Many useful applications of simulation in computational cell biology, e.g. kinetic parameter estimation, Metabolic Control Analysis (MCA), and bifurcation analysis, require a large number of repetitive runs with different input parameters. The heavy requirements imposed by these analysis methods on computational resources has led to an increased interest in parallel- and distributed computing technologies. We have developed a scripting environment that can execute, and where possible, automatically parallelize those mathematical analysis sessions transparently on any of (1) single-processor workstations, (2) Shared-memory Multiprocessor (SMP) servers, (3) workstation clusters, and (4) computational grid environments. This computational framework, E-Cell SessionManager (ESM), is built upon E-Cell System Version 3, a generic software environment for the modeling, simulation, and analysis of whole-cell scale biological systems. Here we introduce the ESM architecture and provide results from, benchmark experiments that addressed 2 typical computationally intensive biological problems, (1) a parameter estimation session of a small hypothetical pathway and (2) simulations of a stochastic E. coli heat-shock model with different random number seeds to obtain the statistical characteristics of the stochastic fluctuations.
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