Programming Bacteria for
Optimization of Genetic Circuits
Principles – Math Problems
• Computation of solutions to Math Problems such
as NP complete problems
– Bacterial computers
• We can encode these math problems in biological terms and
solve prototype versions of them
• We have a problem scaling to enormous sizes because of the
number of bacteria in a culture or the number of DNA
molecule in a reaction
– Silicon computers
• As long as the problem is not too large, they can outperform
bacterial computers at this task
Maybe bacteria cannot beat Bill Gates at his own game…
Principles – Biological Problems
• Computation of solutions to Biological problems such as
Optimization of Genetic Circuits for Synthetic Metabolic
Pathways
– Silicon computers
• Programs have been developed for the determination of the best
genetic circuit elements for use in controlling pathways
• Incomplete inputs and models lead to inaccurate predictions
• Computers can only model the biological system
– Bacteria
• Could be programmed to compute solutions to these problems
• Bacteria are not models of the system, they are the system
But perhaps bacteria can beat Bill Gates at their own game.
Biological Problem
• Say we have a synthetic metabolic pathway
– Examples? How would we pick one? We could pick one
that enables selection
• Assume that we don’t know how to optimize the
output of the pathway in terms of the following
variables
–
–
–
–
Promoters
RBS
Degradation tags
Order and orientation of genes
• How do we built a system that would allow us to
explore combinations of the above variables?
Mathematic Expression of Problem
O = output of metabolic pathway in terms of the concentration of the
product
P = promoter elements
R = RBS elements
D = degradation tags
G = order and orientation of genes
O = fcn (P,R,D,G)
Fitness = fcn (O)
• We need to explore this 4 dimensional sequence space for each of
the genes in the pathway
• We need to examine the relationship between the optimized
function for each of the genes
• We need to connect the output of the pathway to fitness of clones
Genetic Circuit and Metabolic Pathway
Gene Expression A
Gene Expression B
Gene Expression C
Gene Expression D
Precursor X
Enzyme A
Intermediate A
Enzyme B
Intermediate B
Note: Since we are
developing a method
here, we can pick a
pathway that suits our
purpose
Enzyme C
Intermediate C
Enzyme D
Product D
Gene Expression Cassette
=
Gene Expression A
A
LVA
= one of the elements of the promoter set
= one of the elements of the C dog set
A
LVA
= fixed as coding sequence A, B, C, or D
= one of the elements of the degradation set, eg.
LVA, GGA, PEST, Ubi-Lys
Element Insertion
• Use GGA to insert elements
• Elements carry BbsI sites for initial insertion
• But we want to be able to reinsert elements
later, after selection of other elements
• So, elements carry BsaI sites for reinsertion
• Alternate between BsaI and BbsI for multiple
rounds of insertion
GGA - BbsI Element Insertion
BbsI
BsaI
BsaI
BbsI
To be inserted
BbsI, Ligase
BbsI
BbsI
To be replaced
A
BsaI
BsaI
final product
A
Same idea for
A
LVA
GGA - BsaI Element Insertion
BsaI
BbsI
BbsI
BsaI
To be inserted
BsaI, Ligase
BsaI
BsaI
To be replaced
A
BbsI
BbsI
final product
A
Same idea for
A
LVA
Genetic Circuit
A
LVA
B
GGA
C
LVA
D
GGA
Protocol Step 1
• Use GGA in vitro to place one promoter element
from the promoter set into each of the four Gene
Expression Cassettes
• Transform E. coli
• This establishes the Starting Population promoter
allele frequencies
• Culture for one or more generations under
selection for optimal production of product D
• Do minipreps and measure Selected Population
allele frequencies
Genetic Circuit
A
LVA
B
GGA
C
LVA
D
GGA
Protocol Step 2
• Use GGA in vitro to place one C dog element from
the promoter set into each of the four Gene
Expression Cassettes
• Transform E. coli
• This establishes the Starting Population C dog
allele frequencies
• Culture for one or more generations under
selection for optimal production of product D
• Do minipreps and measure Selected Population C
dog allele frequencies
Protocol Step 3
• Use GGA in vitro to place one Degradation Tag
element from the promoter set into each of the
four Gene Expression Cassettes
• Transform E. coli
• This establishes the Starting Population
Degradation Tag allele frequencies
• Culture for one or more generations under
selection for optimal production of product D
• Do minipreps and measure Selected Population
Degradation Tag allele frequencies
Important note: Maybe using degradation tags is redundant with the transcriptional controls
Protocol Step 4
• Express Hin and reshuffle the orientation and order of
the Gene Expression cassettes
– Allow complex effects of readthrough transcription
– Eg. 384 combinations for 4 genes??
