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

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.

Systematic Metabolic Engineering of Recombinant Xylose-Utilizing S. cerevisiae: Strategies for Improving Xylose Uptake Rate

Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

Recent advancements in metabolomics and fluxomics methods and ever-increasing amount of available experimental data necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models capable of giving new insights in cell physiology and providing reliable guidance for metabolic engineering. The development of large scale kinetic models is often hampered by (i) incomplete/no knowledge about rate laws in addition to uncertain or missing information about their kinetic parameters; (ii) difficulties in determining the exact intracellular states due to insufficient experimental information and large degrees of flexibility in the stoichiometry of metabolic networks. ORACLE (Optimization and Risk Analysis of Complex Living Entities), a computational framework that accounts explicitly for mechanistic properties of enzymes and integrates network thermodynamics, physico-chemical constraints in metabolic network and available experimental data can be used to address the aforementioned issues. ORACLE relies on Metabolic Control Analysis (MCA) and Thermodynamic Flux Balance Analysis (TFBA) supported by a range of statistics and systems theory methods, allowing us to uncover all the possible intracellular network states consistent with the available experimental data. This is followed by the computation of control coefficients in the metabolic network to guide the formulation of possible alternatives for metabolic engineering. We demonstrate the application of this methodology in an actual metabolic engineering problem: improving the xylose uptake rate of a glucose-xylose co-utilizing recombinant S. cerevisiae strain through a cycle between experimental lab work and modeling efforts. More specifically, using the given fermentation data we identified network flux and concentration profiles representing possible physiological states of the base strain. Then, we have identified targets that consistently lead to improved flux through xylose transporters (XTR) across these profiles. The targets identified by our kinetic models are experimentally validated demonstrating the predictive capabilities of ORACLE. Time:

2012

Kinetic reconstruction and analysis of sphingolipid metabolism

Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

Lipids are major constituents of the cell. They are responsible for major properties of the cellular membranes: hydrophobicity, selective permeability and being the scaffold of signaling proteins. Many diseases are associated with alterations in the lipid distribution in the cell and the composition of membrane domains. Metabolic syndrome, obesity, atherosclerosis, as well as Alzheimer’s, Huntington’s diseases and cancer, have an impact in the levels of lipids, with observed alterations in their concentrations compared to the healthy state. Applying computational techniques along with systematic modeling of lipid metabolism can provide insights that can guide biomedical research and develop potential strategies for prevention and cure. In the present study we developed a comprehensive model of the sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Sphingolipids are one of the four major lipid categories, along with (glycero)phospholipids, sterols and fatty acids synthesized in the yeast S. cerevisiae. The importance of sphingolipids present in any higher eukaryote has been demonstrated in many recent studies. For this study, S. cerevisiae has been chosen as a model organism due to its high homology of cellular processes with mammalian cells. The developed model will be an essential part towards the construction of a detailed kinetic model of the whole lipidome of the cell. We first constructed a stoichiometric model that contains all the currently known reactions for the biosynthesis of ceramides and complex sphingolipids in the yeast. Additionally, we have accounted for all five reported hydroxylation states, along with the reactions synthesizing the necessary precursor metabolites from other lipid pathways (i.e. palmitate-CoA from fatty acid synthesis and phosphatidylinositol from the phospholipids metabolism). We next developed a kinetic model and we used a large number of lipidomic measurements of wild type yeast to consistently calibrate our model. Curation of the kinetic information of the model came from the comprehensive mining of references for operation of the enzymes as well as ranges of kinetic parameters from online databases. These datasets created the pool for a sampling technique that accounts for the uncertainty in the parameters of the model. This lead to a robust dynamic model, containing mass balances for all the components of the biochemical network as well as terms that accounted for the dillution of these molecules in the cell due to growth. We performed a thorough kinetic analysis of the system by examining the impact of different assumptions in enzyme operation on the levels of ceramides and complex sphingolipids (e.g. substrate competition, (un)competitive inhibition). By applying the principles of Metabolic Control Analysis (MCA) we were able to quantify the effect of enzyme activities on the lipid profiles and we identified enzymes of the biochemical reaction network as efficient targets for metabolic engineering towards a desired state. We also applied a reverse engineering method and we were able to identify enzyme perturbations responsible for an observed altered state compared to the wild type cellular lipidomic profile. Such an approach could lead to the identification of genetic mutations by imploring the information containd within metabolomic measurements. Although we demonstrate the application of the method in models of lipid metabolism it is possible to be applied to biochemical systems of various parts of metabolism and sizes creating a modular platform for kinetic analysis of cellular operations.

2014

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).

2017