title: Flux balance analysis in the era of metabolomics.
abstract: Flux balance analysis (FBA) has emerged as an effective means to analyse biological networks in a quantitative manner. Much progress has been made on the extension of FBA to incorporate a priori biological knowledge, provide more practical descriptions of observed cell behaviours, and predict the outcome of network perturbations. Metabolomics is independently advancing as a set of high-throughput data acquisition tools providing dynamic profiles of metabolites in an unbiased manner. These data sets are neither yet sufficiently comprehensive nor accurate enough for generating large-scale kinetic models. Thus, there is a pressing need to develop quantitative techniques that can make use of the emerging data and embrace the associated uncertainties. This article reviews recent advances in FBA to meet this need and discusses the utility of FBA as a complement to metabolomics and the expected synergy as a result of combining these two techniques.
authors.affiliation: Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908, USA.
authors.forename: Jong Min
authors.initials: JM
authors.lastname: Lee
authors.forename: Erwin P
authors.initials: EP
authors.lastname: Gianchandani
authors.forename: Jason A
authors.initials: JA
authors.lastname: Papin
mesh_terms: D000818:Animals; D001709:Biotechnology / Q000379:methods / Q000639:trends; D002468:Cell Physiological Phenomena; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D004734:Energy Metabolism / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods* / Q000639:trends; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics / Q000379:methods* / Q000639:trends; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome