Analysis of optimality in natural and perturbed metabolic networks

By Daniel Segrè, Dennis Vitkup, G. M. Church. Proceedings of the National Academy of Sciences of the United States of America, 2002

370 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7c8270bc69bdaf983d65b2d088bd7f530c519121

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Citations in the corpus

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review (2014, background): “Wintermute and Silver [67] developed a computational framework based on the minimization of metabolic adjustment (MOMA) [68] to investigate the interactions in synthetic pairs of 46 Escherichia coli auxotrophs.”
Flux balance analysis in the era of metabolomics (2006, methodology): “Once this set of steady-state fluxes is identified, optimization techniques may be utilized to evaluate the performance of the biological system at various perturbations, such as different cellular objectives or variability in the imposed constraints [22].”
Graph theory and networks in Biology. (2006, background): “As pointed out in [51, 132], while the wild-type E.”
BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions (2010, methodology): “The exported SBML files have all been validated and can be used to make predictions about growth rate, predicting the effect of gene deletions (MOMA [ 51 ]), and other COBRA framework methods.”
Flux balance analysis of biological systems: applications and challenges (2009, methodology): “coli, which were not identified by FBA [15].”
Modular epistasis in yeast metabolism (2005, methodology): “For the deletion of gene X , fitness was defined as W x = V ΔX growth / V wild–type growth (refs”
An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. (2006, background): “The obtained results based on biomass maximization are also contrasted against the ones obtained based on the MOMA (Segre et al., 2002) criterion.”
OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (2010, background): “Furthermore, in the absence of kinetic descriptions OptKnock and other methods rely on the maximization of surrogate biological fitness functions (e.g. maximization of biomass yield [21] or minimization of metabolic adjustments MOMA [42]) to estimate flux redirection upon strain engineering.”
Environments that Induce Synthetic Microbial Ecosystems (2010, background): “grow at maximal possible rate? This approach, also known as flux balance analysis, has been described in detail elsewhere [23–26], and given rise to a plethora of interesting discussions on optimality in metabolic network regulation and evolution [23,27–30].”
Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization (2003, background): “The obtained results are also contrasted against using the minimization of metabolic adjustment (Segre et al., 2002) as the cellular objective.”