Study on how underlying uncertainty in the flux values affects metabolic control analysis of optimally grown E. coli

Large-scale kinetic models of metabolism are essential for understanding and predicting the behavior of cellular systems when subject to perturbations. Despite the advances in experimental measurement technologies, the numerous parameters that are required to build kinetic models remain scarce and involve uncertainty. Even after incorporating the partially available experimental data, models still have many degrees of freedom. Due to this parametric uncertainty, some reactions are able to operate in forward and reverse directions. An operational configuration consists of reactions that operate in a unique direction. This flexibility results in the existence of alternative operational configurations representing the same physiology, with very distinct regulatory properties. In this study, we focus on one operational configuration and we investigate how the underlying uncertainty in the flux values affects the robustness of the model predictions and regulatory capabilities. To study this question, we used a large-scale kinetic model and integrated fluxomics and metabolomics data describing the physiology for aerobically grown E. coli. Because of the under-determined nature of the system, there are infinite existing flux solutions within the selected operational configuration. To account for the flux variability within the designated operational configuration, we selected a reference vector of concentrations close to their nominal value. We used the ORACLE (optimization and risk analysis of complex living entities) framework to build populations of kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints. We next performed a systematic analysis of the effect that the flux profiles have on the robustness of the regulatory properties of the system. We used the mean PCA (principle component analysis) value of the flux samples as reference when selecting flux profiles. Flux profiles across the main components of the PCA were studied. We used ORACLE to generate populations of kinetic models for these flux profiles. Then, we computed the distributions of their flux control coefficients (FCCs) along the dimensions with the highest variance. Finally, by comparing the changes among the distributions of the FCCs versus those corresponding to the reference flux profile, we were able to quantify the robustness of the regulatory predictions within a specific operational configuration.

How uncertainty in kinetic parameters affects metabolic control analysis of optimally grown E.coli

Meriç Ataman, Georgios Fengos, Tuure Eelis Hameri, Vassily Hatzimanikatis, Ljubisa Miskovic

Large-scale kinetic models of metabolism are essential for understanding and predicting the behavior of cellular systems when subject to perturbations. Despite the advances in experimental measurement technologies, the numerous parameters that are required to build kinetic models remain scarce and involve uncertainty. Even after incorporating the partially available experimental data, models still have many degrees of freedom. Due to this parametric uncertainty, some reactions are able to operate in forward and reverse directions. An operational configuration consists of reactions that operate in a unique direction. This flexibility results in the existence of alternative operational configurations representing the same physiology, with very distinct regulatory properties. In this study, we focus on one operational configuration and we investigate how the underlying uncertainty in the kinetic parameters affects the robustness of the model predictions and regulatory capabilities. To study this question, we used a large-scale non-linear kinetic model built using integrated fluxomics and metabolomics data describing the physiology for aerobically grown E. coli. Because of the under-determined nature of the system, there are multiple kinetic parameters that can render the model feasible within the selected operational configuration. To account for the variability of the kinetic parameters within the designated operational configuration, we selected a reference vector of fluxes and concentrations close to their nominal values. Then, we used the ORACLE (optimization and risk analysis of complex living entities) framework to build populations of kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints. From these, we built non-linear models to test how the system’s response to perturbations changed with respect to the chosen kinetic parameters. This allowed us to quantify the effect of the uncertainty in the kinetic parameters on the robustness of the regulatory properties of the system.

2016

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

Meriç Ataman, Georgios Fengos, Tuure Eelis Hameri, Vassily Hatzimanikatis, Ljubisa Miskovic

Large-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 grown Escherichia coli into a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of the E. coli aerobic 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 grown E. coli.

2019

Kinetic modeling of the central carbon metabolism of Escherichia Coli

Vassily Hatzimanikatis, Ljubisa Miskovic

Precise information about kinetic properties of enzymes within a biochemical network is a necessary ingredient of any successful metabolic engineering design. Such information inevitably involves uncertainty due to changes in cellular and process parameters. In this contribution, we present a computational methodology that explores the kinetic data space, and accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints. Thus, the proposed framework allows us to characterize the meaningful kinetic responses of metabolic network in the presence of uncertainty. We integrate the kinetic models of the individual reactions with the thermodynamically feasible pairs of substrate and product concentrations to build a population of all biochemically and thermodynamically meaningful models of the metabolic network. The effectiveness of the employed sampling method allows implementing the proposed methodology for modeling of large-scale metabolic networks. We apply the proposed framework for the modeling of the central carbon metabolism of Escherichia Coli.

2010

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

Meriç Ataman, Georgios Fengos, Tuure Eelis Hameri, Vassily Hatzimanikatis, Ljubisa Miskovic

2019

How uncertainty in kinetic parameters affects metabolic control analysis of optimally grown E.coli

Meriç Ataman, Georgios Fengos, Tuure Eelis Hameri, Vassily Hatzimanikatis, Ljubisa Miskovic

Large-scale kinetic models of metabolism are essential for understanding and predicting the behavior of cellular systems when subject to perturbations. Despite the advances in experimental measurement technologies, the numerous parameters that are required to build kinetic models remain scarce and involve uncertainty. Even after incorporating the partially available experimental data, models still have many degrees of freedom. Due to this parametric uncertainty, some reactions are able to operate in forward and reverse directions. An operational configuration consists of reactions that operate in a unique direction. This flexibility results in the existence of alternative operational configurations representing the same physiology, with very distinct regulatory properties. In this study, we focus on one operational configuration and we investigate how the underlying uncertainty in the kinetic parameters affects the robustness of the model predictions and regulatory capabilities. To study this question, we used a large-scale non-linear kinetic model built using integrated fluxomics and metabolomics data describing the physiology for aerobically grown E. coli. Because of the under-determined nature of the system, there are multiple kinetic parameters that can render the model feasible within the selected operational configuration. To account for the variability of the kinetic parameters within the designated operational configuration, we selected a reference vector of fluxes and concentrations close to their nominal values. Then, we used the ORACLE (optimization and risk analysis of complex living entities) framework to build populations of kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints. From these, we built non-linear models to test how the system’s response to perturbations changed with respect to the chosen kinetic parameters. This allowed us to quantify the effect of the uncertainty in the kinetic parameters on the robustness of the regulatory properties of the system.

2016