Automating Manifold Boundary Model Reduction in Michaelis-Menten Reaction Networks

ORAL

Abstract

One of the major issues in understanding complex systems, such as those in systems biology, is the large number of parameters to be fit to data. Methods to approximate and reduce complex models are therefore an important problem. Recent advances in information theory have led to a new method of identifying limiting approximations in complex models known as the Manifold Boundary Approximation Method. I apply this method to systems modeled as coupled differential equations describing networks of Michaelis-Menten reactions. Such models are common in biochemical systems such as developmental biology and cancer. I discuss how this approximation method when applied to such networks can be automated.

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Authors

  • Merrill Asp

    Brigham Young University -- Provo

  • John Colton

    Brigham Young University Dept. of Physics and Astronomy, Brigham Young University, None, The College of William and Mary/Jefferson Lab, Brigham Young University-Idaho, Blue Ridge Research and Consulting LLC, Air Force Research Laboratory - Wright Patterson Air Force Base, Brigham Young Univ - Provo, Blue Ridge Research and Consulting, University of Utah, SRI International, Utah State University, Utah Valley University, Los Alamos National Laboratory, Professor, Graduate, United States Air Force Academy, Arizona State Univ, SiO2 NanoTech, Entrepix Inc, AFRL, Advisor, Brigham Young University- Provo, University of New Mexico, Univ of Utah, University of Wisconsin -- Madison, New Mexico Tech Physics Dept., Retired, Department of Physics and Astronomy, University of Utah, Department of Physics \& Astronomy, University of Hawai'i, JILA and University of Colorado, Boulder, National Institute of Standards and Technology, Boulder, University of Colorado, Boulder, Lawrence Berkeley National Laboratory, National Institute of Standards and Technology, Space Dynamics Lab, New Mexico Tech, BYU Professor, Brigham Young University -- Provo, Northern Arizona University, University of Colorado Boulder, Colorado State University, University of Utah, Department of Physics, New Mexico State University