The apicomplexan Cryptosporidium is a protozoan parasite of humans and other mammals. Cryptosporidium species cause acute gastro-enteritis and diarrheal disease in healthy humans and animals, and cause life-threatening infection in immuno-compromised individuals such as people with AIDS. It has a one-host life cycle and invades intestinal epithelial cells causing diarrhea, or more rarely the pulmonary epithelium. Cryptosporidium carries out all the asexual reproductive stages like several other apicomplexans. Current annotation of this organism predicts it to contain 3884 genes of which only 1581 genes have predicted functions. By using a combination of bioinformatics analysis, biochemical evidence, and high-throughput data, a genome-scale metabolic model of Cryptosporidium hominis is being constructed. The current model is comprised of approximately 213 gene-associated enzymes involved in major metabolic pathways including carbohydrate, nucleotide, amino acid, and energy metabolism. The approach of constructing a genome-scale model provides a link between the genotype and the phenotypic behavior of the organism, making it possible to study and predict behavior based upon genome content. This modeling approach provides an overview for evaluating missing components in a metabolic network and provides an analytical framework for interpreting data as more research becomes available. The goal of constructing this model is to systematically study and analyze various functional behaviors of C. hominis with respect to its stages in life cycle and pathogenicity.

Microbial fuel cells (MFCs) are alternative energy resources that generate electricity from organic matter, where microorganisms such as the Geobacter species oxidize organic waste and transfer electrons to an electrode. Mathematical models are used to study biofilm processes, in hopes of developing MFCs into commercial applications. Existing biofilm models are based on Nernst-Monod type expressions, and are restricted to studying extracellular electrochemical/microbiological components, separated from the metabolic behavior of microorganisms. In this thesis, a model was developed combining extracellular biofilm conditions, with the intracellular metabolic fluxes of microorganisms under spatial heterogeneities (electron donor/acceptor levels) across the biofilm. This model predicts biofilm processes under varying extracellular conditions (presence/absence of NH4+, shear stress in continuous mode MFCs), and intracellular conditions (ATP maintenance fluxes); and also provides a preliminary evaluation of the pH changes across the biofilm. A sensitivity analysis based on the cell density and the biofilm conductivity was also conducted.

Microbial fuel cells (MFCs) are alternative energy resources that generate electricity from organic matter, where microorganisms such as the Geobacter species oxidize organic waste and transfer electrons to an electrode. Mathematical models are used to study biofilm processes, in hopes of developing MFCs into commercial applications. Existing biofilm models are based on Nernst-Monod type expressions, and are restricted to studying extracellular electrochemical/microbiological components, separated from the metabolic behavior of microorganisms. In this thesis, a model was developed combining extracellular biofilm conditions, with the intracellular metabolic fluxes of microorganisms under spatial heterogeneities (electron donor/acceptor levels) across the biofilm. This model predicts biofilm processes under varying extracellular conditions (presence/absence of NH4+, shear stress in continuous mode MFCs), and intracellular conditions (ATP maintenance fluxes); and also provides a preliminary evaluation of the pH changes across the biofilm. A sensitivity analysis based on the cell density and the biofilm conductivity was also conducted.

Spider silk has the potential to be a useful biomaterial due to its high tensile strength and elasticity. It is also biocompatible and biodegradable, making it useful for wound dressings and sutures, tissue and bone scaffolds, vessels for drug delivery, and ligament and tendon replacements. In some studies where spider silk has been used to grow cells, the silk has promoted more cell growth than the control. However, it is difficult to obtain the high volume of silk needed for these undertakings on a large scale. Spiders are territorial and cannibalistic, so they cannot be easily farmed. Therefore, spider silk proteins are frequently produced in other organisms. E. coli is often used for spider silk production due to the relative ease of gene manipulation and the cost effectiveness of large-scale fermentation. However, due to the large protein size of the spider silk and the repeating amino acid motifs, there are some challenges with production in E. coli. Metabolic modeling is a way to model the metabolism of an organism and can help overcome some of the difficulties of spider silk production in E. coli by predicting metabolic engineering strategies. In this study, a metabolic modeling tool known as dynamic FBA predicted that ammonium is depleted during cell growth. Laboratory results confirmed that by adding additional ammonium to the medium, the E. coli cells experienced more cell growth and were able to produce more spider silk protein

