Programming Bacteria for Optimization of Genetic Circuits

# Programming Bacteria for Optimization of Genetic Circuits

## Programming Bacteria for Optimization of Genetic Circuits

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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. C. dog Alex Gittin & Dancho Penev

28. Noise • Introduces variability into gene expression • Probably inevitable • Can attempt to live with it, control it, or integrate it.

29. Types of Noise • Internal • External • Magnitude • Auto-correlation time • Full info at http://www.sciencemag.org/content/309/5743/2010.full.pdf

30. Consequences of Noise • Noise transmits down pathways. • Cells can exhibit variable behavior

31. Control of Noise • Transcriptionally or translationally. Robustness of promoter

32. Beneficial Noise • Competent state=take up DNA • Endogenous circuit: wide competence range • Response to environment • Rewired circuit: narrow competence range http://www.sciencemag.org/content/333/6047/1244.full.pdf

33. 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

34. 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.

35. Pathway Characteristics Longer pathway more sensitive to ligand The longer the circuit, the narrower the concentration it goes form “off” to “on”

36. Pathway Characteristics Though longer pathways are noisier, they also show more tolerance of noisy inputs. Resistant to sub-threshold levels of activation

37. Positive vs. Negative Phenotypic Output • B. subtilislevansucrase 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

38. Linear Pathways Pros Cons There aren’t a lot of linear pathways in the cell • More conducive to modularity

39. Semi synthetic Pros 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 • 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

40. CRIM Plasmids, Degradation Tags, and Transposons Beccaand Kirsten

41. CRIM plasmids Conditional-replication, integration, and modular plasmids

42. Previous problems • Multicopy plasmids • High-copy-number artifacts • Recombining genes on bacterial chromosomes • difficult because often requires manipulating many genes

43. Conditional-replication • Choose copy number • Medium (15 per cell) • High (250 per cell) • Contain a conditional-replication origin

44. Integration • Direct transformation • Helper plasmids • Make Int • Contain attP sites

45. Removal—excision and retrieval • Helper plasmids • Make Xis and Int • Very specific • Removed plasmids are identical to original plasmids