Collective dynamics of evolving populations and their strategies with application to medicine

This dissertation focuses ultimately on the topic of evolution, which is the foundation of modern biology. I hope to understand, in a general sense, evolution on a population scale by investigating individual level interactions. In this dissertation, I present four projects in biophysics performed under the supervision of Professor Nigel Goldenfeld: Population dynamics of viruses and their hosts, game theory and the social life of micro-organisms, a novel mechanism enhancing cooperation in evolutionary game theory, and evolutionary robust strategies for delivery of antibiotics. In the first project, starting with stochastic rate equations for the fundamental interactions between microbes and their viruses, we derive a mean-field theory for the population dynamics of microbe-virus systems, including the effects of lysogeny. In the absence of lysogeny, our model is a generalization of that proposed phenomenologically by Weitz and Dushoff. In the presence of lysogeny, we analyze the possible states of the system, identifying a novel limit cycle, which we interpret physically. To test the robustness of our mean field calculations to demographic fluctuations, we have compared our results with stochastic simulations using the Gillespie algorithm. Finally, we estimate the range of parameters that delineate the various steady states of our model. In the second project, we present a mean field model for the phase diagram of a community of micro- organisms, interacting through their metabolism so that they are, in effect, engaging in a cooperative social game. We show that as a function of the concentration of the nutrients glucose and histidine, the community undergoes a phase transition separating a state in which one strain is dominant to a state which is characterized by coexisting populations. Our results are in good agreement with recent experimental results, correctly reproducing quantitative trends and predicting the phase diagram. In the third project, we propose a novel mechanism to enhance cooperation in evolutionary game theory. Explicitly incorporating stochasticity in the phenotypic decision making process, and the interaction between evolution and ecology in the dynamic landscape, we demonstrate that for a wide variety of cooperative games of the prisoner’s dilemma type, cooperation eventually becomes the dominant strategy as long as the rules are permitted to evolve in response to the changing environment. Therefore, the ubiquitously observed cooperation in nature may come from stochastic phenotype and evolutionary landscape rather than the detailed type of competition. Altruism becomes an advantageous strategy, because it avoids being exploited by selfish agents. In the last project, we treat antibiotic resistance, which is a major concern in public health. Compared with conventional antibiotics, we show that the emergence of antibiotic resistance can be significantly delayed by using narrow and ultra-narrow spectrum antibiotics to target pathogens, rather than the entire microbiome. We also develop a new strategy that involves spoofing quorum sensing channels of commu- nication, causing premature expression of virulence factors. When combined with ultra-narrow spectrum antibiotics, our strategy removes infections and most importantly does not lead to the emergence and spread of antibiotic resistance genes.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Wang, Zhenyu

Contributors dc:contributor

• Goldenfeld, Nigel D.
• Dahmen, Karin A.
• Oono, Yoshitsugu
• Chemla, Yann R.

• antibiotic resistance
• quorum sensing
• cooperation
• evolution
• antibiotics
• game theory
• evolutionary game theory
• altruism
• micro-organism
• stochastic rate equation
• population dynamics
• virus
• lysogen

Statement dc:rights

• Copyright 2012 by Zhenyu Wang. All rights reserved.

Language dc:language

en

Identifier

http://hdl.handle.net/2142/45697

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/45697