Human muscle metabolism, the biochemical reactions which lead storage and usage of energy, is complex, but important in understanding human health and disease. Optimal muscle metabolism can help maintain a healthy organism by adequately storing and utilizing energy for subsequent use in contraction and recovery and adaption from contraction and exercise. Dysregulated muscle metabolism can lead to insulin resistance and obesity among other health problems. Flux balance analysis (FBA) and constraint-based modeling have successfully elucidated important aspects of metabolism in single-celled organisms. However, limited work has been done with multicellular organisms. The foci of this dissertation are (1) to show how novel applications of this technique can aid in the investigation of human nutrition and (2) to elucidate metabolic phenotypes associated with the insulin resistance (IR) characteristics of Type 2 Diabetes (T2D). First, for human nutrition a novel steady-state constraint-based model of skeletal muscle tissue was constructed to investigate the effect of amino acid supplementation on protein synthesis. Several in silico supplementation strategies implemented showed that amino acid supplementation could increase muscle contractile protein synthesis, which is consistent with published data on protein synthesis in a post-resistance exercise state. These results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. Thus, this dissertation introduces the prospect of using systems biology as a framework to investigate how supplementation and nutrition can affect human metabolism and physiology. Second, given the complexity of metabolism, the cause(s) of the altered muscle metabolism in IR remain(s) unknown. Attempting to elucidate this complexity, the constraint-based modeling framework was expanded upon to develop the first in silico analysis of muscle metabolism under varying nutrient conditions and during transitions from fasted to fed states. Systematic perturbations of the metabolic network identified reactions which mimic IR phenotypes: reduced ATP/creatine phosphate synthesis, reduced TCA cycle flux, and reduced metabolic flexibility. Reduced flux through a single reaction is not sufficient to recapitulate the IR phenotypes, but knockdowns in pyruvate dehydrogenase in combination with either reduced lipid uptake or lipid/amino acid oxidative metabolism do so. These combinations also decrease complete lipid oxidation and glycogen storage. Thus, the computational model also provides a novel tool to identify candidate enzymes contributing to dysregulated metabolism in IR.

We propose a new method to integrate genome-scale metabolic models into biogeochemical reaction modeling. This method predicts rates of microbial metabolisms by combining flux balance analysis (FBA) with microbial rate laws. We applied this new hybrid method to methanogenesis by Methanosarcina barkeri. Our results show that the new method predicts well the progress of acetoclastic, methanol, and diauxic metabolism by M. barkeri. The hybrid method represents an improvement over dynamic FBA. We validated genome-scale metabolic models of Methanosarcina barkeri, Methanosarcina acetivorans, Geobacter metallireducens, Shewanella oneidensis, Shewanella putrefaciens and Shewanella sp. MR4 for application to biogeochemical modeling. FBA was used to predict the response of cell metabolism, and ATP and biomass yield. Our analysis provides improvements to these models for the purpose of applications to natural environments. / 2019-07-28

