Nonlinear programming reformulation of dynamic flux balance analysis models

Oliveira, Rafael D. de; Roux, G.; Mahadevan, Radhakrishnan

doi:10.1016/j.compchemeng.2022.108101

Cited by 20 publications

(19 citation statements)

References 38 publications

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“…Genome-scale models can be extended to describe cell metabolism in dynamic conditions by solving dynamic flux balance analysis; however, they often encounter stiff solutions and face challenges in selecting objective functions for different conditions . Here, our results showed that the modeling framework can successfully describe cellular behaviors and patterns in dynamic settings.…”

Section: Resultsmentioning

confidence: 99%

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Nguyen,

Li,

2024

ACS Synth. Biol.

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Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrientaltered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

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Section: Resultsmentioning

confidence: 99%

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Nguyen,

Li,

2024

ACS Synth. Biol.

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“…Incorporating the means to modulate cellular resource allocation using a hybrid modeling paradigm improves fidelity without the need for developing detailed mechanistic models such as whole-cell models or ME-models. Furthermore, by using an adaptive time step, a desired integration accuracy can be ensured without resorting to collocation (St John et al, 2017), which significantly reduces the number of time-steps and by extension, the number of times the FBA problem must be solved (de Oliveira et al, 2023; Zhuang et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Gopalakrishnan,

Johnson,

Valderrama-Gomez

et al. 2023

Preprint

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Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

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“…FBA assumes that the fluxes including nutrient uptake are at steady state, which limits its applications especially when the extracellular environment changes over time. Dynamic FBA (dFBA) extends FBA by incorporating the temporal variations of extracellular nutrients and metabolites, and simulate the metabolic adaptation of cells in response to these changes [4]. However, dFBA still neglects the effects of enzymatic constraints, which may be significant for energy metabolism [5,6], metal utilization [7] and biomass productions [8].…”

Section: Introductionmentioning

confidence: 99%