Tato diplomová práce se zabývá rekonstrukcí dynamického modelu produkce polyhydroxyalkanoátů (PHA) termofilní bakterií Schlegelella thermodepolymerans. První kapitola poskytuje čtenářům krátký úvod do systémové biologie a matematické teorie grafů. Na ni navazuje druhá kapitola zabývající se různými přístupy v dynamickém modelování, včetně běžně používaných nástrojů pro dynamickou analýzu komplexních systémů. Třetí kapitola pak sleduje další pojmy a možnosti týkající se analýzy modelu. Následující kapitola se zaměřuje na metabolomiku a často používané laboratorní techniky a pátá kapitola je pak věnována polyhydroxyalkanoátům, zejména jejich chemické struktuře a vlastnostem. V kapitole šesté je navržen obecný booleovský model pro produkci PHA termofilními bakteriemi. Kapitola sedmá se poté zaměřuje na zdokonalení modelu se zaměřením na S. thermodepolymerans. Výsledný dynamický model je podroben analýze a výsledky jsou diskutovány.

Indiana University-Purdue University Indianapolis (IUPUI) / Metabolic flux analyses and their more comprehensive forms, genome-scale metabolic networks (GSMNs), have gained tremendous attention in industrial and medical research. Saccharomyces cerevisiae (S. cerevisiae) is one of the organisms that has had its GSMN subjected to multiple frequent updates. The objective of this study is to develop a visualization tool for the GSMN of S. cerevisiae for educational and research purposes. This visualization tool is called the Master Metabolic Map of Saccharomyces cerevisiae (MMMSC). In this study, a metabolic database of S. cerevisiae developed by us was transferred to Cytoscape, a useful and efficient bioinformatics software platform for visualizing molecular networks. After the MMMSC was created, nodes, representing metabolites and enzymes, and edges, representing the chemical reactions that connect the nodes, were curated manually to develop a metabolic visualization map of the whole metabolic system of S. cerevisiae (Figure 4). In the discussion, examples are provided regarding possible applications of MMMSC to predict possible effects of the manipulation of the S. cerevisiae metabolome for industrial and medical purposes. Ultimately, it is concluded that further work is needed to complete the metabolic database of S. cerevisiae and the related MMMSC. In future studies, these tools may be integrated with other omics and other approaches, especially the directed-evolution approach, to increase cost and time efficiency of future research and to find solutions for complex and, thus far, poorly managed environmental and health problems.

Trotz weltweiter Bemühungen, die Tropenkrankheit Malaria zurückzudrängen, erkranken jährlich bis zu einer halben Milliarde Menschen an Malaria mit der Folge von über einer Million Todesopfern. Da zur Zeit eine wirksame Impfung nicht in Sicht ist und sich Resistenzen gegen gängige Medikamente ausbreiten, werden dringend neue Antimalariamittel benötigt. Um die Suche nach neuen Angriffsorten für Medikamente zu unterstützen, untersucht die vorliegende Arbeit mit einem rechnergestützten Ansatz den Stoffwechsel von Plasmodium falciparum, dem tödlichsten Malaria-Erreger. Basierend auf einem aus dem aktuellen Forschungsstand rekonstruierten metabolischen Netzwerk des Parasiten werden metabolische Flüsse für die einzelnen Stadien des Lebenszyklus von P. falciparum berechnet. Dabei wird ein im Rahmen dieser Arbeit entwickelter Fluss-Bilanz-Analyse-Ansatz verwendet, der ausgehend von in den jeweiligen Entwicklungsstadien gemessenen Genexpressionsprofilen entsprechende Flussverteilungen ableitet. Für das so ermittelte stadienspezifische Flussgeschehen ergibt sich eine gute Übereinstimmung mit bekannten Austauschprozessen von Stoffen zwischen Parasit und infiziertem Erythrozyt. Knockout Simulationen, die mit Hilfe einer ähnlichen Vorhersagemethode durchgeführte werden, decken essentielle metabolische Reaktionen im Netzwerk auf. Fast 90% eines Sets von experimentell bestimmten essentiellen Enzymen wird wiedergefunden, wenn die Annahme getroffen wird, dass Nährstoffe nur begrenzt aus der Wirtszelle aufgenommen werden können. Die als essentiell vorhergesagten Enzyme stellen mögliche Angriffsorte für Medikamente