Abstract
Gene expression, signal transduction and many other cellular processes are subject to stochastic fluctuations. The analysis of these stochastic chemical kinetics is important for understanding cell-to-cell variability and its functional implications, but it is also challenging. A multitude of exact and approximate descriptions of stochastic chemical kinetics have been developed, however, tools to automatically generate the descriptions and compare their accuracy and computational efficiency are missing. In this manuscript we introduced CERENA, a toolbox for the analysis of stochastic chemical kinetics using Approximations of the Chemical Master Equation solution statistics. CERENA implements stochastic simulation algorithms and the finite state projection for microscopic descriptions of processes, the system size expansion and moment equations for meso- and macroscopic descriptions, as well as the novel conditional moment equations for a hybrid description. This unique collection of descriptions in a single toolbox facilitates the selection of appropriate modeling approaches. Unlike other software packages, the implementation of CERENA is completely general and allows, e.g., for time-dependent propensities and non-mass action kinetics. By providing SBML import, symbolic model generation and simulation using MEX-files, CERENA is user-friendly and computationally efficient. The availability of forward and adjoint sensitivity analyses allows for further studies such as parameter estimation and uncertainty analysis. The MATLAB code implementing CERENA is freely available from http://cerenadevelopers.github.io/CERENA/.
Highlights
Biological processes, including chemical reaction networks, are dynamical systems with inherently stochastic dynamics due to the discrete nature of matter [1]
To improve upon the approximation in the presence of low as well as high copy-number species, hybrid microscopic-mesoscopic approaches such as the method of conditional moments (MCM) [13] and the conditional linear noise approximations [14] have been introduced. All these methods are of reduced computational complexity as they possess significantly fewer state-variables compared to the Chemical Master Equation (CME) or finite state projection (FSP), remain feasible for real-world application problems
A multitude of studies revealed the functional role of cell-to-cell variability in cellular mechanisms [5]
Summary
Biological processes, including chemical reaction networks, are dynamical systems with inherently stochastic dynamics due to the discrete nature of matter [1]. To improve upon the approximation in the presence of low as well as high copy-number species, hybrid microscopic-mesoscopic approaches such as the method of conditional moments (MCM) [13] and the conditional linear noise approximations [14] have been introduced All these methods are of reduced computational complexity as they possess significantly fewer state-variables compared to the CME or FSP, remain feasible for real-world application problems. Efficient implementations of many variants of SSA are available, e.g., in StochKit [18], CERENA is the only package supporting arbitrary, including fast-varying, time-dependent reaction propensities [28] This variety of descriptions renders CERENA unique compared to other relevant software packages (see Fig 1). The approximation accuracies and computation times are assessed, demonstrating the efficiency of the CERENA implementation
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