Implications of the Assumptions on Intracellular Metabolic Operational States in Metabolic Control Analysis

Modeling and analysis of metabolic networks is inherently biased by assumptions regarding the intracellular metabolite and flux states. Despite the advanced metabolomics and fluxomics methods at our disposal, the sheer complexity of metabolic networks prevents us from determining their exact operational state. To address this shortcoming, we have developed a novel framework called Flux Directionality Profile Analysis (FDPA) for characterizing systematically all possible intracellular flux and thermodynamic states using available experimental information, such as metabolite concentration measurements and flux split ratios. In the current study, we demonstrated the utility of this framework in elucidating the different physiologically relevant, yet operationally different, functioning states of wildtype E. coli under optimal aerobic growth condition. Using the available information, we explored the set of possible states to identify the most important and consistent internal operational states for interpreting the observed physiology. To that end, we used the ORACLE framework to study the implications of the choice of different operational paradigms by analyzing their effects on control coefficients. Further analysis of the control coefficients allows us to classify the enzymes in terms of their impact across all, or only specific operational configurations. Using this approach, we can knowledgeably identify the right choice of enzyme targets, which shall help us in meeting metabolic engineering specifications.

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

Computational Studies on Cellular Bioenergetics

Keng Cher Soh

Genome-scale metabolic reconstructions have become indispensable tools for understanding and investigation of the metabolism of any organism in this post-genomic era. However, there are still knowledge gaps in our understanding of the metabolism of even the most well-studied organism, hence a gapfilling algorithm based on novel reaction generating framework was implemented and shown to be able to refine the reconstruction as well as improve our knowledge of the network. The analysis of these large-scale models has their own set of challenges and difficulties that limit their full utility in practical application areas and higher usage by industry and experimentalists. Constraint-based analyses of metabolic models are highly popular methods, as it only requires knowledge of stoichiometry and key input/output fluxes of the system to infer the underlying intracellular fluxes. However due to the underdetermined nature of most large metabolic networks, additional constraints have to be incorporated to ascertain the actual state of the network. Thermodynamic constraints eliminate thermodynamically infeasible solutions and also provide another layer of information onto metabolic networks. Using Thermodynamics-based Flux Balance Analysis (TFBA) of metabolic models and taking into account pH and ionic strength differences, we study how these factors can affect overall cellular energetics. TFBA also enables us to integrate metabolomics data to reduce the solution space of the problem. As genome-scale networks have too many degrees of freedom for systematic analysis and conceptually too difficult to manage, a consistent approach of reducing genome-scale models to core metabolic models that retain most of the network characteristics of the original model is proposed. Such core metabolic models are valuable resources for simplifying analysis that can be extrapolated to the original model. A novel framework for characterizing the flux and thermodynamic state of metabolic networks is proposed in order to systematically analyze the possible intracellular states given the set of known parameters. The characterized states can be further analyzed using metabolic control analysis to provide insights as to how the control of the network is distributed and also provide a bridge to the formulation of large-scale kinetic models.

EPFL2013

Optimization of the production of methyl ethyl ketone in recombinant Pseudomonas putida using large-scale kinetic models

Meriç Ataman, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

The production of second generation of biofuels by conversion of inexpensive feedstocks is a basis for future bio-sustainable economy. Among many proposed molecules, methyl ethyl ketone (MEK) emerged as one of the most prominent fuel candidates due to its high-energy density, low emissions, and good transport properties. There is no known natural producer of MEK, but there were some recent studies to express its production pathway in different organisms. In all of these attempts, reported yields were very low. Among industrial workhorses such as E. coli and S. cerevisiae, P. putida has emerged as one of the most promising biofuel production hosts due to its tolerance to high toxicity compounds. For instance, it is reported that P. putida can grow in the presence of high concentrations of butanol1. This highly adaptive bacterium has been found to survive and grow on a broad range of substrates from pure caffeine to toxic industrial waste. In this study, we used the ORACLE framework to build, for the first time, a population of large-scale kinetic models of recombinant P. putida producing MEK. ORACLE2,3 (Optimization and Risk Analysis of Complex Living Entities) is a suite of computational tools capable of identifying enzymes with the highest impact on the production of target molecules. We started by embedding the MEK production pathway in the genome-scale model iJN7464 of P. putida, and then we derived a consistently reduced stoichiometric model. The obtained core model consists of 302 reactions and 210 mass balances distributed over cytosol, periplasm and extracellular environment. We then performed a series of tests to verify the consistency of the core model with its genome-scale counterpart. We next integrated fluxomics and metabolomics data from experiments and literature and performed Thermodynamic-Based Flux Analysis (TFA) to compute the thermodynamically feasible flux profiles that are consistent with the observed data. We further integrated the available information about the kinetic properties of enzymes involved in the modeled metabolic network. For reactions with missing or incomplete information about kinetic parameters, we performed a Monte Carlo sampling to generate missing information. We then constructed a population of 250’000 models, and we used these models to simultaneously optimize specific productivity of MEK and yield of MEK from glucose as a sole carbon source while taking into consideration the metabolic parameters such as catabolic and anabolic reduction charges. This way, we were able to identify enzymes that are potential candidates for metabolic engineering strategies towards obtaining a P. putida strain with improved specific productivity of MEK and its yield from glucose. This work demonstrates the potential and usefulness of ORACLE for optimizing production of biofuels and biochemicals in metabolic engineering and synthetic biology applications. 1. Rühl, J., Schmid, A. & Blank, L. M. Selected Pseudomonas putida strains able to grow in the presence of high butanol concentrations. Appl. Environ. Microbiol. 75, 4653–6 (2009). 2. Miskovic, L. & Hatzimanikatis, V. Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotechnology 28, 391–7 (2010). 3. Chakrabarti, A., Miskovic, L., Soh, K. C. & Hatzimanikatis, V. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotechnol. J. 8, 1043–57 (2013). 4. Nogales, J., Palsson, B. Ø. & Thiele, I. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst. Biol. 2, 79 (2008).