e-Science
Institute
15 South College Street, Edinburgh
Theme 14 Workshop 3:
6–8 April 2011
Metabolic modelling
and networks
Athel Cornish-Bowden
(cnrs, marseilles)
Aspartate metabolism in Arabidopsis thaliana:
Understanding a classic example of metabolic regulation
in a real system
Thursday, 7 April 2011
e-Science
Institute
15 South College Street, Edinburgh
Theme 14 Workshop 3:
6–8 April 2011
Metabolic modelling
and networks
Athel Cornish-Bowden
(cnrs, marseilles)
Aspartate metabolism in Arabidopsis thaliana:
Understanding a classic example of metabolic regulation
in a real system
Gilles Curien
Université Joseph-Fourier
Grenoble
Thursday, 7 April 2011
María Luz Cárdenas
Bioénergétique et Ingénierie
des Protéines, CNRS
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
feedback inhibition
Biosynthesis of lysine
Aspartate
Aspartate
kinase
Aspartyl phosphate
Aspartate
semialdehyde
dehydrogenase
Aspartate semialdehyde
Dihydrodipicolinate
synthase
Dihydropicolinate
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Lysine
“End-product”
The typical representation of the
biosynthesis of lysine that you can
find in a textbook of biochemistry
contains a serious error of chemistry
(which I’ll discuss later) and a
serious omission, indicating a lack
of understanding of metabolic
regulation (which I’ll consider
now).
feedback inhibition
e-Science
Institute
Biosynthesis of lysine
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Inhibits
Aspartate
semialdehyde
dehydrogenase
Aspartate semialdehyde
Dihydrodipicolinate
synthase
The typical representation of the
biosynthesis of lysine that you can
find in a textbook of biochemistry
contains a serious error of chemistry
(which I’ll discuss later) and a
serious omission, indicating a lack
of understanding of metabolic
regulation (which I’ll consider
now).
Dihydropicolinate
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Lysine
“End-product”
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
D. Voet, J. G. Voet and C. W.
Pratt (2008) Fundamentals of
Biochemistry: Life at the Molecular
Level
feedback inhibition
e-Science
Institute
Biosynthesis of lysine
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Inhibits
Aspartate
semialdehyde
dehydrogenase
Aspartate semialdehyde
Dihydrodipicolinate
synthase
Dihydropicolinate
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Lysine
“End-product”
The typical representation of the
biosynthesis of lysine that you can
find in a textbook of biochemistry
contains a serious error of chemistry
(which I’ll discuss later) and a
serious omission, indicating a lack
of understanding of metabolic
regulation (which I’ll consider
now).
feedback inhibition
e-Science
Institute
Biosynthesis of lysine
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Inhibits
Aspartate
semialdehyde
dehydrogenase
Aspartate semialdehyde
Dihydrodipicolinate
synthase
Dihydropicolinate
Measured concentrations
Elasticities
SUPPLY
Flux distribution
Control coefficients
Flux
Lysine
“End-product”
Concentration
Regulatory effectiveness
DEMAND
What have we learned?
Perspectives
Thursday, 7 April 2011
PROTEINS
The typical representation of the
biosynthesis of lysine that you can
find in a textbook of biochemistry
contains a serious error of chemistry
(which I’ll discuss later) and a
serious omission, indicating a lack
of understanding of metabolic
regulation (which I’ll consider
now).
feedback inhibition
e-Science
Institute
Biosynthesis of lysine
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Inhibits
Aspartate
semialdehyde
dehydrogenase
Aspartate semialdehyde
Dihydrodipicolinate
synthase
Dihydropicolinate
Measured concentrations
Elasticities
SUPPLY
Flux distribution
Control coefficients
Flux
Lysine
“End-product”
Concentration
Regulatory effectiveness
DEMAND
What have we learned?
Perspectives
Thursday, 7 April 2011
PROTEINS
The typical representation of the
biosynthesis of lysine that you can
find in a textbook of biochemistry
contains a serious error of chemistry
(which I’ll discuss later) and a
serious omission, indicating a lack
of understanding of metabolic
regulation (which I’ll consider
now).
This regulatory design allows a cell
to respond to changes in the demand
for end product, as independently as
possible of considerations of supply.
feedback inhibition
e-Science
Institute
Biosynthesis of lysine
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Inhibits
Aspartate
semialdehyde
dehydrogenase
Aspartate semialdehyde
Dihydrodipicolinate
synthase
Dihydropicolinate
Measured concentrations
Elasticities
SUPPLY
Flux distribution
Control coefficients
Flux
Lysine
“End-product”
Concentration
Regulatory effectiveness
DEMAND
What have we learned?
Perspectives
Thursday, 7 April 2011
PROTEINS
The typical representation of the
biosynthesis of lysine that you can
find in a textbook of biochemistry
contains a serious error of chemistry
(which I’ll discuss later) and a
serious omission, indicating a lack
of understanding of metabolic
regulation (which I’ll consider
now).
This regulatory design allows a cell
to respond to changes in the demand
for end product, as independently as
possible of considerations of supply.
When a cell needs more lysine it
simply uses more.
feedback inhibition
e-Science
Institute
Biosynthesis of lysine
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Inhibits
Aspartate
semialdehyde
dehydrogenase
Aspartate semialdehyde
Dihydrodipicolinate
synthase
Dihydropicolinate
Measured concentrations
Elasticities
Flux distribution
Lysine
When a cell needs more lysine it
simply uses more.
“End-product”
How well is this theoretical expectation fulfilled in practice?
Concentration
Regulatory effectiveness
DEMAND
What have we learned?
Perspectives
Thursday, 7 April 2011
PROTEINS
This regulatory design allows a cell
to respond to changes in the demand
for end product, as independently as
possible of considerations of supply.
SUPPLY
Control coefficients
Flux
The typical representation of the
biosynthesis of lysine that you can
find in a textbook of biochemistry
contains a serious error of chemistry
(which I’ll discuss later) and a
serious omission, indicating a lack
of understanding of metabolic
regulation (which I’ll consider
now).
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
Glycolysis in the human erythrocyte (Sam Rapoport; Philip Kuchel;
and others)
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
Rate equations
Glycolysis in the human erythrocyte (Sam Rapoport; Philip Kuchel;
and others)
External metabolites
Glycolysis in Trypanosoma brucei (Barbara Bakker)
Simulation
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
Rate equations
Glycolysis in the human erythrocyte (Sam Rapoport; Philip Kuchel;
and others)
External metabolites
Glycolysis in Trypanosoma brucei (Barbara Bakker)
Simulation
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Threonine synthesis in Escherichia coli (Jean-Pierre Mazat; David Fell)
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
Rate equations
Glycolysis in the human erythrocyte (Sam Rapoport; Philip Kuchel;
and others)
External metabolites
Glycolysis in Trypanosoma brucei (Barbara Bakker)
Simulation
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Threonine synthesis in Escherichia coli (Jean-Pierre Mazat; David Fell)
Sucrose metabolism in sugar cane (Johann Rohwer)
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
Rate equations
Glycolysis in the human erythrocyte (Sam Rapoport; Philip Kuchel;
and others)
External metabolites
Glycolysis in Trypanosoma brucei (Barbara Bakker)
Simulation
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Threonine synthesis in Escherichia coli (Jean-Pierre Mazat; David Fell)
Sucrose metabolism in sugar cane (Johann Rohwer)
Methionine/threonine metabolism in Arabidopsis thaliana (Gilles
Curien)
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
Rate equations
Glycolysis in the human erythrocyte (Sam Rapoport; Philip Kuchel;
and others)
External metabolites
Glycolysis in Trypanosoma brucei (Barbara Bakker)
Simulation
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Threonine synthesis in Escherichia coli (Jean-Pierre Mazat; David Fell)
Sucrose metabolism in sugar cane (Johann Rohwer)
Methionine/threonine metabolism in Arabidopsis thaliana (Gilles
Curien)
These have been very valuable, but they do not address the important
question of the role of feedback inhibition in metabolic regulation, or the
role of multiple isoenzymes that catalyse the same reactions.
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
metabolic models
Databases of computer models of metabolism (e.g. jws online, curated by
Jacky Snoep at Stellenbosch and Amsterdam) now list around 100 models.
However, as Hans Westerhoff pointed out to us a few years ago, this number
is highly misleading, as most of these are stoicheiometric rather than kinetic
models, or they are not based on measured kinetic parameters for the
component enzymes.
If we exclude these, we are left with a much smaller number of models of
real systems with measured parameters that had been published by the
beginning of 2009:
Rate equations
Glycolysis in the human erythrocyte (Sam Rapoport; Philip Kuchel;
and others)
External metabolites
Glycolysis in Trypanosoma brucei (Barbara Bakker)
Simulation
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Threonine synthesis in Escherichia coli (Jean-Pierre Mazat; David Fell)
Sucrose metabolism in sugar cane (Johann Rohwer)
Methionine/threonine metabolism in Arabidopsis thaliana (Gilles
Curien)
These have been very valuable, but they do not address the important
question of the role of feedback inhibition in metabolic regulation, or the
role of multiple isoenzymes that catalyse the same reactions.
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
Homoserine
dehydrogenase
Regulation
Simulation
Rate equations
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Cysteine
S-Adenosyl
methionine
Methionine
Cystathione
γ-synthase
Regulatory effectiveness
Perspectives
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Concentration
What have we learned?
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Lysine
Isoenzymes
Regulation
Homoserine
dehydrogenase
Simulation
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities S-Adenosyl
Flux distribution
methionine
Control coefficients
Flux
Concentration
Methionine
Cystathione
γ-synthase
What have we learned?
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
The Arabidopsis aspartate pathway
provides an excellent opportunity
to test and develop ideas about
metabolic regulation and the
organization of metabolic pathways.
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Lysine
Isoenzymes
Regulation
Homoserine
dehydrogenase
Simulation
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities S-Adenosyl
Flux distribution
methionine
Control coefficients
Flux
Concentration
Methionine
Cystathione
γ-synthase
What have we learned?
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
The Arabidopsis aspartate pathway
provides an excellent opportunity
to test and develop ideas about
metabolic regulation and the
organization of metabolic pathways.
For example is the most highly
regulated step in a pathway the one
that actually controls the flux(es)?
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Lysine
Essential in
humans
Homoserine
dehydrogenase
Simulation
Rate equations
Homoserine
External metabolites
Measured concentrations
Elasticities S-Adenosyl
Flux distribution
methionine
Control coefficients
Flux
Concentration
Regulatory effectiveness
Essential in
humans
Methionine
Cystathione
γ-synthase
Phosphohomoserine
Threonine
synthase
What have we learned?
Essential in
humans
Perspectives
Isoleucine
Thursday, 7 April 2011
Cysteine
Homoserine
kinase
Threonine
deaminase
Essential in
Threonine humans
The Arabidopsis aspartate pathway
provides an excellent opportunity
to test and develop ideas about
metabolic regulation and the
organization of metabolic pathways.
For example is the most highly
regulated step in a pathway the one
that actually controls the flux(es)?
It is a branched pathway: 13
enzymes producing 4 amino acids.
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Lysine
Isoenzymes
Regulation
Homoserine
dehydrogenase
Simulation
The Arabidopsis aspartate pathway
provides an excellent opportunity
to test and develop ideas about
metabolic regulation and the
organization of metabolic pathways.
For example is the most highly
regulated step in a pathway the one
that actually controls the flux(es)?
It is a branched pathway: 13
enzymes producing 4 amino acids.
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities S-Adenosyl
Flux distribution
methionine
Control coefficients
Flux
Concentration
Methionine
Cystathione
γ-synthase
What have we learned?
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
There are several examples of
isoenzymes that respond unequally
to effectors.
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Lysine
Isoenzymes
Regulation
Homoserine
dehydrogenase
Simulation
The Arabidopsis aspartate pathway
provides an excellent opportunity
to test and develop ideas about
metabolic regulation and the
organization of metabolic pathways.
For example is the most highly
regulated step in a pathway the one
that actually controls the flux(es)?
It is a branched pathway: 13
enzymes producing 4 amino acids.
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities S-Adenosyl
Flux distribution
methionine
Control coefficients
Flux
Concentration
Methionine
Cystathione
γ-synthase
What have we learned?
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
There are several examples of
isoenzymes that respond unequally
to effectors.
There are two examples of
bifunctional enzymes.
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Lysine
Isoenzymes
Regulation
Homoserine
dehydrogenase
Simulation
The Arabidopsis aspartate pathway
provides an excellent opportunity
to test and develop ideas about
metabolic regulation and the
organization of metabolic pathways.
For example is the most highly
regulated step in a pathway the one
that actually controls the flux(es)?
It is a branched pathway: 13
enzymes producing 4 amino acids.
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities S-Adenosyl
Flux distribution
methionine
Control coefficients
Flux
Concentration
Methionine
Cystathione
γ-synthase
What have we learned?
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
There are several examples of
isoenzymes that respond unequally
to effectors.
There are two examples of
bifunctional enzymes.
There are many different regulatory
interactions: inhibition, activation,
synergism, antagonism…
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Lysine
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Homoserine
dehydrogenase
Flux balance analysis can’t
address the functions
of most
Homoserine
of these characteristics!
Cysteine
Elasticities S-Adenosyl
Flux distribution
methionine
Control coefficients
Flux
Concentration
Methionine
Cystathione
γ-synthase
What have we learned?
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
The Arabidopsis aspartate pathway
provides an excellent opportunity
to test and develop ideas about
metabolic regulation and the
organization of metabolic pathways.
For example is the most highly
regulated step in a pathway the one
that actually controls the flux(es)?
It is a branched pathway: 13
enzymes producing 4 amino acids.
There are several examples of
isoenzymes that respond unequally
to effectors.
There are two examples of
bifunctional enzymes.
There are many different regulatory
interactions: inhibition, activation,
synergism, antagonism…
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
Homoserine
dehydrogenase
Regulation
Simulation
Rate equations
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Cysteine
S-Adenosyl
methionine
Methionine
Cystathione
γ-synthase
Regulatory effectiveness
Perspectives
Thursday, 7 April 2011
Phosphohomoserine
Threonine
synthase
Concentration
What have we learned?
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
Aspartate
kinase
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
Homoserine
dehydrogenase
Regulation
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
Cystathione
γ-synthase
Flux
Concentration
Methionine
Regulatory effectiveness
Perspectives
Thursday, 7 April 2011
PROTEINS
Phosphohomoserine
Threonine
synthase
PROTEINS
What have we learned?
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
PROTEINS
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
ATP
Aspartate
kinase
ADP
Aspartyl phosphate
Feedback inhibition
Dihydrodipicolinate
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Aspartate
semialdehyde
dehydrogenase
Isoenzymes
Homoserine
dehydrogenase
Regulation
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
Methionine
Cystathione
γ-synthase
Flux
Concentration
PROTEINS
What have we learned?
Thursday, 7 April 2011
PROTEINS
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
PROTEINS
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
ATP
Aspartate
kinase
ADP
Aspartyl phosphate
NADPH
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
+
NADP + Pi dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
NADPH
Regulation
NADP+
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
ATP
Measured concentrations
Elasticities
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
ADP
Methionine
Cystathione
γ-synthase
Flux
Concentration
PROTEINS
What have we learned?
Thursday, 7 April 2011
PROTEINS
Homoserine
kinase
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
dehydrogenase
Isoleucine
Threonine
deaminase
Threonine
PROTEINS
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
ATP
Aspartate
kinase
ADP
Aspartyl phosphate
Feedback inhibition
Dihydrodipicolinate
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Aspartate
semialdehyde
dehydrogenase
Isoenzymes
Homoserine
dehydrogenase
Regulation
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
Methionine
Cystathione
γ-synthase
Flux
Concentration
PROTEINS
What have we learned?
Thursday, 7 April 2011
PROTEINS
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
PROTEINS
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
ATP
Aspartate
kinase
ADP
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
Homoserine
dehydrogenase
Regulation
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
Cystathione
γ-synthase
Flux
Concentration
Methionine
Regulatory effectiveness
Perspectives
Thursday, 7 April 2011
PROTEINS
Phosphohomoserine
Threonine
synthase
PROTEINS
What have we learned?
Homoserine
kinase
Isoleucine
Threonine
deaminase
Threonine
PROTEINS
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
ATP
Aspartate
kinase
ADP
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
Homoserine
dehydrogenase The arrowheads here
Regulation
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
Cystathione
γ-synthase
Flux
Concentration
Methionine
Regulatory effectiveness
Perspectives
Thursday, 7 April 2011
PROTEINS
Homoserine
kinase
Phosphohomoserine
Threonine
synthase
PROTEINS
What have we learned?
indicate the flow
under physiological
conditions.
Isoleucine
Threonine
deaminase
Threonine
PROTEINS
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
ATP
Aspartate
kinase
ADP
Aspartyl phosphate
Feedback inhibition
Aspartate
Dihydrosemialdehyde
dipicolinate
dehydrogenase
synthase
Aspartate semialdehyde
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
Homoserine
dehydrogenase The arrowheads here
Regulation
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
Measured concentrations
Elasticities
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
Cystathione
γ-synthase
Flux
Concentration
Methionine
Regulatory effectiveness
Perspectives
Thursday, 7 April 2011
PROTEINS
Phosphohomoserine
They do not indicate
the equilibrium
constants of the
reactions in question!
Threonine
synthase
PROTEINS
What have we learned?
Homoserine
kinase
indicate the flow
under physiological
conditions.
Isoleucine
Threonine
deaminase
Threonine
PROTEINS
aspartate metabolism in plants
e-Science
Institute
Chloroplasts of Arabidopsis thaliana
PROTEINS
Aspartate
edinburgh
6–8 april 2011
Aspartate
Dihydrosemialdehyde D. Voet, J. G. Voet and
dipicolinate
dehydrogenase C. W. Pratt (2008) Fundasynthase
Aspartate semialdehyde mentals of Biochemistry:
Life at the Molecular Level
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Isoenzymes
Homoserine
dehydrogenase The arrowheads here
Regulation
Simulation
PROTEINS
Rate equations
PROTEINS
Homoserine
External metabolites
Measured concentrations
Flux distribution
Cysteine
S-Adenosyl
methionine
Control coefficients
Methionine
Cystathione
γ-synthase
Flux
Concentration
PROTEINS
What have we learned?
Thursday, 7 April 2011
PROTEINS
Homoserine
kinase
Phosphohomoserine
Threonine
synthase
Regulatory effectiveness
Perspectives
ADP
Aspartyl phosphate
Feedback inhibition
Elasticities
ATP
Aspartate
kinase
Isoleucine
Threonine
deaminase
Threonine
indicate the flow
under physiological
conditions.
They do not indicate
the equilibrium
constants of the
reactions in question!
This is especially
important for
aspartate kinase.
PROTEINS
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Aspartyl P
Feedback inhibition
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
Isoenzymes
Regulation
Simulation
Lys-tRNA
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Aspartyl P
Feedback inhibition
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
Isoenzymes
Regulation
Simulation
Lys-tRNA
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Four isoenzymes of
aspartate kinase (very
intelligently named!)
1
2
I
II
Aspartyl P
Feedback inhibition
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
Isoenzymes
Regulation
Simulation
Lys-tRNA
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Four isoenzymes of
aspartate kinase (very
intelligently named!)
Feedback inhibition
1
2
I
II
Aspartyl P
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
Regulation
Simulation
Lys-tRNA
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Four isoenzymes of
aspartate kinase (very
intelligently named!)
Feedback inhibition
1
2
I
II
Aspartyl P
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
II
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Four isoenzymes of
aspartate kinase (very
intelligently named!)
Feedback inhibition
1
2
I
II
The enzyme concentrations
are not all the same…
Aspartyl P
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
II
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Four isoenzymes of
aspartate kinase (very
intelligently named!)
Feedback inhibition
1
2
I
II
Aspartyl P
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
The enzyme concentrations
are not all the same…
The symbols should be
thought of as 3-dimensional
balls, i.e. they have volume,
and the concentrations are
proportional to the cubes of
the radii.
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
II
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Four isoenzymes of
aspartate kinase (very
intelligently named!)
Feedback inhibition
1
2
I
II
Aspartyl P
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
II
Homoserine
External metabolites
Measured concentrations
Cysteine
The enzyme concentrations
are not all the same…
The symbols should be
thought of as 3-dimensional
balls, i.e. they have volume,
and the concentrations are
proportional to the cubes of
the radii.
There is 46 times as much
aspartate semialdehyde
dehydrogenase as aspartate
kinase 1 — about ten times
as much as the four aspartate kinase isoenzymes
together.
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
Four isoenzymes of
aspartate kinase (very
intelligently named!)
Feedback inhibition
1
2
I
II
Aspartyl P
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
II
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
The enzyme concentrations
are not all the same…
The symbols should be
thought of as 3-dimensional
balls, i.e. they have volume,
and the concentrations are
proportional to the cubes of
the radii.
There is 46 times as much
aspartate semialdehyde
dehydrogenase as aspartate
kinase 1 — about ten times
as much as the four aspartate kinase isoenzymes
together.
Why does the plant need so
much?
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
1
Four isoenzymes of
aspartate kinase
2
I
II
Aspartyl P
Feedback inhibition
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
II
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
1
Four isoenzymes of
aspartate kinase
2
I
II
Aspartyl P
Feedback inhibition
Aspartate kinase I
and homoserine
dehydrogenase I
are activities of the
same protein
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
II
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
aspartate metabolism: isoenzymes
e-Science
Institute
Aspartate
edinburgh
6–8 april 2011
1
Four isoenzymes of
aspartate kinase
2
I
II
Aspartyl P
Feedback inhibition
Two isoenzymes of
dihydrodipicolinate
synthase
2
Metabolic models
Aspartate metabolism in
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Two isoenzymes of
homoserine dehydrogenase
Simulation
Lys-tRNA
Rate equations
Aspartate kinase I
and homoserine
dehydrogenase I
are activities of the
same protein
Aspartate kinase II
and homoserine
dehydrogenase II
are activities of the
same protein
II
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
regulatory interactions
e-Science
Institute
Aspartate
1
edinburgh
6–8 april 2011
2
I
II
Aspartyl P
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
II
Lys-tRNA
Rate equations
Homoserine
External metabolites
Measured concentrations
Cysteine
Elasticities
Flux distribution
AdoMet
Phosphohomoserine
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Aspartyl P
Feedback inhibition
Thr inhibits aspartate
kinase I and II
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
Thursday, 7 April 2011
Thr weakly inhibits
homoserine
dehydrogenase I and II
Cysteine
AdoMet
AdoMet activates threonine
synthase
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
What have we learned?
Perspectives
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
Ile-tRNA
Isoleucine
Threonine
Ile inhibits threonine deaminase
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Aspartyl P
Feedback inhibition
Thr inhibits aspartate
kinase I and II
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
Thursday, 7 April 2011
Thr weakly inhibits
homoserine
dehydrogenase I and II
Cysteine
AdoMet
AdoMet activates threonine
synthase
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
What have we learned?
Perspectives
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
Ile-tRNA
Isoleucine
Threonine
Ile inhibits threonine deaminase
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Aspartyl P
Feedback inhibition
Thr inhibits aspartate
kinase I and II
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
Thursday, 7 April 2011
Thr weakly inhibits
homoserine
dehydrogenase I and II
Which enzymes
control the flux?
Cysteine
AdoMet
AdoMet activates threonine
synthase
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
What have we learned?
Perspectives
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
Ile-tRNA
Isoleucine
Threonine
Ile inhibits threonine deaminase
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Aspartyl P
Feedback inhibition
Thr inhibits aspartate
kinase I and II
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
AdoMet activates threonine
synthase
Thursday, 7 April 2011
Thr weakly inhibits
homoserine
dehydrogenase I and II
Which enzymes
control the flux?
Cysteine
AdoMet
Phosphohomoserine
Valine
How well does the
system cope with
variations in demand?
Val damps the inhibition
of threonine deaminase
What have we learned?
Perspectives
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
Ile-tRNA
Isoleucine
Threonine
Ile inhibits threonine deaminase
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Aspartyl P
Feedback inhibition
Thr inhibits aspartate
kinase I and II
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
AdoMet activates threonine
synthase
Thursday, 7 April 2011
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
Ile-tRNA
Thr weakly inhibits
homoserine
dehydrogenase I and II
Which enzymes
control the flux?
Cysteine
AdoMet
What have we learned?
Perspectives
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
Isoleucine
Threonine
Ile inhibits threonine deaminase
How well does the
system cope with
variations in demand?
Does every isoenzyme
support some flux?
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Aspartyl P
Feedback inhibition
Thr inhibits aspartate
kinase I and II
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
Thursday, 7 April 2011
Cysteine
AdoMet
AdoMet activates threonine
synthase
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
What have we learned?
Perspectives
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
Ile-tRNA
Isoleucine
Threonine
Ile inhibits threonine deaminase
Which enzymes
control
theinhibits
flux?
Thr weakly
homoserine
How
well does
theII
dehydrogenase
I and
system cope with
variations in demand?
Does every isoenzyme
support some flux?
Do the two activities
of the bifunctional
enzymes carry equal
fluxes?
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Aspartyl P
Feedback inhibition
Thr inhibits aspartate
kinase I and II
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
AdoMet activates threonine
synthase
Thursday, 7 April 2011
Cysteine
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
Ile-tRNA
How well does the
system cope with
variations
in demand?
Thr weakly inhibits
homoserine
Does
every isoenzyme
dehydrogenase
I and II
support some flux?
AdoMet
What have we learned?
Perspectives
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
Which enzymes
control the flux?
Isoleucine
Threonine
Ile inhibits threonine deaminase
Do the two activities
of the bifunctional
enzymes carry equal
fluxes?
Do the different
branches respond
independently?
Thr-tRNA
regulatory interactions
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Aspartyl P
Feedback inhibition
Metabolic models
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
Thursday, 7 April 2011
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
AdoMet activates threonine
synthase
Cysteine
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
Ile-tRNA
How well does the
system cope with
variations in demand?
Does every isoenzyme
support
flux?
Thr weaklysome
inhibits
homoserine
Do
the two activities
dehydrogenase
I and II
of the bifunctional
enzymes carry equal
fluxes?
AdoMet
What have we learned?
Perspectives
Which enzymes
Thr
inhibits
control
theaspartate
flux?
kinase I and II
Aspartate metabolism in
External metabolites
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Isoleucine
Threonine
Ile inhibits threonine deaminase
Do the different
branches respond
independently?
Can the system
survive enzyme knockouts?
Thr-tRNA
simulating a metabolic system
e-Science
Institute
Lys inhibits aspartate kinase 1, and
weakly inhibits aspartate kinase 2
edinburgh
6–8 april 2011
Aspartate
1
2
I
II
Aspartyl P
Feedback inhibition
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
1
Isoenzymes
I
Regulation
Simulation
Lys-tRNA
Rate equations
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
Thursday, 7 April 2011
Lys inhibits dihydrodipicolinate
synthase 1, and weakly inhibits
II
dihydrodipicolinate synthase 2
Homoserine
AdoMet potentiates the
weak inhibition of aspartate
kinase 1 by Lys
Cysteine
AdoMet
AdoMet activates threonine
synthase
Phosphohomoserine
Valine
Val damps the inhibition
of threonine deaminase
What have we learned?
Perspectives
Which enzymes
control the flux?
Thr inhibits aspartate
Metabolic models
External metabolites
Ala, Cys, Ile, Ser, Val
activate aspartate kinase
II, and aspartate kinase I
weakly. (We shall not
take account of this)
Ile-tRNA
Isoleucine
Threonine
Ile inhibits threonine deaminase
How
the
kinase well
I anddoes
II
system cope with
variations in demand?
Does every isoenzyme
support some flux?
Thr weakly inhibits
Do
the two activities
homoserine
of
the bifunctional
dehydrogenase I and II
enzymes carry equal
fluxes?
Do the different
branches respond
independently?
Can the system
survive enzyme knockouts?
