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 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

7. 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

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 A BsaI BbsI BbsI, Ligase A BsaI LVA A To be inserted Same idea for To be replaced final product

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

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

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 B GGA C LVA D GGA

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. w x y z

19. w x y z w = 1

20. w x y z z = 2

21. w x y z

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 Repressor 1 Hin Blue SacB Death Gene Bacteriorhodopsin Signal Transduction See 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

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 10 14 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