Data analytics and optimization theory are well-developed techniques to describe, predict and optimize real-world systems, and they have been widely used in engineering and science. This dissertation focuses on applications in biomedical systems, ranging from the scale of microbial communities to problems relating to human disease and health care. Starting from the microbial level, the first problem considered is to design metabolic division of labor in microbial communities. Given a number of microbial species living in a community, the starting point of the analysis is a list of all metabolic reactions present in the community, expressed in terms of the metabolite proportions involved in each reaction. Leveraging tools from Flux Balance Analysis (FBA), the problem is formulated as a Mixed Integer Program (MIP) and new methods are developed to solve large scale instances. The strategies found reveal a large space of nuanced and non-intuitive metabolic division of labor opportunities, including, for example, splitting the Tricarboxylic Acid Cycle (TCA) cycle into two separate halves. More broadly, the landscape of possible 1-, 2-, and 3-strain solutions is systematically mapped at increasingly tight constraints on the number of allowed reactions. The second problem addressed involves the prediction and prevention of short-term (30-day) hospital re-admissions. To develop predictive models, a variety of classification algorithms are adapted and coupled with robust (regularized) learning and heuristic feature selection approaches. Using real, large datasets, these methods are shown to reliably predict re-admissions of patients undergoing general surgery, within 30-days of discharge. Beyond predictions, a novel prescriptive method is developed that computes specific control actions with the effect of altering the outcome. This method, termed Prescriptive Support Vector Machines (PSVM), is based on an underlying SVM classifier. Applied to the hospital re-admission data, it is shown to reduce 30-day re-admissions after surgery through better control of the patient’s pre-operative condition. Specifically, using the new method the patient’s pre-operative hematocrit is regulated through limited blood transfusion. In the last problem in this dissertation, a framework for parameter estimation in Regularized Mixed Linear Regression (MLR) problems is developed. In the specific MLR setting considered, training data are generated from a mixture of distinct linear models (or clusters) and the task is to identify the corresponding coefficient vectors. The problem is formulated as a Mixed Integer Program (MIP) subject to regularization constraints on the coefficient vectors. A number of results on the convergence of parameter estimates for MLR are established. In addition, experimental prediction results are presented comparing the prediction algorithm with mean absolute error regression and random forest regression, in terms of both accuracy and interpretability.

Engineering microorganisms at the systems level is recognized to be the future of metabolic engineering. Thanks to the development of genome annotation, mcroorganisms can be understood, as never before, and be reconstructed in the form of computational models. Flux balance analysis provides a deep insight intocellular metabolism and can guide metabolic engineering strategies. In particular, algorithms can assess the cellular complexity of the metabolism and hint at genetic interventions to improve product productivity. In this work, Synechosystis PCC6803 metabolism was invesetigated in silico. Genetic interventions could besuggested to couple butanol synthesis to growth as a way to improve currentproductivities. Cofactor recycling and, in particular, buffering mechanisms were shown to be important targets. Creating a cofacor imbalance and removing thesebuffering mechanisms is an important driving force. This forces a carbon flux through butanol synthesis to maintain cofactor balance and sustain growth.

Microbes are ubiquitous in virtually all habitats on Earth and affect human life in multiple ways, from the health-balancing role of the human microbiome, to the involvement of microbial communities in the global nitrogen and carbon cycles. The capacity of microbes to survive and grow in diverse environments relates directly to their ability to utilize available resources, be they from other microbes or from the environment itself. Hence, understanding how the environment shapes the metabolic functionality of individual microbes and complex communities constitutes an important area of research. In the first part of my thesis work, I explored how environmental nutrient composition and intracellular transcriptional regulation data can be integrated to provide insight into the temporal metabolic behavior of a bacterium through the use of genome-scale stoichiometric modeling approaches (Flux Balance Analysis). Thus I developed the method of Temporal Expression-based Analysis of Metabolism (TEAM), and applied it to Shewanella oneidensis, a bacterium studied for its important bioenergy and bioremediation applications. I found that TEAM improves on previous models' predictions of S. oneidensis metabolic fluxes, and recovers the overflow metabolism that has been seen experimentally. This study demonstrated the value of incorporating environmental context and transcriptional data for the prediction of time-dependent metabolic behavior. In the second part of my work, I extended the exploration of microbial metabolism from single species to complex communities in order to understand the robustness of metabolic functions. Specifically, I implemented novel mathematical analyses of metagenomic sequencing data to ask how functional stability of microbial communities could ensue despite large taxonomic variability. Upon representing in matrix form the metabolic capabilities of all genera found in 202 available metabolic ecosystem datasets, I compared the different communities with each other and with various randomized analogues. My results reveal new connections between the abundance of an organism in the community and the functions that it encodes. Furthermore, I found that genus abundances govern the metabolic robustness of a community more than the distribution of genetically encoded functions among the community members, suggesting that communities rely largely on ecological interactions to regulate their overall functionality.

