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Project description
SloppyCell is a software environment for simulation and analysis of biomolecular networks. A particular strength of SloppyCell is estimating parameters by fitting experimental data and then calculating the resulting uncertainties on parameter values and model predictions.
SloppyCell was initially developed in the lab of Jim Sethna.
Features
support for much of the Systems Biology Markup Language (SBML) level 2 version 3
deterministic and stochastic dynamical simulations
sensitivity analysis without finite-difference derviatives
optimization methods to fit parameters to experimental data
simulation of multiple related networks sharing common parameters
stochastic Bayesian analysis of parameter space to estimate uncertainties associated with optimal fits
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