Optimally-reduced kinetic models: reaction elimination in large-scale kinetic mechanisms

AbstractLarge-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grownEscherichia coliinto a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of theE. coliaerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grownE. coli.

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Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

10.1101/569152 ◽

2019 ◽

Cited By ~ 1

Author(s):

Milenko Tokic ◽

Ljubisa Miskovic ◽

Vassily Hatzimanikatis

Keyword(s):

Metabolic Engineering ◽

Kinetic Models ◽

Rational Design ◽

Large Scale ◽

Single Gene ◽

Central Carbon Metabolism ◽

Scale Model ◽

Metabolic Responses ◽

Design And Optimization ◽

Genetic Perturbations

AbstractA high tolerance ofPseudomonas putidato toxic compounds and its ability to grow on a wide variety of substrates makes it a promising candidate for the industrial production of biofuels and biochemicals. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. In this work, we developed large-scale kinetic models ofP. putidato predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model ofP. putidaKT2440. We then applied the redGEM and lumpGEM algorithms to reduce the curated iJN1411 model systematically, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism ofP. putida. Using the medium complexity core model as a scaffold, we employed the ORACLE framework to generate populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain ofP. putidaKT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Overall, we demonstrated the potential and predictive capabilities of developed kinetic models that allow for rational design and optimization of recombinantP. putidastrains for improved production of biofuels and biochemicals.

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OPTIMAL SELECTION OF ENZYME LEVELS USING LARGE-SCALE KINETIC MODELS

IFAC Proceedings Volumes ◽

10.3182/20050703-6-cz-1902.02208 ◽

2005 ◽

Vol 38 (1) ◽

pp. 25-30 ◽

Cited By ~ 3

Author(s):

Evgeni V. Nikolaev ◽

Priti Pharkya ◽

Costas D. Maranas ◽

Antonios Armaou

Keyword(s):

Kinetic Models ◽

Large Scale ◽

Optimal Selection ◽

Selection Of

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WinBEST-KIT: Biochemical reaction simulator that can define and customize algebraic equations and events as GUI components

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720019500367 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1950036

Author(s):

Tatsuya Sekiguchi ◽

Hiroyuki Hamada ◽

Masahiro Okamoto

Keyword(s):

Large Scale ◽

System Biology Markup Language ◽

Biochemical Reaction ◽

Engineering System ◽

Algebraic Equations ◽

Biochemical Engineering ◽

Biochemical Reaction Networks ◽

Kinetic Mechanisms ◽

Mathematical Equations ◽

Definition Of

We previously developed Windows-based Biochemical Engineering System analyzing Tool-KIT (WinBEST-KIT), a biochemical reaction simulator for analyzing large-scale and complicated biochemical reaction networks. One particularly notable feature is the ability for users to define original mathematical equations for representing unknown kinetic mechanisms and customize them as GUI components for representing reaction steps. Many simulators support System Biology Markup Language SBML; however, since the definition of the algebraic equations (AssignmentRule) and the events are made through an interface that is distinct from the definition of the reaction steps, there are tough works to define them. Accordingly, we have developed a new version of WinBEST-KIT that allows users to define the algebraic equations and the events through the same interface as those used in the definition of the reaction steps and customize them as GUI components appearing in the symbol selection area. The customized algebraic equations and events can thus be visually arranged at any time and any place. It also allows users to easily understand the roles of the algebraic equations and the events. We have also implemented other useful features, including importing/exporting of SBML format files, exporting to MATLAB, and merging the existing models into the model currently being created. The current version of WinBEST-KIT is freely available at http://winbest-kit.org/ .

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MODELING OF ESSENTIAL OIL EXTRACTION PROCESS: APPLICATION FOR ORANGE, POMELO, AND LEMONGRASS

Vietnam Journal of Science and Technology ◽

10.15625/2525-2518/56/4a/12811 ◽

2018 ◽

Vol 56 (4A) ◽

pp. 182

Author(s):

Thanh Nguyen Dang Binh ◽

Dung Nguyen Trung ◽

Duc Hong Ta

Keyword(s):

Large Scale ◽

Scale Up ◽

Extraction Process ◽

Model Parameters ◽

Two Stage ◽

Kinetic Rate ◽

Kinetic Mechanisms ◽

Extraction Processes ◽

Solid Liquid ◽

Kinetics Of

ABSTRACT - HCTN - 44In this study, the kinetic models of steam distillation of orange (Citrus Sinensis (L.) Osbeck), pomelo (Citrus grandis L.), and lemongrass (Cymbopogon Citratus) for the recovery of essential oils were developed. The model parameters were estimated based on experimental data and comprehensive kinetic mechanisms of the solid-liquid extraction process. Numerical results showed that, the extraction mechanism of the three materials were best fit to the Patricelli two-stage model in which the diffusion of the oil was followed by the washing step. Moreover, the model parameters obtained from the measured data reflected clearly the nature of the two-stage extraction at which the kinetic rate of the washing step (surface extraction) was higher than that of in-tissue diffusion step. Thus, the kinetics of the extraction processes obtained from the present work could be used for the scale-up of the extraction process operating at a large scale and for the purpose of process control as well.

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K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data

10.1101/612994 ◽

2019 ◽

Cited By ~ 3

Author(s):

Saratram Gopalakrishnan ◽

Satyakam Dash ◽

Costas Maranas

Keyword(s):

Steady State ◽

Kinetic Models ◽

Large Scale ◽

Metabolic Networks ◽

Decomposition Approach ◽

E Coli ◽

Environmental Perturbations ◽

Genetic Perturbations ◽

Gradient Based ◽

Speed Up

AbstractKinetic models predict the metabolic flows by directly linking metabolite concentrations and enzyme levels to reaction fluxes. Robust parameterization of organism-level kinetic models that faithfully reproduce the effect of different genetic or environmental perturbations remains an open challenge due to the intractability of existing algorithms. This paper introduces K-FIT, an accelerated kinetic parameterization workflow that leverages a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations followed by a gradient-based update of kinetic parameters until predictions simultaneously agree with the fluxomic data in all perturbed metabolic networks. The applicability of K-FIT to large-scale models is demonstrated by parameterizing an expanded kinetic model forE. coli(307 reactions and 258 metabolites) using fluxomic data from six mutants. The achieved thousand-fold speed-up afforded by K-FIT over meta-heuristic approaches is transformational enabling follow-up robustness of inference analyses and optimal design of experiments to inform metabolic engineering strategies.

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The elucidation of metabolic pathways and their improvements using stable optimization of large-scale kinetic models of cellular systems

Metabolic Engineering ◽

10.1016/j.ymben.2009.08.010 ◽

2010 ◽

Vol 12 (1) ◽

pp. 26-38 ◽

Cited By ~ 31

Author(s):

Evgeni V. Nikolaev

Keyword(s):

Kinetic Models ◽

Metabolic Pathways ◽

Large Scale ◽

Cellular Systems

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