Capturing the essence of a metabolic network: A flux balance analysis approach

Murabito, Ettore; Simeonidis, Evangelos; Smallbone, Kieran; Swinton, Jonathan

doi:10.1016/j.jtbi.2009.06.013

Cited by 32 publications

(22 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include constraints imposed by economy in enzyme usage 43, 51–53 , metabolite dilution 54 , and changes in transcript level 55, 56 . These FBA refinements further decrease the range of feasible reaction fluxes to obtain solutions closely resembling cellular physiology under certain growth conditions.…”

Section: A Phylogeny Of Constraint-based Methodsmentioning

confidence: 99%

Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

2012

View full text Add to dashboard Cite

Section: A Phylogeny Of Constraint-based Methodsmentioning

confidence: 99%

Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

2012

View full text Add to dashboard Cite

“…In addition, the flux of each reaction through the system may be constrained through, for example, knowledge of enzyme maximal activities ( v max ) or irreversibility considerations [32]. Given a typical genome-scale metabolic model such as Yeast 5, at steady state these constraints may be written as:…”

Section: Resultsmentioning

confidence: 99%

Improving metabolic flux predictions using absolute gene expression data

et al. 2012

Self Cite

View full text Add to dashboard Cite

BackgroundConstraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se.ResultsAn alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production.ConclusionDue to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method’s ability to generate condition- and tissue-specific flux predictions in multicellular organisms.

show abstract

“…In this case the filtration and washing step took over 50 s, while the procedure took approximately 10 s when using 10 mbar of vacuum. As the filter clogged when 5 × 10 7 cells were applied (Murabito et al, 2009), which drastically increased the time between filtration and quenching, the applied number of cells used was 10 7 per filter for all samples. Leakage of small metabolites, especially of ATP, started to increase from 20 mbar, while larger test substances such as LDH started to significantly increase from 40 mbar onwards, similar to values published by Volmer et al (2011a).…”

Section: Resultsmentioning

confidence: 99%