Hans V. Westerhoff and friends
Manchester Centre for
Integrative Systems Biology
Netherlands Institute for
Systems Biology,
Amsterdam
(Systems) Biology at last!
Biology neither simple nor without principles
Challenge 1: ……………………………………
Linear FBA prediction fails
Challenge 2: …………………………….
Biomedicine/microbiology is a mess
Challenge 3: ………………………
e-scientists are banking
Challenge 4: ………………
(Systems) Biology at last!
NISB
• Studies the Universe
• From elementary particles to the cosmos
• General principles within this universe
• Simplicity and low energy (high efficiency)
(Systems) Biology at last!
(Systems) Biology at last!
(Systems) Biology at last!
1
DNA
RNA
Enzyme
Chemical 1
Top down:
Pathways
from
genome
v1: Glucose → 2 pyruvate
v2: pyruvate → lactate
v3: pyruvate → CO2
Balance equation:
0=
Process
𝑑𝑝𝑦𝑟𝑢𝑣𝑎𝑡𝑒
= 2𝑣1 − 𝑣2 − 𝑣3
𝑑𝑡
(Systems) Biology at last!
(Systems) Biology at last!
(Systems) Biology at last!
(Systems) Biology at last!
Biology neither simple nor without principles
Challenge 1: do not hide away from complexity
Linear FBA prediction fails
Challenge 2: ………………………….
Biomedicine/microbiology is a mess
Challenge 3: ……………………………………
e-scientists are banking, but
Challenge 4: ……………………………….
(Systems) Biology at last!
Flux balance
analysis, take 2:
glycerol
-2 ATP
alcohol
2 ATP
CO2 only
The simplest??
The most
efficient ??
36 ATP
(Systems) Biology at last!
2
NISB
Turbidostat--permittostat
Permittostat
Feed it with as much as it likes (ad libitum)
{but still under well-defined conditions}
• Maximum growth rate: without
limitation of growth nutrients
• Medium influx at constant rate
• Overflow
• Monitoring of biomass, CO2
production, glucose consumption,
metabolite concentrations in
exometabolome including ethanol
(dissolved and off-gas)
• Is stable system at maximum dilution
rate=maximum growth rate
Computer
Biomass and
gas monitor
Media Pump
Media and
biomass
Air
(Systems) Biology at last!
Metabolite Quantitation
TURBIDOSTAT CULTURES (YEAST)
Flux balance
analysis, take 2:
1.85mM
1.35mM
glycerol
0.47mM
-2 ATP
1.45mM
0.56mM
RAPID SAMPLING, METABOLIC QUENCHING
AND EXTRACTION OF INTRA-CELLULAR
METABOLITES
alcohol
ATP=1.29mM
2 ATP
0.97mM
CO2 only
The simplest??
The most
efficient ??
36 ATP
0.24mM
ANALYSIS BY GC-MS AND LC-MS
DATA PROCESSING
1.18mM
(Systems) Biology at last!
(Systems) Biology at last!
Neither maximally
efficient
Nor simplest
Flux
Flux (mmoles
C/h/g
dryweight)
Flux
percentage of
input
JBiomass
3.2
6
JCO2 (offgas)
9.8
20
Jethanol (exometabolome plus
off gas)
36.1
74
JAcetate (exometabolome)
0.2
0
JGlycerol (exometabolome)
1.0
2
JAcetaldehyde (exometabolome)
0.1
0
JTrehalose (exometabolome)
0.7
1
(Systems) Biology at last!
-2 ATP
2 ATP
(Systems) Biology at last!
3

Reduce to simplicity

Just a can of worms

Minimum energy/maximal efficiency

Just complexity and diversity
(Systems) Biology at last!
(Systems) Biology at last!
NISB
 Preferences
for:
◦High free energy dissipation(cf.
Sergio) if useful (Hans) rather
than efficiency
◦Complexity over simplicity
 All
for a purpose
(Systems) Biology at last!
NISB

Fermentation
2
ATP only
(Systems) Biology at last!
4
Respiration

36 ATP


Extreme pathway 1

Fermentation and
growth
(Systems) Biology at last!



36 ATP
Extreme pathway 2
Respiration and growth
(Systems) Biology at last!
(Systems) Biology at last!

Extreme pathway 3

Fermentation and
maintenance
(Systems) Biology at last!
NISB



36 ATP
Extreme pathway 4
Respiration and
maintenance
Fermentation or respiration (more ATP)?
(Systems) Biology at last!
5





α2= amount of extreme pathway respiration growth
α3= amount of extreme pathway fermentation
maintenance
α4= amount of extreme pathway respiration
maintenance
n=number of ATP from respiration more than
fermentation
p=ATP cost growth
vbiosynthesis 
2  vglycolysis   3   4  p  (n  1)   2





