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LNA++: Linear Noise Approximation with First and Second Order Sensitivities

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11095)

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).

Keywords

Linear noise approximation Automatic construction Numerical simulation Sensitivity analysis MATLAB Python 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational BiologyOberschleißheimGermany
  2. 2.Center for MathematicsTechnische Universität MünchenMunichGermany

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