• Transform E. coli
• This establishes the Starting Population
Order/Orientation allele frequencies
• Culture for one or more generations under selection for
optimal production of product D
• Do minipreps and measure Selected Population
Order/Orientation allele frequencies
Protocol Additional Steps
• Go back and repeat Step 1, if desired
• Repeat Step 2, or Step 3
• Explore the sequence space in whatever way
you want, informed by mathematical
modeling
z
w
y
x
w=1
z
w
y
x
z=2
z
w
y
x
z
w
y
x
Fitness
• We need to connect the optimization of the metabolic
pathway to bacterial cell fitness:
Fitness = fcn (amount of product D)
• Easier Idea
– Product D is tied to cell generation time
• Harder Idea
– Product D will do the following
•
•
Increase Fitness by protecting the cell that makes it (Protection)
Decrease fitness of surrounding cells (Attack?)
Fitness Easier Idea
• Product D will cause derepression of a gene
product that shortens generation time
Product D
Repressor 1
Fitness Gene
Fitness Harder Idea
• Product D will cause Hin and Blue luminescence expression
• Blue luminescence will interact with optogenetic system to express Death
Gene (Attack)
• Hin will enable expression of a repressor that will turn off the Death Gene
expression (Protection)
Product D
Bacteriorhodopsin
Repressor 1
Hin
Flip
Repressor 2
Blue
Signal Transduction
See Jeff Tabor work
“Multichromatic
Control of Gene
Expression” JMB
SacB Death Gene
Important note: this is a placeholder genetic circuit that could certainly be improved upon
Why separate steps for element
insertion?
• We cannot explore all the combinations at
once
• For 16 promoters, 8 C dogs, 4 degradation
tags, and 4 genes in all orders/orientations,
there are over 1014 combinations
Is this just screening?
• Perhaps the answer is Yes, but maybe that is
Ok, since the goal is to optimize a pathway,
not to compute the answer to a math problem
• Perhaps the answer is No, and the bacteria are
computing
– The bacteria are evaluating the inputs and
applying a Fitness function
– The bacteria are rearranging gene
order/orientation
C. dog
Alex Gittin & Dancho Penev
Noise
• Introduces variability into gene expression
• Probably inevitable
• Can attempt to live with it, control it, or
integrate it.
Types of Noise
•
•
•
•
Internal
External
Magnitude
Auto-correlation time
•
Full info at
http://www.sciencemag.org/content/309/5743/2
010.full.pdf
Consequences of Noise
• Noise transmits down pathways.
• Cells can exhibit variable behavior
Control of Noise
• Transcriptionally or translationally.
Robustness of promoter
Beneficial Noise
http://www.sciencemag.org/content/333/6047/1244.full.pdf
• Competent state=take up DNA
• Endogenous circuit: wide competence range
– Response to environment
• Rewired circuit: narrow competence range
KEGG PATHWAY
•
•
•
•
Collection and classification of pathway maps
Identifies parts necessary for pathway manipulation
Green=present in selected organism
Red=identifies selected organism/enzyme/substrate
Pathway Characteristics
In General
Shorter = faster response.
Less potential for noise.
Longer = slower response.
Greater end effect.
More possible noise.
Even vs. odd steps
Figures from:
Campbell, A. M., L. Heyer, and C. Paradise. Integrating Concepts in
Biology. Beta ed. Print.
Pathway Characteristics
Longer pathway more
sensitive to ligand
The longer the circuit, the
narrower the concentration it
goes form “off” to “on”
Pathway Characteristics
Though longer pathways are noisier, they also show more tolerance of
noisy inputs. Resistant to sub-threshold levels of activation
Positive vs. Negative Phenotypic Output
• B. subtilis levansucrase forms levans
– Lethal to E. coli when sucrose present
• Negative Phenotypic Output
Enzyme + sucrose production = death
– Why did cell die?
