Metabolic networks are the collections of all cellular activities taking place in a living cell and all the relationships among biological elements of the cell including genes, proteins, enzymes, metabolites, and reactions. They provide a better understanding of cellular mechanisms and phenotypic characteristics of the studied organism. In order to reconstruct a metabolic network, interactions among genes and their molecular attributes along with their functions must be known. Using this information, proteins are distributed among pathways as sub-networks of a greater metabolic network. Proteins which carry out various steps of a biological process operate in same pathway.<p>The metabolic network of <i>Caenorhabditis elegans</i> was reconstructed based on current genomic information obtained from the KEGG database, and commonly found in SWISS-PROT and WormBase. Assuming proteins operating in a pathway are interacting proteins, currently available protein-protein interaction map of the studied organism was assembled. This map contains all known protein-protein interactions collected from various sources up to the time. Topology of the reconstructed network was briefly studied and the role of key enzymes in the interconnectivity of the network was analysed. The analysis showed that the shortest metabolic paths represent the most probable routes taken by the organism where endogenous sources of nutrient are available to the organism. Nonetheless, there are alternate paths to allow the organism to survive under extraneous variations. <p>Signature content information of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature(s) in their primary structures, the two proteins are more likely to interact. The signature content of proteins was used to measure the extent of similarity between pairs of proteins based on binary similarity score. Pairs of proteins with a binary similarity score greater than a threshold corresponding to confidence level 95% were predicted as interacting proteins. The reliability of predicted pairs was statistically analyzed. The sensitivity and specificity analysis showed that the proposed approach outperformed maximum likelihood estimation (MLE) approach with a 22% increase in area under curve of receiving operator characteristic (ROC) when they were applied to the same datasets. When proteins containing one and two known signatures were removed from the protein dataset, the area under curve (AUC) increased from 0.549 to 0.584 and 0.655, respectively. Increase in the AUC indicates that proteins with one or two known signatures do not provide sufficient information to predict robust protein-protein interactions. Moreover, it demonstrates that when proteins with more known signatures are used in signature profiling methods the overlap with experimental findings will increase resulting in higher true positive rate and eventually greater AUC. <p>Despite the accuracy of protein-protein interaction methods proposed here and elsewhere, they often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positive predicted protein interacting pairs. This algorithm relies on gene ontology (GO) annotations of proteins involved in predicted interactions. A dataset of experimentally confirmed protein pair interactions and their GO annotations was used as a training set to train keywords which were able to recover both their source interactions (training set) and predicted interactions in other datasets (test sets). These keywords along with the cellular component annotation of proteins were employed to set a pair of rules that were to be satisfied by any predicted pair of interacting proteins. When this algorithm was applied to four predicted datasets obtained using phylogenetic profiles, gene expression patterns, chance co-occurrence distribution coefficient, and maximum likelihood estimation for S. cerevisiae and <i>C. elegans</i>, the improvement in true positive fractions of the datasets was observed in a magnitude of 2-fold to 10-fold depending on the computational method used to create the dataset and the available information on the organism of interest. <p>The predicted protein-protein interactions were incorporated into the prior reconstructed metabolic network of <i>C. elegans</i>, resulting in 1024 new interactions among 94 metabolic pathways. In each of 1024 new interactions one unknown protein was interacting with a known partner found in the reconstructed metabolic network. Unknown proteins were characterized based on the involvement of their known partners. Based on the binary similarity scores, the function of an uncharacterized protein in an interacting pair was defined according to its known counterpart whose function was already specified. With the incorporation of new predicted interactions to the metabolic network, an expanded version of that network was resulted with 27% increase in the number of known proteins involved in metabolism. Connectivity of proteins in protein-protein interaction map changed from 42 to 34 due to the increase in the number of characterized proteins in the network.

The metabolism constitutes the universe of biochemical reactions taking place in a cell of an organism. These processes include the synthesis, transformation, and degradation of molecules for an organism to grow, to reproduce and to interact with its environment. A good way to capture the complexity of these processes is the representation as metabolic network, in which sets of molecules are transformed into products by a chemical reaction, and the products are being processed further. The underlying graph model allows a structural analysis of this network using established graphtheoretical algorithms on the one hand, and a visual representation by applying layout algorithms combined with information visualization techniques on the other. In this thesis we will take a look at three different aspects of graph visualization within the context of biochemical systems: the representation and interactive exploration of static networks, the visual analysis of dynamic networks, and the comparison of two network graphs. We will demonstrate, how established infovis techniques can be combined with new algorithms and applied to specific problems in the area of metabolic network visualization. We reconstruct the metabolic network covering the complete set of chemical reactions present in a generalized eucaryotic cell from real world data available from a popular metabolic pathway data base and present a suitable data structure. As the constructed network is very large, it is not feasible for the display as a whole. Instead, we introduce a technique to analyse this static network in a top-down approach starting with an overview and displaying detailed reaction networks on demand. This exploration method is also applied to compare metabolic networks in different species and from different resources. As for the analysis of dynamic networks, we present a framework to capture changes in the connectivity as well as changes in the attributes associated with the network’s elements.

