MEANS: python package for Moment Expansion Approximation, iNference and Simulation | Zendy

Sisi Fan | Zendy; Quentin Geissmann | Zendy; Eszter Lakatos | Zendy; Saulius Lukauskas | Zendy; Angelique Ale | Zendy; Ann C. Babtie | Zendy; Paul Kirk | Zendy; Michael P. H. Stumpf | Zendy

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

Author(s) -

Sisi Fan,

Quentin Geissmann,

Eszter Lakatos,

Saulius Lukauskas,

Angelique Ale,

Ann C. Babtie,

Paul Kirk,

Michael P. H. Stumpf

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw229

Subject(s) - python (programming language) , computer science , ansatz , moment (physics) , inference , stochastic differential equation , mathematical optimization , mathematics , moment closure , theoretical computer science , algorithm , computational science , artificial intelligence , programming language , physics , classical mechanics , turbulence , mathematical physics , thermodynamics

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.

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