A Novel Approach to Design of Metabolic Engineering Strategiesa

Dynamic nonlinear models of metabolism offer significant advantages as compared to traditional steady-state counterparts for the analysis of metabolic networks and identification of metabolic engineering strategies. Uncertainties in the metabolic network structure, kinetic rate laws, and their corresponding parameters are limiting the development of systematic methodologies for construction of large-scale, dynamic metabolic models. In this study, we proposed a novel methodology for design of metabolic engineering strategies that makes use of these models. We illustrated this methodology using a dynamic model of E. coli composed of 225 reactions and 140 metabolites. Implementation of this methodology and subsequent analysis allowed us to identify globally and locally relevant design strategies to from the current state of metabolic network transition towards a desired state. Using dynamic simulations, we analyzed the network responses upon the chosen perturbations of the network components to explore the feasibility and properties of the identified strategies. The methodology can be used for the design of any metabolic network of interest irrespectively of the organism and/or the physiological conditions.

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

Georgios Fengos, Vassily Hatzimanikatis, Ljubisa Miskovic

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.

2016

The design of “Super E. coli” through automated reaction network generation and genome scale models

Noushin Hadadi, Vassily Hatzimanikatis, Keng Cher Soh

Genome-scale metabolic reconstructions capture the known metabolic capabilities of organisms and their analysis have provided many insights into complex biochemical networks. However, this approach relies on a database of known biochemical reactions and hence may fail to take into account unique novel metabolic reactions. Even for well-characterized model organisms like E. coli, new reactions remain to be discovered. Starting from the known biochemistry of E. coli metabolism, we investigated how many more reactions could be evolved and if these reactions could give extra potential to this organism for industrial application in the production of fuels and chemicals. We utilized the BNICE (Biochemical Network Integrated Computational Explorer) methodology which applies known biotransformation rules to generate a “super” network which captures all the possible reactions given a set of E. coli core metabolites and known biotransformation rules in E. coli. This super network is found to capture all the known E. coli reactions as well as novel pathways that can serve as potential novel biosynthesis pathways to valuable chemicals. In order to identify the role of these novel reactions in E. coli metabolic network, we embedded them within the genome scale model of E. coli and performed a thermodynamics flux balance analysis (TFBA) to investigate the feasibility of the novel pathways in the context of the genome scale model. By performing a Flux Variability Analysis (FVA), subject to thermodynamic constraints, the reliable sets of reactions that increase the yield through biomass were identified. To further investigate the feasibility of the novel proposed reactions, we used the BridgIT computational framework for the identification of candidate gene sequences. BridgIT has a database of all the known biochemical reactions and it makes associations between novel predicted reactions and known reactions by using a chemical similarity metrics and it links the novel reactions with known genes in genomes and organisms.

2014

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

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

Georgios Fengos, Vassily Hatzimanikatis, Ljubisa Miskovic

2016

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

Georgios Fengos, Vassily Hatzimanikatis, Ljubisa Miskovic

2016

The design of “Super E. coli” through automated reaction network generation and genome scale models

Noushin Hadadi, Vassily Hatzimanikatis, Keng Cher Soh

2014