Identifying patterns in kinetic parameters that determine the impact of rate-limiting enzymes

Kinetic models are essential for studying complex behavior and properties of metabolism. However, a persistent hurdle for constructing these models is uncertainty in the kinetic properties of enzymes. Currently available methods for building kinetic models cope in an indirect way with uncertainties by integrating data from different biological levels and origins into models. In this context, we have recently proposed iSCHRUNK1 a new approach for characterization and reduction of uncertainty that makes use of the ORACLE2 (Optimization and Risk Analysis of Complex Living Entities) framework and machine learning classification techniques. ORACLE allows us to exploit synergies between different data sources and generate population of nonlinear kinetic models that are consistent with a reference physiological states and the available data. On the other hand, with the machine learning techniques we are able to data mine the integrated datasets together with the outputs of ORACLE in order to discern complex interactions and correlations between the parameters of kinetic models and available partial data such as metabolite concentrations, metabolic fluxes, thermodynamic data and information about kinetic properties of enzymes. The proposed approach is versatile and can be used to infer more accurate ranges of kinetic parameters of all enzymes in large-scale and genome-scale metabolic networks that are consistent with the observed properties of the metabolic network. In this work, we studied which enzymes determine the sign (positive or negative values) of important control coefficients, and then we identified what should be the configuration of the kinetic parameters of identified enzymes that will determine its role as a rate-limiting enzyme. Based on this analysis we categorized the enzymes in the network in groups that act on studied properties: (i) in a concerted fashion; (ii) antagonistically; or (iii) in a mutually independent way. This work will allow us to investigate how to engineer the enzymes in order to achieve a pre-specified metabolic behavior and address the inverse metabolic engineering problems in a new way.

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter 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 posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are 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. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of 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 available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.

2019

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

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

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

2019

Machine Learning for Uncertainty Reduction in Biochemical Kinetic Models

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

2018