Modeling Bacterial Metabolism and Genetic Regulation
Faculty Mentor(s)
Dr. Matthew DeJongh
Document Type
Poster
Event Date
4-10-2015
Abstract
A number of methods have been proposed to incorporate gene expression data to improve metabolic modeling with Flux Balance Analysis (FBA). With the idea of using probabilities of specific genes to be on or off which are computed from gene expression data, one of such methods, PROM (the Probabilistic Regulation of Metabolism) is known to produce better results than traditional methods. However, it suffers from the lack of biological explanation why it uses such probabilities to limit fluxes of reactions instead of setting genes to be on or off. In our method, we use the probabilities to penalize inconsistent gene usage by incorporating gene use variables into the objective function during FBA. We also decided to implement the method on KBase, an open platform for genomics and systems biology, to utilize existing methods and data, and to make it easier to publish and share our method and results in future.
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Comments
This research was supported by the National Science Foundation, Award Number 1330734.