Dynamic flux balance analysis of diauxic growth in Escherichia coli.

By Radhakrishnan Mahadevan, Jeremy S. Edwards, F. Doyle. Biophysical Journal, 2002

888 citations recorded by the source corpus.

Subfields: Molecular Biology, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/8d56a26c56704266b5d7c426ecdfc286962f9112

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Citations in the corpus

OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities (2012, methodology): “The first study to characterize a negative interaction between two microorganisms using genome-scale metabolic models was published by Zhuang et al [31] where similar to [25,27] an extension of the dynamic flux balance analysis [32] was employed to model the competition between Rhodoferax ferrireduc”
Flux balance analysis in the era of metabolomics (2006, background): “Recently, a dynamic optimization approach was developed to account for flux changes in future time steps [39].”
Genome-scale models of microbial cells: evaluating the consequences of constraints (2004, methodology): “Another method, dynamic flux balance analysis, includes additional constraints that limit the rate of change of internal fluxes in attempts to describe the dynamic change in intracellular flux levels 99 .”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, background): “Mahadevan and co-workers [22] extended traditional FBA to account for dynamics and presented two different formulations: the”
Mathematical Modeling of Microbial Community Dynamics: A Methodological Review (2014, methodology): “The integration of FBA with dynamic modeling can be considered in a much simpler context—well-mixed conditions—using the framework called dynamic FBA (dFBA) [123].”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “Temporal Expression-based Analysis of Me-tabolism (TEAM) is a method that combines dynamic Flux Balance Analysis (dFBA) [32] and GIMME to predict time-course flux profiles based on temporal gene expression patterns [33].”
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).”