Dissertation/Thesis Abstract

Investigation of virus capsid self-assembly kinetics using discrete-event stochastic simulation
by Zhang, Tiequan, Ph.D., Carnegie Mellon University, 2007, 131; 3293505
Abstract (Summary)

Self-assembly systems play crucial roles in a broad range of critical biological processes. Investigation of such systems is impeded by the innate stochastic noise of the systems and current experimental limitations. Understanding of such systems, especially complex virus capsid self-assembly systems, is essential for us to build predictive models of cellular function, find novel treatments for many human diseases and build novel nano-machines. Quantitative modeling of virus capsid self-assembly kinetics provides a valuable adjunct to experimental work in understanding self-assembly by allowing us to perform model-assisted interpretation of assembly systems too complex for detailed experimental dissection and to extrapolate results from in vitro experimental conditions to the cellular environment. However, many such computational methods have difficulty in achieving high efficiency and accuracy at the same time.

This thesis develops an efficient computational tool using a local rules model, a stochastic queue-based discrete-event simulation algorithm and the Java language to allow the investigation of virus capsid self-assembly kinetics. The simulation program is then used to study (1) the contribution of oligomer/oligomer binding to capsid assembly kinetics, (2) the scaling effects on virus capsid-like self-assembly, and (3) the parameter space of complex self-assembly.

Indexing (document details)
Advisor: Schwartz, Russell
Commitee:
School: Carnegie Mellon University
School Location: United States -- Pennsylvania
Source: DAI-B 68/12, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Bioinformatics, Virology, Computer science
Keywords: Assembly kinetics, Capsid self-assembly, Discrete-event simulation, Gillespie model, Stochastic simulation, Virology
Publication Number: 3293505
ISBN: 9780549393818
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