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LNA++: Linear noise approximation with first and second order sensitivities.
In: Lecture Notes in Computer Science (16th International Conference on Computational Methods in Systems Biology, 12-14 September 2018, Brno; Czech Republic). 2018. 300-306 ( ; 11095 LNBI)
The linear noise approximation (LNA) provides an approximate description of the statistical moments of stochastic chemical reaction networks (CRNs). LNA is a commonly used modeling paradigm describing the probability distribution of systems of biochemical species in the intracellular environment. Unlike exact formulations, the LNA remains computationally feasible even for CRNs with many reactions. The tractability of the LNA makes it a common choice for inference of unknown chemical reaction parameters. However, this task is impeded by a lack of suitable inference tools for arbitrary CRN models. In particular, no available tool provides temporal cross-correlations, parameter sensitivities and efficient numerical integration. In this manuscript we present LNA++, which allows for fast derivation and simulation of the LNA including the computation of means, covariances, and temporal cross-covariances. For efficient parameter estimation and uncertainty analysis, LNA++ implements first and second order sensitivity equations. Interfaces are provided for easy integration with Matlab and Python. Implementation and availability: LNA++ is implemented as a combination of C/C++, Matlab and Python scripts. Code base and the release used for this publication are available on GitHub (https://github.com/ICB-DCM/LNAplusplus ) and Zenodo (https://doi.org/10.5281/zenodo.1287771 ).
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Publication type Article: Conference contribution
Keywords Automatic Construction ; Linear Noise Approximation ; Matlab ; Numerical Simulation ; Python ; Sensitivity Analysis
ISSN (print) / ISBN 0302-9743
Conference Title 16th International Conference on Computational Methods in Systems Biology
Conference Date 12-14 September 2018
Conference Location Brno; Czech Republic
Proceedings Title Lecture Notes in Computer Science
Quellenangaben Volume: 11095 LNBI, Pages: 300-306
Institute(s) Institute of Computational Biology (ICB)