Thesis advisor: Tim van Opijnen / Each year, the opportunistic pathogen Streptococcus pneumoniae causes millions of illnesses and nearly 300,000 deaths worldwide. Despite widespread vaccination campaigns, S. pneumoniae persists as a public health risk in large part to its high genomic diversity. In previous work, our group has shown that functional pathways, including stress response to antibiotics, are not necessarily conserved between pneumococcal strains. Thus, a holistic pangenome view of S. pneumoniae is a promising avenue to gain understanding of the species and to inform clinical treatment methods. Our group has selected 36 strains, covering 78% of known pneumococcal genetic diversity, for S. pneumoniae pangenome studies. We have previously constructed transposon libraries and performed Tn-seq for 22 of these strains in both in vitro and in vivo conditions. From these studies, our group has constructed pangenome profiles of genes essential for reproduction in culture conditions, infection in a mouse model, and attachment in a human nasopharyngeal epithelial cell line. In this study, we develop and execute a pipeline to construct iSP20, a set of in silico metabolic models for 34 S. pneumoniae strains. We employ these models to predict nutrient and metabolic gene essentiality on both the strain and pangenome level, demonstrating that key patterns in the strains’ essentialomes translate to a metabolic context. Additionally, we perform a functional analysis of the metabolic models, revealing a highly connected metabolic genome and essentialome. We uncover differences in the in vitro and in silico core essentialomes and identify potential sources of discrepancy between the two datasets. Overall, this work demonstrates the utility of strain-specific metabolic models in pangenome essentiality studies and provides enhanced understanding of metabolism in S. pneumoniae. / Thesis (BS) — Boston College, 2020. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Biology.

Metabolism is a key attribute of life on Earth at multiple spatial and temporal scales, involved in processes ranging from cellular reproduction to biogeochemical cycles. While metabolic network modeling approaches have enabled significant progress at the cellular-scale, extending these techniques to address questions at both the ecosystem and planetary-scales remains highly unexplored. In this thesis, I integrate various multi-scale metabolic network modeling approaches to address key questions with regard to both the long-term evolution of metabolism in the biosphere and the metabolic processes that take place in complex microbial communities. The first portion of my thesis work, focused on the evolution of ancient metabolic networks, attempts to model the emergence of ecosystem-level metabolism from simple geochemical precursors. By integrating network-based algorithms, physiochemical constraints, and geochemical estimates of ancient Earth, I explored whether a complex metabolic network could have emerged without phosphate, a key molecular component in modern-day living systems, known to be poorly available at the onset of life. We found that phosphate may have not been essential in early living systems, and that thioesters may have been the primitive energy currency in ancient metabolic networks. By generalizing this approach to explore the scope of geochemical scenarios that could have given rise to living systems, I found that other key biomolecules, including fixed nitrogen, may have not been required at the earliest stages in biochemical evolution. The second portion of my thesis deals with a different aspect of ecosystem-level metabolism, namely the role of metabolism in shaping the structure of microbial communities. I studied the relationship between metabolism and microbial community assembly using microbial communities grown in synthetic laboratory environments. We found that a generalized statistical consumer-resource model recapitulates the emergent phenomena observed in these experiments. Future work could seek to better clarify the connection between the fundamental rules that led to life’s emergence over 4 billion years ago and the laws that shape microbial ecosystems today. An ecosystems-level metabolic perspective may aid in our understanding of both the emergence and maintenance of the biosphere.

An interesting discovery in biology is that most genes in an organism are dispensable. That means these genes have minor effects on survival of the organism in standard laboratory conditions. One explanation of this discovery is that some genes play important roles in specific conditions and are essential genes under those conditions. E. coli is a model organism, which is widely used. It can adapt to many stress conditions, including temperature, pH, osmotic, antibiotic, etc. Underlying mechanisms and associated genes of each stress condition responses are usually different. In our analysis, we combined protein abundance data and mutant conditional fitness data into E. coli constraint-based metabolic models to study conditionally essential metabolic genes under temperature and pH stress conditions. Flux Balance Analysis was employed as the modeling method to analysis these data. We discovered lists of metabolic genes, which are E. coli dispensable genes, but conditionally essential under some stress conditions. Among these conditionally essential genes, atpA in low pH stress and nhaA in high pH stress found experimental evidences from previous studies. Our study provides new conditionally essential gene candidates for biologists to explore stress condition mechanisms.

