This study proposes simulations which present optimized methods for producing fatty acids, fatty alcohols and alkanes using Zymomonas mobilis bacterium by the energy efficient β-oxidation reversal pathway, an eco-friendly alternative to the present petroleum based processes. Zymomonas has advantages of higher carbon intake, higher ethanol tolerance and higher ethanol production efficiency than other organisms. I have improved an earlier Zymomonas genome scale model and used Constraint Based Reconstruction and Analysis (COBRA), a linear optimization based computational tool in Matlab, and to perform flux balance analysis (FBA) based simulations. FBA accounts for formation, consumption, accumulation and removal rate or flux of each metabolite. The results present solution spaces of cell growth rate and product formation rate, which trend with products and their carbon chain length. I have analyzed these solution space trends gaining insight into the Zymomonas’ metabolism, enabling efficient product formation and opening a way for future improvement.

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 / TESIS / Compendio

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

Advances in bioinformatics and computational biology have enabled integration of an enormous amount of known biological interactions. This has enabled researchers to use models and data to design experiments and guide new discovery as well as test for consistency. One such computational method is constraint-based metabolic flux modeling. This is performed using genome-scale metabolic models (GEMs) that are a collection of biochemical reactions, derived from a genome's annotation. This type of flux modeling enables prediction of net metabolite conversion rates (metabolic fluxes) to help understand metabolic activities under specific environmental conditions. It can also be used to derive metabolic engineering strategies that involve genetic manipulations. Over the past decade, GEMs have been constructed for several different microbes, plants, and animal species. Researchers have also developed advanced algorithms to use GEMs to predict genetic modifications for the overproduction of biofuel and valuable commodity chemicals. Many of the predictive algorithms for microbes were validated with experimental results and some have been applied industrially. However, there is much room for improvement. For example, many algorithms lack straight-forward predictions that truly help non-computationally oriented researchers understand the predicted necessary metabolic modifications. Other algorithms are limited to simple genetic manipulations due to computational demands. Utilization of GEMs and flux-based modeling to predict in vivo characteristics of multicellular organisms has also proven to be challenging. Many researchers have created unique frameworks to use plant GEMs to hypothesize complex cellular interactions, such as metabolic adjustments in rice under variable light intensity and in developing tomato fruit. However, few quantitative predictions have been validated experimentally in plants. This research demonstrates the utility of GEMs and flux-based modeling in both metabolic engineering and analysis by tackling the challenges addressed previously with alternative approaches. Here, a novel predictive algorithm, Node-Reward Optimization (NR-Opt) toolbox, was developed. It delivers concise and accurate metabolic engineering designs (i.e. genetic modifications) that can truly improve the efficiency of strain development. As a proof-of-concept, the algorithm was deployed on GEMs of E. coli and Arabidopsis thaliana, and the predicted metabolic engineering strategies were compared with results of well-accepted algorithms and validated with published experimental data. To demonstrate the utility of GEMs and flux-based modeling in analyzing plant metabolism, specifically its response to changes in the signaling pathway, a novel modeling framework and analytical pipeline were developed to simulate changes of growth and starch metabolism in Arabidopsis over multiple stages of development. This novel framework was validated through simulation of growth and starch metabolism of Arabidopsis plants overexpressing sucrose non-fermenting related kinase 1.1 (SnRK1.1). Previous studies suggest that SnRK1.1 may play a critical signaling role in plant development and starch level (a critical carbon source for plant night growth). It has been shown that overexpressing of SnRK1.1 in Arabidopsis can delay vegetative-to-reproductive transition. Many studies on plant development have correlated the delay in developmental transition to reduction in starch turnover at night. To determine whether starch played a role in the delayed developmental transition in SnRK1.1 overexpressor plants, starch turnover was simulated at multiple developmental stages. Simulations predicted no reduction in starch turnover prior to developmental transition. Predicted results were experimentally validated, and the predictions were in close agreement with experimental data. This result further supports previous data that SnRK1.1 may regulate developmental transition in Arabidopsis. This study further validates the utility of GEMs and flux-based modeling in guiding future metabolic research. / Ph. D.