programming bacteria for optimization of genetic circuits
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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 ProblemO = output of metabolic pathway in terms of the concentration of the product
P = promoter elementsR = RBS elementsD = degradation tagsG = 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
Intermediate A
Intermediate B
Intermediate C
Product D
Enzyme C
Enzyme B
Enzyme A
Enzyme D
Note: Since we are developing a method here, we can pick a pathway that suits our purpose
Gene Expression Cassette
A
LVA
Gene Expression A =
A
= one of the elements of the promoter set
= one of the elements of the C dog set
= fixed as coding sequence A, B, C, or D
= one of the elements of the degradation set, eg. LVA, GGA, PEST, Ubi-Lys
LVA
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
A
BsaIBsaI BbsIBbsI
BbsI BbsI
BbsI, Ligase
ABsaI BsaI
LVAA
To be inserted
Same idea for
To be replaced
final product
GGA - BsaI Element Insertion
A
LVAA
BbsIBbsI BsaIBsaI
BsaI BsaI
BsaI, Ligase
ABbsI BbsI
Same idea for
To be inserted
To be replaced
final product
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 frequenciesImportant 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
w
x
y
z
w
x
y
z
w = 1
w
x
y
z
z = 2
w
x
y
z
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
Repressor 1
Hin Blue
SacB Death Gene
Bacteriorhodopsin
Signal TransductionSee Jeff Tabor work“Multichromatic Control of Gene Expression” JMB
Flip
Repressor 2
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
CRIM
• Lambda bacteriophage system commercially available for insertion of plasmid DNA into genome– Uses insertion and excision and attachment
sequences• For a pathway that was too large for plasmids,
we could park circuits into the genome
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