<p>The primary goal of the research is to examine the expander manufacturing process of an industry, observe how the industry may sustainably be manufacturing elements throughout its entire manufacturing process, and analyze the advantages of doing so. It will also look at the challenges that would arise if these manufacturing processes were altered to make them more sustainable and environmentally friendly.</p>

Human exposure to per- and poly-fluoroalkyl substances (PFAS) occurs mainly through two pathways, inhalation and ingestion. Dietary exposure to persistent organic pollutants (POPs), including PFAS, is driven mainly by the consumption of foods of marine or terrestrial animal origin. Therefore, the intake of fish from waters in populated or polluted areas may be a source of human exposure to PFAS. The overall aim of the study was to estimate human exposure to PFAS through the consumption of fish caught in Swedish waters. Analyses of extractable organofluorine (EOF) were performed to serve as an important metric alongside target analysis to better understand the total amount of PFAS in the human sera and fish samples. The serum samples represent individuals who have lived at some point about 5 km from the glass industry in Nybro and Emmaboda. For this study with the Glasbruket study population, the highest concentrations were found for PFOS followed by PFOA, PFNA and PFHxS (medians of 7.9, 1.9, 1.4 and 0.8 ng/mL). Also, the difference in this population between men/women and high/low fish consumers were also addressed. It was stated that there was a statistically significant difference in average Σ17 PFAS concentration between the male and female groups (p &lt; 0.05, two-tailed test). However, the differences between the low and high consumer groups were not statistically significant even though the PFAS levels were higher in the high consumer group. The organofluorine mass balance analysis revealed that 80.1% (ranged from 68.3−93.7%) of the EOF in female samples could not be explained, whereas 57.3% (ranged from 0−99.4%) for the male group was of unidentified origin. Two methods were evaluated for PFAS and EOF analysis of fish muscle, namely, acetonitrile extraction and ion-pair extraction. The selected method, the ion-pair extraction, was performed on fish muscle samples. The fish species included perch (Perca fluviatilis), northern pike (Esox Lucius) and zander (Sander lucioperca) that were collected from seven different lakes in the vicinity of Nyro and Emmaboda. The sum of targeted PFAS (∑14PFAS) across all fish samples analysed ranged from 0.9 to 6.2 ng/g. Mostly, perfluoroalkyl carboxylic acids (PFCAs), precursors to PFCAs and novel PFAS were found in the fish samples. A large part of the EOF content cannot be identified with the targeted PFAS compounds. The average identified EOF fraction for all fish was 2.3% (ranging from 0.8 to 7.2%). For this study, 10 PFAS were found in both sera and fish samples. Therefore, freshwater fish consumption can be identified as one of the contributors to the PFAS concentrations in the Glasbruket population. The fish samples contained precursor compounds as well, that were not found in human serum. These precursor compounds can contribute to the concentrations of PFAAs in serum through biotransformation in the human body to perfluoroalkyl acids (PFAAs). Due to the widespread use of PFAS and their persistence in the environment, it is difficult to determine the relationship between the levels found in serum and fish. The Glasbruket population could be exposed to other sources besides fish.

We are interested in numerical methods for the optimization constrained second order ordinary differential equations arising in biofilm modelling. This class of problems is challenging for several reasons. One of the reasons is that the underlying solution has a steep slope, making it difficult to resolve. We propose a new numerical method with techniques such as domain decomposition and asynchronous iterations for solving certain types of ordinary differential equations more efficiently. In fact, for our class of problems after applying the techniques of domain decomposition with overlap we are able to solve the ordinary differential equations with a steep slope on a larger domain than previously possible. After applying asynchronous iteration techniques, we are able to solve the problem with less time.~We provide theoretical conditions for the convergence of each of the techniques. The other reason is that the second order ordinary differential equations are coupled with an optimization problem, which can be viewed as the constraints. We propose a numerical method for solving the coupled problem and show that it converges under certain conditions. An application of the proposed methods on biofilm modeling is discussed. The numerical method proposed is adopted to solve the biofilm problem, and we are able to solve the problem with larger thickness of the biofilm than possible before as is shown in the numerical experiments. / Mathematics