How can
the system
Thr-tRNA
be simulated?
simulating a metabolic system
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
simulating a metabolic system
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
simulating a metabolic system
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
(1) Kinetic equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
simulating a metabolic system
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
(1) Kinetic equations
(2) Fixed concentrations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
simulating a metabolic system
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
(2) Fixed concentrations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
(1) Kinetic equations
(3) A suitable program
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
sporting systems biologists
e-Science
Institute
sporting systems biologists
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Pedro Mendes
sporting systems biologists
(Taking Hans’s body-building seriously)
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Nathan
Price
Metabolic
models
Aspartate metabolism in
Jamie Wood
Herbert Sauro
Pedro Mendes
António Ferreira
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Markconcentrations
Poolman
Measured
Elasticities
Hans Westerhoff
Hidde de Jong
David Fell
Isaac Pérez
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thomas
Thursday, 7 April 2011
Forth
Gavin Thomas
Tom Rapoport
Stefan Schuster
sporting systems biologists
(Taking Hans’s body-building seriously)
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Nathan
Price
Metabolic
models
Aspartate metabolism in
Jamie Wood
Herbert Sauro
Pedro Mendes
António Ferreira
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Markconcentrations
Poolman
Measured
Elasticities
Hans Westerhoff
Hidde de Jong
David Fell
Isaac Pérez
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thomas
Thursday, 7 April 2011
Forth
Gavin Thomas
Tom Rapoport
Stefan Schuster
simulating a metabolic system
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
(2) Fixed concentrations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
(1) Kinetic equations
(3) A suitable program
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
simulating a metabolic system
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
(2) Fixed concentrations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
(1) Kinetic equations
(3) A suitable program
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
Back reaction must be allowed for
rate equations
vAK1
e-Science
=
[AK1] ·
Institute
edinburgh
6–8 april 2011
5.65 − 1.6[AspP]
!
"
#$2
550
1 + [Lys]/
1 + [AdoMet]/3.5
Asp: 1.5 mM
1
Inhibition by lysine…
2
I
Aspartyl P
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
…potentiated by AdoMet…
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Isoenzymes
Asp semialdehyde
…with cooperativity (h = 2)
Rate equations
(1) Kinetic equations
1
I
Regulation
Simulation
II
II
Lys-tRNA
Homoserine
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
Phosphohomoserine
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
Back reaction must be allowed for
rate equations
vAK1
5.65 − 1.6[AspP]
!
"
#$2
550
1 + [Lys]/
1 + [AdoMet]/3.5
e-Science
=
[AK1] ·
Institute
edinburgh
6–8 april 2011
Asp: 1.5 mM
1
Inhibition by lysine…
2
I
Aspartyl P
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
…potentiated by AdoMet…
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Isoenzymes
Asp semialdehyde
…with cooperativity (h = 2)
Rate equations
External metabolites
(1) Kinetic equations
1
I
Regulation
Simulation
II
Five parameters to be defined (or six
Lys-tRNA
with the enzyme concentration). This is
far from being the most complicated!
Measured concentrations
II
Homoserine
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
Phosphohomoserine
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
rate equations
vAK1 = [AK1] ·
5.65 − 1.6[AspP]
!
"
#$2
550
1 + [Lys]/
1 + [AdoMet]/3.5
vAK2 = [AK2] ·
3.15 − 0.86[AspP]
"
#
[Lys] 1.1
1+
22
Simulating a system like
this requires a very large
amount of information:
0.36 − 0.15[AspP]
vAKI = [AKI HSDH I] ·
#
"
[Thr] 2.6
1+
124
vAKII = [AKII HSDH II] ·
1.35 − 0.22[AspP]
"
#
[Thr] 2
1+
109
(1) Kinetic equations
vASADH = [ASADH] · (0.9[AspP] − 0.23[ASA])
vDHDPS1 = [DHDPS1] · [ASA] ·
1
1 + ([Lys]/10)2
vDHDPS2 = [DHDPS2] · [ASA] ·
1
1 + ([Lys]/33)2
"
vHSDHI = [AKI-HSDH I] · 0.84 · 0.14 +
"
vHSDHII = [AKII-HSDH II] · 0.64 · 0.25 +
vHSK = [HSK] ·
"
0.86
1 + [Thr]/400
#
0.75
1 + [Thr]/8500
#
2.8[Hser]
14 + [Hser]
#
0.42 + 3.5[AdoMet]2 /73
[PHser]
1 + [AdoMet]2 /73
"
#
vTS1 = [TS1] · 
1 + [AdoMet]/0.5
"
#
 250 1 + [AdoMet/1.1 
[P
]
i

 1+
+ [PHser]
2


1000
[AdoMet]
1+
109
vASADH = [ASADH] · (0.9[AspP] − 0.23[ASA])
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
rate equations
vDHDPS1 = [DHDPS1] · [ASA] ·
1
1 + ([Lys]/10)2
vDHDPS2 = [DHDPS2] · [ASA] ·
1
1 + ([Lys]/33)2
"
vHSDHI = [AKI-HSDH I] · 0.84 · 0.14 +
"
vHSDHII = [AKII-HSDH II] · 0.64 · 0.25 +
vHSK = [HSK] ·
0.86
1 + [Thr]/400
#
0.75
1 + [Thr]/8500
#
2.8[Hser]
14 + [Hser]
"
#
0.42 + 3.5[AdoMet]2 /73
[PHser]
1 + [AdoMet]2 /73
"
#
vTS1 = [TS1] · 
1 + [AdoMet]/0.5
"
#
 250 1 + [AdoMet/1.1 
[P
]
i

 1+
+ [PHser]
2


1000
[AdoMet]
1+
140
"
#
30
· [PHser]
1 + 460/[Cys]
#"
#
vCGS = [CGS] · "
2500
[Pi ]
1+
+ [PHser]
1 + 460/[Cys]
2000
vTD = [TD] ·
0.0124[Thr]
!
"
#$3
74[Val]
1 + [Ile]/ 30 +
610 + [Val]
v(Lys)tRNAsth = V AaRS ·
v(Thr)tRNAsth = V AaRS ·
[Lys]
25 + [Lys]
[Thr]
100 + [Thr]
v(Ile)tRNAsth = V AaRS ·
[Ile]
20 + [Ile]
Simulating a system like
this requires a very large
amount of information:
(1) Kinetic equations
13 catalytic constants +
13 enzyme concentrations +
63 other kinetic parameters +
4 fixed concentrations (of
“external” metabolites) =
93 numerical values that need
to be known experimentally.
e-Science
Institute
concentrations of external metabolites
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Aspartyl P
Simulating a system like
this requires a very large
amount of information:
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lysine
A. thaliana chloroplasts
Asp semialdehyde
I
Regulation
Simulation
Rate equations
(1) Kinetic equations
1
Isoenzymes
(2) Fixed concentrations
II
Lys-tRNA
Homoserine
External metabolites
Measured concentrations
(3) A suitable program
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
Phosphohomoserine
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Isoleucine
Threonine
Thr-tRNA
e-Science
Institute
metabolite concentrations in chloroplast stroma
edinburgh
6–8 april 2011
Feedback inhibition
Metabolite
nmol/mg
chlorophyll
Stroma (µM)
AdoMet
ADP
Asp
ATP
Ala
Cys
Ile
Leu
Lys
NADPH + NADP
Pi
Pyruvate
Thr
Ser
Val
—
—
104.5 ± 3.3
—
26.9 ± 1.9
—
3.2 ± 0.8
3.6 ± 0.5
5.6 ± 0.9
—
—
—
20.9 ± 2.2
4.05 ± 0.72
6.4 ± 1.1
20
480
1593
1920
408
15
48
54
85
300–600
10000
1000
317
61
96
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Reference
Curien et al. (2003)
Krause & Heber (1976)
Curien et al. (2005)
Krause & Heber (1976)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
Krause & Heber (1976)
Bligny et al. (1990)
Simons et al. (1999)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
metabolite concentrations in chloroplast stroma
I won’t say anything about these beyond the fact that we had
measured values for most of those we needed, including enzyme
concentrations (not shown on this slide).
Metabolite
nmol/mg
chlorophyll
Stroma (µM)
AdoMet
ADP
Asp
ATP
Ala
Cys
Ile
Leu
Lys
NADPH + NADP
Pi
Pyruvate
Thr
Ser
Val
—
—
104.5 ± 3.3
—
26.9 ± 1.9
—
3.2 ± 0.8
3.6 ± 0.5
5.6 ± 0.9
—
—
—
20.9 ± 2.2
4.05 ± 0.72
6.4 ± 1.1
20
480
1593
1920
408
15
48
54
85
300–600
10000
1000
317
61
96
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Reference
Curien et al. (2003)
Krause & Heber (1976)
Curien et al. (2005)
Krause & Heber (1976)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
Krause & Heber (1976)
Bligny et al. (1990)
Simons et al. (1999)
Curien et al. (2005)
Curien et al. (2005)
Curien et al. (2005)
e-Science
Institute
elasticities (kinetic orders)
How does the rate v of an isolated reaction depend on [Sj]?
AspP
ASA
Thr
Lys
PHser
Ile
Hser
AK1
–0.11
0.00
0.00
–0.84
0.00
0.00
0.00
AK2
–0.11
0.00
0.00
–0.86
0.00
0.00
0.00
AK-I
–0.11
0.00
–1.71
0.00
0.00
0.00
0.00
AK-II
–0.11
0.00
–1.77
0.00
0.00
0.00
0.00
ASADH
3.53
–2.53
0.00
0.00
0.00
0.00
0.00
Isoenzymes
DHDPS1
0.00
1.00
0.00
–1.96
0.00
0.00
0.00
Regulation
DHDPS2
0.00
1.00
0.00
–1.63
0.00
0.00
0.00
HSDH I
0.00
1.00
–0.34
0.00
0.00
0.00
0.00
HSDH II
0.00
1.00
–0.03
0.00
0.00
0.00
0.00
Measured concentrations
HSK
0.00
0.00
0.00
0.00
0.00
0.00
0.94
Elasticities
TS1
0.00
0.00
0.00
0.00
0.97
0.00
0.00
CGS
0.00
0.00
0.00
0.00
0.91
0.00
0.00
TD
0.00
0.00
1.00
0.00
0.00
–2.28
0.00
LysRNA
0.00
0.00
0.00
0.26
0.00
0.00
0.00
ThrRNA
0.00
0.00
0.25
0.00
0.00
0.00
0.00
IleRNA
0.00
0.00
0.00
0.00
0.00
0.25
0.00
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Simulation
Rate equations
External metabolites
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
elasticities (kinetic orders)
How does the rate v of an isolated reaction depend on [Sj]?
AspP
ASA
Thr
Lys
PHser
Ile
Hser
AK1
–0.11
0.00
0.00
–0.84
0.00
0.00
0.00
AK2
–0.11
0.00
0.00
–0.86
0.00
0.00
0.00
AK-I
–0.11
0.00
–1.71
0.00
0.00
0.00
0.00
AK-II
–0.11
0.00
–1.77
0.00
0.00
0.00
0.00
ASADH
3.53
–2.53
0.00
0.00
0.00
0.00
0.00
DHDPS1
0.00
1.00
0.00 –1.96
0.00
0.00
these afterwards — what they mean;
DHDPS2We can
0.00discuss
1.00
0.00 –1.63
0.00
0.00
what we found — if anyone would like, but for the
HSDH I
0.00
1.00 –0.34
0.00 them.
0.00
0.00
moment
I shall skip
HSDH II
0.00
1.00 –0.03 0.00
0.00
0.00
HSK
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.94
TS1
0.00
0.00
0.00
0.00
0.97
0.00
0.00
CGS
0.00
0.00
0.00
0.00
0.91
0.00
0.00
TD
0.00
0.00
1.00
0.00
0.00
–2.28
0.00
LysRNA
0.00
0.00
0.00
0.26
0.00
0.00
0.00
ThrRNA
0.00
0.00
0.25
0.00
0.00
0.00
0.00
IleRNA
0.00
0.00
0.00
0.00
0.00
0.25
0.00
which isoenzymes carry the flux?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
which isoenzymes carry the flux?
74% of the aspartate kinase
flux goes through AK1
Asp: 1.5 mM
1
2
I
II
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
which isoenzymes carry the flux?
74% of the aspartate kinase
flux goes through AK1
Asp: 1.5 mM
1
2
I
II
Almost none (3%)
goes through AKI
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
which isoenzymes carry the flux?
74% of the aspartate kinase
flux goes through AK1
Asp: 1.5 mM
1
2
I
II
Almost none (3%)
goes through AKI
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
The AdoMet flux
is only about 9%
of the Thr flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
0.041
0.008
0.023
0.008
–0.007
–0.065
0.043
0.050
0.000
–0.031
0.033
0.020
0.297
0.202
0.181
0.999
CGS
0.240
0.051
0.010
0.029
0.010
–0.014
–0.127
0.076
0.090
0.000
–0.910
0.967
0.027
0.036
0.272
0.244
1.001
LysRNA
ThrRNA
IleRNA
0.068
0.014
0.003
0.008
0.003
0.010
0.087
–0.041
–0.049
0.000
–0.002
0.002
0.001
0.873
0.012
0.010
0.999
0.364
0.077
0.014
0.043
0.015
–0.021
–0.193
0.116
0.136
0.000
0.047
–0.049
–0.030
0.054
0.698
–0.271
1.000
0.146
0.031
0.006
0.017
0.006
–0.008
–0.077
0.046
0.054
0.000
0.019
–0.020
0.088
0.033
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
LysRNA
ThrRNA
IleRNA
ASADH
CGS
0.196 flux:0.240
0.068through
0.364
Common
all flux passes
ASADH 0.146
0.041
0.051
0.014
0.077
0.031
0.008
0.010
0.003
0.014
0.006
0.023
0.029
0.008
0.043
0.017
0.008
0.010
0.003
0.015
0.006
–0.007
–0.014
0.010
–0.021
–0.008
–0.065
–0.127
0.087
–0.193
–0.077
0.043
0.076
–0.041
0.116
0.046
0.050
0.090
–0.049
0.136
0.054
0.000
0.000
0.000
0.000
0.000
–0.031
–0.910
–0.002
0.047
0.019
0.033
0.967
0.002
–0.049
–0.020
0.020
0.027
0.001
–0.030
0.088
0.297
0.036
0.873
0.054
0.033
0.202
0.272
0.012
0.698
–0.121
0.181
0.244
0.010
–0.271
0.792
0.999
1.001
0.999
1.000
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
0.041
0.008
0.023
0.008
–0.007
–0.065
0.043
0.050
0.000
–0.031
0.033
0.020
0.297
0.202
0.181
0.999
CGS
0.240
0.051
0.010
0.029
0.010
–0.014
–0.127
0.076
0.090
0.000
–0.910
0.967
0.027
0.036
0.272
0.244
1.001
LysRNA
ThrRNA
IleRNA
0.068
0.014
0.003
0.008
0.003
0.010
0.087
–0.041
–0.049
0.000
–0.002
0.002
0.001
0.873
0.012
0.010
0.999
0.364
0.077
0.014
0.043
0.015
–0.021
–0.193
0.116
0.136
0.000
0.047
–0.049
–0.030
0.054
0.698
–0.271
1.000
0.146
0.031
0.006
0.017
0.006
–0.008
–0.077
0.046
0.054
0.000
0.019
–0.020
0.088
0.033
–0.121
0.792
1.001
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
flux control coefficients
LysRNA
IleRNA
d ln
J
J eidJ Thr
ASADH
CGS
RNA
Flux control coefficient for Ei: Ci =
=
Jde
ln ei
AK1
0.196
0.240
0.068 i d0.364
0.146
This isAK2
a quantitative0.041
answer to 0.051
the following
question:
how sensitive
0.014
0.077
0.031 is
the fluxAK-I
to the concentration
the ith enzyme
0.008 ei of
0.010
0.003 Ei? 0.014
0.006
Just one flux at a time
n
AK-II
0.023
0.029 !
