Metabolica: A statistical research tool for analyzing metabolic networks

Authors:
Jenni Heino;Daniela Calvetti;Erkki Somersalo
Affiliations:
Department of Mathematics, Helsinki University of Technology, PO Box 1100, FIN-02015 TKK, Finland;Department of Mathematics, Case Western Reserve University, 10900 Euclid Avenue, OH 44106, USA;Department of Mathematics, Case Western Reserve University, 10900 Euclid Avenue, OH 44106, USA
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

Citing 7
Cited 0

WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility

Statistics and Computing
Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology

Bioinformatics
COPASI---a COmplex PAthway SImulator

Bioinformatics
Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing (Surveys and Tutorials in the Applied Mathematical Sciences)

Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing (Surveys and Tutorials in the Applied Mathematical Sciences)
GNU MCSim

Bioinformatics
Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Steady state flux balance analysis (FBA) for cellular metabolism is used, e.g., to seek information on the activity of the different pathways under equilibrium conditions, or as a basis for kinetic models. In metabolic models, the stoichiometry of the system, commonly completed with bounds on some of the variables, is used as the constraint in the search of a meaningful solution. As model complexity and number of constraints increase, deterministic approach to FBA is no longer viable: a multitude of very different solutions may exist, or the constraints may be in conflict, implying that no precise solution can be found. Moreover, the solution may become overly sensitive to parameter values defining the constraints. Bayesian FBA treats the unknowns as random variables and provides estimates of their probability density functions. This stochastic setting naturally represents the variability which can be expected to occur over a population and helps to circumvent the drawbacks of the classical approach, but its implementation can be quite tedious for users without background in statistical computations. This article presents a software package called Metabolica for performing Bayesian FBA for complex multi-compartment models and visualization of the results.