Dissertations / Theses: 'Flux-balance analysi'

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Favero, Francesco. "Development of two new approaches for NGS data analysis of DNA and RNA molecules and their application in clinical and research fields." Doctoral thesis, Università del Piemonte Orientale, 2019. http://hdl.handle.net/11579/102446.

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The aim of this study is focused on two main areas of NGS analysis data: RNA-seq(with a specific interest in meta-transcriptomics) and DNA somatic mutations detection. We developed a simple and efficient pipeline for the analysis of NGS data derived from gene panels to identify DNA somatic point mutations. In particular we optimized a somatic variant calling procedure that was tested on simulated datasets and on real data. The performance of our system has been compared with currently available tools for variant calling reviewed in literature. For RNA-seq analysis, in this work we tested and optimized STAble, an algorithm developed originally in our laboratory for the de novo reconstruction of transcripts from non reference based RNA-seq data. At the beginning of this study, the first module of STAble was already been written. The first module is the one which reconstructs a list of transcripts starting from RNA-seq data. The aim of this study, particularly, consisted in adding a new module to STAble, developed in collaboration with Cambridge University, based on the flux-balance analysis in order to link the metatranscriptomic analysis to a metabolic approach. This goal has been achieved in order to study the metabolic fluxes of microbiota starting from metatranscriptomic data.

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Gomez, Jose Alberto Ph D. Massachusetts Institute of Technology. "Simulation, sensitivity analysis, and optimization of bioprocesses using dynamic flux balance analysis." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117325.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 301-312).
Microbial communities are a critical component of natural ecosystems and industrial bioprocesses. In natural ecosystems, these communities can present abrupt and surprising responses to perturbations, which can have important consequences. For example, climate change can influence drastically the composition of microbial communities in the oceans, which in turn affects the entirety of the food chain, and changes in diet can affect drastically the composition of the human gut microbiome, making it stronger or more vulnerable to infection by pathogens. In industrial bioprocesses, engineers work with these communities to obtain desirable products such as biofuels, pharmaceuticals, and alcoholic beverages, or to achieve relevant environmental objectives such as wastewater treatment or carbon capture. Mathematical models of microbial communities are critical for the study of natural ecosystems and for the design and control of bioprocesses. Good mathematical models of microbial communities allow scientists to predict how robust an ecosystem is, how perturbed ecosystems can be remediated, how sensitive an ecosystem is with respect to specific perturbations, and in what ways and how fast it would react to environmental changes. Good mathematical models allow engineers to design better bioprocesses and control them to produce high-quality products that meet tight specifications. Despite the importance of microbial communities, mathematical models describing their behavior remain simplistic and only applicable to very simple and controlled bioprocesses. Therefore, the study of natural ecosystems and the design of complex bioprocesses is very challenging. As a result, the design of bioprocesses remains experiment-based, which is slow, expensive, and labor-intensive. With high throughput experiments large datasets are generated, but without reliable mathematical models critical links between the species in the community are often missed. The design of novel bioprocesses rely on informed guesses by scientists that can only be tested experimentally. The expenses incurred by these experiments can be difficult to justify. Predictive mathematical models of microbial communities can provide insights about the possible outcomes of novel bioprocesses and guide the experimental design, resulting in cheaper and faster bioprocess development. Most mathematical models describing microbial communities do not take into account the internal structure of the microorganisms. In recent years, new knowledge of the internal structures of these microorganisms has been generated using highthroughput DNA sequencing. Flux balance analysis (FBA) is a modeling framework that incorporates this new information into mathematical models of microbial communities. With FBA, growth and exchange flux predictions are made by solving linear programs (LPs) that are constructed based on the metabolic networks of the microorganisms. FBA can be combined with the mathematical models of dynamical biosystems, resulting in dynamic FBA (DFBA) models. DFBA models are difficult to simulate, sensitivity information is challenging to obtain, and reliable strategies to solve optimization problems with DFBA models embedded are lacking. Therefore, the use of DFBA models in science and industry remains very limited. This thesis makes DFBA simulation more accessible to scientists and engineers with DFBAlab, a fast, reliable, and efficient Matlab-based DFBA simulator. This simulator is used by more than a 100 academic users to simulate various processes such as chronic wound biofilms, gas fermentation in bubble column bioreactors, and beta-carotene production in microalgae. Also, novel combinations of microbial communities in raceway ponds have been studied. The performance of algal-yeast cocultures and more complex communities for biolipids production has been evaluated, gaining relevant insights that will soon be tested experimentally. These combinations could enable the production of lipids-rich biomass in locations far away from power plants and other concentrated CO 2 sources by utilizing lignocellulosic waste instead. Following reliable DFBA simulation, the mathematical theory required for sensitivity analysis of DFBA models, which happen to be nonsmooth, was developed. Methods to compute generalized derivative information for special compositions of functions, hierarchical LPs, and DFBA models were generated. Significant numerical challenges appeared during the sensitivity computation of DFBA models, some of which were resolved. Despite the challenges, sensitivity information for DFBA models was used to solve for the steady-state of a high-fidelity model of a bubble column bioreactor using nonsmooth equation-solving algorithms. Finally, local optimization strategies for different classes of problems with DFBA models embedded were generated. The classes of problems considered include parameter estimation and optimal batch, continuous steady-state, and continuous cyclic steady-state process design. These strategies were illustrated using toy metabolic networks as well as genome-scale metabolic networks. These optimization problems demonstrate the superior performance of optimizers when reliable sensitivity information is used, as opposed to approximate information obtained from finite differences. Future work includes the development of global optimization strategies, as well as increasing the robustness of the computation of sensitivities of DFBA models. Nevertheless, the application of DFBA models of microbial communities for the study of natural ecosystems and bioprocess design and control is closer to reality.
by Jose Alberto Gomez.
Ph. D.

