Journal of Global Optimization

, Volume 40, Issue 4, pp 719–738

Deterministic parallel global parameter estimation for a model of the budding yeast cell cycle

Authors

  • Thomas D. Panning
    • Departments of Computer Science and Biological SciencesVirginia Polytechnic Institute and State University
    • Departments of Computer Science and MathematicsVirginia Polytechnic Institute and State University
  • Nicholas A. Allen
    • Departments of Computer Science and Biological SciencesVirginia Polytechnic Institute and State University
  • Katherine C. Chen
    • Departments of Computer Science and Biological SciencesVirginia Polytechnic Institute and State University
  • Clifford A. Shaffer
    • Departments of Computer Science and Biological SciencesVirginia Polytechnic Institute and State University
  • John J. Tyson
    • Departments of Computer Science and Biological SciencesVirginia Polytechnic Institute and State University
Article

DOI: 10.1007/s10898-007-9273-7

Cite this article as:
Panning, T.D., Watson, L.T., Allen, N.A. et al. J Glob Optim (2008) 40: 719. doi:10.1007/s10898-007-9273-7

Abstract

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.

Keywords

DIRECT (DIviding RECTangles) algorithmDirect searchMADS (Mesh Adaptive Direct Search) algorithmComputational biology

Copyright information

© Springer Science+Business Media, LLC. 2008