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Team:Yale/Modeling
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Not all MAGE-induced mutations will be at the intended sites; to identify likely unintended mutations, we scripted a search of the genome using BLAST+ to find subsequences with four base pairs or more matching oligos in the MAGE oligo pool, and estimates the change in Gibbs energy likely upon hybridization at each such off-target pairing, using the UNAFold software package. | Not all MAGE-induced mutations will be at the intended sites; to identify likely unintended mutations, we scripted a search of the genome using BLAST+ to find subsequences with four base pairs or more matching oligos in the MAGE oligo pool, and estimates the change in Gibbs energy likely upon hybridization at each such off-target pairing, using the UNAFold software package. | ||
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| - | <gallery> | + | <gallery caption="Figures generated by our MAGE model"> |
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Image:MAGEfig2.png | Image:MAGEfig2.png | ||
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Image:MAGEhistogram.png | Image:MAGEhistogram.png | ||
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| + | Both of these scripts will be bundled into an cloud-based tool for genomic engineering (unpublished work). | ||
Revision as of 03:58, 4 October 2012
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Modeling the evolution of a population during MAGE
The distribution of specific mutations in MAGE is a stochastic process that we model as as a function of each oligo's predicted efficiency of allelic replacement (which can be estimated in E. coli as discussed in "Programming cells by multiplex genome engineering and accelerated evolution," Wang & Isaacs et al 2009), assuming that each mutation event is binary and exclusive. Then a population after c cycles is a weighted sum of n Bernoulli trials, each zero if the oligo does not mutate its target 'i' and otherwise equal to the number r of mutations it induces. Given efficiencies of allelic replacement p, this probability mass function becomes:
In doing this, we have derived a more general form of the binomial distribution. Computing this PMF involves solving the subset sum problem, but we optimized our algorithm to avoid slowdowns by using a recursive formula (Wadyicki, Shah et al. 1973) for the occasional, simpler case when all oligos carry the same number of mutations, and in other cases a branched, dynammic programming algorithm (Horowitz and Sahni 1974).
Survey for off-target binding sites
Not all MAGE-induced mutations will be at the intended sites; to identify likely unintended mutations, we scripted a search of the genome using BLAST+ to find subsequences with four base pairs or more matching oligos in the MAGE oligo pool, and estimates the change in Gibbs energy likely upon hybridization at each such off-target pairing, using the UNAFold software package.
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Both of these scripts will be bundled into an cloud-based tool for genomic engineering (unpublished work).
