Large Scale Dynamic Models of Metabolism as a Tool for Analysis and Design of Metabolic Engineering and Synthetic Biology Strategies

Large-scale dynamic models of metabolism remain an overarching ambition of efforts that aim to analyze and explain complex behavior of living entities. However, intrinsic nonlinearity of dynamics of living organisms and structural and quantitative uncertainties at several biological levels hinder the development of such models. Specifically, the knowledge about kinetic mechanisms of the metabolism is still limited and the corresponding parameters are frequently not available. Moreover, identification of the missing parameters would necessitate a prohibitively large number of experimental measurements. We are presenting a systematic methodology for generating a population of large-scale dynamic nonlinear models of metabolism. We start from the reference steady-state flux and we use the ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) framework to integrate available metabolomics and kinetic data into these dynamic descriptions and we computed the corresponding parameters such as Michaelis constants and maximal velocities. The parameter estimation method proposed here is tailored for metabolic kinetic models and can be used as an alternative to standard parameter estimation methods in the biologically relevant cases when the parameter estimation problem does not have an unique solution. In addition, the obtained models are locally stable around the reference state and consistent with the physico-chemical laws and thermodynamics. We demonstrated the potential and features of the proposed methodology for the case of optimally grown E. coli, where we constructed a population of large-scale nonlinear models that involved 283 metabolites and 409 reactions. For the analyzed physiology we: (i) identified and analyzed multiple steady-states; (ii) characterized basins of attraction around the identified steady-states; and (iii) investigated the responses of E. coli metabolism upon large perturbations such as gene knockouts. We further investigated the potential of the proposed methodology for the design of metabolic engineering and synthetic biology strategies.

Analysis and Design of Metabolic Engineering and Synthetic Biology Strategies using Large Scale Dynamic Models of Metabolism

Georgios Fengos, Vassily Hatzimanikatis, Ljubisa Miskovic

Systematic analysis for the redirection of carbon flux in metabolite-producing microorganisms requires the comprehensive understanding of their complex metabolic processes. The use of large-scale dynamic models of metabolism plays key role in the understanding of these processes and the study of possible metabolic engineering interventions. However, the generation of such models is hampered by the intrinsic nonlinearities of enzymatic reactions, and the uncertainties at different levels. In particular there is limited knowledge about the exact kinetic mechanisms, and many of the parameters involved in these mechanisms remain largely unknown. In this study we propose a systematic methodology to generate large populations of dynamic non-linear models of metabolism using the ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) framework. Instead of seeking for an optimal value of the estimated kinetic model parameter values, we integrate thermodynamics, available omics, and kinetic data to construct populations of models that are locally stable, and consistent with the observed physiology. To demonstrate the utility of this methodology we constructed a population of large-scale dynamical models of optimally grown E. coli that involves 283 metabolites and 409 reactions. We used these models to (i) study the response of E. coli metabolism upon large-scale perturbations, such as gene knockouts; (ii) identify and analyze multiple steady states; and (iii) characterize basins of attraction around the identified steady states. The aforementioned analyses provide valuable insight for the design of metabolic engineering strategies towards amplification of desired product-forming pathways. 1. Miskovic, L. & Hatzimanikatis, V. Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotechnology 28, 391–7 (2010).

2016

Analysis and Design of Metabolic Engineering and Synthetic Biology Strategies using Large Scale Dynamic Models of Metabolism

Georgios Fengos, Vassily Hatzimanikatis, Ljubisa Miskovic

Systematic analysis for the redirection of carbon flux in metabolite-producing microorganisms requires the comprehensive understanding of their complex metabolic processes. The use of large-scale dynamic models of metabolism plays key role in the understanding of these processes and the study of possible metabolic engineering interventions. However, the generation of such models is hampered by the intrinsic nonlinearities of enzymatic reactions, and the uncertainties at different levels. In particular there is limited knowledge about the exact kinetic mechanisms, and many of the parameters involved in these mechanisms remain largely unknown. In this study we propose a systematic methodology to generate large populations of dynamic non-linear models of metabolism using the ORACLE (Optimization and Risk Analysis of Complex Living Entities) framework. Instead of seeking for an optimal value of the estimated kinetic model parameter values, we integrate thermodynamics, available omics, and kinetic data to construct populations of models that are locally stable, and consistent with the observed physiology. To demonstrate the utility of this methodology we constructed a population of large-scale dynamical models of optimally grown E. coli. We used these models to perform large-scale perturbations of E. coli metabolism and to analyze their simulated responses. The aforementioned analyses provides valuable insight for the design of metabolic engineering strategies towards amplification of desired product-forming pathways.

2016

Metabolic control analysis of the central carbon pathway in optimally grown E. coli

Stefano Andreozzi, Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

The engineering of the metabolism requires the building of reliable kinetic models of the metabolic pathways through the integration of the information from heterogeneous data sources. Unfortunately, the quantitative knowledge about fluxes, mechanisms or kinetic parameters of the constituent reactions of the metabolic network is most of the times uncertain or missing, due to limitations in the measurement techniques of the relevant biological properties of the cell and variability in experimental conditions. In this contribution, we employ ORACLE (Optimization and Risk Analysis of Complex Living Entities), the computational framework for the analysis and optimization of metabolic networks that addresses these issues. ORACLE allows us to generate populations of kinetic models that account explicitly for the physico-chemical and thermodynamic features and constraints of metabolic networks. We show how ORACLE incorporates fluxomics and metabolomics data and constraints into the central carbon core model of E. coli to uncover its possible steady state behaviors, when the cells are cultivated under the physiological condition of optimal growth on different carbon sources, both anaerobically and aerobically. Alternative cases of possible intracellular flux and thermodynamic states, corresponding to the given experimental data, are identified and subsequently the kinetic analysis is performed to evaluate how the control over fluxes and concentrations is distributed over the metabolic network. We demonstrate that ORACLE methodology provides guidance for metabolic engineering in spite of uncertain/missing information in the local/global properties of the metabolic network, integrating a statistical account on the semi-quantitative available data with consistent thermodynamic information and plausible hypotheses.

2012

Analysis and Design of Metabolic Engineering and Synthetic Biology Strategies using Large Scale Dynamic Models of Metabolism

Georgios Fengos, Vassily Hatzimanikatis, Ljubisa Miskovic

2016