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Jaques, Colin Mark. "Modelling of metabolic pathways for Saccharopolyspora erythraea using flux balance analysis." Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/1446668/.

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The objective of this thesis is to use metabolic modelling techniques to investigate primary and secondary metabolism in S. erythraea and from this to identify key factors controlling flux distribution during secondary metabolism. S. erythraea is a member of the actinomycetes a group of bacteria responsible for the production of a number of commercially important small molecules. Actinomycete physiology is considerably more complicated than that seen in "simple" bacteria such as E. coli. The conjecture investigated in this thesis is that metabolic modelling techniques that take into account this extra complexity should be more useful in designing strategies for overproduction of desired metabolites than simpler models. The thesis gives the first detailed description of the dynamic changes in biomass composition seen during the batch cultivation of S. erythraea. It further shows that incorporation of this information into a flux balance model of the organism's metabolism significantly improves the flux distributions generated especially in the stationary phase. Using this improved technique growth phase and stationary phase metabolism are investigated. Some of the unusual stationary phase behaviour is shown to be the result of glucose uptake being independent of demand. Rigid control of branch points in the metabolic network is not found suggesting that the organism's metabolism is flexible. A reverse metabolic engineering strategy is applied, two variants of the wild type organism are compared with an industrial strain. The industrial strain is found to have a considerably lower glucose uptake rate than the parental strain. The relationship between TCA cycle flux, oxidative phosphorylation and organic acid secretion is investigated using an uncoupler. This project demonstrates that applied correctly flux balance analysis is a powerful tool for investigating actinomycete physiology. The insights gained are of direct relevance to the commercial production of secondary metabolites in S. erythraea.

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Desouki, Abdelmoneim [Verfasser]. "Algorithms for Improving the Predictive Power of Flux Balance Analysis / Abdelmoneim Desouki." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2016. http://d-nb.info/1125658738/34.

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Guidi, Lionel. "Particle flux transformation in the mesopelagic water column: process analysis and global balance." Diss., Texas A&M University, 2008. http://hdl.handle.net/1969.1/85946.

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Marine aggregates are an important means of carbon transfers downwards to the deep ocean as well as an important nutritional source for benthic organism communities that are the ultimate recipients of the flux. During these last 10 years, data on size distribution of particulate matter have been collected in different oceanic provinces using an Underwater Video Profiler. The cruise data include simultaneous analyses of particle size distributions as well as additional physical and biological measurements of water properties through the water column. First, size distributions of large aggregates have been compared to simultaneous measurements of particle flux observed in sediment traps. We related sediment trap compositional data to particle size (d) distributions to estimate their vertical fluxes (F) using simple power relationships (F=Ad^b). The spatial resolution of sedimentation processes allowed by the use of in situ particle sizing instruments lead to a more detailed study of the role of physical processes in vertical flux. Second, evolution of the aggregate size distributions with depth was related to overlying primary production and phytoplankton size-distributions on a global scale. A new clustering technique was developed to partition the profiles of aggregate size distributions. Six clusters were isolated. Profiles with a high proportion of large aggregates were found in high-productivity waters while profiles with a high proportion of small aggregates were located in low-productivity waters. The aggregate size and mass flux in the mesopelagic layer were correlated to the nature of primary producers (micro-, nano-, picophytoplankton fractions) and to the amount of integrated chlorophyll a in the euphotic layer using a multiple regression technique on principal components. Finally, a mesoscale area in the North Atlantic Ocean was studied to emphasize the importance of the physical structure of the water column on the horizontal and vertical distribution of particulate matter. The seasonal change in the abundance of aggregates in the upper 1000 m was consistent with changes in the composition and intensity of the particulate flux recorded in sediment traps. In an area dominated by eddies, surface accumulation of aggregates and export down to 1000 m occured at mesoscale distances (<100 km).

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Coze, Fabien. "Régulation du métabolisme primaire et biosynthèse d’antibiotiques par la souche d’intérêt industriel Streptomyces." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112323.