0.008
0.043
0.017
J
Fundamental property (the
C
=
1
ASADH
0.008
0.010
0.003
0.015
0.006
i
summation relationship):
DHDPS1
–0.007
–0.014 i=10.010 Sum
–0.021
–0.008
over all enzymes
DHDPS2
–0.065
–0.127
0.087
–0.193
–0.077
The sum of the flux control coefficients over all enzymes (not over all
HSDH
I are more
0.043
0.076
0.046
fluxes:
if there
than one
distinct–0.041
flux, as in a0.116
branched pathway,
J
in every
term
same flux)–0.049
is 1.
HSDH
II must refer
0.050to the 0.090
0.136
0.054
HSK that in general
0.000 most
0.000
0.000
0.000must be
0.000
This implies
flux control
coefficients
small.
TS1 v
–0.031
–0.910
–0.002
0.047
0.019
CGS J
0.033
0.967
0.002
–0.049
This idea
has been –0.020
exploited in
enzymes 0.001
TD
0.020 Many0.027
–0.030
0.088Mazat
particular
by Jean-Pierre
LysRNA
in his analysis
0.297
0.036
0.873
0.054 of mitochondrial
0.033
One
myopathies.
ThrRNA
0.202
0.272
0.012
0.698
–0.121
enzyme
IleRNA 0
0.181
0.244 [I] 0.010
–0.271
0.792
0
Sums
0.999
1.001
0.999
1.000
1.001
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
flux control coefficients
LysRNA
IleRNA
d ln
J
J eidJ Thr
ASADH
CGS
RNA
Flux control coefficient for Ei: Ci =
=
Jde
ln ei
AK1
0.196
0.240
0.068 i d0.364
0.146
This isAK2
a quantitative0.041
answer to 0.051
the following
question:
how sensitive
0.014
0.077
0.031 is
the fluxAK-I
to the concentration
the ith enzyme
0.008 ei of
0.010
0.003 Ei? 0.014
0.006
Just one flux at a time
n
AK-II
0.023
0.029 !
0.008
0.043
0.017
J
Fundamental property (the
C
=
1
ASADH
0.008
0.010
0.003
0.015
0.006
i
summation relationship):
DHDPS1
–0.007
–0.014 i=10.010 Sum
–0.021
–0.008
over all enzymes
DHDPS2
–0.065
–0.127
0.087
–0.193
–0.077
The sum of the flux control coefficients over all enzymes (not over all
HSDH
I are more
0.043
0.076
0.046
fluxes:
if there
than one
distinct–0.041
flux, as in a0.116
branched pathway,
J
in every
term
same flux)–0.049
is 1.
HSDH
II must refer
0.050to the 0.090
0.136
0.054
HSK that in general
0.000 most
0.000
0.000
0.000must be
0.000
This implies
flux control
coefficients
small.
TS1 v
–0.031
–0.910
–0.002
0.047
0.019
CGS J
0.033
0.967
0.002
–0.049
This idea
has been –0.020
exploited in
enzymes 0.001
TD
0.020 Many0.027
–0.030
0.088Mazat
particular
by Jean-Pierre
LysRNA
in his analysis
0.297
0.036
0.873
0.054 of mitochondrial
0.033
One
myopathies.
ThrRNA
0.202
0.272
0.012
0.698
–0.121
enzyme
IleRNA 0
0.181
0.244 [I] 0.010
–0.271
0.792
0
0.999 fluxes1.000
1.001
In the Sums
Arabidopsis case0.999
there are 1.001
several distinct
to be considered…
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured
M concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
0.041
0.008
0.023
0.008
–0.007
–0.065
0.043
0.050
0.000
–0.031
0.033
0.020
0.297
0.202
0.181
0.999
LysRNA
ThrRNA
IleRNA
CGS
0.240flux: this
0.068
0.364
0.146
CGS
is the flux
to AdoMet
0.051
0.014
0.077
0.031
0.010
0.003
0.014
0.006
0.029
0.008
0.043
0.017
0.010
0.003
0.015
0.006
–0.014
0.010
–0.021
–0.008
–0.127
0.087
–0.193
–0.077
0.076
–0.041
0.116
0.046
0.090
–0.049
0.136
0.054
0.000
0.000
0.000
0.000
–0.910
–0.002
0.047
0.019
0.967
0.002
–0.049
–0.020
0.027
0.001
–0.030
0.088
0.036
0.873
0.054
0.033
0.272
0.012
0.698
–0.121
0.244
0.010
–0.271
0.792
1.001
0.999
1.000
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
KSimulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
I
Control coefficients
T
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
0.041
0.008
0.023
0.008
–0.007
–0.065
0.043
0.050
0.000
–0.031
0.033
0.020
0.297
0.202
0.181
0.999
LysRNA
ThrRNA
IleRNA
CGS
0.240
0.068
0.364 amino
0.146acids
tRNA fluxes
of the principal
0.051
0.014
0.077
0.031
0.010
0.003
0.014
0.006
0.029
0.008
0.043
0.017
0.010
0.003
0.015
0.006
–0.014
0.010
–0.021
–0.008
–0.127
0.087
–0.193
–0.077
0.076
–0.041
0.116
0.046
0.090
–0.049
0.136
0.054
0.000
0.000
0.000
0.000
–0.910
–0.002
0.047
0.019
0.967
0.002
–0.049
–0.020
0.027
0.001
–0.030
0.088
0.036
0.873
0.054
0.033
0.272
0.012
0.698
–0.121
0.244
0.010
–0.271
0.792
1.001
0.999
1.000
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
J
ASADH
CAK1
ASADH
∂ ln JASADH
≡ AK1
= 0.196
∂ ln[AK1]
AK2
0.041
AK-I
0.008
AK-II
0.023
ASADH
0.008
DHDPS1
–0.007
DHDPS2
–0.065
HSDH I
0.043
HSDH II
0.050
HSK
0.000
TS1
–0.031
CGS
0.033
TD
0.020
LysRNA
0.297
ThrRNA
0.202
IleRNA
0.181
Sums
0.999
CGS
0.240
0.051
0.010
0.029
0.010
–0.014
–0.127
0.076
0.090
0.000
–0.910
0.967
0.027
0.036
0.272
0.244
1.001
LysRNA
ThrRNA
IleRNA
0.068
0.014
0.003
0.008
0.003
0.010
0.087
–0.041
–0.049
0.000
–0.002
0.002
0.001
0.873
0.012
0.010
0.999
0.364
0.077
0.014
0.043
0.015
–0.021
–0.193
0.116
0.136
0.000
0.047
–0.049
–0.030
0.054
0.698
–0.271
1.000
0.146
0.031
0.006
0.017
0.006
–0.008
–0.077
0.046
0.054
0.000
0.019
–0.020
0.088
0.033
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
LysRNA
ThrRNA
IleRNA
ASADH
CGS
0.196
0.240
0.068
0.364
0.146
0.041
0.051
0.014
0.077
0.031
0.008
0.010
0.003
0.014
0.006
0.023
0.029
0.008
0.043
0.017
0.008
0.010
0.003
0.015
0.006
–0.007
–0.014
0.010
–0.021
–0.008
–0.065
–0.127
0.087
–0.193
–0.077
0.043
0.076
–0.041
0.116
0.046
0.050
0.090
–0.049
0.136
0.054
0.000
0.000
0.000
0.000
0.000
–0.031
–0.910
–0.002
0.047
0.019
0.033
0.967
0.002
–0.049
–0.020
0.020
0.027
0.001
–0.030
0.088
0.297
0.036
0.873
0.054
0.033
0.202 add 0.272
0.012
0.698
–0.121
All columns
up to 1.000
— but then
they must!
0.181
0.244
0.010
–0.271
0.792
0.999
1.001
0.999
1.000
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
0.041
0.008
0.023
0.008
–0.007
–0.065
0.043
0.050
0.000
–0.031
0.033
0.020
0.297
0.202
0.181
0.999
CGS
0.240
0.051
0.010
0.029
0.010
–0.014
–0.127
0.076
0.090
0.000
–0.910
0.967
0.027
0.036
0.272
0.244
1.001
LysRNA
ThrRNA
IleRNA
0.068
0.014
0.003
0.008
0.003
0.010
0.087
–0.041
–0.049
0.000
–0.002
0.002
0.001
0.873
0.012
0.010
0.999
0.364
0.077
0.014
0.043
0.015
–0.021
–0.193
0.116
0.136
0.000
0.047
–0.049
–0.030
0.054
0.698
–0.271
1.000
0.146
0.031
0.006
0.017
0.006
–0.008
–0.077
0.046
0.054
0.000
0.019
–0.020
0.088
0.033
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
LysRNA
ThrRNA
IleRNA
ASADH
CGS
0.196
0.240
0.068 values,
0.364
There are
some negative
but they0.146
are
0.041
0.051 enough
0.014
0.077 with
0.031
mostly small
not to interfere
the
expectation
positive values
0.008
0.010that most
0.003of the 0.014
0.006must
be small.
0.023
0.029
0.008
0.043
0.017
0.008
–0.007
–0.065
0.043
0.050
0.000
–0.031
0.033
0.020
0.297
0.202
0.181
0.999
0.010
–0.014
–0.127
0.076
0.090
0.000
–0.910
0.967
0.027
0.036
0.272
0.244
1.001
0.003
0.010
0.087
–0.041
–0.049
0.000
–0.002
0.002
0.001
0.873
0.012
0.010
0.999
0.015
–0.021
–0.193
0.116
0.136
0.000
0.047
–0.049
–0.030
0.054
0.698
–0.271
1.000
0.006
–0.008
–0.077
0.046
0.054
0.000
0.019
–0.020
0.088
0.033
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
LysRNA
ThrRNA
IleRNA
ASADH
CGS
0.196
0.240
0.068 values,
0.364
There are
some negative
but they0.146
are
0.041
0.051 enough
0.014
0.077 with
0.031
mostly small
not to interfere
the
expectation
positive values
0.008
0.010that most
0.003of the 0.014
0.006must
There is0.008
one exception,
be small.
0.023
0.029
0.043however…
0.017
0.008
–0.007
–0.065
0.043
0.050
0.000
–0.031
0.033
0.020
0.297
0.202
0.181
0.999
0.010
–0.014
–0.127
0.076
0.090
0.000
–0.910
0.967
0.027
0.036
0.272
0.244
1.001
0.003
0.010
0.087
–0.041
–0.049
0.000
–0.002
0.002
0.001
0.873
0.012
0.010
0.999
0.015
–0.021
–0.193
0.116
0.136
0.000
0.047
–0.049
–0.030
0.054
0.698
–0.271
1.000
0.006
–0.008
–0.077
0.046
0.054
0.000
0.019
–0.020
0.088
0.033
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
–0.065
CGS
0.240
–0.127
0.076
0.090
LysRNA
ThrRNA
IleRNA
0.068
0.364
0.077
0.146
–0.193
0.116
0.136
–0.077
0.010
0.087
0.054
–0.910
0.967
0.088
0.297
0.202
0.181
0.999
0.873
0.272
0.244
1.001
0.999
0.054
0.698
–0.271
1.000
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
–0.065
CGS
0.240
–0.127
0.076
0.090
–0.910
0.967
0.297
0.202
0.181
0.999
0.272
0.244
1.001
LysRNA
ThrRNA
IleRNA
0.068
0.364
0.077
0.146
–0.193
0.116
0.136
–0.077
0.010
0.087
0.054
Cystathione γ-synthase controls
the flux through its own0.088
reaction,
but…
0.873
0.054
0.698
–0.121
–0.271
0.792
0.999
1.000
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
–0.065
CGS
0.240
–0.127
0.076
0.090
–0.910
0.967
0.297
0.202
0.181
0.999
0.272
0.244
1.001
LysRNA
ThrRNA
IleRNA
0.068
0.364
0.077
0.146
–0.193
0.116
0.136
–0.077
0.010
0.087
0.054
Cystathione γ-synthase controls
the flux through its own0.088
reaction,
but…
0.873
0.054
…threonine synthase exerts
0.698
–0.121
strong negative control (why?),
–0.271
0.792
0.999
1.000
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
–0.065
CGS
0.240
–0.127
0.076
0.090
–0.910
0.967
0.297
0.202
0.181
0.999
0.272
0.244
1.001
LysRNA
ThrRNA
IleRNA
0.068
0.364
0.077
0.146
–0.193
0.116
0.136
–0.077
0.010
0.087
0.054
Cystathione γ-synthase controls
the flux through its own0.088
reaction,
but…
0.873
0.054
…threonine synthase exerts
0.698
–0.121
strong negative control (why?),
–0.271
0.792
balanced by three non-negligible
0.999positive
1.000
other
values. 1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
CGS
0.240
LysRNA
ThrRNA
IleRNA
0.068
0.364
0.077
0.146
However, even with the small values
blanked out it’s not easy to see the trends
in a table of numbers.
–0.065
–0.127
0.076
0.090
0.010
0.087
–0.193
0.116
0.136
–0.077
0.054
–0.910
0.967
0.088
0.297
0.202
0.181
0.999
0.873
0.272
0.244
1.001
0.999
0.054
0.698
–0.271
1.000
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
CGS
0.240
LysRNA
ThrRNA
IleRNA
0.068
0.364
0.077
0.146
However, even with the small values
blanked out it’s not easy to see the trends
in a table of numbers.
0.010
So–0.065
we shall–0.127
look at0.087
it a
0.076
way.
