The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Groot, Daan H. de; Lischke, Julia; Muolo, Riccardo; Planquè, Robert; Bruggeman, Frank J.; Teusink, Bas

doi:10.1101/679019

Cited by 10 publications

(12 citation statements)

References 45 publications

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“…If instead of a single constraint on cellular density, multiple density constraints are imposed simultaneously (e.g., to describe separate constraints on different cellular compartments), then the solutions may in some cases correspond to positive linear combinations of EGSs 30,63 , and the treatment below needs to be generalized. Multiple density constraints may play a role in the emergence of overflow metabolism in E. coli 54,64 , although overflow metabolism can also arise in balanced growth models with a single density constraint 5 .…”

Section: Methodsmentioning

confidence: 99%

An analytical theory of balanced cellular growth

2020

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The biological fitness of microbes is largely determined by the rate with which they replicate their biomass composition. Mathematical models that maximize this balanced growth rate while accounting for mass conservation, reaction kinetics, and limits on dry mass per volume are inevitably non-linear. Here, we develop a general theory for such models, termed Growth Balance Analysis (GBA), which provides explicit expressions for protein concentrations, fluxes, and growth rates. These variables are functions of the concentrations of cellular components, for which we calculate marginal fitness costs and benefits that are related to metabolic control coefficients. At maximal growth rate, the net benefits of all concentrations are equal. Based solely on physicochemical constraints, GBA unveils fundamental quantitative principles of cellular resource allocation and growth; it accurately predicts the relationship between growth rates and ribosome concentrations in E. coli and yeast and between growth rate and dry mass density in E. coli.

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Section: Methodsmentioning

confidence: 99%

An analytical theory of balanced cellular growth

2020

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“…de Groot et al . (2019) show that GEM predict overflow metabolism when two growth‐limiting constraints are hit regardless of their biological interpretation. Therefore, Yeast8 can be modified to predict overflow metabolism by adding a second constraint such as a maximum oxygen uptake rate (Famili et al ., 2003).…”

Section: Discussionmentioning

confidence: 99%

Enzyme‐constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed‐batch bioreactors

Moreno‐Paz

Schmitz

Santos

et al. 2022

Microbial Biotechnology

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Summary Genome‐scale, constraint‐based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bioprocess design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme‐constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well‐known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyse flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell‐based processes and, thus, the usefulness of such models.

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“…The constraint-based model shows a performance comparable to that of the kinetic models, with its accuracy degrading with higher dilution rates. This limitation was described earlier (33) and is attributed to the challenges of predicting activation of overflow metabolism without incorporating additional constraints (35) .…”

Section: Changes In Dilution Ratementioning

confidence: 98%

Benchmarking kinetic models ofEscherichia colimetabolism

Shepelin

Machado

Nielsen

et al. 2020

Preprint

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Predicting phenotype from genotype is the holy grail of quantitative systems biology.Kinetic models of metabolism are among the most mechanistically detailed tools for phenotype prediction. Kinetic models describe changes in metabolite concentrations as a function of enzyme concentration, reaction rates, and concentrations of metabolic effectors uniquely enabling integration of multiple omics data types in a unifying mechanistic framework. While development of such models for Escherichia coli has been going on for almost twenty years, multiple separate models have been established and systematic independent benchmarking studies have not been performed on the full set of models available. In this study we compared systematically all recently published kinetic models of the central carbon metabolism of Escherichia coli . We assess the ease of use of the models, their ability to include omics data as input, and the accuracy of prediction of central carbon metabolic flux phenotypes. We conclude that there is no clear winner among the models when considering the resulting tradeoffs in performance

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The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Cited by 10 publications

References 45 publications

An analytical theory of balanced cellular growth

An analytical theory of balanced cellular growth

Enzyme‐constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed‐batch bioreactors

Benchmarking kinetic models ofEscherichia colimetabolism

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