Dissertations / Theses on the topic 'Genome-Scale Metabolic Network (GSMN)'

Face à la résistance accrue aux antibiotiques menaçant la santé publique, la prospection de nouvelles molécules biologiquement actives est pressante. Les champignons filamenteux se distinguent par leur capacité à synthétiser une large gamme de produits naturels, sous l’influence de clusters de gènes biosynthétiques (BGC) qui orchestrent la production de métabolites spécialisés. Toutefois, de nombreux produits issus de ces BGCs n’ont pas encore été caractérisés et leur chimiodiversité demeure sous-explorée en raison de l’incapacité à activer l’ensemble de leur potentiel en laboratoire. L’approche OSMAC (One Strain Many Compound) permet de solliciter ce potentiel en variant les conditions de culture. Cependant, cette méthode reste complexe et coûteuse en raison de son caractère aléatoire et du grand nombre d’expérimentations nécessaires. L’optimisation de ces processus nécessite l’intégration de stratégies plus rationnelles et efficaces. A l’aide d’approches systémiques liées à la biologie des systèmes, les réseaux métaboliques à l’échelle du génome (GSMN) offrent une modélisation détaillée des voies métaboliques, des enzymes impliquées et des gènes associés, fournissant un aperçu précis du métabolisme. Dans ce cadre, nous proposons une stratégie alternative: l’OSMAC in silico. En reconstruisant un GSMN actualisé pour Penicillium rubens, nous avons pu étudier les réponses de son métabolisme sous divers scénarios nutritionnels. Cette modélisation a permis d’évaluer l’influence de différentes sources de carbone et d’azote sur sa croissance et la production de métabolites spécialisés, ouvrant ainsi de nouvelles perspectives pour optimiser la production de produits naturels
Given the pressing issue of increasing antibiotic resistance threatening public health, new biologically active molecule research is urgent. Filamentous fungi are charcterised by their ability to synthesise a wide range of natural products, driven by biosynthetic gene clusters (BGCs) that orchestrate the production of specialised metabolites. However, many products derived from these BGCs remain uncharacterised, and their chemodiversity is underexplored due to the inability to activate their full potential in laboratory settings. The OSMAC (One Strain Many Compounds) approach seeks to harness this potential through culture condition variations. Nevertheless, this method remains complex and costly due to its randomness and vast number of experiments required. Therefore, optimising these processes needs the integration of more rational and efficient strategies. Using systems biology approaches, genome-scale metabolic networks (GSMNs) provide detailed modeling of metabolic pathways, involved enzymes, and associated genes, offering a precise overview of metabolism. In this context, we propose an alternative strategiy: in silico OSMAC. By reconstructing an updated GSMN for Penicillium rubens , we studied its metabolic responses under various nutritional scenarios. This modelling enabled us to assess the influence of different carbon and nitrogen sources on growth and the production of specialised metabolites, thereby opening new prospects for optimising the production of natural products

La compréhension des organismes vivant a été une quête pendant longtemps. Depuisles premiers progrès des derniers siècles, nous sommes arrivés jusqu’au point où desquantités massives de données et d’information sont constamment générées. Bien que,jusqu’au présent la plupart du travail a été concentré sur la génération d’un catalogued’éléments biologiques, ce n’est pas que récemment qu’un effort coordonné pour découvrirles réseaux de relations entre ces parties a été constaté. Nous nous sommes intéressésà comprendre non pas seulement ces réseaux, mais aussi la façon dont, à partir de sesconnexions, émergent des fonctions biologiques.Ce travail se concentre sur la modélisation et l’exploitation d’un de ces réseaux :le métabolisme. Un réseau métabolique est un ensemble des réactions biochimiquesinterconnectées qui se produisent à l’intérieur, ou dans les proximité d’une cellulevivante. Une nouvelle méthode de découverte, ou de reconstruction des réseaux métaboliquesest proposée dans ce travail, avec une emphase particulière sur les organismeseucaryotes.Cette nouvelle méthode est divisée en deux parties : une nouvelle approche pour lamodélisation de la reconstruction basée sur l’instanciation des éléments d’un modèlesquelette existant, et une nouvelle méthode de réécriture d’association des gènes. Cetteméthode en deux parties permet des reconstructions qui vont au-delà de la capacitédes méthodes de l’état de l’art, permettant la reconstruction de modèles métaboliquesdes organismes eucaryotes, et fournissant une relation détaillée entre ses réactions etses gènes, des connaissances cruciales pour des applications biotechnologiques.Les méthodes de reconstruction développées dans ce travail, ont été complétéespar un workflow itératif d’édition, de vérification et d’amélioration du modèle. Ceworkflow a été implémenté dans un logiciel, appelé Pathtastic.Comme une étude de cas de la méthode développée et implémentée dans le présenttravail, le réseau métabolique de la levure oléagineuse Yarrowia lipolytica, connucomme contaminant alimentaire et utilisé pour la biorestauration et comme usinecellulaire, a été reconstruit. Une version préliminaire du modèle a été générée avecPathtastic, laquelle a été améliorée par curation manuelle, à travers d’un travail avecdes spécialistes dans le domaine de cette espèce. Les données expérimentales, obtenuesà partir de la littérature, ont été utilisées pour évaluer la qualité du modèle produit.La méthode de reconstruction chez les eucaryotes, et le modèle reconstruit deY. lipolytica peuvent être utiles pour les communautés scientifiques respectives, lepremier comme un pas vers une meilleure reconstruction automatique des réseauxmétaboliques, et le deuxième comme un soutien à la recherche, un outil pour desapplications biotechnologiques et comme un étalon-or pour les reconstructions futures
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

