Browsing by Author "Panning, Thomas D."

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    Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle
    Panning, Thomas D. (Virginia Tech, 2006-05-25)
    Two parallel deterministic direct search algorithms are combined to find improved parameters for a system of differential equations designed to simulate the cell cycle of budding yeast. Comparing the model simulation results to experimental data is difficult because most of the experimental data is qualitative rather than quantitative. An algorithm to convert simulation results to mutant phenotypes is presented. Vectors of the 143 parameters defining the differential equation model are rated by a discontinuous objective function. Parallel results on a 2200 processor supercomputer are presented for a global optimization algorithm, DIRECT, a local optimization algorithm, MADS, and a hybrid of the two. A second formulation is presented that uses a system of smooth inequalities to evaluate the phenotype of a mutant. Preliminary results of this formulation are given.
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    Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle
    Panning, Thomas D.; Watson, Layne T.; Allen, Nicholas A.; Chen, Katherine C.; Shaffer, Clifford A.; Tyson, John J. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2006)
    Two parallel deterministic direct search algorithms are used to find improved parameters for a system of differential equations designed to simulate the cell cycle of budding yeast. Comparing the model simulation results to experimental data is difficult because most of the experimental data is qualitative rather than quantitative. An algorithm to convert simulation results to mutant phenotypes is presented. Vectors of parameters defining the differential equation model are rated by a discontinuous objective function. Parallel results on a 2200 processor supercomputer are presented for a global optimization algorithm, DIRECT, a local optimization algorithm, MADS, and a hybrid of the two.
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    A Mathematical Programming Formulation for the Budding Yeast Cell Cycle
    Panning, Thomas D.; Watson, Layne T.; Shaffer, Clifford A.; Tyson, John J. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2007)
    The budding yeast cell cycle can be modeled by a set of ordinary differential equations with 143 rate constant parameters. The quality of the model (and an associated vector of parameter settings) is measured by comparing simulation results to the experimental data derived from observing the cell cycles of over 100 selected mutated forms. Unfortunately, determining whether the simulated phenotype matches experimental data is difficult since the experimental data tend to be qualitative in nature (i.e., whether the mutation is viable, or which development phase it died in). Because of this, previous methods for automatically comparing simulation results to experimental data used a discontinuous penalty function, which limits the range of techniques available for automated estimation of the differential equation parameters. This paper presents a system of smooth inequality constraints that will be satisfied if and only if the model matches the experimental data. Results are presented for evaluating the mutants with the two most frequent phenotypes. This nonlinear inequality formulation is the first step toward solving a large-scale feasibility problem to determine the ordinary differential equation model parameters.