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Ce travail décrit l’analyse de la distribution des flux de carbones au sein de deux souches de Streptomyces coelicolor A3(2) : la souche sauvage nommée M145 et son mutant M1146 incapable de produire les antibiotiques actinorhodine, undecylprodigiosine, et l’antibiotique dépendant du calcium. Metabolite Balance Analysis et Isotopomer Balance Analysis sont mis en œuvre pour proposer un modèle de distribution des flux de carbones de S. coelicolor en phase exponentielle de croissance. Les souches M145 et M1146 sont cultivées dans un milieu minimum limitant en azote et leurs comportements métaboliques sont comparés. Dans la souche non productrice M1146, un taux de croissance plus élévé, un flux plus important dans la voie des pentoses phosphates, un flux plus faible au niveau du cycle de Krebs ainsi qu’une activité respiratoire plus faible sont mis en évidence. Cela traduit le coût énergétique important associé à la production d’actinorhodine par M145. De plus, ce travail propose un rôle important de la nicotinamide nucléotide transhydrogénase pour le maintien de l’homéostasie du NADPH lors de la production d’actinorhodine par M145. Comme il existe de bonnes corrélations entre les données expérimentales et celles issues de la modélisation au niveau des bilans carbones, des bilans de pouvoir réducteur et des échanges gazeux, il sera intéressant d’utiliser cette modélisation avec la technique de Flux Balance Analysis pour prédire les variations de la distribution des flux de carbones dans des mutants de S. coelicolor pour lesquels des gènes auraient été sur-exprimés ou délétés
This work describes an analysis of carbon flux distribution in two strains of Streptomyces coelicolor A3(2), namely the wild type strain M145 and its derivative M1146 that is no longer able to produce the antibiotics actinorhodin, undecylprodigiosin and the calcium dependent antibiotic. Metabolite Balance Analysis and Isotopomer Balance Analysis were used to propose a model for carbon flux distribution in S. coelicolor during the exponential phase of growth. Strains M145 and M1146 were grown under nitrogen limitation in minimal medium and their metabolic behaviour were compared. In the non-producing strain M1146, a higher growth rate, a higher flux via the pentose phosphate pathway, a decreased flux through the TCA cycle and a decreased respiratory activity were evidenced. This highlighted the high energetic cost for actinorhodin production in M145. In this paper, we also propose a key role for the nicotinamide nucleotide transhydrogenase in NADPH homeostasis in M145 during actinorhodin production. As there are good correlations between experimental data and the model in terms of carbon balance, reducing power balance and gas exchanges, this model will be of great interest for Flux Balance Analysis to predict carbon-flux distribution changes in S. coelicolor strains in which gene are deleted or overexpressed

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Shabestery, Kiyan. "Metabolisk modellering av butanol produktion i cyanobakterie." Thesis, KTH, Skolan för bioteknologi (BIO), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172095.

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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.

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Chongcharoentaweesuk, Pasika. "Hydrogen production by Rhodobacter sphaeroides and its analysis by metabolic flux balancing." Thesis, University of Manchester, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.603211.

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There is a global need for sustainable, renewable and clean energy sources. Microbial production of hydrogen from renewable carbon sources, biorefinery compounds such as succinic acid or from food and drinks industry waste meets all these criteria. Although it has been studied for several decades, there is still no large scale bio-hydrogen production because the rate and yield of hydrogen production are not high enough to render the process economical. The dependency of biological hydrogen production of incipient light energy is also an important factor affecting economics. In order to improve the prospects of biohydrogen as a renewable and sustainable energy alternative, the genetic and process engineering approaches should be helped and targeted by metabolic engineering tools such as metabolic flux balance analysis. The overall aim of this research was the development of computational metabolic flux balance analysis for the study of growth and hydrogen production in Rhodobacter sphaeroides. The research reported in this thesis had two approaches; experimental and computational. Batch culture experiments for growth and hydrogen production by Rhodobacter sphaeroides were performed with either malate or succinate as carbon source and with glutamate as the nitrogen source. Other conditions investigated included; i) aerobic and anaerobic growth, ii) light and dark fermentation for growth, and iii) continuous light and cycled light/dark conditions for hydrogen production. The best growth was obtained with succinate under anaerobic photoheterotrophic conditions with the maximum specific growth rate of 0.0467 h– 1, which was accompanied with the maximum specific hydrogen production rate of 1.249 mmol(gDW.h)– 1. The range of the photon flux used was 5.457 - 0.080 mmol(gDW.h)– 1. The metabolic flux balance model involved 218 reactions and 176 metabolites. As expected the optimised specific rates of growth and hydrogen production were higher than those of the experimental values. The best prediction was for hydrogen production on succinate with computed specific hydrogen production rates in the range of 2.314 - 1.322 mmol(gDW.h)– 1. Sensitivity analyses indicated that the specific growth rate was affected by the nitrogen source uptake rate under aerobic dark condition whereas the flux of protein formation had the largest effect on the specific growth rate under anaerobic light condition.

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Yen, Jiun Yang. "Systems metabolic engineering of Arabidopsis for increased cellulose production." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/54589.

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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

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Iizuka, Kazuki. "A novel approach to dynamic flux balance analysis that accounts for the dynamic transfer of information by internal metabolites." Thesis, University of York, 2016. http://etheses.whiterose.ac.uk/21661/.

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Understanding the dynamics of information feedback amongst components of complex biological systems is crucial to the success of engineering desirable metabolic phenotypes. Flux Balance Analysis (FBA) is a structural metabolic modelling procedure that allows for local topological constraints to be related to steady-state global behaviors of metabolic systems. A vast majority of biological systems of interest, such as microbial communities, however do not exist under steady-state conditions. Therefore, extending FBA methods to the dynamical setting has been a major challenge to metabolic modelling. In dynamic FBA (dFBA), the representation of feedback dynamics is made possible by combining the methods of FBA with those of Ordinary Differential Equations (ODE). Although numerous dFBA models have been constructed to date, very little effort has gone into the theoretical analysis of how static FBA models and dynamic ODE models should be combined in dFBA. To develop a better understanding of the mathematical structure of dFBA, we investigate the properties of FBA. In order to predict time-derivatives of population growth, every dFBA model must make the assumption that the underlying metabolic network modeled via FBA optimizes a phenotypic function of growth rate. We show however, that under certain circumstances, this requirement introduces a rigid correspondence between growth rate, and a related quantity, the growth yield. The consequence of this is that the dFBA models become rigid in its predictions, effectively becoming a near-static representation of metabolism. In this thesis, we show that this tight correspondence between yield and rate may be broken by combining two inversely related approaches to formulating the FBA problem.

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Pardelha, Filipa Alexandra Guerreiro. "Constraint-based modelling of mixed microbial populations: Application to polyhydroxyalkanoates production." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/13111.