Mycobacterium tuberculosis (Mtb), a pathogenic bacterium, is the causative agent in the vast majority of human tuberculosis (TB) cases. Nearly one-third of the world’s population has been affected by TB and annually two million deaths result from the disease. Because of the high cost of medication for a long term treatment with multiple drugs and the increase of multidrug-resistant Mtb strains, faster-acting drugs and more effective vaccines are urgently demanded. Several metabolic pathways of Mtb are attractive for identifying novel drug targets against TB. Hence, a high quality genome-scale metabolic network of Mtb (HQMtb) was reconstructed to investigate its whole metabolism and explore for new drug targets. The HQMtb metabolic network was constructed using an unbiased approach by extracting gene annotation information from various databases and consolidating the data with information from literature. The HQMtb consists of 686 genes, 607 intracellular reactions, 734 metabolites and 471 E.C. numbers, 27 of which are incomplete. The HQMtb was compared with two recently published Mtb metabolic models, GSMN-TB by Beste et al. and iNJ661 model by Jamshidi and Palsson. Due to the different reconstruction methods used, the three models have different characteristics. The 68 new genes and 80 new E.C. numbers were found only in the HQMtb and resulting in approximately 52 new metabolic reactions located in various metabolic pathways, for example biosynthesis of steroid, fatty acid metabolism, and TCA cycle. Through a comparison of HQMtb with a previously published human metabolic network (EHMN) in terms of protein signatures, 42 Mtb metabolic genes were proposed as new drug targets based on two criteria: (a) their protein functional sites do not match with any human protein functional sites; (b) they are essential genes. Interestingly, 13 of them are found in a list of current validated drug targets. Among all proposed drug targets, Rv0189c, Rv3001c and Rv3607c are of interest to be tested in the laboratory because they were also proposed as drug target candidates from two research groups using different methods.

In this thesis, we presented three different methods for enumerating special subnetworks containedin a metabolic network: metabolic stories, minimal precursor sets and chemical organisations. Foreach of the three methods, we gave theoretical results, and for the two first ones, we further providedan illustration on how to apply them in order to study the metabolic behaviour of living organisms.Metabolic stories are defined as maximal directed acyclic graphs whose sets of sources and targets arerestricted to a subset of the nodes. The initial motivation of this definition was to analyse metabolomicsexperimental data, but the method was also explored in a different context. Metabolic precursor setsare minimal sets of nutrients that are able to produce metabolites of interest. We present threedifferent methods for enumerating minimal precursor sets and we illustrate the application in a studyof the metabolic exchanges in a symbiotic system. Chemical organisations are sets of metabolites thatare simultaneously closed and self-maintaining, which captures some stability feature in the

