A Design-Build-Test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology, and also to assist in the rational design of bioreactor or fermentation processes. ORACLE (Optimization and Risk Analysis of Complex Living Entities), a prominent computational framework for construction of large-scale kinetic models, can be used as guidance for formulating alternative metabolic engineering strategies. We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose-xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations we characterized network flux and concentration profiles representing possible physiological states of the analysed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with experimental data collected on the HXK2 deficient S. cerevisiae strain. We present a design-build-test cycle comprised of modeling efforts and experiments with a glucose-xylose co-utilizing recombinant S. cerevisiae and its HXK2 deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The presented study demonstrates the potential of integrated “modeling & experiments” systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.

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

Deciphering ambiguous control over fluxes through characterization and reduction of uncertainty

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

The development of kinetic models is still facing the challenges such as large uncertainties in available data. Uncertainty originating from various sources including metabolite concentration levels, flux values, thermodynamic and kinetic data propagates to the kinetic parameter space and prevents an accurate identification of parameters. The uncertainty in parameters propagates further to the outputs of the corresponding kinetic models, such as control coefficients and time evolutions of metabolic fluxes and concentrations, and it can have deleterious effects on metabolic engineering and synthetic biology decisions. We have introduced a framework recently, iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which allows us to characterize uncertainty in the parameters of studied kinetic models and integrate this information into a new population of kinetic models with reduced uncertainty. To this end, iSCHRUNK combines the ORACLE framework with parameter classification techniques. With ORACLE, we first construct a set of large-scale and genome-scale kinetic models that are consistent with the integrated datasets and physicochemical laws. We then employ parameter classification techniques to data-mine the integrated datasets together with the outputs of ORACLE. This way, iSCHRUNK allows us to uncover complex relationships between the integrated data and parameters of kinetic models, and to use the obtained information for constructing an improved set of kinetic models with reduced uncertainty in ORACLE. In this work, we analyzed possible improvements of xylose uptake rate (XTR) in a glucose-xylose co-utilizing S. cerevisiae strain. For this purpose, we computed a population of 200’000 large-scale kinetic models, and we determined the key enzymes controlling XTR. However, large uncertainties due to a limited number of measured flux and metabolite concentration values and lack of data about kinetic parameters led to widespread distributions around zero values for control coefficients of some enzymes such as hexokinase (HXK). We used iSCHRUNK to identify the enzymes and their kinetic parameters that determine negative and positive control of HXK over XTR. Our study showed that by engineering only three saturation states related to two enzymes we could enforce either negative or positive control of HXK over XTR. This result implies that for kinetic modeling of metabolism only a small number of kinetic parameters should be accurately determined and that we can disregard the remaining parameters.

2017

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

Deciphering ambiguous control over fluxes through characterization and reduction of uncertainty

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

2017

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

Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

2012