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Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica
The combined use of mixed microbial cultures (MMC) and fermented feedstock as substrate may significantly decrease polyhydroxyalkanoates (PHA) production costs and make them more competitive in relation to conventional petroleum-based polymers. However, there still exists a lack of knowledge at metabolic level that limits the development of strategies to make this process more effective. In this thesis, system biology computational tools were developed and applied to PHA production by MMC from fermented sugar cane molasses, rich in volatile fatty acids (VFA). Firstly, a metabolic network able to describe the uptake of complex mixtures of VFA and PHA production was defined. This metabolic network was applied to metabolic flux analysis (MFA) to describe substrate uptake and PHA production fluxes over the enrichment time of a culture submitted to the feast and famine regimen. Then, the minimization of the tricarboxylic acid cycle (TCA) fluxes was identified as the key metabolic objective of a MMC subjected to this regimen by flux balance analysis (FBA). This model enabled to predict, with an acceptable accuracy, the PHA fluxes and biopolymer composition. Subsequently, data gathered from microautoradiography-fluorescence in situ hybridization (MAR-FISH) was used to develop a segregated FBA model able to predict the flux distribution for the three populations identified in the enriched culture. These results were slightly better than those obtained by the non-segregated FBA and were consistent with MFA results. Finally, a dynamic metabolic model was proposed based on the previous models and on a regulatory factor for VFA uptake and PHA production. This model allowed to identify the dynamics of the process and regulatory factor as well as to validate the previous results. Globally, this thesis enabled to demonstrate the potential of using computational tools to understand and optimize PHA production by MMC.

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Shapiro, Benjamin. "A New Method of Genome-Scale Metabolic Model Validation for Biogeochemical Application." Thesis, University of Oregon, 2017. http://hdl.handle.net/1794/22679.

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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

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Soubeyrand, Eric. "Etude de la régulation par l’azote de la biosynthèse des anthocyanes dans les cellules de vigne, par une approche intégrative." Thesis, Bordeaux 2, 2013. http://www.theses.fr/2013BOR22118/document.

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Les anthocyanes sont une famille de polyphénols très répandus chez les végétaux. Chez la vigne, elles sont responsables de la coloration des baies des cépages rouges, et sont impliquées dans les propriétés organoleptiques des vins. Une nutrition azotée faible induit la production des anthocyanes dans les cellules de la pellicule de raisin des cépages rouges via des mécanismes de régulation qui ne sont pas encore totalement élucidés. Dans ce contexte, nous avons étudié les mécanismes moléculaires impliqués dans la réponse de l’accumulation des anthocyanes pour différents niveaux d’apports azotés. Deux matériels biologiques complémentaires ont été utilisés : des suspensions cellulaires de vigne (lignée GT3) et des plants de Cabernet-Sauvignon, cultivés au vignoble.L’augmentation de la synthèse d’anthocyanes en réponse à la diminution de la nutrition azotée a été confirmée dans les baies et les cellules de vigne en culture. Les analyses transcriptomiques globales (génome complet) et ciblées (qPCR) ont mis en lumière des modifications de l’expression génique, notamment de gènes liés au métabolisme des flavonoïdes, en réponse à la nutrition azotée. L’expression de nombreux gènes structuraux impliqués dans la voie de biosynthèse des anthocyanes est induite par une faible nutrition azotée. La variation de l’apport azoté influence également de façon coordonnée l’expression des gènes régulateurs positifs (facteurs de transcription de type MYB) et négatifs (protéine de type Lateral organ Boundary Domain (LBD)) des gènes de la biosynthèse des flavonoïdes chez la Vigne. L’expression de gènes liés à la production d’énergie (NADH, NADPH), est également affectée.En parallèle, une approche intégrative a été développée sur les suspensions cellulaires, en combinant des mesures d’activités enzymatiques, des dosages de métabolites primaires et secondaires, avec un modèle de balance de flux (Flux Balance Analysis, FBA). Les cartes de flux obtenues prédisent que la diminution de l’apport azoté entraîne une augmentation des flux métaboliques dans la voie du shikimate et des phénylpropanoïdes ; ainsi qu’une répression de la majorité des flux dans les différentes voies du métabolisme primaire, à l’exception de la voie des pentoses phosphates, dont le flux est maintenu, et de la voie de synthèse de l’amidon qui est accrue. Les résultats obtenus plaident en faveur d’un lien fort entre synthèse des anthocyanes et statut énergétique (ATP, NADPH) des cellules vigne
Anthocyanins are polyphenol compounds very abundant in most of the plants. In grapevine, they give color to red berries and they improve red wine quality and increase the organoleptic properties of the wine. Low nitrogen supply stimulates anthocyanin production in berry skin cells of red grape varieties through regulation mechanisms that are far from being fully understood. In this context, we worked on the molecular mechanisms involved in anthocyanin biosynthesis response to nitrogen supply. Two complementary biological materials were used: grapevine cell suspensions (GT3 line) that originate from a teinturier cultivar and produce anthocyanins under normal conditions; and red grape berries of cv. Cabernet-Sauvignon cultivated in a commercial vineyard. Increases of anthocyanins synthesis in response to low nitrogen levels were confirmed in the field-grown berries and the cells suspensions. Both comparative global (microarrays) and targeted (qPCR) transcriptomic analysis showed different regulations on the expression of the genes involved in the secondary (especially the anthocyanin) and nitrogen metabolisms. The expression of most structural genes of the anthocyanin biosynthesis pathway was induced by a low nitrogen supply. Nitrogen controls also the expression of the positive (MYB transcription factors) and negative (Lateral organ Boundary Domain family protein LBD39) regulatory genes of the flavonoid pathway in grapevine. Furthermore, some genes improved in energy production (ATP, NADPH) were affected. In parallel, an integrative approach combining enzymatic activities and primary and secondary metabolites measurements with developing a Flux Balance Analysis (FBA) modeling approach was used on cells suspensions GT3. The flux maps deciphered that low nitrogen increases metabolic fluxes in shikimate and phenylpropanoid pathways and represses the majority metabolic fluxes in different pathways of primary metabolism. The two exceptions included the pentose phosphate pathway, which the flux metabolism was maintained, and the starch synthesis pathway, which was enhanced. The results obtained showed a strong link between anthocyanin synthesis and energy status (ATP, NADPH) in the berry cell suspensions