Clostridium botulinum (C. botulinum) produces a neurotoxin which can be used in a clinical environment to treat diseases and disorders characterised by muscle hypertension or spasm. However, previous research has mostly focused on the biochemical mode of action of the toxin and the disease it manifests. In order to increase our understanding of the process further, this study aimed to investigate the metabolism of various biomarkers, thought to be correlated with neurotoxin biosynthesis. The objective was to increase our understanding of the metabolism which drives the production of C. botulinum toxin using a Genome Scale Metabolic Network (GSMN) enhanced surrogate system. A linear correlation was established between the accumulation of intracellular Poly β hydroxybutyrate (PHB) and neurotoxin in silico (R2 = 0.988). This correlation was confirmed by chemostat experiments in C. sporogenes demonstrating that increased supply of gaseous carbon dioxide (CO2) to the culture results in increased accumulation of PHB and in silico neurotoxin in C. botulinum. Experiments revealed the correlation is a result of modulation of carbon flux partitioning between glycolysis and the TCA cycle, ultimately increasing the availability of carbon for storage as PHB. Phosphate limitation and supplementation with Homoserine and other oxaloacetate derived amino acids, gave rise to increased PHB, owing to reduced activity and/or demand of the TCA cycle increasing the availability of acetyl CoA, the energy storage polymer’s precursor. Altering the growth medium to decrease TCA activity also resulted in decreased flagellin biosynthesis. The results of this study can be used to design a C. botulinum production process based on experimentally proven correlations and pathway analysis to yield a process which promotes neurotoxin biosynthesis over competing pathways, such as flagellin biosynthesis.

The current investigation is aimed at the reconstruction and analysis of genome-scale metabolic models. Specifically, it is focused on the use of mathematical-computational simulations to predict the cellular metabolism behavior towards bio-products production. The photosynthetic cyanobacterium Synechococcus elongatus PCC7942 was studied as biological system. This prokaryotic has been used in several studies as a biological platform for the synthesis of several substances for industrial interest. These studies are based on the advantage of autotrophic systems, which basically requires light and CO2 for growth. The main objective of this thesis is the integration of different types of biological information, whose interaction can be extract applicable knowledge for economic interests. To this end, our study was addressed to the use of methods for modeling, analyzing and predicting the behavior of metabolic phenotypes of cyanobacterium. The work has been divided into chapters organized sequentially, where the starting point was the in silico metabolic reconstruction network. This process intent to join in a metabolic model of all chemical reactions codified in genome. The stoichiometric coefficients of each reactions, can be arranged into a sparse matrix (stoichiometric matrix), where the columns corresponds to reactions and rows to metabolites. As a result of this process the first model was obtained (iSyf646) than later was updated to another (iSyf714). Both were generated from data ¿omics published in databases, scientific reviews as well as textbooks. To validate them, each one of the stoichiometric matrix together with relevant constraints were used by simulation techniques based on linear programming. These reconstructions have to be flexible enough to allow autotrophic growth under which the organism grows in nature. Once the reconstructions were validated, environmental variations can be simulated and we were able to study its effects through changes in outline system parameters. Subsequently, synthetic capabilities were evaluated from the in silico models in order to design metabolic engineering strategies. To do this a genetic variation was simulated in reactions network, where the disturbed stoichiometric matrix was the object of the quadratic optimization methods. As a results sets of optimal solutions were generated to enhanced production of various metabolites of energetic interest such as: ethanol, n-butanol isomers, lipids and hydrogen, as well as lactic acid as the compound which is an interest to the industry. Furthermore, functionally coupled reactions have been studied and have been weighted to the importance in the production of metabolites. Finally, genome-scale metabolic models allow us to establish criteria to integrate different types of data to help of find important points of regulation that may be subject to genetic modification. These regulatory centers have been investigated under drastic changes of illumination and have been inferred operational principles of cyanobacterium metabolism. In general, this thesis presents the metabolic capabilities of photosynthetic cyanobacterium Synechococcus elongatus PCC7942 to produce substances of interest, being a potential biological platform for clean and sustainable production.
Triana Dopico, J. (2014). Model-based analysis and metabolic design of a cyanobacterium for bio-products synthesis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39351
TESIS

碩士
國立中正大學
化學工程研究所
106
This study used the liver as the main axis to exploit the significant flux distribution differences between normal cells and cancer cells to find biomarkers or oncogenes under the case of cancer. Based on the human metabolic network model Recon2.2, and use tissue-specific data from the Human Protein Atlas (HPA), the Virtual Metabolic Human (VMH) to provide the nutrients and Cost Optimization Reaction Dependency Assessment (CORDA) algorithm to generate the model reconstructs of liver health and cancer. Through the use of the Nested Hybrid Differential Evolution (NHDE) and Mutant Flux Balance Analysis (mFBA) developed in the laboratory, the phenomenon of the Warburg Effect as indicators of research to simulate the reprogramming of liver cancer metabolism. Most of the found oncogenes are related to the enzyme enzymes involved in fat metabolism. They will induce human body metabolic network disorders to promote the growth of tumors and the development of cancer. The studies of liver cancer, predict biomarkers, give medical care a clear direction in the further .

