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

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

11. Genetic Circuit
LVA A
GGA B
LVA C
GGA 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
LVA A
GGA B
LVA C
GGA 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. Alex Gittin & Dancho Penev
C. dog
Alex Gittin & Dancho Penev

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

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
Competent state=take up DNA
Endogenous circuit: wide competence range
Response to environment
Rewired circuit: narrow competence range
Bacteria cells have gene circuit that controls when cells enter competent state and can take up DNA from environment
ComK must be expressed for cell to be in competent state
When ComS concentration decreases, ComS more susceptible to noise (seen with white region)
Alter pathway to hold ComS constant and alter MecA concentration
Cells exit competent state at high MecA concentration
Leads to narrower competence range with less noise
Wide competence range due to noise in ComS may be beneficial
Allows cells to respond to changes in environment
At least some cells in competent state over longer period of time

38. KEGG PATHWAY Collection and classification of pathway maps
KEGG PATHWAY is part of larger KEGG Database that also maps genomes, makes genome comparisons, and maps how diseases affect biologic pathways
Collection of pathway maps
Green indicates present in selected organism
In this case a specific strain of E. coli
KEGG PATHWAY Live
Organized into categories based on type of pathway: ex. metabolic
Divided in metabolic category by what you are metabolizing
Click organism menu to find out about selected pathway in similar organisms
Pathway for your chosen organism highlighted in red
Click box for information on enzyme
Gives amino acid and nucleotide sequence
Genome map gives visual of where region that encodes for enzyme
Click box for enzyme
Gives structure (click Jmol for 3D structure)
Go to tetracycline resistance biosynthesis
Choose substrate anhydrotetracycline
Shows enzyme that produces the substrate
Shows other pathways substrate is in
Ex. Biosynthesis of type II polyketide products
Selected substrate highlighted in red
Theoretically could link these pathways because share common substrate
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
B. subtilis levansucrase is an enzyme synthesizes levans (high molecular weight fructose polymers)
E. coli die when enzyme and sucrose are present
Optimal pathway has positive phenotypic output
In this example a pathway that leads to degrading sucrose rather than forming sucrose
Example of negative phenotypic output is selecting for cell death by adding enzyme and pathway that produces sucrose
If use pathway that forms sucrose not certain cells died because successfully made sucrose or another factor led to cell death
Also cannot continue work with cells that died so would need to continuously reengineer cells
Example of positive phenotypic output is selecting for cell survival by adding enzyme and pathway that degrades sucrose
More certain cell lives because successfully degraded sucrose
Can continue to use cells

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

44. Semi synthetic Pros Cons
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
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. Conditional-replication, integration, and modular plasmids
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 Contain attP sites
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. Ubiquitin
Pupylome

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

62. Double Cloning with Type IIs REs
Duke and Ben

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

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

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

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

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