TY - GEN
T1 - LNA++
T2 - 16th International Conference on Computational Methods in Systems Biology, CMSB 2018
AU - Feigelman, Justin
AU - Weindl, Daniel
AU - Theis, Fabian J.
AU - Marr, Carsten
AU - Hasenauer, Jan
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - 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 ).
AB - 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 ).
KW - Automatic construction
KW - Linear noise approximation
KW - MATLAB
KW - Numerical simulation
KW - Python
KW - Sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=85053221755&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99429-1_19
DO - 10.1007/978-3-319-99429-1_19
M3 - Conference contribution
AN - SCOPUS:85053221755
SN - 9783319994284
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 300
EP - 306
BT - Computational Methods in Systems Biology - 16th International Conference, CMSB 2018, Proceedings
A2 - Safranek, David
A2 - Ceska, Milan
PB - Springer Verlag
Y2 - 12 September 2018 through 14 September 2018
ER -