System Biology proposes to study biological components, as well as the interactions between them, to understand and predict systems’ behaviour through the use of mathematical models. Under this scope, Genome-Scale Metabolic Models (GSMMs) can be regarded as mathematical representations of the intrinsic metabolic capabilities of a given organism, encoded in its genome, and can be used in a variety of applications like predicting the phenotypical behaviour of a given organism in different environmental and genetic perturbations. The reconstruction of these models comprehends four fundamental stages, namely Genome Annotation, Assembling of a Metabolic Network from the Genome, the Conversion of the Network to a Stoichiometric Model and finally the Validation of the Metabolic Model. Although this procedure is currently relatively standardized in some stages, a significant amount of work still needs to be done by the community before the reconstruction process becomes semi-automated and reproducible. The present work aims at contributing to this field through the development of several tools for aiding the reconstruction process, while simultaneously applying some of those tools to an industrially relevant organism, the yeast Kluyveromyces lactis. The genome annotation stage is critical, as an inadequate annotation may delay, or even impair, the development of the model. The genome metabolic annotation consists on identifying and attributing functions to metabolic genes, i.e., genes encoding enzymes and transport proteins. While the identification of enzyme encoding genes can be performed by assigning Enzyme Commission numbers to the proteins encoded in the genes, the transport proteins encoding genes annotation is not straightforward. In this work, an automatic system to detect and classify all potential transport proteins from a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The integration of the data provided by this methodology with highly curated models allowed the identification of new transport reactions. This tool was included in the merlin tool, a user-friendly Java application developed under the scope of this thesis that performs the reconstruction of GSMMs for any organism that has its genome sequenced. It performs several steps of the reconstruction process, including the functional genomic annotation of the whole genome. merlin 2.0 also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome, and thus the localisation of the metabolites involved in the reactions induced by such proteins. Finally, merlin 2.0 expedites the transition from genome-scale data to SBML (the standard Systems Biology Markup Language) metabolic models, allowing the user to have a preliminary view of the biochemical network. The yeast Kluyveromyces lactis has long been considered a model organism for studies in genetics and physiology, mainly due to its ability to metabolize lactose and to express recombinant proteins. Although the genome of Kluyveromyces lactis has been publicly available for some years, until now no complete metabolic functional annotation has been performed to the proteins encoded in the Kluyveromyces lactis genome and consequently no GSMM has been made available. In this work, a new metabolic genome-wide functional re-annotation of the proteins encoded in the Kluyveromyces lactis genome was performed, resulting in the annotation of 1759 genes with metabolic functions, and the development of a methodology supported by merlin. The new annotation includes novelties, such as the assignment of transporter superfamily numbers to genes identified as transporter proteins. The methodology developed throughout this work can be used to re-annotate any yeast or, with a little tweak of the reference organism, the proteins encoded in any sequenced genome. The new annotation provided by this study served as the basis for the reconstruction of a compartmentalized, genome-scale metabolic model for Kluyveromyces lactis. The partially compartmentalised (4 compartments) genome-scale metabolic model of Kluyveromyces lactis, the iOD962 metabolic model, comprises 962 genes, 2038 reactions and 1561 metabolites. Previous chemostat experiments were used to adjust both growth and non-growth associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. Also, the in silico knockouts predicted accurately the in vivo phenotypes, when compared to published experiments. This model allowed determining a minimal medium for cultivating Kluyveromyces lactis and will surely bring new insights on the milk yeast metabolism, identifying engineering targets for the improvement of the yields of products of interest by performing in silico simulations.
A Biologia de Sistemas propõe-se estudar os componentes biológicos e as interações entre eles, para compreender e prever o comportamento dos sistemas através do uso de modelos matemáticos. Nesse âmbito, os Modelos Metabólicos à Escala Genómica (MMEGs) podem ser considerados representações matemáticas das capacidades metabólicas intrínsecas de um dado organismo, codificadas no seu genoma, e podem ser usados numa grande variedade de aplicações tais como a previsão do comportamento fenotípico de um determinado organismo face a diferentes perturbações ambientais e genéticas. O processo de reconstrução destes modelos compreende quatro fases fundamentais: anotação do genoma, desenvolvimento da rede metabólica, conversão da rede num modelo estequiométrico e, finalmente, a validação do modelo metabólico. Apesar de algumas destas fases estarem já relativamente normalizadas, existe ainda uma lacuna significativa na comunidade no que se refere à (semi-) automação e reprodutibilidade deste processo. O presente trabalho apresenta-se como uma contribuição para esta área, através do desenvolvimento de várias ferramentas de apoio à construção de modelos metabólicos e, simultaneamente da sua aplicação ao organismo Kluyveromyces lactis, uma levedura de elevado interesse industrial. A fase de anotação do genoma é uma fase crítica, pois uma anotação inadequada pode atrasar, ou mesmo comprometer o desenvolvimento de um modelo metabólico. A anotação metabólica do genoma consiste na identificação e atribuição de funções aos genes metabólicos, ou seja, genes que codificam enzimas e proteínas de transporte. Enquanto que a identificação de enzimas codificadas nos genes pode ser realizada através da atribuição de números da Comissão para as Enzimas, a anotação de genes que codificam as proteínas de transporte é um processo mais complexo. Neste trabalho é proposto um sistema automático para a deteção e classificação de proteínas de transporte. Este sistema é baseado na identificação e classificação dos genes que codificam proteínas transmembranares. A integração dos dados fornecidos por esta metodologia com modelos metabólicos curados permitiu a identificação de novas reações de transporte em organismos bem estudados. Esta ferramenta está incluída na ferramenta bioinformática merlin desenvolvida no âmbito desta tese, que é uma aplicação Java de fácil utilização, direcionada para a reconstrução de modelos metabólicos à escala genómica. Esta aplicação executa várias etapas do processo de reconstrução, incluindo a anotação funcional do genoma. O merlin 2.0 também efetua a compartimentação do modelo, prevendo a localização das proteínas codificadas no genoma, e consequentemente dos metabolitos envolvidos nas reações induzidas por essas proteínas. Finalmente, merlin 2.0 acelera a transição de dados do genoma para modelos metabólicos no formato SBML (Systems Biology Markup Language), possibilitando uma visão preliminar da rede bioquímica. A levedura Kluyveromyces lactis tem sido considerada um organismo modelo para estudos de genética e fisiologia, principalmente devido à sua capacidade de metabolizar a lactose e pela sua capacidade de expressar proteínas recombinantes. Apesar de o genoma da Kluyveromyces lactis ter sido disponibilizado publicamente há alguns anos, até agora não foi efetuada uma anotação funcional completa para identificar as proteínas codificadas no genoma da Kluyveromyces lactis. Consequentemente, não existe ainda nenhum MMEG para esta levedura. Neste trabalho foi efetuada uma re-anotação funcional das proteínas codificadas no genoma da Kluyveromyces lactis, resultando na anotação de 1759 genes com funções metabólicas, e no desenvolvimento de uma metodologia apoiada na aplicação merlin. A nova anotação do genoma inclui novidades, tais como a atribuição de números de superfamílias de transportadores a genes que codificam proteínas de transporte. A metodologia desenvolvida ao longo deste trabalho pode ser usada para reanotar qualquer levedura ou, com um ajuste do organismo de referência, as proteínas codificadas em qualquer genoma sequenciado. A nova anotação fornecida por este estudo serviu de base para a reconstrução de um modelo metabólico à escala genómica da Kluyveromyces lactis. Este modelo metabólico, parcialmente compartimentado (4 compartimentos), designado iOD962, inclui 962 genes, 2038 reações e 1561 metabolitos. Foram utilizadas experiências em quimiostato publicadas anteriormente para ajustar os requisitos energéticos associados à manutenção celular, e o modelo mostrou precisão na previsão dos rendimentos de biomassa, de dióxido de carbono e de oxigénio. Além disso, as simulações in silico previram com precisão os fenótipos in vivo, quando comparadas com as experiências publicadas. Este modelo permitiu determinar um meio mínimo para o cultivo de Kluyveromyces lactis e certamente trará novas perspectivas sobre o metabolismo desta levedura, identificando alvos de engenharia metabólica para a melhoria dos rendimentos dos produtos de interesse através da realização de simulações in silico.

