MEANS: python package for Moment Expansion Approximation, iNference and Simulation
File(s)Bioinformatics-2016-Fan-bioinformatics-btw229.pdf (182.28 KB)
Published version
Author(s)
Type
Journal Article
Abstract
MOTIVATION: Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the system's moments and apply a closure ansatz to obtain a closed set of differential equations; that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems. RESULTS: We present a free, user-friendly tool implementing an efficient moment expansion approximation with parametric closures that integrates well with the IPython interactive environment. Our package enables the analysis of complex stochastic systems without any constraints on the number of species and moments studied and the type of rate laws in the system. In addition to the approximation method our package provides numerous tools to help non-expert users in stochastic analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/theosysbio/means CONTACTS: m.stumpf@imperial.ac.uk or e.lakatos13@imperial.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
Date Issued
2016-05-05
Date Acceptance
2016-04-21
ISSN
1367-4803
Publisher
Oxford University Press
Start Page
2863
End Page
2865
Journal / Book Title
Bioinformatics
Volume
32
Issue
18
Copyright Statement
© The Author 2016. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Subjects
Bioinformatics
Mathematical Sciences
Biological Sciences
Information And Computing Sciences
Publication Status
Published