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Yen, Jiun Yang. "Model-guided Analysis of Plant Metabolism and Design of Metabolic Engineering Strategies." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/85179.

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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.

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Ghozlane, Amine. "Développement de méthodes bioinformatiques dédiées à la prédiction et l'analyse des réseaux métaboliques et des ARN non codants." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14617/document.

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L'identification des interactions survenant au niveau moléculaire joue un rôle crucial pour la compréhension du vivant. L'objectif de ce travail a consisté à développer des méthodes permettant de modéliser et de prédire ces interactions pour le métabolisme et la régulation de la transcription. Nous nous sommes basés pour cela sur la modélisation de ces systèmes sous la forme de graphes et d'automates. Nous avons dans un premier temps développé une méthode permettant de tester et de prédire la distribution du flux au sein d'un réseau métabolique en permettant la formulation d'une à plusieurs contraintes. Nous montrons que la prise en compte des données biologiques par cette méthode permet de mieux reproduire certains phénotypes observés in vivo pour notre modèle d'étude du métabolisme énergétique du parasite Trypanosoma brucei. Les résultats obtenus ont ainsi permis de fournir des éléments d'explication pour comprendre la flexibilité du flux de ce métabolisme, qui étaient cohérentes avec les données expérimentales. Dans un second temps, nous nous sommes intéressés à une catégorie particulière d'ARN non codants appelés sRNAs, qui sont impliqués dans la régulation de la réponse cellulaire aux variations environnementales. Nous avons développé une approche permettant de mieux prédire les interactions qu'ils effectuent avec d'autres ARN en nous basant sur une prédiction des interactions, une analyse par enrichissement du contexte biologique de ces cibles, et en développant un système de visualisation spécialement adapté à la manipulation de ces données. Nous avons appliqué notre méthode pour l'étude des sRNAs de la bactérie Escherichia coli. Les prédictions réalisées sont apparues être en accord avec les données expérimentales disponibles, et ont permis de proposer plusieurs nouvelles cibles candidates
The identification of the interactions occurring at the molecular level is crucial to understand the life process. The aim of this work was to develop methods to model and to predict these interactions for the metabolism and the regulation of transcription. We modeled these systems by graphs and automata.Firstly, we developed a method to test and to predict the flux distribution in a metabolic network, which consider the formulation of several constraints. We showed that this method can better mimic the in vivo phenotype of the energy metabolism of the parasite Trypanosoma brucei. The results enabled to provide a good explanation of the metabolic flux flexibility, which were consistent with the experimental data. Secondly, we have considered a particular class of non-coding RNAs called sRNAs, which are involved in the regulation of the cellular response to environmental changes. We developed an approach to better predict their interactions with other RNAs based on the interaction prediction, an enrichment analysis, and by developing a visualization system adapted to the manipulation of these data. We applied our method to the study of the sRNAs interactions within the bacteria Escherichia coli. The predictions were in agreement with the available experimental data, and helped to propose several new target candidates

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Sun, Chenhao. "Dynamic metabolic studies of C. necator producing PHB from glycerol." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/dynamic-metabolic-studies-of-c-necator-producing-phb-from-glycerol(4b627cd4-c6c9-4f2c-9a2a-2e3da8d4f68a).html.

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The development of human society, which is highly dependent on fossil fuels, is now facing a range of global issues, such as rising energy prices, energy security and climate changes. To successfully tackle the resultant issues, the energy transition from fossil fuels to renewable energy sources, such as solar energy, tide energy, hydroelectric power, geothermal heat and biofuels, is under way. Biodiesel, as an important type of biofuels, has been increasingly produced from vegetable oil or used cooking oil, especially in Europe. Nevertheless, considering the high production cost of biodiesel, there is still much to be done to improve the economics of biodiesel industry. Utilisation of crude glycerol, the main by-product of the biodiesel industry, to produce value-added products appears to be a promising solution. Poly(3-hydroxybutyric acid) (PHB), a biodegradable plastic, can be converted from glycerol by Cupriavidus necator DSM 545 under unbalanced growth conditions, such as nitrogen limitation. One way to enhance the batch production of PHB is to genetically engineer the strain of C. necator, which requires insights of the dynamic impact of extracellular environment on cell phenotypes. Hence in this thesis, we aim to perform metabolic modelling based on experimental measurements to gain a better understanding of the behaviour of the metabolic network of Cupriavidus necator DSM 545 and identify potential bottlenecks of the process. Initially, C. necator DSM 545 is a strain that hardly grows on glycerol, so in a preliminary study, we investigate the process by which the strain was adapted to consume glycerol through serial subcultivation. It is found that the adaptation can be achieved within 15 cell generations over three passages in basal mineral medium, and the acquired phenotype is sufficiently stable upon further passage. The study of metabolism started with the reconstruction of the cell's metabolic network, followed by a thermodynamic analysis to check the feasibility and reversibility of all the biochemical reactions included. Then the static flux balance analysis was extended and applied to analyse the shift of metabolic states during the microbial fermentation in different batch conditions. The resulting patterns of flux distribution reveal the TCA cycle to be the major competitor for PHB synthesis at the ACCoA node. Cells have the potential to enter an efficient PHB-production phase that features minimal TCA/PHB flux split ratio, and the length of the phase can be manipulated by aeration. Although low aeration rate favours optimal flux split ratio, such condition that limits respiration also limits nutrient uptake, leading to low PHB productivity overall. To identify the actual limiting factors of PHB synthesis in the system, we further performed metabolic control analysis based on the calculated flux distributions. The analysis demonstrated how the distribution of the metabolic control can vary widely, depending on the aeration conditions used and the flux split ratios. Glycerolipid pathway, glycolysis, PHB metabolism, as well as the electron transport chain are revealed to be potential engineering targets as they contribute to the great majority of the positive control of PHB flux.

