Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties

Mišković, Ljubiša; Béal, Jonas; Moret, Michael; Hatzimanikatis, Vassily

doi:10.1101/427716

Cited by 3 publications

(3 citation statements)

References 83 publications

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“…This rather constant variability as we go toward a higher number of gene manipulations suggests that variability among 19 sets is primarily determined by the activity of a relatively small number of enzymes, which predominantly have control over the glucose uptake rate. This finding is in line with previous studies of metabolic systems demonstrating that just a few enzymes in the network (or corresponding parameters) determine the key metabolic properties such as system stability (Andreozzi et al, 2016b) or control over production fluxes (Miskovic et al, 2019a). A similar observation was reported in a more general context of biological systems (Daniels et al, 2008; Gutenkunst et al, 2007).…”

Section: Resultssupporting

confidence: 91%

Constraint-based metabolic control analysis for rational strain engineering

Tsouka¹,

Ataman²,

Hameri³

et al. 2020

Preprint

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The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolic concentrations, it fails to account for the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework based on MCA, Network Response Analysis (NRA), for the rational genetic strain design that incorporates biologically relevant constraints, as well as genome editing restrictions. The NRA core constraints being similar to the ones of Flux Balance Analysis, allow it to be used for a wide range of optimization criteria and with various physiological constraints. We show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

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Section: Resultssupporting

confidence: 91%

Constraint-based metabolic control analysis for rational strain engineering

Tsouka¹,

Ataman²,

Hameri³

et al. 2020

Preprint

Self Cite

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“…To resolve the challenges arising from the uncertainties in the parameter values, we used Bayesian statistical learning 26 , which is a probabilistic framework that has been successfully applied for quantifying and reducing uncertainties in various fields including deep learning 28 , ordinary differential equations 29 and biochemical kinetic models 30 . The approach uses experimental observations ( ) to update Prior distributions ( ( )) of model parameters to Posterior ones ( ( | )) ( Fig 1e).…”

Section: Introduction Resultsmentioning

confidence: 99%

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

Wang

et al. 2020

Preprint

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The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovered enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) was predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtained a thermotolerant strain that outgrew the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.Temperature is the most common environmental and evolutionary factor that shapes the physiology of living cells. Organisms have successfully adapted to survive in diverse temperature ranges 1-3 , where minor deviations from the optimal temperature by merely a few degrees can dramatically impair cell growth. For instance, the model eukaryotic organism Saccharomyces cerevisiae has an optimal growth temperature of ~30°C, whereas a temperature of 42°C is already lethal to the organism 4,5 . Since cell growth fundamentally requires all cellular components to be functional in the temperature window of cell growth, proteins, the most abundant group of biomolecules that carry out the majority of catalytic functions and are also the most sensitive to changes in temperature 5-7 , are considered to have the largest effect on cell physiology in relation to temperature. However, despite all our knowledge of temperature effects at both the cellular and molecular levels, including recent breakthroughs in temperature-dependent protein folding 7-10 and enzyme kinetics 11,12 , the temperature association between proteins and cell physiology is still poorly understood.Multiple studies have attempted to model the temperature effects on cell growth with very few proteome wide parameters. For instance, the dominant activation barrier and the number of essential proteins to cell growth 13 , activation energy of the growth process and the free energy change of protein denaturation 14 and others (reviewed in 15 ). These models showed excellent performance when describing the general cell growth rate at various temperatures, however, they could not pinpoint the specific rate-limiting enzymes, nor predict the amount of improvement in growth rate by replacing these ...

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“…Ideally models should propagate uncertainty from training data and parameters to the actual predictions (47,48) . Some kinetic frameworks try to perform uncertainty quantification in form of Monte Carlo sampling (49) or using a full Bayesian framework (50) , but there is no published E. coli kinetic model using these frameworks that we could test in our benchmark. We hope that uncertainty quantification would be integrated into the next generation of models enabling exploration of the whole set of predictions rather than only the point estimates.…”

Section: Uncertaintymentioning

confidence: 99%

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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Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties

Cited by 3 publications

References 83 publications

Constraint-based metabolic control analysis for rational strain engineering

Constraint-based metabolic control analysis for rational strain engineering

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

Benchmarking kinetic models ofEscherichia colimetabolism

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