dar. Anhand der Flussverteilungen, die für die einzelnen Entwicklungsstadien berechnet wurden, können diese potenziellen Targets nach Relevanz für Malaria Prophylaxe und Therapie sortiert werden, je nachdem, in welchem Stadium die Enzyme als aktiv vorhergesagt wurden. Dies bietet einen vielversprechenden Startpunkt für die Entwicklung von neuen Antimalariamitteln. / Despite enormous efforts to combat malaria, the disease still afflicts up to half a billion people each year, of which more than one million die. Currently no effective vaccine is within sight, and resistances to antimalarial drugs are wide-spread. Thus, new medicines against malaria are urgently needed. In order to aid the process of drug target detection, the present work carries out a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. A comprehensive compartmentalized metabolic network is assembled, which is able to reproduce metabolic processes known from the literature to occur in the parasite. On the basis of this network metabolic fluxes are predicted for the individual life cycle stages of P. falciparum. In this context, a flux balance approach is developed to obtain metabolic flux distributions that are consistent with gene expression profiles observed during the respective stages. The predictions are found to be in good accordance with experimentally determined metabolite exchanges between parasite and infected erythrocyte. Knockout simulations, which are conducted with a similar approach, reveal indispensable metabolic reactions within the parasite. These putative drug targets cover almost 90% of a set of experimentally confirmed essential enzymes if the assumption is made that nutrient uptake from the host cell is limited. A comparison demonstrates that the applied flux balance approach yields target predictions with higher specificity than the topology based choke-point analysis. The previously predicted stage-specific flux distributions allow to filter the obtained set of drug target candidates with respect to malaria prophylaxis, therapy or both, providing a promising starting point for further drug development.

Polihidroxialcanoatos (PHA) são polímeros biodegradáveis, biocompatíveis e podem ser produzidos a partir de matérias primas renováveis. Pseudomonas sp. é capaz de produzir PHA com composição monomérica variada e com teor controlável, o que confere grande variedade de aplicações. Flux Balance Analysis (FBA) foi utilizado no ambiente Matlab pelo uso do software COBRA. Para a análise foi necessário construir um core de Pseudomonas pela modificação dum core de E. coli, e assim fosse possível obter um modelo mais aproximado. Como resultado foi gerada uma rede metabólica viável contendo os genes da via de Weimberg de C. crescentus no core construído. Paralelamente, a Pseudomonas sp. LFM046 foi repicada sucessivas vezes em meio mineral (MM). Testes simples foram feitos para verificar o perfil das colônias isoladas, além de amplificar e sequenciar o gene 16S rDNA. O resultado do BLAST foi uma identidade de 99% com o gene da Pseudomonas sp. IPT 046, confirmando que a bactéria pertence a género Pseudomonas e tem a capacidade de consumir xilose. / Polyhydroxyalkanoates (PHA) are biodegradable, biocompatible polymers and can be produced from renewable raw materials. Pseudomonas sp. is capable of producing PHA with varied monomer composition and with controllable content, which confers a great variety of applications. Flux Balance Analysis (FBA) was used in the Matlab environment by the use of COBRA software. For the analysis, it was necessary to construct a Pseudomonas core by modifying an E. coli core, so that a more approximate model could be obtained. As a result a viable metabolic network was generated containing the C. crescentus Weimberg pathway genes in the constructed core. In parallel, Pseudomonas sp. LFM046 was repeated successively in mineral medium (MM). Simple tests were done to verify the profile of the isolated colonies, in addition to amplifying and sequencing the 16S rDNA gene. The BLAST result was 99% identity with the Pseudomonas sp gene. IPT 046, confirming that the bacterium belongs to the genus Pseudomonas and has the ability to consume xylose.

Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states, which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems. To better harness microorganisms, plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells via multi-omics approaches (e.g., transcriptomics and proteomics analysis). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., metabolic fluxes) of microorganisms. Therefore, in this study, I have applied computational modeling approaches (i.e., 13C assisted pathway and flux analysis, flux balance analysis, and machine learning) to both planktonic and biofilm cells for better understanding intracellular metabolisms and providing valuable biological insights. First, I have summarized recent advances in synergizing 13C assisted pathway and flux analysis and metabolic engineering. Second, I have applied 13C assisted pathway and flux analysis to investigate the intracellular metabolisms of planktonic and biofilm cells. Various biological insights have been elucidated, including the metabolic responses under mixed stresses in the planktonic states, the metabolic rewiring in homogenous and heterologous chemical biosynthesis, key pathways of biofilm cells for electricity generation, and mechanisms behind the electricity generation. Third, I have developed a novel platform (i.e., omFBA) to integrate multi-omics data with flux balance analysis for accurate prediction of biological insights (e.g., key flux ratios) of both planktonic and biofilm cells. Fourth, I have designed a computational tool (i.e., CRISTINES) for the advanced genome editing tool (i.e., CRISPR-dCas9 system) to facilitate the sequence designs of guide RNA for programmable control of metabolic fluxes. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the scientists and engineers to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I will apply 13C assisted pathway analysis to investigate the metabolism of pathogenic biofilm cells for reducing their antibiotic resistance. / Ph. D.

Esta tesis se centra en la construcción y usos de los modelos metabólicos a escala genómica para obtener biocombustibles de manera eficiente, como etanol e hidrógeno. Como organismo objetivo, se ha elegido a la cianobacteria Synechocystis sp. PCC6803. Este organismo ha sido estudiado como una potencial plataforma de producción alimentada por fotones, dada su capacidad de crecer solamente a partir de dióxido de carbono y fotones. Esta tesis versa acerca de los métodos para modelar, analizar, estimar y predecir el comportamiento del metabolismo de las células. La principal meta es extraer conocimiento de los diferentes aspectos biológicos de un organismo con el fin de utilizarlo para un objetivo industrial pertinente. Esta tesis ha sido estructurada en capítulos organizados de acuerdo con las sucesivas tareas que terminan con la construcción de una célula in silico que se comporta, idealmente, como la que está basada en el carbono. Este proceso suele comenzar con los archivos de anotación del genoma y termina con un modelo metabólico a escala genómica capaz de integrar datos -ómicos. El primer objetivo de la presente tesis es la reconstrucción de un modelo del metabolismo de esta cianobacteria que tenga en cuenta todas las reacciones presentes en la misma. Esta reconstrucción tenía que ser lo suficientemente flexible como para permitir el crecimiento en las distintas condiciones ambientales bajo las cuales este organismo crece en la naturaleza, así como permitir la integración de diferentes niveles de información biológica. Una vez que se cumplió este requisito, se pudieron simular variaciones ambientales y estudiar sus efectos desde una perspectiva de sistema. Se han estudiado hasta cinco diferentes condiciones de crecimiento en este modelo metabólico y sus diferencias han sido evaluadas. La siguiente tarea fue definir estrategias de producción para sopesar la viabilidad de este organismo como una plataforma de producción. Se simularon perturbaciones genéticas para e / This thesis is focused on the construction and uses of genome-scale metabolic models to efficiently obtain biofuels, such as ethanol and hydrogen. As a target organism, cyanobacterium Synechocystis sp. PCC6803 was chosen. This organism has been studied as a potential photon-fuelled production platform, for its ability to grow only from carbon dioxide, water and photons. This dissertation verses about methods to model, analyse, estimate and predict the metabolic behaviour of cells. Principal goal is to extract knowledge from the different biological aspects of an organism in order to use it for