DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

Laurence Yang*, Ali Ebrahim, Colton J. Lloyd, Michael A. Saunders, Bernhard Palsson

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
JournalBMC Systems Biology
Volume13
Issue number2
Number of pages15
ISSN1752-0509
DOIs
Publication statusPublished - 2019

Keywords

  • Constraint-based modeling
  • Metabolism
  • Proteome
  • Dynamic simulation
  • Batch culture

Cite this

Yang, Laurence ; Ebrahim, Ali ; Lloyd, Colton J. ; Saunders, Michael A. ; Palsson, Bernhard. / DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression. In: BMC Systems Biology. 2019 ; Vol. 13, No. 2.
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title = "DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression",
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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author = "Laurence Yang and Ali Ebrahim and Lloyd, {Colton J.} and Saunders, {Michael A.} and Bernhard Palsson",
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DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression. / Yang, Laurence; Ebrahim, Ali; Lloyd, Colton J.; Saunders, Michael A.; Palsson, Bernhard.

In: BMC Systems Biology, Vol. 13, No. 2, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

AU - Yang, Laurence

AU - Ebrahim, Ali

AU - Lloyd, Colton J.

AU - Saunders, Michael A.

AU - Palsson, Bernhard

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Constraint-based modeling

KW - Metabolism

KW - Proteome

KW - Dynamic simulation

KW - Batch culture

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DO - 10.1186/s12918-018-0675-6

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JO - B M C Systems Biology

JF - B M C Systems Biology

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