Distributed Cell Biology Simulations with the E-Cell System
Analytical techniques in computational cell biology such as kinetic parameter estimation, Metabolic Control Analysis (MCA) and bifurcation analysis require large numbers of repetitive simulation runs with different input parameters. The requirements for significant computational resources imposed by those analytical methods have led to an increasing interest in the use of parallel and distributed computing technologies.
We developed a Python-scripting environment that can execute the above mathematical analyses. Also, where possible, it automatically and transparently parallelizes them on either (1) stand-alone PCs, (2) shared-memory multiprocessor (SMP) servers, (3) cluster systems, or (4) a computational grid infrastructure. We named this environment E-Cell Session Manager (ESM). It involves user-friendly flat application program interfaces (APIs) for scripting and a pure object-oriented programming environment for sophisticated implementation of a user’s analysis.
In this chapter, fundamental concepts related to the design and the ESM architecture are introduced. We also describe an estimation of the parameters with some script examples executed on ESM.
KeywordsGenetic Algorithm Grid Infrastructure Metabolic Control Analysis Cluster Machine Kinetic Parameter Estimation
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- 8.Sugimoto M, Takahashi K, Kitayama T et al. Distributed cell biology simulations with E-Cell System Lecture Notes in Computer Science, Berlin, Springer-Verlag 2005; 3370:20–31.Google Scholar