ArticleBMC systems biology2019
DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression.
Article in BMC systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Dynamic Constrained Allocation Flux Balance Analysis (dCAFBA).Methods in molecular biology (Clifton, N.J.) · 2026Article
- Recent trends in microbial production of alkanes.World journal of microbiology & biotechnology · 2025Review
- Minimization of proteome reallocation explains metabolic transition in hierarchical utilization of carbon sources.mSystems · 2025Article
- Genome-scale metabolic modeling reveals specific vaginalMicrobiology spectrum · 2025Article
- New insights in metabolism modelling to decipher plant-microbe interactions.The New phytologist · 2025Review
- A Mathematical Exploration of SDH-b Loss in Chromaffin Cells.Bulletin of mathematical biology · 2025Article
- UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale.Bioinformatics (Oxford, England) · 2025Article
- Charting the state of GEMs in microalgae: progress, challenges, and innovations.Frontiers in plant science · 2025Review
- Integration of proteomic data with genome-scale metabolic models: A methodological overview.Protein science : a publication of the Protein Society · 2024Review
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Genome-scale metabolic modeling in antimicrobial pharmacology.Engineering microbiology · 2022Review
- Modeling and Optimization of a Molecular Biocontroller for the Regulation of Complex Metabolic Pathways.Frontiers in molecular biosciences · 2022Article
- Glycolysis/gluconeogenesis specialization in microbes is driven by biochemical constraints of flux sensing.Molecular systems biology · 2022Article
- Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.Frontiers in oncology · 2022Review
- Time-Optimal Adaptation in Metabolic Network Models.Frontiers in molecular biosciences · 2022Article
- Synthetic Perturbations in IL6 Biological Circuit Induces Dynamical Cellular Response.Molecules (Basel, Switzerland) · 2021Article
- Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm.Biophysical journal · 2021Article
- Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Opportunities and Challenges for Microbial Synthesis of Fatty Acid-Derived Chemicals (FACs).Frontiers in bioengineering and biotechnology · 2021Review
- The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale.Microorganisms · 2020Review
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5 authors.
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Abstract
backgroundGenome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype.
resultsWe develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics ("inertia") alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation.
conclusionsOverall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.
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