Dissertação de mestrado em Bioinformatics
The dairy food industry is constantly changing as novel biotechnological techniques improve the manufacturing process of dairy products. Widely used over the years in the yogurt and cheese manufacturing, Streptococcus thermophilus is now considered as an extremely valuable lactic acid bacterium for the annual market of the dairy industry. A specific, but of easy-access knowledge regarding the thermophilic bacteria metabolism would be a plus for the continuous growth of such industry. In this work, we present the Genome-Scale Metabolic (GSM) model for the LMD- 9 strain of S. thermophilus together with the detailed description of the species metabolic capabilities at the cellular level. The reconstruction of the genome-scale metabolic model, was performed using Metabolic Models Reconstruction Using Genome-Scale Information (merlin) together with COBRApy tool and OptFlux platform. S. thermophilus LMD-9 genome was functionally annotated and the encoded metabolic information was afterwards used to assemble a draft network. After extensive manual curation, the metabolic network was converted to a comprehensive metabolic model. The assembled GSM model was then validated against experimental data. The metabolism of this important stater for the dairy industry has been accessed in detail through the reconstruction. The organism possesses a simple machinery for central carbon metabolism and shows a narrow spectrum of carbohydrate utilization. The genome-scale metabolic model additionally suggests the existence of several pyruvate dissipating pathways which end in the synthesis of various compounds of interest. In silico simulations demonstrated the production of lactate and residual amounts of formate, acetolactate and acetaldehyde. Regarding the amino acid metabolism, the organism possesses complete pathways for the biosynthesis of all amino acids, except for lysine, methionine and cysteine. Furthermore, the GSM model can be used to simulate other relevant features of the S. thermophilus metabolism, such as the aroma compounds and Exopolysaccharides (EPS) synthesis, oxygen tolerance, absence of complete citrate cycle and pentose phosphate pathway, urea metabolism or amino acid catabolism.
A indústria dos lacticínios encontra-se em constante mudança devido ao aparecimento de novas técnicas biotecnológicas que permitem o melhoramento da produção dos laticínios. Amplamente utilizado ao longo dos anos na produção de iogurte e queijo, Streptococcus thermophilus é agora considerado extremamente valioso para o mercado anual desta indústria. Portanto, conhecimento especifico, mas facilmente acessível e compreensivo sobre o metabolismo da bactéria seria uma vantagem para o crescimento continuo desta industria. Nesta tese, apresentamos o modelo metabólico à escala genómica para a estirpe LMD-9 de S. thermophilus, juntamente com um estudo aprofundado das suas capacidades metabólicas. Para obter a reconstrução do modelo metabólico à escala genómica, foi usada principalmente a ferramenta merlin com o apoio da ferramenta COBRApy e a plataforma OptFlux. O genoma de S. thermophilus LMD-9 foi anotado e as informações metabólicas codificadas foram usadas para construir uma rede rascunho. Após curação manual, a rede metabólica foi convertida num modelo metabólico à escala genómica. Posteriormente, o modelo de S. thermophilus foi validado contra dados experimentais. O metabolismo desta bactéria acido láctica foi estudado em detalhe através da reconstrução. O organism dispõe de um metabolismo de carbono muito simples e um espectro de utlização de hidratos de carbono bastante reduzido. Além disso, o modelo desenvolvido sugere a existência de várias vias metabólicas que se iniciam no piruvato e terminam na síntese de vários compostos de interesse, embora as simulações in silico tenham demonstrado apenas a produção de lactato e quantidades residuais de formato, acetolactato e acetaldeído. No que diz respeito ao metabolismo dos aminoácidos, o organismo possui as vias completas para a biossíntese de todos aminoácidos, à exceção da lisina, metionina e cisteína. O modelo pode ser usado para simular outras características relevantes de S. thermophilus, tais como a síntese de EPS e compostos aromáticos, tolerância ao oxigénio, ausência de um ciclo completo do ácido cítrico ou da via das pentoses fosfato, metabolismo da ureia ou catabolismo de aminoácidos.

