StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes

Timo R Maarleveld,Frank J Bruggeman,Brett G Olivier,Jérémie Bourdon

doi:10.1371/journal.pone.0079345

Timo R Maarleveld, Frank J Bruggeman + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0079345

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Journal: PloS one	Publication Date: Nov 18, 2013
Citations: 99	License type: CC BY 4.0

Affiliation: Centrum Wiskunde & Informatica, Cancer Genomics Centre

Abstract

Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (Stochastic modeling in Python), which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool.

Highlights

Experiments at the level of single cells indicate large cell-to-cell variability in copy numbers of molecules [1]
Software Implementation The StochPy software has been designed around three core principles
Functionality, it should implement a variety of Stochastic simulation algorithms (SSAs), allow for the intuitive description of models, and provide high-level, userfriendly access tailored for interactive use. Flexibility, it should support high-level statistical and plotting functions for interrogating both model and data as well as provide programmatic access to low-level functions and data structures. To satisfy these principles StochPy has been developed as a console application using the Python language, taking advantage of its pure object-oriented nature, portability, extensive standard library, and ability to seamlessly glue together scientific libraries written in compiled languages

Summary

Introduction

Experiments at the level of single cells indicate large cell-to-cell variability in copy numbers of molecules [1]. StochPy provides various SSAs for the simulation of stochastic dynamics and supports model definition in either plain text or the Systems Biology Markup Language (SBML) [17]. In addition it includes statistical functions for the numerical analysis of stochastic simulations as well as plotting facilities for the visualization of amongst other features time-correlations, propensities, and event waiting times.

Results

Conclusion