Computational biology enabled us to manage vast amount of experimental data and make inferences on observations that we had not made. Among the many methods, predicting metabolic functions with genome-scale models had shown promising results in the recent years. Using sophisticated algorithms, such as flux balance analysis, OptKnock, and OptForce, we can predict flux distributions and design metabolic engineering strategies at a greater efficiency. The caveat of these current methods is the accuracy of the predictions. We proposed using flux balance analysis with flux ratios as a possible solution to improving the accuracy of the conventional methods. To examine the accuracy of our approach, we implemented flux balance analyses with flux ratios in five publicly available genome-scale models of five different organisms, including Arabidopsis thaliana, yeast, cyanobacteria, Escherichia coli, and Clostridium acetobutylicum, using published metabolic engineering strategies for improving product yields in these organisms. We examined the limitations of the published strategies, searched for possible improvements, and evaluated the impact of these strategies on growth and product yields. The flux balance analysis with flux ratio method requires a prior knowledge on the critical regions of the metabolic network where altering flux ratios can have significant impact on flux redistribution. Thus, we further developed the reverse flux balance analysis with flux ratio algorithm as a possible solution to automatically identify these critical regions and suggest metabolic engineering strategies. We examined the accuracy of this algorithm using an Arabidopsis genome-scale model and found consistency in the prediction with our experimental data. / Master of Science

