Author: Nicolas Loira

Publisher:

Published: 2012

Total Pages: 0

ISBN-13:

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Understanding living organisms has been a quest for a long time. Since the advancesof the last centuries, we have arrived to a point where massive quantities of data andinformation are constantly generated. Even though most of the work so far has focusedon generating a parts catalog of biological elements, only recently have we seena coordinated effort to discover the networks of relationships between those parts. Notonly are we trying to understand these networks, but also the way in which, from theirconnections, emerge biological functions.This work focuses on the modeling and exploitation of one of those networks:metabolism. A metabolic network is a net of interconnected biochemical reactionsthat occur inside, or in the proximity of, a living cell. A new method of discovery, orreconstruction, of metabolic networks is proposed in this work, with special emphasison eukaryote organisms.This new method is divided in two parts: a novel approach to reconstruct metabolicmodels, based on instantiation of elements of an existing scaffold model, and a novelmethod of assigning gene associations to reactions. This two-parts method allows reconstructionsthat are beyond the capacity of the state-of-the-art methods, enablingthe reconstruction of metabolic models of eukaryotes, and providing a detailed relationshipbetween its reactions and genes, knowledge that is crucial for biotechnologicalapplications.The reconstruction methods developed for the present work were complementedwith an iterative workflow of model edition, verification and improvement. This workflowwas implemented as a software package, called Pathtastic.As a case study of the method developed and implemented in the present work,we reconstructed the metabolic network of the oleaginous yeast Yarrowia lipolytica,known as food contaminant and used for bioremediation and as a cell factory. A draftversion of the model was generated using Pathtastic, and further improved by manualcuration, working closely with specialists in that species. Experimental data, obtainedfrom the literature, were used to assess the quality of the produced model.Both, the method of reconstruction in eukaryotes, and the reconstructed model ofY. lipolytica can be useful for their respective research communities, the former as astep towards better automatic reconstructions of metabolic networks, and the latteras a support for research, a tool in biotechnological applications and a gold standardfor future reconstructions.

Download or read book Scaffold-based Reconstruction Method of Genome-scale Metabolic Models written by Nicolas Loira and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding living organisms has been a quest for a long time. Since the advancesof the last centuries, we have arrived to a point where massive quantities of data andinformation are constantly generated. Even though most of the work so far has focusedon generating a parts catalog of biological elements, only recently have we seena coordinated effort to discover the networks of relationships between those parts. Notonly are we trying to understand these networks, but also the way in which, from theirconnections, emerge biological functions.This work focuses on the modeling and exploitation of one of those networks:metabolism. A metabolic network is a net of interconnected biochemical reactionsthat occur inside, or in the proximity of, a living cell. A new method of discovery, orreconstruction, of metabolic networks is proposed in this work, with special emphasison eukaryote organisms.This new method is divided in two parts: a novel approach to reconstruct metabolicmodels, based on instantiation of elements of an existing scaffold model, and a novelmethod of assigning gene associations to reactions. This two-parts method allows reconstructionsthat are beyond the capacity of the state-of-the-art methods, enablingthe reconstruction of metabolic models of eukaryotes, and providing a detailed relationshipbetween its reactions and genes, knowledge that is crucial for biotechnologicalapplications.The reconstruction methods developed for the present work were complementedwith an iterative workflow of model edition, verification and improvement. This workflowwas implemented as a software package, called Pathtastic.As a case study of the method developed and implemented in the present work,we reconstructed the metabolic network of the oleaginous yeast Yarrowia lipolytica,known as food contaminant and used for bioremediation and as a cell factory. A draftversion of the model was generated using Pathtastic, and further improved by manualcuration, working closely with specialists in that species. Experimental data, obtainedfrom the literature, were used to assess the quality of the produced model.Both, the method of reconstruction in eukaryotes, and the reconstructed model ofY. lipolytica can be useful for their respective research communities, the former as astep towards better automatic reconstructions of metabolic networks, and the latteras a support for research, a tool in biotechnological applications and a gold standardfor future reconstructions.