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Ryan Gutenkunst
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SloppyCell
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