α2= amount of extreme pathway respiration growth
α3= amount of extreme pathway fermentation
maintenance
α4= amount of extreme pathway respiration
maintenance
n=number of ATP from respiration more than
fermentation
p=ATP cost growth
Y
n
vbiosynthesis
vglycolysis
 2
1  ( p  1)   2
n  1  ( p  n)   2   3   4
Y
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Y
0
vbiosynthesis
vglycolysis
5
10
15
respiration
20
(Systems) Biology at last!
Growth yield
Growth yield
(Systems) Biology at last!
Y'
25
1  ( p  1)   2
 2
n  1  ( p  n)   2   3   4
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Y
0
5
10
15
respiration
(Systems) Biology at last!
20
Y'
25
(Systems) Biology at last!
NISB
Flux balance
analysis, take 2:
glycerol
Nonlinear FBA predicts:
•Preferential respiration at low growth rate
•Preferential fermentation at high growth rate
-2 ATP
alcohol
2 ATP
CO2 only
The simplest??
The most
efficient ??
36 ATP
(Systems) Biology at last!
6
 Preferences
2.5
0.1
0.09
2
0.08
0.06
ETOH
1.5
0.05
1
0.04
biomass
0.07
ETOH
biomass
0.03
0.5
0.02
0.01
0
0
0
0.2
0.4
0.6
0.8
for:
◦Optimal energy ?
◦Complexity
◦Objective/selective
advantage
1
c
(Systems) Biology at last!
(Systems) Biology at last!
NISB
Biology neither simple nor without principles
Challenge 1: do not hide away from complexity
Linear FBA prediction fails
Challenge 2: Develop Nonlinear FBA
Biomedicine/microbiology is a mess
……………………………………
e-scientists are banking, but
Challenge 4: ……………………………….
(Systems) Biology at last!
•Characterize
each
component inclusive i9ts
interactive properties: rate
equations
•Characterize networks:
balance equaitons
•Integrate this: discover
system properties by
computational biology
(Systems) Biology at last!
(Systems) Biology at last!
7
http://jjj.mib.ac.uk/index.html
(Systems) Biology at last!
(Systems) Biology at last!
NISB
Find the most fragile step in the network
(Systems) Biology at last!
NISB
step
Fragility=C
1/robustness
(doubled glc
transporter)
Glucose transport
0.887
0.011
GAPdh
0.024
0.249
?
0.024
0.051
0.001
0.002
0.004
0.018
ALD
0.026
0.354
TPI
0.002
0.016
HK
PGI
PFK
?
GDH
0.015
0.166
GPO
-0.004
-0.068
PGK
0.016
0.144
PK
0.001
0.014
0
0.003
ATPase
GlyK
Sum
0.003
0.039
0.999
0.999
But …. drug safety …….
Kill the parasite, but not the host!
Differential network-based
drug design
(Systems) Biology at last!
8
Differential fragility analysis TRYP and ERY
Fragility of
ATP synthesis
flux
0.00
TRYPANOSOME
ERYTHROCYTE
0.68
0.03
BAD
TARGET
0.00 0.001
BAD
TARGET
0.02
0.02
0.05
0.000.01
0.00
0.00
0
us et al.
T. brucei…..
0.005
-0.01
0.00
GOOD
TARGET
0.01
0.03
Red blood cell
0.06
FAIR
TARGET
Holzhütter et al.
0.07
0.94
0.001
(Bakker, Holzhütter, Snoep, Westerhoff)
(Systems) Biology at last!
(Systems) Biology at last!
Virtual liver (BMBF).
Noble, Kohl et al.
liver
us et al.
T. brucei…..
heart
pancreas
organism
level
…….
brain
Doyle et al.
muscle
Web services
interfaced
models of
modules;
Software
engineering
Red blood cell
Holzhütter et al.
(Systems) Biology at last!
Biology neither simple nor without principles
Challenge 1: do not hide away from complexity
Linear FBA prediction fails
Challenge 2: Develop Nonlinear FBA
Biomedicine/microbiology is a mess
Challenge 3: Body building
e-scientists are banking, but
Challenge 4: ……………………………….
(Systems) Biology at last!
NISB
EU-FP7/8 Flagship programme
Lehrach, Zatloukal, Westerhoff, Girolami, Brand,
Church, Hunter, etc, etc. ……
(Systems) Biology at last!
9
Brueghel
Brueghel
(Systems) Biology at last!
(Systems) Biology at last!
NISB
NISB
Integration
Integration
Of international reseach activities
But also of
molecular, cell biological, PD and PK
effects
Integration through IT
IT based on the anatomy of the
problem
Virtual liver (BMBF).
Noble, Kohl et al.
liver
us et al.
T. brucei…..
heart
pancreas
organism
level
…….
brain
Doyle et al.
muscle
Web services
interfaced
models of
modules;
Software
engineering
Red blood cell
Lipid synthesis
Ribosome composition
supercoiling
muRNA
cell level
Membrane structure
translation
glycolyis
transcription
Holzhütter et al.
(Systems) Biology at last!
(Systems) Biology at last!
10
Web
services
interfaced
models of
modules;
E-science
(Systems) Biology at last!
Biology neither simple nor without principles
Challenge 1: do not hide away from complexity
Linear FBA prediction fails
Challenge 2: Develop Nonlinear FBA
Biomedicine/microbiology is a mess
Challenge 3: Body building
e-scientists are banking, but
Challenge 4: should be integrating IT for
Biology
(Systems) Biology at last!
The MCISB:
The DTC students
Mara Nardelli,
Jacky Snoep and
Ettore Murabito
and the Bruggeman/Teusink/Westerhoff
groups in Amsterdam:
BBSRC, EPSRC, NWO, EU-FP7,
FEBS, ESF, AstraZeneca
(Systems) Biology at last!
11