Dissertação de mestrado em Bioinformatics
The constant growth of high-throughput data generation and omics approaches require informatics support and (semi) automated processes to be developed. With increasing number of sequenced genomes available, metabolic engineering processes will allow a rational alteration of the genetic architecture to achieve specific phenotypes. These alterations will allow to generate and optimize features of some organisms with economic and health interest. Lactobacillus helveticus is an important industrial lactic-acid bacterium being used in the production of several types of cheese. The metabolic activities of the bacterium contribute to the cheese flavour and reduce bitterness. Lb. helveticus is a growing body of literature on the health-promoting properties of its various strains and generally accepted as probiotic for its anti-mutagenic, immunomodulatory and anti-diarrheal effects. The aim of this project was to reconstruct a genome-scale metabolic network of Lb. helveticus CNRZ32, based on its genome sequence annotation as well as known biochemical and physiological characteristics. The generated model contained 790 reactions, 894 metabolites and 1687 genes. The growth rate predicted by the model on sugar was comparable to the reported in literature. This model provides the basis for a constraint-based mathematical model capable of simulating the phenotype of the organism under different growth conditions and guiding indepth physiological studies and hypothesis generation.
O crescimento constante do volume de dados de alto rendimento gerados e de abordagens ómicas urgem de desenvolvimento de suporte informático e processos (semi) automatizados. O aumento do número de genomas sequenciados disponíveis, os processos de engenharia metabólica permitirá uma alteração racional da arquitetura genética para alcançar fenótipos específicos. Estas alterações irão permitir gerar e otimizar características de organismos com interesse económico e de saúde. Lactobacillus helveticus é uma bactéria lática com importância para o uso industrial e utilizada na produção de vários tipos de queijo. A atividade metabólicas da bactéria contribui para o sabor do queijo e para a redução da sua acidez. Lb. Helveticus é geralmente aceite como probiótico, com um crescente volume de literatura sobre as suas propriedades que contribuem positivamente para a saúde em várias das suass estirpes, assim como os seus efeitos antimutagénicos, imunomoduladores e antidiarreicos. O objetivo deste projeto é gerar uma reconstrução da rede metabólica à escala geneomica de Lb. helveticus CNRZ32 baseado na anotação de sequência do genoma, bem como das suas características bioquímicas e fisiológicas. O modelo gerado continha 790 reações, 894 reações e 1687 genes. A taxa de crescimento prevista pelo modelo sobre o açúcar é comparável ao relatado na literatura. A reconstrução deste modelo serve como base para a rescontrução de modelo matemático baseado em restrições capaz de simular o fenótipo do organismo sob diferentes condições de crescimento e orientar estudos fisiológicos em profundidade e geração de hipóteses.

