Date of Award:

8-2014

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Nicholas Flann

Abstract

Iterative competition between organisms for limited resources gives rise to different social strategies including cooperation. One specific problem in the cooperating but competing species in that cost associated in exhibiting cooperative traits provokes "cheating". Cheaters acquire relatively higher fitness by reaping the benefits of cooperation without contributing towards community beneficial goods. While the relatively fit cheaters can drive the contributors to extinction, the contributors exhibit different strategies to gain preferential benefits of cooperation. The facultative benefit of cooperation to cheaters drives the population to an equilibrium frequency of cooperators and cheaters. Here we develop a multi-scale modeling approach to simulate the dynamics of such cooperation within mixed population of contributors and cheaters. We recursively use genome-scale metabolic models to estimate the fitness of the organism based on the current ecological state. In addition, a series of ordinary differential equations estimate the dynamics of the population and ecological conditions. We use our approach to investigate alternative strategies whereby the cooperating strain may improve its fitness and find that regulation of gene expression is superior to modulation of enzyme activity in our system.

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