# Programming Bacteria for Optimization of Genetic Circuits

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Programming Bacteria for Optimization of Genetic Circuits

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…

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.

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?

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

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

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

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

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

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

Genetic Circuit LVA A GGA B LVA C GGA D

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

Genetic Circuit LVA A GGA B LVA C GGA D

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

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

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

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

z w y x

w = 1 z w y x

z = 2 z w y x

z w y x

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?)

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

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

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

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

Alex Gittin & Dancho Penev
C. dog Alex Gittin & Dancho Penev

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

Types of Noise Internal External Magnitude Auto-correlation time
Full info at

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

Control of Noise Transcriptionally or translationally. Robustness of promoter

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

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

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.

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

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

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

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

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

CRIM Plasmids, Degradation Tags, and Transposons
Becca and Kirsten

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

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

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

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

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

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)

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

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

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

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

Ubiquitin Pupylome

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

Can selectively remove proteins - E.g., regulatory proteins Strategy for quick, efficient control of cell - E.g., checkpoints for cellular processes

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”

Replicative

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

Double Cloning with Type IIs REs
Duke and Ben

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

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

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

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

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