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Guo, Weihua. "Computational Modeling of Planktonic and Biofilm Metabolism." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79669.

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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.

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Křápková, Monika. "Dynamický model produkce polyhydroxyalkonoátů termofilní bakterií S. thermodepolymerans." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442582.

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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.

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Norris, Shaun W. "A Pipeline for Creation of Genome-Scale Metabolic Reconstructions." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4667.

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The decreasing costs of next generation sequencing technologies and the increasing speeds at which they work have lead to an abundance of 'omic datasets. The need for tools and methods to analyze, annotate, and model these datasets to better understand biological systems is growing. Here we present a novel software pipeline to reconstruct the metabolic model of an organism in silico starting from its genome sequence and a novel compilation of biological databases to better serve the generation of metabolic models. We validate these methods using five Gardnerella vaginalis strains and compare the gene annotation results to NCBI and the FBA results to Model SEED models. We found that our gene annotations were larger and highly similar in terms of function and gene types to the gene annotations downloaded from NCBI. Further, we found that our FBA models required a minimal addition of transport reactions, sources, and escapes indicating that our draft pathway models were very complete. We also found that on average our solutions contained more reactions than the models obtained from Model SEED due to a large amount of baseline reactions and gene products found in ASGARD.

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Hoffmann, Sabrina. "Minimale Flussmoden als theoretisches Konzept für die funktionelle Analyse und modulare Beschreibung zellulärer Stoffwechselnetzwerke." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16444.

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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.

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Huthmacher, Carola. "Applying systems biology methods to identify putative drug targets in the metabolism of the malaria pathogen Plasmodium falciparum." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2010. http://dx.doi.org/10.18452/16256.

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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.

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Hawari, Aliah H. "Metabolic modelling of tomato fruit ripening." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:a5e98a25-cdfa-4371-8d08-e6305c61f517.

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Tomatoes are the fourth most valuable commodity in agriculture after rice, wheat and soybeans globally with 151 million tonnes of fruit being produced in 2012. The tomato fruit is also a model system for fleshy fruit development. During ethylene-regulated fruit ripening there are complex changes in fruit chemical composition due to degradation and synthesis of a number of soluble and volatile metabolites. Ultimately, these changes control the composition of the ripe fruit and dictate its flavour and texture. It is known that ripening can proceed when mature green fruit are removed from the plant (and indeed this is standard commercial practice) but the extent to which metabolic changes are sustained when fruit are ripened in this way has yet to be established. A modelling approach such as constraints-based modelling can provide system-level insights into the workings of the complex tomato metabolic network during ripening. The first aim of this thesis was therefore to construct a genome-scale metabolic network model for tomato and to use this model to explore metabolic network flux distributions during the transitions between the stages of fruit ripening. The flux distributions predicted provided insight into the production and usage of energy and reductants, into routes for climacteric CO₂ release, and the metabolic routes underlying metabolite conversions during ripening. The second aim of this thesis was to use the model to explore metabolic engineering strategies for increased production of lycopene in tomato fruit. The model predictions showed that rearrangement of dominant metabolic fluxes were required to cope with the increased demand for reductants at high lycopene accumulation, which came at a cost of a lower accumulation of other secondary metabolites. Overall the thesis provides an approach to connect underlying metabolic mechanisms to the known metabolic processes that happen during ripening.

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Morales, Pérez Yeimy Liceth. "Constraint-based metabolic models and their application in industrial biotechnology." Doctoral thesis, Universitat de Girona, 2016. http://hdl.handle.net/10803/394040.