0.090
–0.193
–0.077
more
graphical
0.116
0.136
0.054
–0.910
0.967
0.088
0.297
0.202
0.181
0.999
0.873
0.272
0.244
1.001
0.999
0.054
0.698
–0.271
1.000
–0.121
0.792
1.001
flux control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
ASADH
0.196
CGS
0.240
LysRNA
ThrRNA
IleRNA
0.068
0.364
0.077
0.146
However, even with the small values
blanked out it’s not easy to see the trends
in a table of numbers.
0.010
So–0.065
we shall–0.127
look at0.087
it a
0.076
way.
0.090
–0.193
–0.077
more
graphical
0.116
0.136
0.054
First, however, we shall look briefly at
–0.910 control coefficients
the concentration
0.967
0.088
0.297
0.202
0.181
0.999
0.873
0.272
0.244
1.001
0.999
0.054
0.698
–0.271
1.000
–0.121
0.792
1.001
concentration control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
AspP
0.413
0.087
0.016
0.049
–0.266
–0.023
–0.208
–0.203
–0.240
0.000
–0.018
0.019
0.012
0.138
0.118
0.106
0.000
ASA
0.499
0.105
0.020
0.059
0.021
–0.029
–0.264
–0.301
–0.354
0.000
–0.013
0.014
0.008
0.074
0.085
0.076
0.000
Thr
1.460
0.309
0.058
0.174
0.060
–0.085
–0.774
0.465
0.547
0.000
0.187
–0.199
–0.120
0.218
–1.215
–1.089
–0.004
Lys
0.259
0.055
0.010
0.031
0.011
0.036
0.330
–0.156
–0.184
0.000
–0.007
0.007
0.004
–0.480
0.044
0.039
–0.001
PHser
0.263
0.055
0.010
0.031
0.011
–0.015
–0.139
0.083
0.098
0.000
–0.996
–0.036
0.030
0.039
0.298
0.267
–0.001
Ile
0.577
0.122
0.023
0.069
0.024
–0.034
–0.305
0.183
0.216
0.000
0.074
–0.078
0.347
0.086
–0.479
–0.823
0.002
Hser
0.271
0.057
0.011
0.032
0.011
–0.016
–0.143
0.086
0.101
–1.067
–0.047
0.050
0.030
0.040
0.307
0.275
–0.002
e-Science
Institute
concentration control coefficients
eidsjPHser
d ln sj Ile
s
ASA
ThrE : Lys
Hser
ConcentrationAspP
control coefficient
for
=
i Ci =
s
de
d ln ei 0.577 0.271
AK1
0.413 0.499 1.460 0.259j i 0.263
AK2
0.087 0.105 0.309 0.055 0.055 0.122 0.057
This is a quantitative answer to the following question: how sensitive is
AK-I
0.016 0.020 0.058 0.010 0.010 0.023 0.011
the concentration sj of a metabolite Sj to the concentration ei of the ith
AK-IIE ?
0.049 0.059 0.174 0.031 0.031 0.069 0.032
enzyme
i
ASADH
–0.266 0.021 0.060 0.011Just 0.011
0.024at a0.011
one metabolite
time
DHDPS1 –0.023 –0.029 –0.085 n0.036 –0.015 –0.034 –0.016
!
sj–0.139 –0.305 –0.143
DHDPS2 property
–0.208 –0.264
Fundamental
(the –0.774 0.330
C
=
0
i
summation
HSDH I relationship):
–0.203 –0.301 0.465 –0.156 0.083 0.183 0.086
i=1
HSDH II –0.240 –0.354 0.547 –0.184 0.098 0.216 0.101
Sum over all enzymes
HSK
0.000 0.000 0.000 0.000 0.000 0.000 –1.067
TS1
–0.018 –0.013 0.187 –0.007 –0.996 0.074 –0.047
CGS
0.019 0.014 –0.199 0.007 –0.036 –0.078 0.050
TD
0.012 0.008 –0.120 0.004 0.030 0.347 0.030
LysRNA
0.138 0.074 0.218 –0.480 0.039 0.086 0.040
ThrRNA
0.118 0.085 –1.215 0.044 0.298 –0.479 0.307
IleRNA
0.106 0.076 –1.089 0.039 0.267 –0.823 0.275
Sums
0.000 0.000 –0.004 –0.001 –0.001 0.002 –0.002
j
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
e-Science
Institute
concentration control coefficients
eidsjPHser
d ln sj Ile
s
ASA
ThrE : Lys
Hser
ConcentrationAspP
control coefficient
for
=
i Ci =
s
de
d ln ei 0.577 0.271
AK1
0.413 0.499 1.460 0.259j i 0.263
AK2
0.087 0.105 0.309 0.055 0.055 0.122 0.057
This is a quantitative answer to the following question: how sensitive is
AK-I
0.016 0.020 0.058 0.010 0.010 0.023 0.011
the concentration sj of a metabolite Sj to the concentration ei of the ith
AK-IIE ?
0.049 0.059 0.174 0.031 0.031 0.069 0.032
enzyme
i
ASADH
–0.266 0.021 0.060 0.011Just 0.011
0.024at a0.011
one metabolite
time
DHDPS1 –0.023 –0.029 –0.085 n0.036 –0.015 –0.034 –0.016
!
sj–0.139 –0.305 –0.143
DHDPS2 property
–0.208 –0.264
Fundamental
(the –0.774 0.330
C
=
0
i
summation
HSDH I relationship):
–0.203 –0.301 0.465 –0.156 0.083 0.183 0.086
i=1
HSDH II –0.240 –0.354 0.547 –0.184 0.098 0.216 0.101
Sum over all enzymes
HSK
0.000 0.000 0.000 0.000 0.000 0.000 –1.067
TS1
–0.018
–0.013
0.187metabolites
–0.007 –0.996
0.074
–0.047
In the
Arabidopsis
case the
principal
to consider
are AspP,
CGS semialdehyde
0.019 0.014
–0.078 0.050
aspartate
(ASA),–0.199
Thr, Lys,0.007
PHser, –0.036
Ile and Hser…
TD
0.012 0.008 –0.120 0.004 0.030 0.347 0.030
LysRNA
0.138 0.074 0.218 –0.480 0.039 0.086 0.040
ThrRNA
0.118 0.085 –1.215 0.044 0.298 –0.479 0.307
IleRNA
0.106 0.076 –1.089 0.039 0.267 –0.823 0.275
Sums
0.000 0.000 –0.004 –0.001 –0.001 0.002 –0.002
j
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
concentration control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
AspP
0.413
0.087
0.016
0.049
–0.266
–0.023
–0.208
–0.203
–0.240
0.000
–0.018
0.019
0.012
0.138
0.118
0.106
0.000
ASA
0.499
0.105
0.020
0.059
0.021
–0.029
–0.264
–0.301
–0.354
0.000
–0.013
0.014
0.008
0.074
0.085
0.076
0.000
Thr
1.460
0.309
0.058
0.174
0.060
–0.085
–0.774
0.465
0.547
0.000
0.187
–0.199
–0.120
0.218
–1.215
–1.089
–0.004
Lys
0.259
0.055
0.010
0.031
0.011
0.036
0.330
–0.156
–0.184
0.000
–0.007
0.007
0.004
–0.480
0.044
0.039
–0.001
PHser
0.263
0.055
0.010
0.031
0.011
–0.015
–0.139
0.083
0.098
0.000
–0.996
–0.036
0.030
0.039
0.298
0.267
–0.001
Ile
0.577
0.122
0.023
0.069
0.024
–0.034
–0.305
0.183
0.216
0.000
0.074
–0.078
0.347
0.086
–0.479
–0.823
0.002
Hser
0.271
0.057
0.011
0.032
0.011
–0.016
–0.143
0.086
0.101
–1.067
–0.047
0.050
0.030
0.040
0.307
0.275
–0.002
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
concentration control coefficients
AspP ASA
Thr
Lys
PHser
Ile
Hser
AK1
0.413 0.499 1.460 0.259 0.263 0.577 0.271
AK2
0.087 0.105 0.309 0.055 0.055 0.122 0.057
AK-I
0.016 0.020 0.058 0.010 0.010 0.023 0.011
AK-II
0.049 0.059 0.174 0.031 0.031 0.069 0.032
ASADH
–0.266 0.021 0.060 0.011 0.011 0.024 0.011
DHDPS1 –0.023 –0.029 –0.085 0.036 –0.015 –0.034 –0.016
DHDPS2 –0.208 –0.264 –0.774 0.330 –0.139 –0.305 –0.143
HSDH I –0.203 –0.301 0.465 –0.156 0.083 0.183 0.086
HSDH II –0.240 –0.354 0.547 –0.184 0.098 0.216 0.101
HSK
0.000 0.000 0.000 0.000 0.000 0.000 –1.067
TS1 Homoserine
–0.018 –0.013
0.187
–0.007 over
–0.996
0.074 –0.047
kinase has
no control
any metabolite
CGS concentration
0.019 0.014
–0.036 –0.078
apart –0.199
from that0.007
of its substrate,
for which0.050
it
a strong
negative
control.
TD exerts
0.012
0.008
–0.120
0.004 0.030 0.347 0.030
LysRNA
0.138 0.074 0.218 –0.480 0.039 0.086 0.040
ThrRNA
0.118 0.085 –1.215 0.044 0.298 –0.479 0.307
IleRNA
0.106 0.076 –1.089 0.039 0.267 –0.823 0.275
Sums
0.000 0.000 –0.004 –0.001 –0.001 0.002 –0.002
concentration control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
AspP
0.413
0.087
0.016
0.049
–0.266
–0.023
–0.208
–0.203
–0.240
0.000
–0.018
0.019
0.012
0.138
0.118
0.106
0.000
ASA
0.499
0.105
0.020
0.059
0.021
–0.029
–0.264
–0.301
–0.354
0.000
–0.013
0.014
0.008
0.074
0.085
0.076
0.000
Thr
1.460
0.309
0.058
0.174
0.060
–0.085
–0.774
0.465
0.547
0.000
0.187
–0.199
–0.120
0.218
–1.215
–1.089
–0.004
Lys
0.259
0.055
0.010
0.031
0.011
0.036
0.330
–0.156
–0.184
0.000
–0.007
0.007
0.004
–0.480
0.044
0.039
–0.001
PHser
0.263
0.055
0.010
0.031
0.011
–0.015
–0.139
0.083
0.098
0.000
–0.996
–0.036
0.030
0.039
0.298
0.267
–0.001
Ile
0.577
0.122
0.023
0.069
0.024
–0.034
–0.305
0.183
0.216
0.000
0.074
–0.078
0.347
0.086
–0.479
–0.823
0.002
Hser
0.271
0.057
0.011
0.032
0.011
–0.016
–0.143
0.086
0.101
–1.067
–0.047
0.050
0.030
0.040
0.307
0.275
–0.002
concentration control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
AspP
0.413
0.087
0.016
0.049
–0.266
–0.023
–0.208
–0.203
–0.240
0.000
–0.018
0.019
0.012
0.138
0.118
0.106
0.000
ASA
0.499
0.105
0.020
0.059
0.021
–0.029
–0.264
–0.301
–0.354
0.000
–0.013
0.014
0.008
0.074
0.085
0.076
0.000
Thr
1.460
0.309
0.058
0.174
0.060
–0.085
–0.774
0.465
0.547
0.000
0.187
–0.199
–0.120
0.218
–1.215
–1.089
–0.004
Lys
PHser
Ile
Hser
0.259 0.263 0.577 0.271
0.055 0.055 0.122 0.057
There are
many0.023
more non-trivial
0.010
0.010
0.011
values than we saw with the flux
0.031 0.031 0.069 0.032
control coefficients
0.011 0.011 0.024 0.011
0.036 –0.015 –0.034 –0.016
0.330 –0.139 –0.305 –0.143
–0.156 0.083 0.183 0.086
–0.184 0.098 0.216 0.101
0.000 0.000 0.000 –1.067
–0.007 –0.996 0.074 –0.047
0.007 –0.036 –0.078 0.050
0.004 0.030 0.347 0.030
–0.480 0.039 0.086 0.040
0.044 0.298 –0.479 0.307
0.039 0.267 –0.823 0.275
–0.001 –0.001 0.002 –0.002
concentration control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
AspP
0.413
0.087
0.016
0.049
–0.266
–0.023
–0.208
–0.203
–0.240
0.000
–0.018
0.019
0.012
0.138
0.118
0.106
0.000
ASA
0.499
0.105
0.020
0.059
0.021
–0.029
–0.264
–0.301
–0.354
0.000
–0.013
0.014
0.008
0.074
0.085
0.076
0.000
Thr
1.460
0.309
0.058
0.174
0.060
–0.085
–0.774
0.465
0.547
0.000
0.187
–0.199
–0.120
0.218
–1.215
–1.089
–0.004
Lys
PHser
Ile
Hser
0.259 0.263 0.577 0.271
0.055 0.055 0.122 0.057
0.010 0.010 0.023 0.011
The
concentration
of threonine
0.031
0.031 0.069
0.032is
sensitive to the activity of almost
0.011enzyme.
0.011 0.024 0.011
every
0.036 –0.015 –0.034 –0.016
0.330 –0.139 –0.305 –0.143
–0.156 0.083 0.183 0.086
–0.184 0.098 0.216 0.101
0.000 0.000 0.000 –1.067
–0.007 –0.996 0.074 –0.047
0.007 –0.036 –0.078 0.050
0.004 0.030 0.347 0.030
–0.480 0.039 0.086 0.040
0.044 0.298 –0.479 0.307
0.039 0.267 –0.823 0.275
–0.001 –0.001 0.002 –0.002
concentration control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
AspP
0.413
0.087
0.016
0.049
–0.266
–0.023
–0.208
–0.203
–0.240
0.000
–0.018
0.019
0.012
0.138
0.118
0.106
0.000
ASA
0.499
0.105
0.020
0.059
0.021
–0.029
–0.264
–0.301
–0.354
0.000
–0.013
0.014
0.008
0.074
0.085
0.076
0.000
Thr
1.460
0.309
0.058
0.174
0.060
–0.085
–0.774
0.465
0.547
0.000
0.187
–0.199
–0.120
0.218
–1.215
–1.089
–0.004
Lys
PHser
Ile
Hser
0.259 0.263 0.577 0.271
0.055 0.055 0.122 0.057
0.010 0.010 0.023 0.011
The
concentration
of threonine
0.031
0.031 0.069
0.032is
sensitive to the activity of almost
0.011enzyme.