Le paludisme est responsable de la mort de près d'un million de personnes chaque année. Cette maladie est causée par le Plasmodium, parasite protozoaire appartenant à la famille des Apicomplexes. Dans cette thèse, nous avons développé des approches de biologie de systèmes pour l'étude du métabolisme des phospholipides (PL) métabolisme et de sa régulation chez Plasmodium. Ces voies métaboliques sont d'une importance primordiale pour la survie du parasite. À l'étape intra-érythrocytaire du développement, les espèces de Plasmodium exploitent un nombre important de voies de synthèse phospholipidique, qui sont rarement trouvées ensemble dans un seul organisme : (i) la voie dépendante ancestrale CDP-diacylglycerol des procaryotes ( ii) les voies eucaryotes de novo CDP- choline et CDP-éthanolamine (Kennedy) ( iii ) de plus P.falciparum et P. knowlesi emploient des réactions supplémentaires qui relient une à l'autre certaines de ces routes. Une voie de synthèse caractéristique aux plantes, qui utilise la sérine en tant que source supplémentaire de phosphatidyl-choline (PC) et de phosphatidyl-éthanolamine (PE), est nommée la voie méthyltransférase décarboxylase - phosphoéthanolamine sérine (SDPM). Pour comprendre la dynamique d'acquisition et le métabolisme des phospholipides chez Plasmodium, nous avons construit un modèle cinétique quantitatif basé sur des données fluxomiques. La dynamique in vitro d'incorporation de phospholipides révèle plusieurs voies de synthèse. Nous avons construit un réseau métabolique détaillé et nous avons identifié les valeurs de ses paramètres cinétiques (taux maximaux et constantes Michaelis). Afin d'obtenir une recherche globale dans l'espace de paramètres, nous avons conçu une méthode d'optimisation hybride, discrète et continue. Des paramètres discrets ont été utilisés pour échantillonner le cône des flux admissibles, alors que les constantes des Michaelis et les taux maximaux ont été obtenus par la minimisation locale d'une fonction objective. Cette méthode nous a également permis de prédire la répartition des flux au sein du réseau pour différents précurseurs métaboliques. Cette analyse quantitative a également été utilisée pour comprendre les liens éventuels entre les différentes voies. La principale source de PC est la voie Kennedy CDP-choline. Des expériences de knock-out in silico ont montré l'importance comparable des voies phosphoéthanolamine-N-méthyltransférase (PMT) et de la phosphatidyléthanolamine-N-méthyltransférase (PEMT) pour la synthèse de PC. Les valeurs des flux indiquent que plus grande partie de la PE dérivée de la sérine est formée par décarboxylation, alors que la synthèse de PS est majoritairement effectuée par des réactions d'échange de base. L'analyse de sensitivité de la voie CDP- choline montre que l'entrée de choline dans le parasite et la réaction cytidylyltransferase de la phosphocholine ont les plus grands co-efficients de contrôle sur cette voie, mais ne permet pas de distinguer une réaction comme l'unique étape limitante. Ayant comme objectif la compréhension de la régulation de l'expression génique chez Plasmodium falciparum et son influence sur le fonctionnement métabolique, nous avons effectué une étude bioinformatique intégrative des données du transcriptome et du métabolome pour les principales enzymes impliquées dans le métabolisme PL. L'étude de la dépendance temporelle des variables métaboliques et transcriptomiques au cours du cycle intra-érythrocytaire, a mis en évidence deux modes d'activation des voies PL. Les voies Kennedy sont activées pendant la phase schizogonique et au début de la phase anneau, alors que les voies SDPM et d'échange de bases sont activées lors de la fin de la phase anneau cycle et lors de la phase tropozoïte. / Malaria is responsible of the death of up to one million people each year. This disease is caused by Plasmodium, a protozoan parasite. In this thesis we have developed systems biology approaches to the study of phospholipid (PL) metabolism and its regulation in Plasmodium. These pathways are of primary importance for the survival of the parasite. At the blood stage, Plasmodium species display a bewildering number of PL synthetic pathways that are rarely found together in a single organism (i) the ancestral prokaryotic CDPdiacylglycerol dependent pathway (ii) the eukaryotic type de novo CDP-choline and CDPethanolamine (Kennedy) pathways (iii) P. falciparum and P. knowlesi exhibits additional reactions that bridge some of these routes. A plant-like pathway that relies on serine to provide additional PC and PE, is named the serine decarboxylase-phosphoethanolamine methyltransferase (SDPM) pathway. To understand the dynamics of PL acquisition and metabolism in Plasmodium we have used fluxomic data to build a quantitative kinetic model. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimisation method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors. The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway. In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis. The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas the phosphatidylserine (PS) is mainly predominated by base-exchange reactions. Metabolic control analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest control coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step.With a vision to understand regulation of gene expression in Plasmodium falciparum and its influence on the metabolite expression, we have performed an integrative bioinformatic studies. The study integrates transcriptome and metabolome data for the main enzymes involved in PL metabolism. The study of the correlated time dependence of metabolic and transcriptomic variables during the intraerythrocytic cycle showed that there are two modes of activation of PL pathways. Kennedy pathways are activated during schizogony and early ring stages, whereas SDPM and base exchange pathways are activated during late ring and tropozoite stages.

