Benchmarking kinetic models of<i>Escherichia coli</i>metabolism

Shepelin, Denis; Machado, Daniel; Nielsen, Lars K.; Mj, Herrgård

doi:10.1101/2020.01.16.908921

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…It should be noted that the method places no restriction on the size or scale of the model as well as the biological purpose of the associated network as long as its reactions are governed by MAKRL. Thus the method is applicable for models of core metabolism (for instance E.coli central carbon metabolism models reviewed in [ 55 ]) as well as models of regulatory networks. It can also be applied for small models like the one of NAR considered in this paper as well as genome-scaled models as in [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

Parameter Estimation for Kinetic Models of Chemical Reaction Networks from Partial Experimental Data of Species’ Concentrations

Gasparyan,

Rao

2023

Bioengineering

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The current manuscript addresses the problem of parameter estimation for kinetic models of chemical reaction networks from observed time series partial experimental data of species concentrations. It is demonstrated how the Kron reduction method of kinetic models, in conjunction with the (weighted) least squares optimization technique, can be used as a tool to solve the above-mentioned ill-posed parameter estimation problem. First, a new trajectory-independent measure is introduced to quantify the dynamical difference between the original mathematical model and the corresponding Kron-reduced model. This measure is then crucially used to estimate the parameters contained in the kinetic model so that the corresponding values of the species’ concentrations predicted by the model fit the available experimental data. The new parameter estimation method is tested on two real-life examples of chemical reaction networks: nicotinic acetylcholine receptors and Trypanosoma brucei trypanothione synthetase. Both weighted and unweighted least squares techniques, combined with Kron reduction, are used to find the best-fitting parameter values. The method of leave-one-out cross-validation is utilized to determine the preferred technique. For nicotinic receptors, the training errors due to the application of unweighted and weighted least squares are 3.22 and 3.61 respectively, while for Trypanosoma synthetase, the application of unweighted and weighted least squares result in training errors of 0.82 and 0.70 respectively. Furthermore, the problem of identifiability of dynamical systems, i.e., the possibility of uniquely determining the parameters from certain types of output, has also been addressed.

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Section: Discussionmentioning

confidence: 99%

Parameter Estimation for Kinetic Models of Chemical Reaction Networks from Partial Experimental Data of Species’ Concentrations

Gasparyan,

Rao

2023

Bioengineering

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“…To address this parameterization challenge, a number of approaches have been developed (9,10). These approaches can be loosely classified into parameter sampling, "topdown" parameterization, and "bottom-up" parameterization methods.…”

Section: Introductionmentioning

confidence: 99%

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Zielinski,

Matos,

de Bree

et al. 2023

Preprint

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Kinetic models of enzymes have a long history of use for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of ‘bottom up’ parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, andin vitro-in vivodifferences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard ‘macroscopic’ kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and ‘microscopic’ rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either withinin vitroexperiments or betweenin vitroandin vivoenzyme function. The code and case studies are provided in the MASSef package built on top of the MASS Toolbox in Mathematica. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.Author SummaryDetailed kinetic models of metabolism offer the promise of enabling new predictions of metabolic behavior and prospective design of metabolic function. However, parameterizing such models remains a substantial challenge. Historically, the kinetics of many enzymes have been measured usingin vitroassays, but integrating this data into consistent large-scale models and filling gaps in available data has been a primary difficulty. Here, we provide an algorithmic approach to parameterize enzyme kinetic models using diverse enzyme kinetic data. The approach reconciles inconsistent data and addresses the issue of gaps in available data implicitly through sampling alternative parameter sets. We provide a number of case studies demonstrating the approach on different enzymes. This work empowers the use of the large amount of historical and machine learning-estimated enzyme data and will aid in the construction of biochemically-accurate models of metabolism.

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Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Zielinski,

Matos,

de Bree

et al. 2024

Metabolic Engineering Communications

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Benchmarking kinetic models ofEscherichia colimetabolism

Cited by 3 publications

References 51 publications

Parameter Estimation for Kinetic Models of Chemical Reaction Networks from Partial Experimental Data of Species’ Concentrations

Parameter Estimation for Kinetic Models of Chemical Reaction Networks from Partial Experimental Data of Species’ Concentrations

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

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