0.011 0.024 0.011
every
0.036 –0.015 –0.034 –0.016
–0.139 concentration
–0.305 –0.143
Is0.330
the threonine
a
signal
outside the
–0.156to enzymes
0.083 0.183
0.086
pathway
defined?
–0.184 as
0.098
0.216 0.101
0.000 0.000 0.000 –1.067
–0.007 –0.996 0.074 –0.047
0.007 –0.036 –0.078 0.050
0.004 0.030 0.347 0.030
–0.480 0.039 0.086 0.040
0.044 0.298 –0.479 0.307
0.039 0.267 –0.823 0.275
–0.001 –0.001 0.002 –0.002
concentration control coefficients
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
AK1
AK2
AK-I
AK-II
ASADH
DHDPS1
DHDPS2
HSDH I
HSDH II
HSK
TS1
CGS
TD
LysRNA
ThrRNA
IleRNA
Sums
AspP ASA
Thr
Lys
PHser
Ile
0.413 0.499 1.460 0.259 0.263 0.577
0.087
0.105 0.309
0.055 0.055
0.122
Every metabolite
concentration,
especially
[Thr],
the activity
0.016 varies
0.020according
0.058 to0.010
0.010of AK1
0.023
0.049 0.059 0.174 0.031 0.031 0.069
–0.266 0.021 0.060 0.011 0.011 0.024
–0.023 –0.029 –0.085 0.036 –0.015 –0.034
–0.208 –0.264 –0.774 0.330 –0.139 –0.305
–0.203 –0.301 0.465 –0.156 0.083 0.183
–0.240 –0.354 0.547 –0.184 0.098 0.216
0.000 0.000 0.000 0.000 0.000 0.000
–0.018 –0.013 0.187 –0.007 –0.996 0.074
0.019 0.014 –0.199 0.007 –0.036 –0.078
0.012 0.008 –0.120 0.004 0.030 0.347
0.138 0.074 0.218 –0.480 0.039 0.086
0.118 0.085 –1.215 0.044 0.298 –0.479
0.106 0.076 –1.089 0.039 0.267 –0.823
0.000 0.000 –0.004 –0.001 –0.001 0.002
Hser
0.271
0.057
0.011
0.032
0.011
–0.016
–0.143
0.086
0.101
–1.067
–0.047
0.050
0.030
0.040
0.307
0.275
–0.002
flux control coefficients
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
flux
control
coefficients
Asp:
1.5 mM
0.742
edinburgh
6–8 april 2011
1
Asp: 1.5 mM 0.092
0.157 0.028
1
2
I
II
2
I
II ak
Asp-P: 0.34 µM
Feedback inhibition
Asp-P: 0.34
µM
2
Lys: 69 µM
ASA: 0.96 µM
1.02
1
Metabolic models
+
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Lys:
69concentrations
µM
Measured
Elasticities
Flux distribution
rs
Control coefficients
Flux
Concentration
2
0.285
Lys-tRNA
Thursday, 7 April 2011
I
II
aars
dhdps
ASA: Cys:
0.96
µM
15 µM
0.38
0.031
1
AdoMet: 20 µM
PHser: 45 µM
I
hsdh
Lys
0.323
Val: 100 µM
aars
II
What have we learned?
Lys-tRNA
ak1
Hser: 0.94 µM
Regulatory effectiveness
Perspectives
asadh
Common flux
Ile-tRNA
HSer:
0.94 µM
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
flux
control
coefficients
Asp:
1.5 mM
0.742
edinburgh
6–8 april 2011
1
Asp: 1.5 mM 0.092
0.157 0.028
1
2
I
II
2
I
II ak
Asp-P: 0.34 µM
Feedback inhibition
Asp-P: 0.34
µM
2
Lys: 69 µM
ASA: 0.96 µM
1.02
1
Metabolic models
+
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Lys:
69concentrations
µM
Measured
Elasticities
Flux distribution
rs
Control coefficients
Flux
Concentration
2
0.285
Lys-tRNA
Thursday, 7 April 2011
aars
dhdps
ASA: Cys:
0.96
µM
15 µM
0.38
0.031
1
AdoMet: 20 µM
PHser: 45 µM
I
hsdh
Lys
0.323
Val: 100 µM
aars
II
What have we learned?
Lys-tRNA
II
Aspartate
kinase 1
ak
1(20%)
Hser: 0.94 µM
Regulatory effectiveness
Perspectives
asadh
I
Common flux
Ile-tRNA
HSer:
0.94 µM
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
flux
control
coefficients
Asp:
1.5 mM
0.742
edinburgh
6–8 april 2011
1
Asp: 1.5 mM 0.092
0.157 0.028
1
2
I
II
2
I
II ak
Asp-P: 0.34 µM
Feedback inhibition
Asp-P: 0.34
µM
2
Lys: 69 µM
ASA: 0.96 µM
1.02
1
Metabolic models
+
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Lys:
69concentrations
µM
Measured
Elasticities
Flux distribution
rs
Control coefficients
Flux
Concentration
2
0.285
Lys-tRNA
Thursday, 7 April 2011
aars
Aminoacyl tRNA
synthetases (18–30%)
dhdps
ASA: Cys:
0.96
µM
15 µM
0.38
0.031
1
AdoMet: 20 µM
PHser: 45 µM
I
hsdh
Lys
0.323
Val: 100 µM
aars
II
What have we learned?
Lys-tRNA
II
Aspartate
kinase 1
ak
1(20%)
Hser: 0.94 µM
Regulatory effectiveness
Perspectives
asadh
I
Common flux
Ile-tRNA
HSer:
0.94 µM
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
+
Asp-P:
0.34
µM
flux
control
coefficients
Asp:
1.5 mM
1.02
Asp: 1.5 mM 0.092
0.157 0.028
0.742
1
2
I asadh
II
1
2
ak
I
II
0.285
2
Asp-P: 0.34 µM
dhdps
ASA: 0.96 µM
Asp-P:
0.34
µM
2
Aspartate metabolism in
0.38
0.031
+A. thaliana chloroplasts
1
Lys: 69 µM
ASA: 0.96 µM
Lys
1.02
Aspartate
kinase 1
aars
ak
1
Lysine
flux
I
1
Isoenzymes
(20%)
I
hsdh
Lys
0.323
Regulation
aarsasadh
IIAminoacyl tRNA
Simulation
II
0.285
synthetases
(18–30%)
aars
Lys-tRNA
2
Rate equations
Hser: 0.94 µM
External metabolites
HSer: 0.94 µM
dhdps
Lys:
69concentrations
µM
ASA: Cys:
0.96
µM
Measured
15 µM
Elasticities
Lys-tRNA
0.38
0.031
1
Flux distribution
hsk
AdoMet: 20 µM
Cys:
15
µM
rs
PHser: 45 µM
I
Control coefficients
0.703
hsdh
Flux
Lys
0.323
Val: 100 µM
aars
II
Concentration
cgs
cgs
PHser: 45 µM
Feedback inhibition
Metabolic models
Lys: 69 µM
Regulatory effectiveness
What have we learned?
Perspectives
Lys-tRNA
Thursday, 7 April 2011
tsIle-tRNA
HSer:
0.94 µM
Ile: 59 µM
Thr: 303 µM
0.058
+
ts1
0.645
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
+
Asp-P:
0.34
µM
flux
control
coefficients
Asp:
1.5 mM
1.02
Asp: 1.5 mM 0.092
0.157 0.028
0.742
1
2
I asadh
II
1
2
ak
I
II
0.285
2
Asp-P: 0.34 µM
dhdps
ASA: 0.96 µM
Asp-P:
0.34
µM
2
Aspartate metabolism in
0.38
0.031
+A. thaliana chloroplasts
1
Lys: 69 µM
ASA: 0.96 µM
Lys
1.02
Aspartate
kinase 1
aars
ak
1
Lysine
flux
I
1
Isoenzymes
(20%)
I
hsdh
Lys
0.323
Regulation
aarsasadh
IIAminoacyl tRNA
Simulation
II
0.285
synthetases
(18–30%)
aars
Lys-tRNA
2
Rate equations
Lysyl tRNA
Hser: 0.94 µM
External metabolites
HSer: 0.94 µM
synthetase (90%)
dhdps
Lys:
69concentrations
µM
ASA: Cys:
0.96
µM
Measured
15 µM
Elasticities
Lys-tRNA
0.38
0.031
1
Flux distribution
hsk
AdoMet: 20 µM
Cys:
15
µM
rs
PHser: 45 µM
I
Control coefficients
0.703
hsdh
Flux
Lys
0.323
Val: 100 µM
aars
II
Concentration
cgs
cgs
PHser: 45 µM
Feedback inhibition
Metabolic models
Lys: 69 µM
Regulatory effectiveness
What have we learned?
Perspectives
Lys-tRNA
Thursday, 7 April 2011
tsIle-tRNA
HSer:
0.94 µM
Ile: 59 µM
Thr: 303 µM
0.058
+
ts1
0.645
Thr-tRNA
III
hsdh
Asp-P:
0.34
µM
0.323
aars flux
control
coefficients
II
Asp:
1.5
mM
+
e-Science
HSer: 0.94 µM
1.02
Institute
Asp: 1.5 mM 0.092
0.157
0.028
0.742
HSer:
0.94
µM
1
2
I asadh
II
Lys-tRNA
edinburgh
1
2
ak
6–8 april 2011
hsk
I
II
0.285
Cys:
15
µM
2
Lys-tRNA
Asp-P:0.703
0.34 µM
hsk
Cys: 15dhdps
µM
Feedback inhibition
Lys: 69 cgs
µM
ASA:
0.96
µM
cgs
Metabolic models
0.703
PHser:
45
µM
Asp-P:
0.34
µM
2
Aspartate metabolism in
0.38
cgs
0.031
+A. thaliana chloroplasts
1
Lys: 69 µM
cgs
ASA:45
0.96 µM
PHser:
µM
Lys
1.02
Aspartate
kinase 1
aars
0.645
ak
1
I
0.058
1
ts
Isoenzymes
(20%)
+
I
ts
1
hsdh
Lys
0.323
Regulation
0.645 II
aarsasadh
0.058
ts
Aminoacyl tRNA
Simulation
+
ts
1
II
0.285
synthetases
(18–30%)
aars
Lys-tRNA
2
Rate equations
Lysyl tRNA
Hser: µM
0.94 µM
Thr:
303
External metabolites
HSer: 0.94 µM
synthetase (90%)
Met:
20
µM
dhdps
0.323
Lys:
69concentrations
µM
ASA: Cys:
0.96
µM
Measured
Thr:
303
µM Threonine flux
15 µM
td 0.32
Elasticities
Threonyl tRNA
doMet:
20 µM Lys-tRNA
Thr
0.323
0.38
aars
synthetase
0.031
1 20 µM
Flux distributionIle
hsk
AdoMet:
Cys:
15
µM
td
aars
rs
0.32
Isoleucine
flux
PHser:
45 µM
Thr
Thr
I
Control coefficients
aars
aars
0.703
hsdh
Ile
Flux
Isoleucyl
tRNA
Lys
0.323
ak1
aars
Val: 100 µM
II
Thraars
synthetase aars
Ile: 59 µM cgs
Concentration
cgs
PHser:
45
µM
ak1
Regulatory effectiveness
Ile: 59 µM
What have we learned?
HSer:
0.94
µM
Ile:
59
µM
Thr-tRNA
0.645
Thr: 303 µM
Thr-tRNA
tsIle-tRNA 0.058
Perspectives
+
ts
1
Ile
Lys-tRNA
aars
Thr-tRNA
Val: 100 µM
Lys
Thursday, 7 April 2011
III
hsdh
0.38
Asp-P:
0.34
µM
0.323
0.031
1
aars
flux
control
coefficients
II
Lys
Asp:
1.5
mM
aars
+
e-Science
HSer: 0.94I µM
1.02
Institute
hsdh
Lys
0.323
Asp:
1.5
mM
0.092
0.157
0.028
0.742
aars
II
HSer:
0.94
µM
1
2
I asadh
II
Lys-tRNA
edinburgh
1
2
ak
6–8 april 2011
hsk
I
II
0.285
Cys:
15
µM
2 HSer:Asp-P:
Lys-tRNA
0.94
µM
0.34 µM
0.703
hsk
Cys: 15dhdps
µM
Feedback inhibition
Lys: 69 cgs
µM
ASA:
0.96
µM
cgs
Metabolic models
0.703
Lys-tRNA
PHser:
45
µM
Asp-P:
0.34
µM
2
Aspartate metabolism in
hsk
0.38
cgs
Cys:
15
µM
0.031
+A. thaliana chloroplasts
1
Lys: 69 µM
cgs
ASA:45
0.96 µM
PHser:
µM
Lys
1.02
aars
0.645
ak
1
0.703
I
0.058
1
Isoenzymes ts
AdoMet flux
+
I
ts
1
hsdh
Lys
0.323
Regulation
0.645
asadh
aars
cgs
II
0.058
ts
cgs
PHser:
45
µM
Simulation
+
ts
1 II
0.285
aars
Lys-tRNA
2
Rate equations
Hser: µM
0.94 µM
Thr:
303
External metabolites
HSer:0.645
0.94 µM
0.058
ts
Met:
20
µM
dhdps
0.323
Lys:
69concentrations
µM
ASA:+Cys:
0.96
µM
Measured
ts
1
Thr:
303
µM
15 µM
td 0.32
Elasticities
doMet:
20 µM Lys-tRNA
Thr
0.323
0.38
aars
0.031
1 20 µM
Flux distributionIle
hsk
AdoMet:
Cys:
15
µM
td
aars
rs
0.32
PHser:
45 µM
Thr
Thr
I Thr:
Control coefficients
303
µM
aars
aars
0.703
hsdh
Flux
Lys
0.323
doMet:
20IleµM
ak1
aarsaars
Val: 100 µM
II
Thraars
0.323
Ile: 59 µM cgs
Concentration
cgs
PHser:
45
µM
td
0.32
akThr
1
Regulatory effectiveness
aars
Ile:
59
µM
Ile
What have we learned?
