Title page for ETD etd-05242008-153640


Type of Document Master's Thesis
Author Wang, Pengyuan
Author's Email Address wpy@vt.edu
URN etd-05242008-153640
Title Bridging the Gap between Deterministic and Stochastic Modeling with Automatic Scaling and Conversion
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Cao, Yang Committee Chair
Baumann, William T. Committee Member
Shaffer, Clifford A. Committee Member
Watson, Layne T. Committee Member
Keywords
  • biomolecular regulatory networks
  • pathway modeling
  • stochastic simulation
Date of Defense 2008-05-07
Availability unrestricted
Abstract
During the past decade, many successful deterministic models of macromolecular regulatory networks have been built. Deterministic simulations of these models can show only average dynamics of the systems. However, stochastic simulations of macromolecular regulatory models can account for behaviors that are introduced by the noisy nature of the systems but not revealed by deterministic simulations. Thus, converting an existing model of value from the most common deterministic formulation to one suitable for stochastic simulation enables further investigation of the regulatory network. Although many different stochastic models can be developed and evolved from deterministic models, a direct conversion is the first step in practice.

This conversion process is tedious and error-prone, especially for complex models. Thus, we seek to automate as much of the conversion process as possible. However, deterministic models often omit key information necessary for a stochastic formulation. Specifically, values in the model have to be scaled before a complete conversion, and the scaling factors are typically not given in the deterministic model. Several functionalities helping model scaling and converting are introduced and implemented in the JigCell modeling environment. Our tool makes it easier for the modeler to include complete details as well as to convert the model.

Stochastic simulations are known for being computationally intensive, and thus require high performance computing facilities to be practical. With parallel computation on Virginia Tech's System X supercomputer, we are able to obtain the first stochastic simulation results for realistic cell cycle models. Stochastic simulation results for several mutants, which are thought to be biologically significant, are presented. Successful deployment of the enhanced modeling environment demonstrates the power of our techniques.

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