MEANS: Python package for Moment Expansion Approximation, iNference and Simulation

TY - JOUR

T1 - MEANS

T2 - Python package for Moment Expansion Approximation, iNference and Simulation

AU - Fan, Sisi

AU - Geissmann, Quentin

AU - Lakatos, Eszter

AU - Lukauskas, Saulius

AU - Ale, Angelique

AU - Babtie, Ann C.

AU - Kirk, Paul D.W.

AU - Stumpf, Michael P.H.

N1 - Publisher Copyright: © 2016 The Author. Published by Oxford University Press. All rights reserved.

PY - 2016/9

Y1 - 2016/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84992189458&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btw229

DO - 10.1093/bioinformatics/btw229

M3 - Journal article

C2 - 27153663

AN - SCOPUS:84992189458

SN - 1367-4803

VL - 32

SP - 2863

EP - 2865

JO - Bioinformatics

JF - Bioinformatics

IS - 18

ER -