HSer:
0.94
µM
aars
Ile:
59
µM
Thr-tRNA
Thr303
0.645
Thr:
µM
Thr-tRNA
aars
tsIle-tRNA 0.058
Perspectives
+
ts
1
Ile
ak
1
Lys-tRNA
aars
Thr-tRNA
Val: 100 µM
Ile: 59 µM
Lys
Aspartate kinase 1
(20%)
Cystathione
γ-synthase
Aminoacyl tRNA
synthetases (18–30%)
Lysyl tRNA
synthetase (90%)
Threonyl tRNA
synthetase
Isoleucyl tRNA
synthetase
Thursday, 7 April 2011
III
hsdh
0.38
Asp-P:
0.34
µM
0.323
0.031
1
aars
flux
control
coefficients
II
Lys
Asp:
1.5
mM
aars
+
e-Science
HSer: 0.94I µM
1.02
Institute
hsdh
Lys
0.323
Asp:
1.5
mM
0.092
0.157
0.028
0.742
aars
II
HSer:
0.94
µM
1
2
I asadh
II
Lys-tRNA
edinburgh
1
2
ak
6–8 april 2011
hsk
I
II
0.285
Cys:
15
µM
2 HSer:Asp-P:
Lys-tRNA
0.94
µM
0.34 µM
0.703
hsk
Cys: 15dhdps
µM
Feedback inhibition
Lys: 69 cgs
µM
ASA:
0.96
µM
cgs
Metabolic models
0.703
Lys-tRNA
PHser:
45
µM
Asp-P:
0.34
µM
2
Aspartate metabolism in
hsk
0.38
cgs
Cys:
15
µM
0.031
+A. thaliana chloroplasts
1
Lys: 69 µM
cgs
ASA:45
0.96 µM
PHser:
µM
Lys
1.02
aars
0.645
ak
1
0.703
I
0.058
1
Isoenzymes ts
AdoMet flux
+
I
ts
1
hsdh
Lys
0.323
Regulation
0.645
asadh
aars
cgs
II
0.058
ts
cgs
PHser:
45
µM
Simulation
+
ts
1 II
0.285
aars
Lys-tRNA
2
Rate equations
Hser: µM
0.94 µM
Thr:
303
External metabolites
HSer:0.645
0.94 µM
0.058
ts
Met:
20
µM
dhdps
0.323
Lys:
69concentrations
µM
ASA:+Cys:
0.96
µM
Measured
ts
1
Thr:
303
µM
15 µM
td 0.32
Elasticities
doMet:
20 µM Lys-tRNA
Thr
0.323
0.38
aars
0.031
1 20 µM
Flux distributionIle
hsk
AdoMet:
Cys:
15
µM
td
aars
rs
0.32
PHser:
45 µM
Thr
Thr
I Thr:
Control coefficients
303
µM
aars
aars
0.703
hsdh
Flux
Lys
0.323
doMet:
20IleµM
ak1
aarsaars
Val: 100 µM
II
Thraars
0.323
Ile: 59 µM cgs
Concentration
cgs
PHser:
45
µM
td
0.32
akThr
1
Regulatory effectiveness
aars
Ile:
59
µM
Ile
What have we learned?
HSer:
0.94
µM
aars
Ile:
59
µM
Thr-tRNA
Thr303
0.645
Thr:
µM
Thr-tRNA
aars
tsIle-tRNA 0.058
Perspectives
+
ts
1
Ile
ak
1
Lys-tRNA
aars
Thr-tRNA
Val: 100 µM
Ile: 59 µM
Lys
Aspartate kinase 1
(20%)
Cystathione
γ-synthase
Threonine
synthase
Aminoacyl tRNA
synthetases (18–30%)
Lysyl tRNA
synthetase (90%)
Threonyl tRNA
synthetase
Isoleucyl tRNA
synthetase
Thursday, 7 April 2011
0.38
0.031
flux control coefficients
I
hsdh
Lys
0.323
Asp:
1.5
mM
aars
II
1
2
I
II
1
aars
e-Science
Lys
Institute
edinburgh
6–8 april 2011
HSer:Asp-P:
0.94
µM
0.34 µM
Feedback inhibition
Lys-tRNA
Metabolic models
Cys:
15 µM
Lys: 69 µM
Aspartate metabolism in
A. thaliana chloroplasts
Regulation
Simulation
cgs
PHser: 45II µM
Lys-tRNA
Rate equations
0.058
ts
Measured concentrations
Threonine
External metabolites
synthase
0.703
ASA: 0.96 µM
I
cgs
Cystathione
γ-synthase
hsk
1
AdoMet flux
Isoenzymes
2
+
Hser: 0.94 µM
Cys: 15 µM
ts1
0.645
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Thr:
303 µM
0.323
td 0.32
Control coefficients
Flux
doMet:
20 µM
Val: 100 µM
Concentration
Regulatory effectiveness
aars
Ile
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Ile: 59 µM
aars
Thr303 µM
Thr:
aars
Thr
Thr-tRNA
ak1
0.38
0.031
flux control coefficients
I
hsdh
Lys
0.323
Asp:
1.5
mM
aars
II
1
2
I
II
1
aars
e-Science
Lys
Institute
edinburgh
6–8 april 2011
HSer:Asp-P:
0.94
µM
0.34 µM
Feedback inhibition
Lys-tRNA
Metabolic models
Cys:
15 µM
Lys: 69 µM
Aspartate metabolism in
A. thaliana chloroplasts
Regulation
Simulation
cgs
PHser: 45II µM
Lys-tRNA
Rate equations
0.058
ts
Measured concentrations
Threonine
External metabolites
synthase
0.703
ASA: 0.96 µM
I
cgs
Cystathione
γ-synthase
hsk
1
AdoMet flux
Isoenzymes
2
+
Why not?
Cys: 15 µM
Hser: 0.94 µM
ts1
0.645
Elasticities
Flux distribution
AdoMet: 20 µM
Control coefficients
Flux
doMet:
20 µM
Val: 100 µM
Concentration
Regulatory effectiveness
aars
Ile
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
PHser: 45 µM
Thr:
303 µM
0.323
Why?td
0.32
Ile: 59 µM
Ile: 59 µM
aars
Thr303 µM
Thr:
aars
Thr
Thr-tRNA
ak1
0.38
0.031
flux control coefficients
I
hsdh
Lys
0.323
Asp:
1.5
mM
aars
II
1
2
I
II
1
aars
e-Science
Lys
Institute
edinburgh
6–8 april 2011
HSer:Asp-P:
0.94
µM
0.34 µM
Feedback inhibition
Lys-tRNA
Metabolic models
Cys:
15 µM
Lys: 69 µM
Aspartate metabolism in
A. thaliana chloroplasts
cgs
Regulation
Simulation
A small variation in the
PHser: 45II µM
threonine synthase flux
Lys-tRNA
Rate equations
0.058
ts
Measured concentrations
Threonine
External metabolites
synthase
0.703
ASA: 0.96 µM
I
cgs
Cystathione
γ-synthase
hsk
1
AdoMet flux
Isoenzymes
2
+
Why not?
Cys: 15 µM
Hser: 0.94 µM
ts1
Elasticities
Flux distribution
AdoMet: 20 µM
Control coefficients
Flux
doMet:
20 µM
Val: 100 µM
Concentration
Regulatory effectiveness
aars
Ile
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
produces a huge insta0.645
bility in the cystathione
γ-synthase flux.
PHser: 45 µM
Thr:
303 µM
0.323
Why?td
0.32
Ile: 59 µM
Ile: 59 µM
aars
Thr303 µM
Thr:
aars
Thr
Thr-tRNA
ak1
0.38
0.031
flux control coefficients
I
hsdh
Lys
0.323
Asp:
1.5
mM
aars
II
1
2
I
II
1
aars
e-Science
Lys
Institute
edinburgh
6–8 april 2011
HSer:Asp-P:
0.94
µM
0.34 µM
Feedback inhibition
Lys-tRNA
Metabolic models
Cys:
15 µM
Lys: 69 µM
Aspartate metabolism in
A. thaliana chloroplasts
cgs
Regulation
Simulation
I
cgs
Cystathione
γ-synthase
A small variation in the
PHser: 45II µM
threonine synthase flux
Lys-tRNA
Rate equations
0.058
ts
Measured concentrations
Threonine
External metabolites
synthase
0.703
ASA: 0.96 µM
1
AdoMet flux
Isoenzymes
hsk
2
Hser: 0.94 µM
+
Why not?
Cys: 15 µM
ts1
Elasticities
Flux distribution
Activation by AdoMet
PHser: 45 µM helps to counteract that
Thr:
303 µM
effect.
AdoMet: 20 µM
Control coefficients
Flux
doMet:
20 µM
Val: 100 µM
Concentration
Regulatory effectiveness
aars
Ile
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
produces a huge insta0.645
bility in the cystathione
γ-synthase flux.
Why?td
Ile: 59 µM
Ile: 59 µM
0.32
aars
Thr303 µM
Thr:
0.323
aars
Thr
Thr-tRNA
ak1
0.38
0.031
flux control coefficients
I
hsdh
Lys
0.323
Asp:
1.5
mM
aars
II
1
2
I
II
1
aars
e-Science
Lys
Institute
edinburgh
6–8 april 2011
HSer:Asp-P:
0.94
µM
0.34 µM
Feedback inhibition
Lys-tRNA
Metabolic models
Cys:
15 µM
Lys: 69 µM
Aspartate metabolism in
A. thaliana chloroplasts
cgs
Regulation
Simulation
I
cgs
Cystathione
γ-synthase
A small variation in the
PHser: 45II µM
threonine synthase flux
Lys-tRNA
Rate equations
0.058
ts
Measured concentrations
Threonine
External metabolites
synthase
0.703
ASA: 0.96 µM
1
AdoMet flux
Isoenzymes
hsk
2
Hser: 0.94 µM
+
Why not?
Cys: 15 µM
ts1
Elasticities
Flux distribution
Activation by AdoMet
PHser: 45 µM helps to counteract that
Thr:
303 µM
effect.
AdoMet: 20 µM
Control coefficients
Flux
doMet:
20 µM
Val: 100 µM
Concentration
Regulatory effectiveness
aars
Ile
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
produces a huge insta0.645
bility in the cystathione
γ-synthase flux.
Why?td
Ile: 59 µM
Ile: 59 µM
0.32
aars
Thr303 µM
Thr:
0.323
(Activation is in generThr
aars
al rare as a regulatory
mechanism.)
Thr-tRNA
ak1
e-Science
Institute
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
1. If lysine is consumed in
substantial amounts in
other pathways does that
make it less available for
making protein?
1
II
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
Regulation
Rate equations
I
Asp-P: 0.34 µM
Aspartate metabolism in
Simulation
2
Lys-tRNA
Lysine
ketoglutarate
reductase
I
II
Hser: 0.94 µM
External metabolites
WASTE
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
1. If lysine is consumed in
substantial amounts in
other pathways does that
make it less available for
making protein?
1
2
Lys: 69 µM
Simulation
Rate equations
Lys-tRNA
0.4
I
Lysine
ketoglutarate
reductase
II
Hser: 0.94 µM
External metabolites
WASTE
Measured concentrations
0.0
Cys: 15 µM
Elasticities
Flux distribution
II
Flux, ASA: 0.96 µM
1 s–1
µM
Isoenzymes
Regulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
0
15
30
[Lysine ketoglutarate reductase], µM
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
1. If lysine is consumed in
substantial amounts in
other pathways does that
make it less available for
making protein?
1
2
Lys: 69 µM
Simulation
Rate equations
Lys-tRNA
0.4
I
Lysine
ketoglutarate
reductase
II
Lysine to waste
Hser: 0.94 µM
External metabolites
WASTE
Measured concentrations
0.0
Cys: 15 µM
Elasticities
Flux distribution
II
Flux, ASA: 0.96 µM
1 s–1
µM
Isoenzymes
Regulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
0
15
30
[Lysine ketoglutarate reductase], µM
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
1. If lysine is consumed in
substantial amounts in
other pathways does that
make it less available for
making protein?
1
2
Lys: 69 µM
Simulation
Rate equations
Lys-tRNA
0.4
Threonine to protein
I
Lysine
ketoglutarate
reductase
II
Lysine to waste
Hser: 0.94 µM
External metabolites
WASTE
Measured concentrations
0.0
Cys: 15 µM
Elasticities
Flux distribution
II
Flux, ASA: 0.96 µM
1 s–1
µM
Isoenzymes
Regulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
0
15
30
[Lysine ketoglutarate reductase], µM
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
1. If lysine is consumed in
substantial amounts in
other pathways does that
make it less available for
making protein? No!
1
2
Lys: 69 µM
Simulation
Rate equations
Lys-tRNA
0.4
Threonine to protein
I
Lysine
ketoglutarate
reductase
II
Lysine to waste
Hser: 0.94 µM
External metabolites
WASTE
Measured concentrations
0.0
Cys: 15 µM
Elasticities
Flux distribution
II
Flux, ASA: 0.96 µM
1 s–1
Lysine to protein
µM
Isoenzymes
Regulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
0
15
30
[Lysine ketoglutarate reductase], µM
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
2. If threonine is consumed
in substantial amounts in
other pathways does that
make it less available for
making protein?
1
II
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Rate equations
I
Asp-P: 0.34 µM
Aspartate metabolism in
Simulation
2
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
2. If threonine is consumed
in substantial amounts in
other pathways does that
make it less available for
making protein? No!
1
2
Regulation
Rate equations
II
Hser: 0.94 µM
External metabolites
0.0
Cys: 15 µM
Elasticities
Flux distribution
0.4
Lys-tRNA
Measured concentrations
0
PHser: 45 µM
Control coefficients
Threonine to waste
4
8
[Threonine aldolase], µM
AdoMet: 20 µM
Flux
II
Lysine to protein
Flux,ASA: 0.96 µM
1 µM s –1
I Threonine to protein
Lys: 69 µM
Isoenzymes
Simulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
Val: 100 µM
Threonine
aldolase
WASTE
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
3. We can ask the same
question in relation to
methionine by modulating the concentration of Sadenosylmethionine.
1
II
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Rate equations
I
Asp-P: 0.34 µM
Aspartate metabolism in
Simulation
2
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
Met
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
3. We can ask the same
question in relation to
methionine by modulating the concentration of Sadenosylmethionine.
1
2
0.5
I
Regulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
0.0
Measured concentrations
Cys: 15 µM
0
Elasticities
Flux distribution
II
Flux, ASA: 0.96 µM
1µM s –1
Lys: 69 µM
Isoenzymes
Simulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
AdoMet: 20 µM
Met
PHser: 45 µM
Control coefficients
Flux
15
[AdoMet], µM
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
30
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
3. We can ask the same
question in relation to
methionine by modulating the concentration of Sadenosylmethionine.
