Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis

By Sriram Chandrasekaran, Nathan D. Price. Proceedings of the National Academy of Sciences of the United States of America, 2010

394 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/47814558428c367ec12698619b3ac2457f5e0dbc

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Citations in the corpus

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “When ample transcriptomic data and candidate regulatory interactions (for example, from chromatin immunoprecipitation followed by sequencing (ChIP–seq)) are available, PROM can build a probabilistic model of the transcription-regulatory network of an organism and integrate it with the metabolic netw”
Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “Similarly, regulatory interactions can be added to affect the presence/absence of a gene product based on condition-specific activity of a transcription factor (Chandrasekaran and Price, 2010).”
Integrative approaches for finding modular structure in biological networks (2013, methodology): “For example, the probabilistic regulation of metabolism (PROM) method was developed to facilitate automated and quantitative integration of regulatory interactions and other high-throughput data for constraint-based modelling of metabolic networks 156 .”
Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “The integrated E. coli metabolic regulatory model was more accurate in predicting transcription factor-knockout phenotypes than pre vious attempts that used integrated models 90 the TRN modifies the CBM of metabolism, the calculated flux distributions and the metabolites that are present do not feed”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “Probabilistic regulation of metabolism (PROM) is a method for integration of regulatory and metabolic networks [25].”
A Whole-Cell Computational Model Predicts Phenotype from Genotype (2012, neutral): “…had previously developed methods to integrate ODEs with Boolean, probabilistic, and constraint-based submodels (Covert et al., 2001, 2004, 2008; Chandrasekaran and Price, 2010), the current effort involved so many different cellular functions and mathematical representations that a more…”