an industrial relevant objective. This dissertation has been structured in chapters accordingly organized as the successive tasks that end up building an in silico cell that behaves as the carbon-based one. This process usually starts with the genome annotation files and ends up with a genome-scale metabolic model able to integrate ¿omics data. First objective of present thesis is to reconstruct a model of this cyanobacteria¿s metabolism that accounts for all the reactions present in it. This reconstruction had to be flexible enough as to allow growth under the different environmental conditions under which this organism grows in nature as well as to allow the integration of different levels of biological information. Once this requisite was met, environmental variations could be simulated and their effect studied under a system-wide perspective. Up to five different growth conditions were simulated on this metabolic model and differences were evaluated. Following assignment was to define production strategies to weigh this organism¿s viability as a production platform. Genetic perturbations were simulated to design strains with an enhanced production of three industrially-relevant metabolites: succinate, ethanol and hydrogen. Resulting sets of genetic modifications for the overproduction of those metabolites are, thus, proposed. Moreover, functional reactions couplings were studied and weighted to their metabolite production importance. Finally, genome-scale metabolic models allow establishing integrative approaches to include different types of data that help to find regulatory hotspots that can be targets of genetic modification. Such regulatory hubs were identified upon light/dark shifts and general metabolism operational principles inferred. All along this process, blind spots in Synechocystis sp. PCC6803 metabolism, and more importantly, blind spots in our understanding of it, are revealed. Overall, the work presented in this thesis unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean production platform. / Esta tesis es centra en la construcció i els usos del models metabòlics a escala genòmica per a obtenir eficientment biocombustibles, com etanol i hidrogen. Com a organisme diana, s¿elegí el cianobacteri Synechocystis sp. PCC6803. Aquest organisme ha segut estudiat com una plataforma de producció nodrida per fotons, per la seva habilitat per créixer a partir únicament de diòxid de carboni, aigua i fotons. Aquesta tesi versa sobre mètodes per a modelitzar, analitzar, estimar i predir el comportament metabòlic de cèl¿lules. La principal meta és extreure coneixement del diferents aspectes biològics d¿un organisme de manera que s¿usen per a un objectiu industrial rellevant. La tesi ha segut estructurada en capítols organitzats d¿acord a les successives tasques que acaben construint una cèl¿lula in silico que es comporta, idealment, com la que està basada en carboni. Aquest procés generalment comença amb els arxius de l¿anotació del genoma i acaba amb un model metabòlic a escala genòmica capaç d¿integrar dades ¿òmiques. El primer objectiu de la present tesi és la reconstrucció d¿un model del metabolisme d¿aquest cianobacteri que tinga en compte totes les reaccions que hi estan presents. Esta reconstrucció havia de ser prou flexible com per permetre la simulació del creixement en les diferents condicions ambientals en les quals aquest cianobacteri creix en la natura, així com permetre la integració de diferents nivells d¿informació biològica. Una vegada que aquest requisit fou assolit, es pogueren simular variacions ambientals i estudiar els seus efectes amb una perspectiva de sistema. S¿han simulat fins a cinc condicions de creixement en este model metabòlic i les seves diferències han segut avaluades. La següent tasca fou definir estratègies de producció per a valorar la viabilitat d¿aquest organisme com a plataforma de producció. Es simularen pertorbacions genètiques per al disseny de soques amb producció millorada de metabòlits de rellevància industrial: succinat, etanol i hidrogen. Així, es proposen conjunts de modificacions genètiques per a la sobreproducció d¿aquests metabòlits. També s'han estudiat reaccions acoblades funcionalment i s¿ha ponderat la seva importància en la producció de metabòlits. Finalment, els models metabòlics a escala genòmica permeten establir criteris per integrar diferents tipus de dades que ens ajuden a trobar punts importants de regulació. Eixos centres reguladors, que poden ser objecte de modificacions genètiques, han segut investigats baix canvis dràstics d¿il¿luminació i s¿han inferit principis operacionals del metabolisme. Al llarg d'aquest procés, s¿han revelat punts cecs al metabolisme de Synechocystis sp. PCC6803 i, el més important, punts cecs en la nostra comprensió d'aquest metabolisme. En general, el treball presentat en aquesta tesi dona a conèixer les capacitats industrials del cianobacteri Synechocystis sp. PCC6803 per a produir metabòlits d'interès, tot sent una plataforma de producció neta i sostenible. / Montagud Aquino, A. (2012). Modelling and analysis of biological systems to obtain biofuels [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17319 / Palancia

