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

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:

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

Machine Learning for Uncertainty Reduction in Biochemical Kinetic Models

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

The primary goal of kinetic models is to capture the systemic properties of the metabolic networks, and we need large-scale kinetic models for reliable in silico analyses of the complex dynamic behavior of metabolism. However, parameter uncertainty hinders the development of kinetic models and uncertainty levels increase with the model size. Current methods for building kinetic models within constraint-based modeling frameworks address uncertainty indirectly by integrating data from different biological origins. We recently proposed iSCHRUNK, a computational approach which combines Monte Carlo sampling methods and machine learning techniques to characterize the uncertainties and to reveal complex relationships between the kinetic parameters and the responses of the metabolic networks. Monte Carlo sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive values of kinetic parameters consistent with the observed physiology. In this work, we modified iSCHRUNK to address a design question: can we identify the kinetic parameters and their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. As an illustration, we used the proposed methodology to find parameters that ensure a rate improvement of the xylose uptake (XTR) in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of omics data and extract the information necessary for metabolic engineering and synthetic biology decisions.

2018

Quantification of uncertainty in metabolic networks through the sampling of kinetic data space

Vassily Hatzimanikatis, Ljubisa Miskovic

Study of a metabolic network with respect to the sensitivity of the metabolite concentrations and the metabolic fluxes to genetic, biochemical, and environmental variations requires knowledge of the kinetic properties of the enzymes in the network. Experimentally observed data about the in vivo enzyme kinetics are subject to large variations of underlying physiochemical parameters. In this contribution, we present a computational framework that allows us to model the uncertainty in the kinetic data and to predict quantitatively the steady-state responses of metabolic networks in the presence of genetic and environmental perturbations. The proposed methodology relies on formalism from process control and metabolic control analysis and uses a Monte Carlo sampling method. In addition, it accounts explicitly for mechanistic properties of enzymes and physicochemical and thermodynamic constraints. The method employs a novel, efficient sampling procedure that allows us to simulate all possible meaningful states of a metabolic network. We demonstrate the properties of the proposed framework through an example of a prototypical module of biosynthetic pathways - the branched pathway.

2009

Machine Learning for Uncertainty Reduction in Biochemical Kinetic Models

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

2018