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This thesis is focused on the application of small constraint-based models to analyze and predict the behavior of wild type and modified strains of Pichia pastoris. The presented work deals with the common limitations that industrial environment imposes: measurements are scarce, models are not detailed, the modelled organisms are not always well-known and, in most cases, they are genetically modified. The results have been divided in three articles. The first presents the validation of a small FBA (flux balance analysis) model of unmodified P. pastoris cells, based on the assumption of “maximizing growth” as evolved biological objective for the cells. The model has been validated in heterogeneous experimental situations. In the second article, I exploit a feature of constraint-based models: they are easily extendable.In particular, the FBA model has been extended to represent and predict the behavior of genetically modified cells of P. pastoris producing a recombinant protein. The new model represents the energetic requirements of the protein production process, and also the impact that protein production has over the cells growth. The model predictions for growth and even for protein production have been validated against multiple experimental datasets. Finally, a software toolbox is presented. It implements two MFA-wise methods to get estimations from small, constraint-based models in uncertain scenarios. These implementations simplify and extend the application of MFA (Metabolic flux analysis) when measurements are scarce and imprecise. The thesis is an application of small, constraint-based models to P. pastoris. It illustrates how these models can be a valuable tool to analyze, estimate or predict the behavior of unmodified and modified P. pastoris cells. The approaches followed in this work account for some of the limitations of industrial environments, and thus, they may be of use when modelling other microorganisms of industrial interest.
Esta tesis está enfocada en la aplicación de pequeños modelos basados en restricciones con el fin de analizar y predecir el comportamiento de cepas salvajes y genéticamente modificadas de Pichia pastoris. El trabajo presentado afronta las limitaciones comunes que los ambientes industriales imponen: las mediciones son escasas, los modelos no son detallados, los organismos modelados no son siempre bien conocidos y en muchos casos han sido modificados genéticamente. Los resultados han sido divididos en tres artículos. El primero presenta la validación de un pequeño modelo FBA (flux balance analysis) para organismos no modificados de P.pastoris basado en la suposición de ‘’maximizar el crecimiento’’ como objetivo biológico de la evolución de las células. El modelo ha sido validado en situaciones experimentales heterogéneas. En el segundo artículo, he explotado una característica de los modelos basado en restricciones: estos modelos se pueden ampliar para representar y predecir el comportamiento de células genéticamente modificadas de P. pastoris produciendo una proteína recombinante. El nuevo modelo representa los requerimientos energéticos del proceso de producción de proteína, además del impacto que tiene este proceso sobre el crecimiento celular. Las predicciones del modelo para crecimiento e incluso para la producción de proteína han sido validadas usando múltiples conjuntos de medidas experimentales. Finalmente, se presenta una herramienta software. Esta implementa dos métodos MFA-wise para obtener estimaciones de pequeños modelos basados en restricciones en escenarios con incertidumbre. Esta implementación facilita y extiende la aplicación de MFA (Metabolic flux analysis) cuando las mediciones son escasas e imprecisas. La tesis es una aplicación de pequeños modelos basados en restricciones a P. pastoris. Esta ilustra cómo estos modelos pueden ser una herramienta útil para analizar, estimar o predecir el comportamiento de células de P. pastoris modificadas o salvajes. Los enfoques seguidos en este trabajo consideran algunas de las limitaciones de ambientes industriales y en consecuencia, estos tal vez pueden ser de uso cuando se modelen otros organismos de interés industrial.

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Montagud, Aquino Arnau. "Modelling and analysis of biological systems to obtain biofuels." Doctoral thesis, Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/17319.

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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

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Hedfors, Jim. "Force Budget Analysis of Glacier Flow : Ice Dynamical Studies on Storglaciären, Sweden, and Ice Flow Investigations of Outlet Glaciers in Dronning Maud Land, Antarctica." Doctoral thesis, Uppsala University, Department of Earth Sciences, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4219.

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This thesis contributes to the understanding of glacier response to climate change by ice dynamical studies on Storglaciären, Sweden, and Bonnevie-Svendsenbreen, Kibergbreen and Plogbreen in Dronning Maud Land, Antarctica. Ice surface velocities, ice geometry and temperature information is fed through a force budget model to calculate ice mass outflux of these glacial systems via three-dimensional stress distributions for a flux-gate.

Field data were collected through repeated DGPS and GPR observations on Storglaciären between July 2000 to September 2001 and on Kibergbreen and Plobreen during the SWEDARP 2002/03 expedition to Antarctica. The work was strongly supported by remotely-sensed information.

The results from Storglaciären show a strength in the force budget model to discern both spatial and temporal variability in ice dynamical patterns. It highlights the influence of seasonality and bedrock topography upon glacier flow. A modeling experiment on Bonnevie-Svendsenbreen suggested that ice temperature increases substantially under conditions of high stress (≥0.4 MPa) due to strain-heating. This provides a positive feedback loop, increasing ice deformation, as long as it overcomes the advection of cool ice from the surface. These results explain, to some extent, the mechanism behind fast flowing ice streams. Mass flux caclulations from Bonnevie-Svendsenbreen suggest that the outflux given from force budget calculations can be used as a gauge for influx assuming steady state conditions. Plogbreen receives an influx of 0.48±0.1 km³ a^-1 and expedites a discharge volume of 0.55±0.05 km³ a^-1. This indicative negative mass balance is explained by a falling trend in upstream accumulation and the recent rise in global sea level, as it is likely to induce glacier acceleration due to a reduction in resistive forces at the site of the gate. This result is comparable with other Antarctic studies reporting negative mass balances, e.g. from WAIS, as caused by changes in the global atmospheric circulation pattern.

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Rezgui, Cyrine. "Etude du potentiel d'introduction de la culture du pois d'hiver dans les successions culturales en Normandie : conséquences sur les communautés microbiennes du sol et les flux d'azote Impacts of the winter pea crop (instead of rapeseed) on soil microbial communities, nitrogen balance and wheat yield Quantification et analyse des exsudats racinaires de pois, de blé et de colza : mise au point d’une méthodologie de collecte des exsudats racinaires N rhizodeposition quantification and root exudates characterization of pea (Pisum Sativum L.), rapeseed (Brassica napus L.) and wheat (Triticum aestivum L.) under controlled conditions Linking soil microbial community to C and N dynamics during crop residues decomposition." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMR047.

