**Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?**

Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur GraphSearch.

Publication# iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Stefano Andreozzi, Vassily Hatzimanikatis, Ljubisa Miskovic

*Academic Press Inc Elsevier Science, *2016

Article

Article

Résumé

Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces.

Official source

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

Concepts associés (16)

Métabolisme

Le métabolisme est l'ensemble des réactions chimiques qui se déroulent à l'intérieur de chaque cellule d'un être vivant et lui permettent notamment de se maintenir en vie, de se reproduire (se divis

Théorie cinétique des gaz

La théorie cinétique des gaz a pour objet d'expliquer le comportement macroscopique d'un gaz à partir des caractéristiques des mouvements des particules qui le composent. Elle permet notamment de donn

Physiologie

La physiologie (du grec φύσις, phusis, la nature, et λόγος, logos, l'étude, la science) étudie le rôle, le fonctionnement et l'organisation mécanique, physique et biochimique des organismes vivants

Publications associées (47)

Chargement

Chargement

Chargement

Stefano Andreozzi, Vassily Hatzimanikatis, Ljubisa Miskovic

Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a novel computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces.

2015Stefano Andreozzi, Vassily Hatzimanikatis, Ljubisa Miskovic

Information about kinetics and enzyme activities while essential for quantitative understanding of metabolic dynamics remains rarely available and always involves uncertainty. In this work, we introduce the first computational methodology capable of determining the important enzymes in the network along with the operating ranges of their saturation by substrates and products and their parameters that correspond to a given metabolic flux and a given metabolite concentration. The proposed approach is based on the ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) framework and machine learning methods and it offers information about enzymes that supplements the one obtained by experimental techniques. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed method can be considered also as a new parameter estimation procedure since it can identify enzymes whose saturations, if constrained to a narrow range, allow us to build the kinetic models capable to describe the studied physiology, and by this mean to provide accurate estimates of ranges of kinetic parameters relevant for the studied physiology. Furthermore, the proposed approach sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces.

2016Anirikh Chakrabarti, Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

Mathematical modeling is an essential tool for a comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information about enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works consider only the mass action kinetics for the reactions in the metabolic networks. In this work, we applied the ORACLE framework and constructed a large-scale, mechanistic kinetic model of optimally grown E. coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of the metabolic fluxes and metabolite concentrations. Our results further suggest that the enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of the cellular metabolism.

2013