Der Stoffwechsel der Zelle besteht aus chemischen Reaktionen und Transportprozessen, deren Umsatzraten (Stoffflüsse) das Ergebnis genetischer, translationaler und metabolischer Kontrolle sind. Stoffflüsse erlauben daher wertvolle Einblicke in das interne Zellgeschehen, sind jedoch -- wenn überhaupt -- nur unter großem Aufwand experimentell bestimmbar. Ihre Vorhersage mittels mathematischer Modelle ist ebenfalls komplex; vereinfachend wird angenommen, der Stoffwechsel unterliege einer optimalen Regulation, wobei Optimalität vielfältig interpretiert wird. Die in dieser Arbeit entwickelte Methode zur Flussvorhersage basiert auf der Annahme, dass sich die Synthesewege wichtiger Metabolite im Laufe der Evolution optimiert haben und unabhängig voneinander reguliert werden. Dies ermöglicht den Organismen: 1. sich einer variierenden Umgebung schnell anzupassen und 2. Störungen und Schäden auf kleinere Teilsysteme (Module) zu begrenzen. Kern der Methode ist die Vorhersage optimaler Synthese-Module: stationärer Flusszustände, die jeweils nur einen Metaboliten synthetisieren und dabei eine vorgegebene Zielfunktion minimieren oder maximieren. Diese minimalen Flussmoden (\textit{MinModen}) sind schnell und ohne Kenntnis enzymspezifischer Parameter zu berechnen, womit sie sich auch zur systematischen Überprüfung der Synthesekapazität großer Netzwerke eignen. Durch lineare Kombination der MinModen kann das Flussgeschehen komplexer Stoffwechselleistungen abgebildet werden. Hinsichtlich verfügbarer experimenteller Daten ist die Qualität der so gewonnenen Flussvorhersagen vergleichbar mit bisherigen Konzepten, und das, obwohl die Kombination optimaler Synthesen ein suboptimales Gesamtflussgeschehen ergibt. Vorteil der MinModen-Methode ist die flexible Integration zusätzlich verfügbarer Daten. So können beispielsweise durch Berücksichtigung Freier Gibbs-Energien und recherchierter Metabolitkonzentrationsbereiche thermodynamisch zulässige Flusszustände vorhergesagt werden. / The metabolism of a cell consists of chemical transformations and transport processes. Their rates (fluxes) are the result of genetic, translational and metabolic control and therefore carry valuable information about the internal state of a cell. However, metabolic fluxes are hard to determine by experiment and are therefore subject of mathematical prediction methods. In this work, a conceptually new method for the prediction of fluxes in large scale metabolic networks is developed. The method is based on the assumption of optimally evolved synthesis pathways that are regulated independently of each other. This enables organisms: (i) to quickly adapt to a varying and complex environment and (ii) to modularly organize its metabolism in order to restrict internal disturbances and damage to smaller subsystems. The core of this method is the prediction of optimal ``synthesis-modules'''': stationary flux modes, each of which synthesizes a single metabolite while minimizing or maximizing a so-called objective function. These so-called minimal flux modes (MinModes) are rapidly calculable without knowledge of enzyme kinetics. As such they are suited for the determination of the synthesis capacity and the set of blocked reactions of large networks. Linearly combined, they allow for the representation of complex metabolic tasks. In contrast to previous approaches that optimize for the concerted accomplishment of complex metabolic tasks (e.g. biomass formation), optimizing single syntheses results in a rather suboptimal total network flux. However, with respect to available experimental data the prediction quality is comparable to previous (FBA) approaches. As major benefit, the method relies on a flexible structure that allows for the integration of diverse experimentally observed data. Here, incorporating free Gibbs-energy and metabolite concentration values enabled the prediction of thermodynamically feasible flux modes without prior restriction of flux directions.