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Team:UIUC Illinois/Software/Evolvalvability
From 2014.igem.org
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<p><b>Description:</b></p> | <p><b>Description:</b></p> | ||
<p>The basis is how evolution depends on both mutational rate on a single organism and the selection of organisms within a group in terms of who receives certain mutations. There are many steps that must be accounted in order to proximate more accurately. This is why we must be modular in our approach. As a preliminary step, and due to the specificity of the data we will be using, we specifically choose a common strain of E. coli which is K12. | <p>The basis is how evolution depends on both mutational rate on a single organism and the selection of organisms within a group in terms of who receives certain mutations. There are many steps that must be accounted in order to proximate more accurately. This is why we must be modular in our approach. As a preliminary step, and due to the specificity of the data we will be using, we specifically choose a common strain of E. coli which is K12. | ||
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| + | <p><b>Overall Plan:</b></p> | ||
| + | <p> The first thing that must be done is to account for the probability of mutational change on one organism. | ||
| + | <p><b>1. Single base pair mutation:</b></p> | ||
| + | <p> Find polymerase error rate for our organisms. (1/6000 per generation) Using the length of nucelotide sequence, we can find | ||
| + | the probability of a nucleotide experiencing an error. | ||
| + | <p> Pr(Mutation at Nucleotide at location N) = f(error rate and size)</p> | ||
| + | <p><b>2. Rate of experiencing error of single base:</b></p> | ||
| + | <p> Find the transition & transversion matrix parameter for K12 strain. </p> | ||
| + | <p><b>3. The probability that, if the single base experiences an error and if the error causes it to change into a different base, the change will cause another change in amino acid expression</b></p> | ||
| + | |||
</p> | </p> | ||
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Eliminate unnecessary components from the organisms genome or utilize an orthogonal gene expression or signaling system (with the goal of reducing genetic and metabolic cost)<br> | Eliminate unnecessary components from the organisms genome or utilize an orthogonal gene expression or signaling system (with the goal of reducing genetic and metabolic cost)<br> | ||
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Revision as of 03:36, 18 October 2014
Do you fear that your plasmid will be lost by mutation?
Here is our EvoTracer software that will predict mutational tolerance of plasmid
in K12 E.Coli strain!!
Description:
The basis is how evolution depends on both mutational rate on a single organism and the selection of organisms within a group in terms of who receives certain mutations. There are many steps that must be accounted in order to proximate more accurately. This is why we must be modular in our approach. As a preliminary step, and due to the specificity of the data we will be using, we specifically choose a common strain of E. coli which is K12.
Overall Plan:
The first thing that must be done is to account for the probability of mutational change on one organism.
1. Single base pair mutation:
Find polymerase error rate for our organisms. (1/6000 per generation) Using the length of nucelotide sequence, we can find the probability of a nucleotide experiencing an error.
Pr(Mutation at Nucleotide at location N) = f(error rate and size)
2. Rate of experiencing error of single base:
Find the transition & transversion matrix parameter for K12 strain.
3. The probability that, if the single base experiences an error and if the error causes it to change into a different base, the change will cause another change in amino acid expression
Additional Constraints
Genetic Reliability: Synthetic biology devices commonly impose a burden on their host organisms that is at odds with their long-term survival.Metabolic toll of constructing additional RNAs and proteins
Heterologous genetic parts that interfere with the efficient operation of native cellular processes.
Genetic reliability is defined in terms of an evolutionary half-life : the number of cell doublings over which 50% of the total function of an engineered device persists in a cell population.
Mutations usually cause the evolutionary meltdown of circuits with 3-6 days.
Q: Could quorum sensing be used to limit cell reproducibility and prolong circuit lifespan? Would slow evolution.
Factor: Cell populations that use quorum sensing would evolve slower.
Slowing down evolution:
Choose DNA sequences that are less prone to mutations
Manipulate the organisms copying and repair mechanisms
Overlap the sequence encoding genetic parts with information for some activity that is required for cell survival, so that some mutations are no longer favored by selection
Eliminate unnecessary components from the organisms genome or utilize an orthogonal gene expression or signaling system (with the goal of reducing genetic and metabolic cost)
illinoisigem@gmail.com 