1
2
I
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
0.0
Measured concentrations
Cys: 15 µM
0
Elasticities
Flux distribution
Reference
concentration
0.5
Regulation
Rate equations
II
Flux, ASA: 0.96 µM
1µM s –1
Lys: 69 µM
Isoenzymes
Simulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
AdoMet: 20 µM
Met
PHser: 45 µM
Control coefficients
Flux
15
[AdoMet], µM
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
30
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
3. We can ask the same
question in relation to
methionine by modulating the concentration of Sadenosylmethionine.
1
2
II
Lys-tRNA
Hser: 0.94 µM
0.0
30 µM
Cys: 15 µM
0
Elasticities
Flux distribution
AdoMet: 20 µM
Met
2 µM
15
[AdoMet], µM
PHser: 45 µM
Control coefficients
Flux
…increase
by 50%
I
External metabolites
Measured concentrations
Reference
concentration
0.5
Regulation
Rate equations
II
Flux, ASA: 0.96 µMDecrease by
90% or…
1µM s –1
Lys: 69 µM
Isoenzymes
Simulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
30
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
3. We can ask the same
question in relation to
methionine by modulating the concentration of Sadenosylmethionine.
1
2
0.5
Hser: 0.94 µM
0.0
30 µM
Cys: 15 µM
0
Elasticities
Flux distribution
AdoMet: 20 µM
Met
2 µM
15
[AdoMet], µM
PHser: 45 µM
Control coefficients
Flux
Flux through
methionine
II
Lys-tRNA
External metabolites
Measured concentrations
Reference
concentration
I
Regulation
Rate equations
II
Threonine to protein
Flux, ASA: 0.96 µM
1µM s –1
Lysine to protein
Lys: 69 µM
Isoenzymes
Simulation
I
Asp-P: 0.34 µM
Aspartate metabolism in
A. thaliana chloroplasts
2
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
30
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
4. What about decreasing or
increasing the demand for
protein? Can the system
respond satisfactorily?
1
2
I
II
Asp-P: 0.34 µM
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
4. What about decreasing or
increasing the demand for
protein? Can the system
respond satisfactorily?
1
2
I
II
Asp-P: 0.34 µM
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
Flux,ASA: 0.96 µM
1
µM s –1
“Normal”
I
Isoenzymes
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
0.0
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
1.2
0
1
2
Demand for protein, µM s–1
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
4. What about decreasing or
increasing the demand for
protein? Can the system
respond satisfactorily?
1
2
I
II
Asp-P: 0.34 µM
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
Flux,ASA: 0.96 µM
1
µM s –1
“Normal”
I
Isoenzymes
Regulation
Simulation
Rate equations
Lysine
Threonine
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
0.0
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
1.2
0
Isoleucine
1
2
Demand for protein, µM s–1
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
effectiveness of the regulatory mechanisms
Are the fluxes well regulated according to demand for products ?
Asp: 1.5 mM
4. What about decreasing or
increasing the demand for
protein? Can the system
respond satisfactorily? Yes!
1
2
I
II
Asp-P: 0.34 µM
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
1
Isoenzymes
Regulation
Simulation
Rate equations
1.2
No steady
Flux,ASA: 0.96 µM state
µM s –1
I
“Normal”
Threonine
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
0.0
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
Lysine
0
Isoleucine
1
2
Demand for protein, µM s–1
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
II
Asp-P: 0.34 µM
Feedback inhibition
1. The very large con-
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
centration of aspartate semialdehyde
dehydrogenase is
needed to overcome
the unfavourable
equilibrium in the
aspartate kinase
reaction.
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
2. The mass of inhibitAsp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
I
Regulation
Rate equations
ory effects allow
production of
unwanted metabolites to be switched
off.
1
Isoenzymes
Simulation
II
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
2. The mass of inhibitAsp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
I
Regulation
Rate equations
ory effects allow
production of
unwanted metabolites to be switched
off.
1
Isoenzymes
Simulation
II
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
3. The flux to SAsp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
II
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
adenosylmethionine is much smaller
than the flux to
threonine:
activation of
threonine synthase
by S-adenosylmethionine avoids a
“branchpoint
effect” whereby
small changes in the
synthesis of
threonine would
perturb the
regulation of Sadenosylmethionine
production.
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
3. The flux to SAsp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
II
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
adenosylmethionine is much smaller
than the flux to
threonine:
activation of
threonine synthase
by S-adenosylmethionine avoids a
“branchpoint
effect” whereby
small changes in the
synthesis of
threonine would
perturb the
regulation of Sadenosylmethionine
production.
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
4. The synergistic
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
II
Lys-tRNA
amplification by Sadenosylmethionine of the weak
inhibition of
aspartate kinase 1
by lysine allows
demand for Sadenosylmethionine to regulate its
production.
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
what have we learned from this?
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
4. The synergistic
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
II
Lys-tRNA
amplification by Sadenosylmethionine of the weak
inhibition of
aspartate kinase 1
by lysine allows
demand for Sadenosylmethionine to regulate its
production.
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
1. What purpose is
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
1
I
Regulation
Rate equations
served by the presence of isoenzymes
that carry almost no
flux?
ASA: 0.96 µM
Isoenzymes
Simulation
II
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
1. What purpose is
Asp-P: 0.34 µM
served by the presence of isoenzymes
that carry almost no
flux?
ASA: 0.96 µM
The flux results
refer to one set of
conditions, but the
plant must survive
in different circumstances.
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
1. What purpose is
Asp-P: 0.34 µM
served by the presence of isoenzymes
that carry almost no
flux?
ASA: 0.96 µM
The flux results
refer to one set of
conditions, but the
plant must survive
in different circumstances.
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
II
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Isoenzymes that do
very little in the
reference state can
take the strain if
there are knockouts.
(I have results for
knockouts if you
wish to see them.)
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
2. What purpose is
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
served by the presence of two isoenzymes that carry
almost the same
flux and have the
same regulatory
properties?
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
2. What purpose is
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
II
AdoMet: 20 µM
served by the presence of two isoenzymes that carry
almost the same
flux and have the
same regulatory
properties?
These properties
are similar, but
they are by no
means identical:
they can respond
differently to
particular stresses.
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
3. What purpose is
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
I
Regulation
Rate equations
served by having
two bifunctional
proteins catalysing
non-consecutive
processes?
1
Isoenzymes
Simulation
II
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
3. What purpose is
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
II
Lys-tRNA
served by having
two bifunctional
proteins catalysing
non-consecutive
processes?
(This is a property
conserved since the
divergence of plants
and bacteria!)
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
4. Why does valine
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
Lys: 69 µM
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Rate equations
damp the inhibition
by isoleucine of
isoleucine synthesis?
2
A. thaliana chloroplasts
Simulation
II
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
some points for further reflection
e-Science
Institute
Asp: 1.5 mM
1
edinburgh
6–8 april 2011
2
I
4. Why does valine
Asp-P: 0.34 µM
Feedback inhibition
Metabolic models
Aspartate metabolism in
2
Lys: 69 µM
A. thaliana chloroplasts
ASA: 0.96 µM
1
Isoenzymes
I
Regulation
Simulation
Rate equations
II
II
Lys-tRNA
Hser: 0.94 µM
External metabolites
Measured concentrations
damp the inhibition
by isoleucine of
isoleucine synthesis?
This is probably
related to the fact
that parts of the
valine and isoleucine
molecules are derived from pyruvate
(not considered in
the model).
Cys: 15 µM
Elasticities
Flux distribution
AdoMet: 20 µM
PHser: 45 µM
Control coefficients
Flux
Val: 100 µM
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
Ile-tRNA
Ile: 59 µM
Thr: 303 µM
Thr-tRNA
co
raibh
maith
agat
as
do
aire
(Thank you for your attention)
acornish@ifr88.cnrs-mrs.fr
http://bip.cnrs-mrs.fr/bip10/homepage.htm
Thursday, 7 April 2011
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
enzyme knock-outs
Can the system cope?
Table 1: Effects of knock-outs on fluxes and intermediate concentrations
Step
JAK ≡ JASADH
[Thr]
[Lys]
[Ile]
Wild type
1.016
296
69.2
58.8
AK1
AK2
AK1 and 2
AK2, I and II
AK I
AK II
AK I and II
0.812
0.974
0.749
0.869
1.010
0.978
0.869
92.7
223
70.3
122
285
228
206
54.6
65.7
50.6
58.2
68.8
66.0
64.8
32.9
52.3
26.4
39.2
58.0
52.9
50.6
DHDPS 1
DHDPS 2
DHSPS 1 and 2
1.025
1.141
LystRNA
ThrtRNA
IletRNA
0.543
0.757
0.789
330
66.8
7760
37.9
— no steady state —
104
11400
6600
5 × 108
81.9
79.0
61.3
179.2
35.5
203
2 × 1013
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
enzyme knock-outs
Can the system cope?
Table 1: Effects of knock-outs on fluxes and intermediate concentrations
Step
JAK ≡ JASADH
Wild type
1.016
AK1
AK2
AK1 and 2
AK2, I and II
AK I
AK II
AK I and II
0.812
0.974
0.749
0.869
1.010
0.978
0.869
DHDPS 1
DHDPS 2
DHSPS 1 and 2
1.025
1.141
LystRNA
ThrtRNA
IletRNA
0.543
0.757
0.789
[Thr]
[Lys]
[Ile]
296
69.2
58.8
Knock-out of AK1 still allows
92.7
54.6 flux, 32.9
80%
of the wild-type
223
65.7
52.3
70.3
50.6
26.4
122
58.2
39.2
285
68.8
58.0
228
66.0
52.9
206
64.8
50.6
330
66.8
7760
37.9
— no steady state —
104
11400
6600
5 × 108
81.9
79.0
61.3
179.2
35.5
203
2 × 1013
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
enzyme knock-outs
Can the system cope?
Table 1: Effects of knock-outs on fluxes and intermediate concentrations
Step
JAK ≡ JASADH
Wild type
1.016
AK1
AK2
AK1 and 2
AK2, I and II
AK I
AK II
AK I and II
0.812
0.974
0.749
0.869
1.010
0.978
0.869
DHDPS 1
DHDPS 2
DHSPS 1 and 2
1.025
1.141
LystRNA
ThrtRNA
IletRNA
0.543
0.757
0.789
[Thr]
[Lys]
[Ile]
296
69.2
58.8
Knock-out of AK1 still allows
92.7
54.6 flux, 32.9
80%
of the wild-type
52.3
and223
the double65.7
knock-out of
70.3
50.6 75% of
26.4
AK1
and AK2 allows
58.2
39.2
the122
wild-type flux.
285
68.8
58.0
228
66.0
52.9
206
64.8
50.6
330
66.8
7760
37.9
— no steady state —
104
11400
6600
5 × 108
81.9
79.0
61.3
179.2
35.5
203
2 × 1013
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
enzyme knock-outs
Can the system cope?
Table 1: Effects of knock-outs on fluxes and intermediate concentrations
Step
JAK ≡ JASADH
[Thr]
Wild type
1.016
296
However, this flux
[Lys]
[Ile]
stability is achieved
at the cost of large
69.2
variations in58.8
[Thr]:
AK1
AK2
AK1 and 2
AK2, I and II
AK I
AK II
AK I and II
0.812
0.974
0.749
0.869
1.010
0.978
0.869
92.7
223
70.3
122
285
228
206
31% of wild-type
54.6
32.9
65.7
52.3
24% of wild-type
50.6
26.4
58.2
39.2
68.8
58.0
66.0
52.9
64.8
50.6
DHDPS 1
DHDPS 2
DHSPS 1 and 2
1.025
1.141
LystRNA
ThrtRNA
IletRNA
0.543
0.757
0.789
330
66.8
7760
37.9
— no steady state —
104
11400
6600
5 × 108
81.9
79.0
61.3
179.2
35.5
203
2 × 1013
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Simulation
Rate equations
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
enzyme knock-outs
Can the system cope?
Table 1: Effects of knock-outs on fluxes and intermediate concentrations
Step
JAK ≡ JASADH
[Thr]
[Lys]
[Ile]
Wild type
1.016
296
69.2
58.8
AK1
AK2
AK1 and 2
AK2, I and II
AK I
AK II
AK I and II
0.812
0.974
0.749
0.869
1.010
0.978
0.869
92.7
223
70.3
122
285
228
206
54.6
65.7
50.6
58.2
68.8
66.0
64.8
32.9
52.3
26.4
39.2
58.0
52.9
50.6
DHDPS 1
DHDPS 2
DHSPS 1 and 2
1.025
1.141
LystRNA
ThrtRNA
IletRNA
330
66.8
7760
37.9
— no steady state —
61.3
179.2
8
0.543
104 2 (weakly
5 × 10inhibited
35.5
Knock-out
of DHDPS
by
Lys)
produces only
a modest81.9
increase in the
0.757
11400
203
flux
(to 112%) but
huge increases
0.789
6600
79.0 in [Ile]
2 ×(to
1013
305%) and [Thr] (to 2600%).
e-Science
Institute
edinburgh
6–8 april 2011
Feedback inhibition
Metabolic models
Aspartate metabolism in
A. thaliana chloroplasts
Isoenzymes
Regulation
Rate equations
0
8
Simulation
External metabolites
Measured concentrations
Elasticities
Flux distribution
Control coefficients
Flux
Concentration
Regulatory effectiveness
What have we learned?
Perspectives
Thursday, 7 April 2011
enzyme knock-outs
Can the system cope?
Table 1: Effects of knock-outs on fluxes and intermediate concentrations
Step
JAK ≡ JASADH
[Thr]
[Lys]
[Ile]
Wild type
1.016
296
69.2
58.8
AK1
AK2
AK1 and 2
AK2, I and II
AK I
AK II
AK I and II
0.812
0.974
0.749
0.869
1.010
0.978
0.869
92.7
223
70.3
122
285
228
206
54.6
65.7
50.6
58.2
68.8
66.0
64.8
32.9
52.3
26.4
39.2
58.0
52.9
50.6
DHDPS 1
DHDPS 2
DHSPS 1 and 2
1.025
1.141
LystRNA
ThrtRNA
IletRNA
0.543
0.757
0.789
330
66.8
7760
37.9
— no steady state —
104
11400
6600
5 × 108
81.9
79.0
61.3
179.2
35.5
203
2 × 1013