– Reengineer cells
• Positive Phenotypic Output
Enzyme+ sucrose degradation= survival
– More certain cell lives because pathway works
Linear Pathways
Pros
• More conducive to
modularity
Cons
• There aren’t a lot of linear
pathways in the cell
Semi synthetic
Pros
• Don’t have to import as
many components into the
cell
• In fully synthetic pathways
we can be sure that any
output we get is completely
due to our modifications
Cons
• Obviously, the cell already
has a specific role assigned
to the naturally occurring
enzymes and this may
influence the results
• Create a strong selection
pressure for fully synthetic
pathways
• Places an extra energy
demand on the cell
CRIM Plasmids,
Degradation Tags, and Transposons
Becca and Kirsten
CRIM plasmids
Conditional-replication, integration, and
modular plasmids
Previous problems
• Multicopy plasmids
– High-copy-number artifacts
• Recombining genes on bacterial chromosomes
– difficult because often requires manipulating
many genes
Conditional-replication
• Choose copy number
– Medium (15 per cell)
– High (250 per cell)
• Contain a conditional-replication origin
Integration
• Direct transformation
• Helper plasmids
– Make Int
• Contain attP sites
Removal—excision and retrieval
• Helper plasmids
– Make Xis and Int
• Very specific
• Removed plasmids
are identical to
original plasmids
Modular
• Integration
• Can be removed from the chromosome
– Verify cause of phenotypes
– Gene or mutant libraries
• The genes in the plasmids are replaceable
(stuffer fragments)
Benefits
• Choose copy number
• Easy to integrate, excise and retrieve
– Specificity
• Modularity
• Familiar with protocol
Degradation Tags
• Short peptide sequence that identifies a
protein for destruction
• Reduces half life  reduces concentration
N-end rule
• The half-life of a protein is determined by the
nature of its N-terminal residue
• Specific amino acid residue will cause a
protein to be either stable or unstable
• Universal rule, though mechanisms differ
Pupylome
• Post-translational protein modifier
• Similar to ubiquitin in Eukaryotes
• Found in Mycobacterium tuberculosis
Ubiquitin
Pupylome
ssrA tags
• Add a short peptide
sequence to the Cterminal
• AANDENYALVA
• Not a post-translational
tag
Benefits of Degradation Tags
• Can selectively remove proteins
- E.g., regulatory proteins
• Strategy for quick, efficient control of cell
- E.g., checkpoints for cellular processes
Transposons
• “Jumping genes”
• Small piece of DNA that can insert itself into
another place in the genome
– Conservative— “cut and paste”
– Replicative— “copy and paste”
Replicative
Sources
• Conditional-Replication, Integration, Excision, and Retrieval Plasmid-Host
systems for Gene Structure-Function Studies of Bacteria
– Andreas Haldimann and Barry L. Wanner
• The N-end rule pathway for regulated proteolysis: prokaryotic and
eukaryotic strategies
– Axel Mogk, Ronny Schmidt and Bernd Bakau
• Prokayrotic Ubiquitin-Like Protein (Pup) Proteome of Mycobacterium
tuberculosis
– Richard A. Festa, Fiona McAllister, Michael J. Pearce, Julian Mintseris, Kristin E.
Burns, Steven P. Gygi, K. Heran Darwin
• New Unstable Variants of Green Fluorescent Protein for Studies of
Transient Gene Expression in Bacteria
– Jens Bo Andersen, Claus Sternberg, Lars Kongsbak Poulsen, Sara Petersen Bjørn,
Michael Givskove, and Søren Molin
• http://www.micro.siu.edu/micr302/transpose.html
– http://utminers.utep.edu/rwebb/assets/images/img010.jpg
• http://www.sci.sdsu.edu/~smaloy/MicrobialGenetics/topics/phage/lambd
a-att-sites.html
• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2846642/figure/F1/
Double Cloning with Type IIs REs
Duke and Ben
8-cutters:
•
•
•
•
•
A
LVA
AscI
SbfI
FseI
SgrAI
NotI-HF
B
GGA
C
LVA
D
GGA
GGA - E1 Element Insertion
E1
E2
E2
E1
To be inserted
E1, Ligase
E1
E1
To be replaced
A
E2
E2
final product
A
Same idea for
A
LVA
GGA - E2 Element Insertion
E2
E1
E1
E2
To be inserted
E2, Ligase
E2
E2
To be replaced
A
E1
E1
final product
A
Same idea for
A
LVA
Pairs of Enzymes:
E2
•
•
•
•
•
•
E1
BbsI, BsaI
FokI, SfaNI
BsmAI, BsmBI
BsmFI, BtgZI
BseYI, BssSI
EarI, SapI
E1
E2
Barcodes for Multiplex PCR
• General primers on ends (one for
each orientation)
• Barcodes
• Unique primers associated with
each arrangement of parts
• Optimally different
• Similar GC content
A
LVA
B
GGA
C
LVA
D
GGA