碩士
國立中正大學
化學工程研究所
102
Metabolic activities in normal cells rely primarily on mitochondrial oxidative phosphorylation to generate ATP for energy. Unlike in normal cells, glycolysis is enhanced and oxidative phosphorylation capacity is reduced in various cancer cells. This phenomenon is corresponding to the Warburg Effect. Recon2 liver model used in this study include 2168 metabolites, 3041 reaction, eight compartments and 1410 genes. For data comparison against other Recon2 sub-model (liver cells, lung macrophages, epidermal cells), analysis of their common features and differences. And other common hepatic cell model (for example: Nielsen etc. liver model and Nathan D Price etc. liver model) do cross comparison data to illustrate Recon2 liver model features and benefits. Analysis of metabolic pathways Recon2 liver model includes about metabolic pathways that play an important role in the model. In this study, the metabolic network model of human liver cells Recon2 liver model, the analysis method (Flux balance analysis, FBA) is calculated optimal flux balance. Then the tumor suppressor gene Mir122a shave target gene, the method for analysis (Mutant Flux balance analysis, mFBA) mutant analog flux balance calculation. Steady-state flux distribution obtained FBA, in steady-state flux distribution with mFBA results do compare, then use flux variability analysis (Flux Variability Analysis, FVA) results for the verification and found that knockout metabolic flux changes after meet the Warburg effect (Warburg Effect).

The increasing prevalence of apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium represents a significant global healthcare burden. Treatment options are increasingly limited due to the emergence of new resistant strains. We postulate that parasites have evolved distinct metabolic strategies critical for growth and survival during human infections, and therefore susceptible to drug targeting using a systematic approach. I developed iCS306, a fully characterized metabolic network reconstruction of the model organism Toxoplasma gondii via extensive curation of available genomic and biochemical data. Using available microarray data, metabolic constraints for six different clinical strains of Toxoplasma were modeled. I conducted various in silico experiments using flux balance analysis in order to identify essential metabolic processes, and to illustrate the differences in metabolic behaviour across Toxoplasma strains. The results elucidate probable explanations for the underlying mechanisms which account for the similarities and differences among strains of Toxoplasma, and among species of Apicomplexa.

