Flux balance analysis of biological systems: applications and challenges


Autoria(s): Raman, Karthik; Chandra, Nagasuma
Data(s)

01/07/2009

Resumo

Systems level modelling and simulations of biological processes are proving to be invaluable in obtaining a quantitative and dynamic perspective of various aspects of cellular function. In particular, constraint-based analyses of metabolic networks have gained considerable popularity for simulating cellular metabolism, of which flux balance analysis (FBA), is most widely used. Unlike mechanistic simulations that depend on accurate kinetic data, which are scarcely available, FBA is based on the principle of conservation of mass in a network, which utilizes the stoichiometric matrix and a biologically relevant objective function to identify optimal reaction flux distributions. FBA has been used to analyse genome-scale reconstructions of several organisms; it has also been used to analyse the effect of perturbations, such as gene deletions or drug inhibitions in silico. This article reviews the usefulness of FBA as a tool for gaining biological insights, advances in methodology enabling integration of regulatory information and thermodynamic constraints, and finally addresses the challenges that lie ahead. Various use scenarios and biological insights obtained from FBA, and applications in fields such metabolic engineering and drug target identification, are also discussed. Genome-scale constraint-based models have an immense potential for building and testing hypotheses, as well as to guide experimentation.

Formato

application/pdf

Identificador

http://eprints.iisc.ernet.in/21140/1/fulltext.pdf

Raman, Karthik and Chandra, Nagasuma (2009) Flux balance analysis of biological systems: applications and challenges. In: Briefings in bioinformatics, 10 (4). pp. 435-449.

Publicador

Oxford university press

Relação

http://bib.oxfordjournals.org/cgi/content/abstract/bbp011

http://eprints.iisc.ernet.in/21140/

Palavras-Chave #BioInformatics Centre
Tipo

Journal Article

PeerReviewed