Tesis por compendio / [ES] Saccharomyces cerevisiae, además de ser un importante organismo modelo en biología, es indiscutiblemente la especie de levadura más utilizada en procesos fermentativos industriales, incluyendo el sector enológico. Su capacidad de fermentar en concentraciones elevadas de azúcares, tolerar concentraciones altas de etanol y soportar la adición de sulfitos, son algunos de los factores que explican su éxito en fermentaciones vínicas. El metabolismo fermentativo de S. cerevisiae en condiciones enológicas se conoce bien gracias a una amplia bibliografía científica. En cambio, aún se sabe poco sobre el metabolismo de las especies de Saccharomyces criotolerantes, S. uvarum y S. kudriavzevii, quienes han suscitado recientemente el interés del sector vitivinícola por sus buenas propiedades fermentativas a bajas temperaturas, tales como la producción de vinos con mayor contenido en glicerol y alta complejidad aromática, llegando a veces a reducir su contenido en etanol. En este contexto, esta tesis pretende ampliar nuestros conocimientos sobre el metabolismo fermentativo de S. uvarum y S. kudriavzevii en condiciones enológicas, profundizando en el entendimiento de las diferencias existentes con el de S. cerevisiae, así como entre cepas de S. cerevisiae de distintos orígenes. Para ello, hemos utilizado varias técnicas ómicas para analizar la dinámica de los metabolomas (intra- y extracelulares) y/o transcriptomas de cepas representativas de S. cerevisiae, S. uvarum y S. kudriavzevii a alta (25 °C) y baja (12 °C) temperatura de fermentación. También, hemos desarrollado un modelo metabólico a escala de genoma que, junto a un análisis de balance de flujos, es capaz de cuantificar los flujos a través del metabolismo del carbono y del nitrógeno de levaduras en cultivo de tipo batch. Así, el conjunto de estos trabajos nos ha permitido identificar rasgos metabólicos y/o transcriptómicos relevantes para el sector enológico en estas especies. También se aporta nueva información sobre las especificidades de redistribución de flujos en la red metabólica de levaduras del género Saccharomyces acorde a la especie y las fluctuaciones ambientales que ocurren durante una fermentación vínica. / [CAT] Saccharomyces cerevisiae, a més de ser un important organisme model en biologia, és indiscutiblement l'espècie de llevat més utilitzat en processos fermentatius industrials, incloent el sector enològic. La seua capacitat de fermentar grans concentracions de sucres, tolerar concentracions altes d'etanol i suportar l'addició de sulfits, són alguns dels factors que expliquen el seu èxit en fermentacions víniques. D'aquesta manera, el metabolisme fermentatiu de S. cerevisiae en condicions enològiques està ben descrit i es beneficia d'una àmplia bibliografia científica. En canvi, poc se sap encara sobre el metabolisme de les espècies de Saccharomyces criotolerants, S. uvarum i S. kudriavzevii, els qui han recentment suscitat l'interés del sector vitivinícola per les seues bones propietats fermentatives a baixes temperatures, com ara la producció de vins amb major contingut en glicerol, alta complexitat aromàtica i arribant a vegades a reduir el seu contingut en etanol. En aquest context, aquesta tesi pretén ampliar els nostres coneixements sobre el metabolisme fermentatiu de S. uvarum i S. kudriavzevii en condicions enològiques, aprofundint en l'enteniment de les diferències existents amb el de S. cerevisiae, així també com entre ceps de S. cerevisiae de diferents orígens. Per a això, hem utilitzat diverses tècniques omiques per a analitzar la dinàmica dels metabolomes (intra- i extracelul·lars) i/o transcriptomes de ceps representatius de S. cerevisiae, S. uvarum i S. kudriavzevii a alta (25 °C) i baixa (12 °C) temperatures de fermentació. També, hem desenvolupat un model metabòlic a escala del genoma que, al costat d'una anàlisi de balanç de fluxos, és capaç de quantificar els fluxos a través del metabolisme carbonat i nitrogenat de llevats en cultius de tipus batch. Així, el conjunt d'aquests treballs ens ha permés identificar trets metabòlics i/o transcriptómics rellevants per al sector enològic en aquestes espècies. També aporta nova informació sobre les especificitats de redistribució de fluxos en la xarxa metabòlica de llevats del gènere Saccharomyces concorde a l'espècie i les fluctuacions ambientals ocorrent durant una fermentació vínica. / [EN] Saccharomyces cerevisiae, besides being an important model organism in biology, is undoubtedly the most widely used yeast species in industrial fermentation processes, including the winemaking sector. Its ability to ferment at high levels of sugars, tolerate high ethanol concentrations and withstand the addition of sulfites are some of the factors explaining its success in wine fermentation. Accordingly, the fermentative metabolism of S. cerevisiae under oenological conditions is well described and benefits from a large scientific literature. In contrast, little is known about the metabolism of the cryotolerant Saccharomyces species, S. uvarum and S. kudriavzevii, which have recently attracted the interest of the wine industry for their good fermentative properties at low temperatures, such as the production of wines with higher glycerol content, high aromatic complexity and sometimes even reduced ethanol content. In this context, this thesis aims to expand our knowledge on the fermentative metabolism of S. uvarum and S. kudriavzevii under oenological conditions, deepening our understanding of the existing differences with that of S. cerevisiae, as well as between S. cerevisiae strains of different origins. For this purpose, we have used several omics techniques to analyze the dynamics of the (intra- and extracellular) metabolomes and/or transcriptomes of representative strains of S. cerevisiae, S. uvarum and S. kudriavzevii at high (25 °C) and low (12 °C) fermentation temperatures. Also, we have developed a genome-scale metabolic model that, together with a flux balance analysis, is able to quantify fluxes through carbon and nitrogen metabolism of yeast in batch culture. Taken together, this work has allowed us to identify metabolic and/or transcriptomic traits relevant to the oenological sector in these species. It also provides new information on the specificities of flux redistribution in the metabolic network of Saccharomyces yeasts according to the species and environmental fluctuations occurring during wine fermentation. / The present work has been carried out at the Department of Food Biotechnology of the IATA (CSIC). Romain Minebois was funded by a FPI grant (REF: BES-2016-078202) and supported by projects AGL2015-67504-C3-1R and RTI2018-093744-BC31 of the Ministerio de Ciencia e Inovación awarded to Amparo Querol. / Minebois, RCM. (2021). Study of the differences in the fermentative metabolism of S. cerevisiae, S. uvarum and S. kudriavzevii species [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176018 / Compendio