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.

Cells are able to function and survive due to a delicate orchestration of the expression of genes and their downstream products at the genetic, transcriptomic, proteomic, and metabolic levels. Since metabolites are ultimately the causative agents for physiological responses and responsible for much of the functionality of the organism, a comprehensive understanding of cellular functioning mandates deep insights into how metabolism works. Gaining these insights is impeded by the fact that the regulation and dynamics of metabolic networks are often too complex to allow intuitive predictions, which thus renders mathematical modeling necessary as a means for assessing and understanding metabolic systems. The most difficult step of the modeling process is the extraction of information regarding the structure and regulation of the system from experimental data. The work presented here addresses this "inverse" task with three new methods that are applied to models within Biochemical Systems Theory (BST). Alternating Regression (AR) dissects the nonlinear estimation task into iterative steps of linear regression by utilizing the fact that power-law functions are linear in logarithmic space. Eigenvector Optimization (EO) is an extension of AR that is particularly well suited for the identification of model structure. Dynamic Flux Estimation (DFE) is a more general approach that can involve AR and EO and resolves open issues of model validity and quality beyond residual data fitting errors. The necessity of fast solutions to biological inverse problems is discussed in the context of concept map modeling, which allows the conversion of hypothetical network diagrams into mathematical models.

Optimization and control theory are well developed techniques to quantize, model, understand and optimize real world systems and they have been widely used in engineering, economics, and science. In this thesis, we focus on applications in biomedical systems ranging from cells to microbial communities, and to something as complex as the human body. The first problem we consider is that of medication dosage control for drugs delivered intravenously to the patient. We focus specifically on a blood thinner (called bivalirudin) used in the post cardiac surgery Intensive Care Unit (ICU). We develop two approaches (a model-free and a model-based one) that predict the effect of bivalirudin. After obtaining the model and its best fit parameters by solving a non-linear optimization problem, we develop automatic dosage controllers that adaptively regulate its effect to desired levels. Our algorithms are validated using actual data from a large hospital in the Boston area. In the second problem, we introduce a cellular objective function inference mechanism in metabolic networks. We develop an inverse optimization method, called InvFBA (Inverse Flux Balance Analysis), to infer the objective functions of growing cells by using their reaction fluxes. InvFBA can be seen as an inverse version of FBA (Flux Balance Analysis) which predicts the distribution of the cell's reaction fluxes by using a hypothetical objective function. The objective functions can be linear, quadratic and non-parametric. The efficiency of the InvFBA approach matches the structure of the FBA and ensures scalability to large networks and optimality of the solution. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression data, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories. In the final problem in this thesis, we formulate a novel resource allocation problem in microbial ecosystems. We consider a given number of microbial species living symbiotically in a community and a list of all metabolic reactions present in the community, expressed in terms of the metabolite proportions involved in each reaction. We are interested in allocating reactions to organisms so that each organism maintains a minimal level of growth and the community optimizes certain objectives, such as maximizing growth and/or the uptake of specific compounds from the common environment. We leverage tools from Flux Balance Analysis (FBA) and formulate the problem as a mixed integer linear programming problem. We test our method in a toy model involving two organisms that can only survive through cross-feeding, demonstrating that the method can recover this interaction. We also test the method in a community of two simplified bacteria described in terms of their core, simplified metabolic network. We demonstrate that the method can obtain syntrophic cross-feeding species that would be very difficult to design manually.

