Programming Bacteria for Optimization of Genetic Circuits

1. Programming Bacteria for Optimization of Genetic Circuits

2. 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…

3. 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.

4. 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?

5. 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

6. 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 Enzyme C Note: Since we are developing a method here, we can pick a pathway that suits our purpose Intermediate C Enzyme D Product D

7. Gene Expression Cassette LVA Gene Expression A LVA = 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 A

8. 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

9. GGA - BbsI Element Insertion BbsI BsaI BsaI BbsI To be inserted LVA BbsI, Ligase BbsI BbsI To be replaced A BsaI final product BsaI A A Same idea for

10. GGA - BsaI Element Insertion BsaI BbsI BbsI BsaI To be inserted LVA BsaI, Ligase BsaI BsaI To be replaced A BbsI final product BbsI A A Same idea for

11. Genetic Circuit A LVA GGA LVA GGA B C D

12. 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

13. Genetic Circuit A LVA GGA LVA GGA B C D

14. 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

15. 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

16. 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

17. 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

18. z w y x

19. w = 1 z w y x

20. z = 2 z w y x

21. z w y x

22. 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?)

23. Fitness Easier Idea • Product D will cause derepression of a gene product that shortens generation time Product D Repressor 1 Fitness Gene

24. 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 Signal Transduction See Jeff Tabor work “Multichromatic Control of Gene Expression” JMB Hin Blue Flip Repressor 2 SacB Death Gene Important note: this is a placeholder genetic circuit that could certainly be improved upon

25. 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

26. 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

27. 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