LNA++: Linear Noise Approximation with First and Second Order Sensitivities
Feigelman J, Weindl D, Theis FJ, Marr C, Hasenauer J (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11095 LNBI
Pages Range: 300-306
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Brno, CZE
ISBN: 9783319994284
DOI: 10.1007/978-3-319-99429-1_19
Abstract
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 ).
Involved external institutions
Germany (DE)How to cite
APA:
Feigelman, J., Weindl, D., Theis, F.J., Marr, C., & Hasenauer, J. (2018). LNA++: Linear Noise Approximation with First and Second Order Sensitivities. In David Safranek, Milan Ceska (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 300-306). Brno, CZE: Springer Verlag.
MLA:
Feigelman, Justin, et al. "LNA++: Linear Noise Approximation with First and Second Order Sensitivities." Proceedings of the 16th International Conference on Computational Methods in Systems Biology, CMSB 2018, Brno, CZE Ed. David Safranek, Milan Ceska, Springer Verlag, 2018. 300-306.
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