Análise de Redes Metabólicas foi aplicada à cepa de Saccharomyces cerevisiae CEN.PK113-7D, e a alguns mutantes interrompidos em genes que codificam para proteínas regulatórias envolvidas no fenômeno de repressão por glicose. Todas as cepas foram cultivadas em aerobiose, em meio mínimo contendo [1-13C]glicose como substrato limitante. As células eram recolhidas em situação de crescimento balanceado e submetidas à hidrólise, seguida de derivação e posterior injeção da amostra resultante num cromatógrafo gasoso acoplado a um espectrômetro de massa, para análise da marcação em alguns fragmentos de metabólitos intracelulares. Estes dados serviram como base para a identificação da atividade de algumas vias metabólicas no metabolismo central de S. cerevisiae. Além disto, utilizando-os juntamente com um modelo estequiométrico, foi possível obter uma estimativa para os fluxos no metabolismo central na cepa referência e nos mutantes estudados. Num primeiro momento, a metodologia foi validada para cultivos contínuos e descontínuos. Calculou-se um desvio padrão para a medida da marcação em cada fragmento de metabólito detectado pela metodologia empregada. Na cepa referência, observou-se que o ciclo de Krebs opera de forma cíclica em células que respiram e de forma não cíclica em células que apresentam metabolismo respiratório-fermentativo. Verificou-se que uma maior parte da glicose consumida é desviada para a via das pentoses fosfato no primeiro caso, em relação ao segundo. Foram encontradas evidências para a biossíntese de glicina através da enzima treonina aldolase e para a atividade da enzima málica. A ausência das proteínas Mig1 e Mig2 não altera os padrões de crescimento, produção de etanol e de marcação em metabólitos intracelulares de S. cerevisiae. Já a ausência de Hxk2, Reg1 ou Grr1 provoca alívio na repressão por glicose, observado pelo aumento das atividades respiratórias. / Metabolic Network Analysis was applied to the reference strain CEN.PK113-7D of Saccharomyces cerevisiae, as well as to some mutants disrupted in genes which code for regulatory proteins involved in the glucose repression cascade. All strains were cultivated under aerobic conditions, using minimal medium with [1-13C]glucose as the limiting substrate. Cells were harvested under balanced growth conditions and submitted to hydrolysis, derivatization and injection of the sample into a gas chromatograph coupled to a mass spectrometer for analysis of the labeling pattern in some fragments of intracellular metabolites. These data were used for identifying the activity of some pathways in the central metabolism of S. cerevisiae. Furthermore, using the data together with a stoichiometric model, it was possible to estimate the fluxes in the central metabolism of the reference strain and in the mutant strains. First, the methodology was validated for batch and continuous cultivations. Standard deviations were calculated for the measurement of the fractional labeling in each of the detected fragments. In the reference strain, it was observed that the Krebs cycle operates in a cyclic manner in respiratory cells, whereas it operates in a non cyclic manner under respiro-fermentative metabolism. It was also seen that a greater part of the glucose consumed by the cells enters the pentose phosphate pathway in the former than in the later case. Evidence for the activity of the threonine aldolase and the malic enzyme catalyzed reactions was also found. The absence of the Mig1 and Mig2 proteins does not alter the growth, ethanol formation and labeling pattern of intracellular metabolites in S. cerevisiae. In contrast, the absence of Hxk2, Reg1, or Grr1 provoques a relief in glucose repression, which was observed by an increased respiratory activity.

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.

The metabolism constitutes the universe of biochemical reactions taking place in a cell of an organism. These processes include the synthesis, transformation, and degradation of molecules for an organism to grow, to reproduce and to interact with its environment. A good way to capture the complexity of these processes is the representation as metabolic network, in which sets of molecules are transformed into products by a chemical reaction, and the products are being processed further. The underlying graph model allows a structural analysis of this network using established graphtheoretical algorithms on the one hand, and a visual representation by applying layout algorithms combined with information visualization techniques on the other. In this thesis we will take a look at three different aspects of graph visualization within the context of biochemical systems: the representation and interactive exploration of static networks, the visual analysis of dynamic networks, and the comparison of two network graphs. We will demonstrate, how established infovis techniques can be combined with new algorithms and applied to specific problems in the area of metabolic network visualization. We reconstruct the metabolic network covering the complete set of chemical reactions present in a generalized eucaryotic cell from real world data available from a popular metabolic pathway data base and present a suitable data structure. As the constructed network is very large, it is not feasible for the display as a whole. Instead, we introduce a technique to analyse this static network in a top-down approach starting with an overview and displaying detailed reaction networks on demand. This exploration method is also applied to compare metabolic networks in different species and from different resources. As for the analysis of dynamic networks, we present a framework to capture changes in the connectivity as well as changes in the attributes associated with the network’s elements.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19112号 / 情博第558号 / 新制||情||99(附属図書館) / 32063 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 阿久津 達也, 教授 岡部 寿男, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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