Regulatory on/off minimization of metabolic flux changes after genetic perturbations.

By Tomer Shlomi, Omer Berkman, Eytan Ruppin. Proceedings of the National Academy of Sciences of the United States of America, 2005

486 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fcd1f2865e5962b29b789b9657b9a7534fdc0f39

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Citations in the corpus

Flux balance analysis in the era of metabolomics (2006, background): “ROOM (regulatory on/off minimization) is a recent development that identifies the metabolic flux state of mutants by minimizing the number of ‘significant’ flux changes from the wild-type flux distribution [36].”
A protocol for generating a high-quality genome-scale metabolic reconstruction (2010, background): “Known incapabilities (Steps 81 and 82): So far we compared whether the model can reproduce growth on a certain substrate, secrete a particular by-product and so on.”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “To address additional questions concerning gene deletion, new methods have been introduced such as MOMA 50 , regulatory on– off minimization (ROOM) 62 and metabolite essentiality analysis (MEA) 63 (FIG.”
Flux balance analysis of biological systems: applications and challenges (2009, background): “Techniques such as MoMA [15, 27] and ROOM [28], which alter the objective for optimization, resulting in QP and MILP formulations respectively, have already been discussed earlier.”
Network-based prediction of human tissue-specific metabolism (2008, methodology): “Notably, previous studies have used MILP in the context of applying constraint-based methods to studying metabolism in microbial organism”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, background): “They predict knockout mutant growth phenotypes by minimizing either the overall ﬂux change [MoMA (Segre` et al ., 2002)] or the number of regulatory changes [ROOM (Shlomi et al ., 2005)] between the wild-type strain and the mutant strain (see Table 5).”
OptFlux: an open-source software platform for in silico metabolic engineering (2010, methodology): “OptFlux has three available methods for conducting the simulations: FBA (see for example [30]), MOMA [21] or ROOM [22].”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, methodology): “The suboptimal state of the cell can also be predicted, but the most utilized method (Segre et al , 2002) provides little insight into the biological driving force for suboptimal performance (Shlomi et al , 2005).”
Evolutionary programming as a platform for in silico metabolic engineering (2005, methodology): “There are, however, some other approaches to determine flux distributions, especially for deletion mutants that might not be capable of realizing the same objective function as the wild-type strain [19-21].”
Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli (2008, methodology): “Two extensions of FBA of interest here are the use of multiple FBA steps to simulate growth dynamics (Luo et al ., 2006; Mahadevan et al ., 2002; Varma and Palsson, 1994), and the incorporation of transcriptional regulatory network models (Covert et al ., 2001; Shlomi et al ., 2005, 2007).”