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Programming Bacteria for Optimization of Genetic Circuits.

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Presentation on theme: "Programming Bacteria for Optimization of Genetic Circuits."— Presentation transcript:

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 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

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

34 Types of Noise Internal External Magnitude Auto-correlation time Full info at 010.full.pdf 010.full.pdf

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

36 Control of Noise Transcriptionally or translationally. Robustness of promoter

37 Beneficial Noise Competent state=take up DNA Endogenous circuit: wide competence range – Response to environment Rewired circuit: narrow competence range

38 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

39 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.

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

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

42 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

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

44 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

45 CRIM Plasmids, Degradation Tags, and Transposons Becca and Kirsten

46 CRIM plasmids Conditional-replication, integration, and modular plasmids

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

48 Conditional-replication Choose copy number – Medium (15 per cell) – High (250 per cell) Contain a conditional-replication origin

49 Integration Direct transformation Helper plasmids – Make Int Contain attP sites

50 Removal—excision and retrieval Helper plasmids – Make Xis and Int Very specific Removed plasmids are identical to original plasmids

51 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)

52 Benefits Choose copy number Easy to integrate, excise and retrieve – Specificity Modularity Familiar with protocol

53 Degradation Tags Short peptide sequence that identifies a protein for destruction Reduces half life  reduces concentration

54 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

55 Pupylome Post-translational protein modifier Similar to ubiquitin in Eukaryotes Found in Mycobacterium tuberculosis

56 UbiquitinPupylome

57 ssrA tags Add a short peptide sequence to the C- terminal AANDENYALVA Not a post-translational tag

58 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

59 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”

60 Replicative

61 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 – a-att-sites.html

62 Double Cloning with Type IIs REs Duke and Ben

63 A LVA B GGA C LVA D GGA 8-cutters: AscI SbfI FseI SgrAI NotI-HF

64 GGA - E1 Element Insertion A E2 E1 E1, Ligase A E2 LVA A To be inserted Same idea for To be replaced final product

65 GGA - E2 Element Insertion A LVA A E1 E2 E2, Ligase A E1 Same idea for To be inserted To be replaced final product

66 Pairs of Enzymes: BbsI, BsaI FokI, SfaNI BsmAI, BsmBI BsmFI, BtgZI BseYI, BssSI EarI, SapI E1 E2

67 Barcodes for Multiplex PCR A LVA B GGA C LVA D GGA General primers on ends (one for each orientation) Barcodes Unique primers associated with each arrangement of parts Optimally different Similar GC content


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