Genome-scale microbial in silico models: the constraints-based approach.

By Nathan D. Price, Jason A. Papin, C. Schilling, Bernhard O. Palsson. Trends in Biotechnology, 2003

429 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9996629f4e86969079f63a1d2ecaa198db535084

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Citations in the corpus

Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis (2010, neutral): “In constraint-basedanalysis,thefunctionalstatesanetworkcanchoosearelimited byvariousphysicochemicalconstraints(11).”
An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) (2003, background): “This approach to genome-scale model building has been reviewed in detail [3-6].”
Evolutionary programming as a platform for in silico metabolic engineering (2005, background): “Genome-scale models of microbial organisms [6], comprising different levels of information, primarily on the stoichiometry of the many different reactions but possibly also comprising some information about regulation, could offer a suitable platform for developing systems level tools for analyzing ”
Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization (2003, methodology): “This information, used in the context of the flux balance analysis (FBA) modeling framework (Varma and Palsson, 1993), has been employed extensively to explore the integrated functions of metabolic networks (Burgard and Maranas, 2001; Burgard et al., 2001; Papin et al., 2003; Price et al., 2003).”
Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data (2006, background): “FBA employs linear programming to optimize a suitable cellular objective, while assuming steady state for the mass balances (Kauffman et al , 2003; Price et al , 2003).”
Exploring the diversity of complex metabolic networks (2005, positive): “The cellular feasibility of the predicted pathways can be further evaluated by implementing a constraints-based approach (Price et al., 2003).”
A protocol for generating a high-quality genome-scale metabolic reconstruction (2010, methodology): “12| Add subsystem information to the reaction.”
The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum (2013, methodology): “[1] homocitrate synthase (r0683); [2] homocitrate dehydrase (r0684); [3] homoaconitate hydrase (r0685); [4] homoisocitrate dehydrogenase (r0688); [5] a-aminoadipate aminotransferase (r0689); [6] homoserine transacetylase (r0600); [7] O-acetylhomoserine sulfhydrylase (r0601); [8] cystathione-b-syntha”
Flux coupling analysis of genome-scale metabolic network reconstructions. (2004, background): “…abundance of complete genome sequences has enabled the generation of genome-scale metabolic reconstructions for various microorganisms (Covert et al. 2001; Price et al. 2003; Reed and Palsson 2003) These models provide a largely complete skeleton of the metabolic reactions present in an organism .”
Network-based prediction of human tissue-specific metabolism (2008, background): “Specifically, a highly expressed reaction is considered to be active if it carries a significant positive flux that is greater than a positive threshold e (equation (3)) or a significant negative flux o –e (equation (4) for reversible reactions).”
Genome-scale models of microbial cells: evaluating the consequences of constraints (2004, background): “In mathematical terms, the range of allowable network states is described by a solution space that represents the phenotypic potential of an organis”