Due to vast improvements in sequencing methods over the past few decades, the availability of genomic data is rapidly increasing, thus bringing about the need for functional characterization tools. Considering the breadth of data involved, functional assays would be impractical and only a computational method could afford fast and cost-effective functional annotations. Therefore, homology-based computational methods are routinely used to assign putative molecular functions that can later be confirmed with targeted experiments. These methods are particularly well suited to predict the function of enzymes because most metabolic pathways are conserved across organisms. However, the current methods have limitations, especially when considering enzymes that have very low sequence and structure homology to well-annotated enzymes. We hypothesized that two enzymes with the same molecular function shared significant sequence homology in the region surrounding the active site, even if they appear diverged at the global sequence level. First, we have investigated the limits of sequence and structure conservation for enzymes with the same function during divergent evolution. The goal of this was to determine the sequence identity threshold beyond which functional annotations should not be transferred between two sequences; that is the level of homology beyond which the pair of proteins would not be expected to have the same function. Our analysis, which compares several models of sequence evolution, shows that the sequences of orthologous proteins catalyzing the same reaction rarely diverge beyond 30 % identity, even after approximately 3.5 billion years of evolution. As for structure conservation, enzymes catalyzing the same reactions rarely diverge beyond 3 Ã… root-mean-square distance (RMSD). We have also explored sequence conservation constraints as a function of the distance to the active site. Although residues closer to the protein active site (within a radius of 10 Ã… around the catalytic residues) are mutating significantly slower, the requirement to preserve the molecular function also constrains residues at other parts of the protein. From these results, we have developed a structure-based function prediction method where we employ active site conservation in addition to global sequence homology for functional characterization. We then integrated this method with a probabilistic whole-genome function prediction framework previously developed in the Vitkup group, GLOBUS. The original version of GLOBUS uses sampling of probability space to assign functions to all putative metabolic genes in an input genome by considering sequence homology to known enzymes, gene-gene context and EC co-occurrence. Applying this novel method to the whole-genome metabolic reconstruction of Mycobacterium tuberculosis, we made several novel predictions for genes with apparent links to pathogenesis. Notably, our predictions allowed us to reconstruct the cholesterol degradation pathway in M. tuberculosis, which has been implicated in bacterial persistence in the literature but remains to be fully characterized. This pathway is absent from previously published metabolic models of M. tuberculosis. Our new model can now be used to simulate different environments and conditions in order to gain a better understanding of the metabolic adaptability of M. tuberculosis during pathogenesis.

碩士
國立中正大學
化學工程研究所
107
According to the RNA Seq and protein expression, data of various tissues provided by the Cancer Genome Atlas (TCGA) database and the Human Protein Atlas (HPA) website, and based on the human genome-scale metabolic network model (Recon 2.2 Model). This study used two different algorithms: Cost Optimization Reaction Dependency Assessment (CORDA) and Integrative Metabolic Analysis Tool (iMAT) to reconstruct four different metabolic network models of colon cell. Considering the requirements of the experiment, this study used the components of DMEM and RPMI-1640 as the conditions for the uptake of substance, and used Mutant Flux Balance Analysis (MFBA) to simulate the metabolism reprogramming occurred in colon cancer cells This study successfully calculated potential oncogenes in four different colon metabolic models, which can enable future researchers to develop targeted therapies for these oncogenic genes that cause metabolic reprogramming of colon cancer cells.

Release of chloroethene compounds into the environment often results in groundwater contamination, which puts people at risk of exposure by drinking contaminated water. cDCE (cis-1,2-dichloroethene) accumulation on subsurface environments is a common environmental problem due to stagnation and partial degradation of other precursor chloroethene species. Polaromonas sp. strain JS666 apparently requires no exotic growth factors to be used as a bioaugmentation agent for aerobic cDCE degradation. Although being the only suitable microorganism found capable of such, further studies are needed for improving the intrinsic bioremediation rates and fully comprehend the metabolic processes involved. In order to do so, a metabolic model, iJS666, was reconstructed from genome annotation and available bibliographic data. FVA (Flux Variability Analysis) and FBA (Flux Balance Analysis) techniques were used to satisfactory validate the predictive capabilities of the iJS666 model. The iJS666 model was able to predict biomass growth for different previously tested conditions, allowed to design key experiments which should be done for further model improvement and, also, produced viable predictions for the use of biostimulant metabolites in the cDCE biodegradation.