Les réseaux métaboliques à l’échelle génomique décrivent les relations entre milliers de réactions et molécules biochimiques pour améliorer notre compréhension du métabolisme. Ils trouvent des applications dans les domaines chimiques, pharmaceutiques, et dans la biorestauration.La complexité de modèles métaboliques mets des obstacles á l’inférence des modèles, à la comparaison entre eux, ainsi que leur analyse, curation et amélioration par des experts humains. Parce que l’abondance des détailles dans les réseaux à grande échelle peut cacher des erreurs et des adaptations importantes de l’espèce qui est étudié, c’est important de trouver les correct niveaux d’abstraction qui sont confortables pour les experts humains : on doit mettre en évidence la structure essentiel du modèle ainsi que les divergences de celle-là (par exemple les chemins alternatives et les réactions manquantes), tout en masquant les détails non significatifs.Pour répondre a cette demande nous avons défini une généralisation des modèles métaboliques, fondée sur les connaissances, qui permet la création des vues abstraites de réseaux métaboliques. Nous avons développé une méthode théorétique qui regroupe les métabolites en classes d’équivalence et factorise les réactions reliant ces classes d’équivalence. Nous avons réalisé cette méthode comme une bibliothèque Python qui peut être téléchargée depuis metamogen.gforge.inria.fr.Pour valider l’intérêt de notre méthode, nous l’avons appliquée à 1 286 modèles métaboliques que nous avons extraits de la ressource Path2Model. Nous avons montré que notre méthode aide l’expert humain à relever de façon automatique les adaptations spécifiques de certains espèces et à comparer les modèles entre eux.Après en avoir discuté avec des utilisateurs, nous avons décidé de définir trois niveaux hiérarchiques de représentation de réseaux métaboliques : les compartiments, les modules et les réactions détaillées. Nous avons combiné notre méthode de généralisation et le paradigme des interfaces zoomables pour développer Mimoza, un système de navigation dans les réseaux métaboliques qui crée et visualise ces trois niveaux. Mimoza est accessible en ligne et pour le téléchargement depuis le site mimoza.bordeaux.inria.fr. / Genome-scale metabolic models describe the relationships between thousands of reactions and biochemical molecules, and are used to improve our understanding of organism’s metabolism. They found applications in pharmaceutical, chemical and bioremediation industries.The complexity of metabolic models hampers many tasks that are important during the process of model inference, such as model comparison, analysis, curation and refinement by human experts. The abundance of details in large-scale networks can mask errors and important organism-specific adaptations. It is therefore important to find the right levels of abstraction that are comfortable for human experts. These abstract levels should highlight the essential model structure and the divergences from it, such as alternative paths or missing reactions, while hiding inessential details.To address this issue, we defined a knowledge-based generalization that allows for production of higher-level abstract views of metabolic network models. We developed a theoretical method that groups similar metabolites and reactions based on the network structure and the knowledge extracted from metabolite ontologies, and then compresses the network based on this grouping. We implemented our method as a python library, that is available for download from metamogen.gforge.inria.fr.To validate our method we applied it to 1 286 metabolic models from the Path2Model project, and showed that it helps to detect organism-, and domain-specific adaptations, as well as to compare models.Based on discussions with users about their ways of navigation in metabolic networks, we defined a 3-level representation of metabolic networks: the full-model level, the generalized level, the compartment level. We combined our model generalization method with the zooming user interface (ZUI) paradigm and developed Mimoza, a user-centric tool for zoomable navigation and knowledgebased exploration of metabolic networks that produces this 3-level representation. Mimoza is available both as an on-line tool and for download atmimoza.bordeaux.inria.fr.

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