Constraint-based metabolic control analysis for rational strain engineering

Meriç Ataman, Tuure Eelis Hameri, Vassily Hatzimanikatis, Ljubisa Miskovic, Sofia Tsouka
2021
Journal paper

Abstract

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

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https://infoscience.epfl.ch/record/285672?ln=en

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Meriç Ataman, Tuure Eelis Hameri, Vassily Hatzimanikatis, Ljubisa Miskovic, Sofia Tsouka
2021
Journal paper

Abstract

Official source

https://infoscience.epfl.ch/record/285672?ln=en

About this result

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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

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

Modelling and analysis of biological systems is crucial in order to quantitatively explain and predict their behaviour. The importance of modelling in systems biology becomes even more evident when tackling complex, large systems. Approaches that rely on modelling can be used to tackle a range of problems, such as cellular signalling or using genetically modified bacteria to synthesize human proteins for medical purposes. These phenomena range across different spatiotemporal scales, whether it is predicting the decay of red blood cells for blood transfusion or finding engineering targets in order to increase the productivity of bioprocesses. Reconciling models with experimental data and quantifying how uncertainty propagates through these models is crucial to exploiting them to their full potential. Uncertainty impacts not only the calibration and validation of a model, but also the predictions extracted from it. Developing and implementing uncertainty quantification methods into these models is a key step in exploring their behaviour and accelerating the adoption of such methods in metabolic engineering. In this thesis I firstly propose to implement and explore uncertainty quantification methods on control coefficients obtained by Metabolic Control Analysis (MCA) of Genome Scale Metabolic Models (GEMs), with the goal of quantifying uncertainty propagation from model parameters to control coefficients. I additionally want to build Single Protein Models that describe the effects of Post Translational Modifications (PTMs). The nature of the experimental data available to calibrate these models makes uncertainty an inherent property, and therefore Global Sensitivty Analysis (GSA) techniques are well suited to describing the links between parameter uncertainty and model behaviour.

EPFL2021

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2012