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La transition agroécologique vise la triple performance agronomique, écologique et sociétale des exploitations agricoles. Un certain nombre de pratiques agricoles permet d’envisager la construction et le développement de systèmes de culture répondant à ces contraintes. Les légumineuses, par leur capacité à fixer l’azote atmosphérique, sont une alternative intéressante aux intrants azotés. Outre l’absence de fertilisation lors de leur culture, elles fournissent de l’azote à la culture suivante. Il existe cependant un manque de références sur certaines légumineuses à graines et notamment la culture du pois d’hiver. En effet, si des données acquises dans différentes régions françaises sont disponibles, aucune référence n’a été publiée pour la Normandie où la culture du pois d’hiver connaît un récent regain d’intérêt. Cette thèse propose d’évaluer, sur une période de deux ans, l’effet du remplacement du colza par le pois d’hiver en tête de rotation en réalisant une analyse comparative de ces deux successions (pois d’hiver-blé et colza-blé). L’objectif était d’évaluer l’effet de ce changement de tête de rotation (pois d’hiver vs colza) sur l’état biologique du sol et les flux d’azote à différentes échelles spatiotemporelles. Les résultats ont révélé une forte variabilité spatio-temporelle dans la réponse des communautés microbiennes du sol, et mis en évidence l’importance du contexte pédoclimatique dans le déterminisme de l’abondance et de l’activité des communautés microbiennes du sol. Ils ont montré par ailleurs, l’effet positif du pois d’hiver sur la disponibilité de l’azote minéral au cours du cycle cultural et pour les cultures suivantes, ici le blé puis l’orge. Les apports d’azote minéral dans le sol lié à la contribution des parties racinaires via la rhizodéposition et à la dégradation des résidus de culture après récolte ont été évalués au cours de ce travail de thèse. En effet, si la rhizodéposition s’est révélée plus importante sous pois d’hiver, elle n’a pas eu d’impact significatif sur les communautés microbiennes rhizosphériques. Contrairement à ces observations, la dégradation des résidus de culture a significativement modifié la composition des communautés bactériennes en lien avec leur composition biochimique initiale. La succession culturale incluant le pois a enrichi le sol en azote minéral mais des risques de perte d’azote par lixiviation de l’ordre de 23 kg N. ha-1 ont été estimés. Ces constats soulignent l’importance d’adapter la conduite des systèmes de culture incluant le pois d’hiver, en limitant les pertes d’azote par lixiviation et en maximisant son utilisation par les cultures suivantes. Les résultats de ces travaux ont confirmé la diminution des quantités d’engrais azoté utilisées dans la succession contenant le pois, sans préjudice de productivité, ni pour le pois, ni pour la culture suivante, ici, le blé. Finalement, introduire le pois d’hiver dans la rotation de culture en région Normandie, semble permettre de répondre à la problématique d’augmentation du coût des intrants, et aux enjeux de transition agroécologique et d’autonomie protéique régionale
The agroecological transition targets triple agronomic, ecological, and societal performance of farms. Some new agricultural practices had emerged to develop a new cropping system to respond to these constraints. Legumes constitute an interesting alternative. Indeed, legumes are advantageous for soils due to their symbiotic relationship with nitrogen-fixing bacteria. The presence of compatible rhizobia combined to nitrogen-limited conditions promotes symbiosis which is the most efficient way for legumes to acquire more nitrogen. Compared with non-nodulated plants, symbiosis provides a competitive advantage by increasing soil nitrogen pool. However, some grain legumes, notably winter pea, are rarely studied, especially in the Normandy region where no reference has been published for this crop. The objective of this study is to compare two crop successions for a period of two years (winter pea-wheat and rapeseed-wheat), in order to assess the effect of replacing rapeseed by winter pea at the head of the rotation .We evaluated the effect of these two crops (winter pea vs rapeseed) on the biological state of the soil and nitrogen fluxes at different spatio-temporal scales. The results showed a significant spatio-temporal effect on the response of soil microbial communities and highlighted the importance of the pedoclimatic context in determining the abundance and activity of soil microbial communities. A positive effect of winter pea has been demonstrated on the availability of mineral nitrogen during the crop cycle and for following crops (wheat and barley). The supply of nitrogen to the soil is linked to the rhizodeposition of nitrogen via plants roots and the degradation of crop residues after harvest. Our results showed that winter pea exhibited the greatest amount of nitrogen rhizodeposition. However, rhizodeposition did not have a significant impact on rhizospheric microbial communities. Contrary to these observations, the degradation of crop residues significantly modified the composition of bacterial communities linked to their initial biochemical composition. Crop succession including winter pea enriched the soil with mineral nitrogen but simulation with STICS software revealed a nitrogen leaching around of 23 kg N. ha-1 during the cropping cycle. These findings underline the importance of adapting an adequate crop management system, including winter pea, to limit nitrogen losses. The results showed also that wheat yields after winter pea without the use of nitrogen fertilizers were equivalent to those obtained after rapeseed. However, rapeseed required significant nitrogen fertilization. Including winter pea in crop rotation in Normandy region may be a key to enhance productivity, to respond to the challenges of agroecological transition, regional protein autonomy, and to reduce environmental and economic costs, by reducing notably, the costs of fertilizers production and uses

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Raja, Farhan. "Flux Balance Analysis of Plasmodium falciparum Metabolism." Thesis, 2010. http://hdl.handle.net/1807/25900.

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Plasmodium falciparum is the causative agent of malaria, one of the world‟s most prevalent infectious diseases. The emergence of strains resistant to current therapeutics creates the urgent need to identify new classes of antimalarials. Here we present and analyse a constraints-based model (iMPMP427) of P. falciparum metabolism. Consisting of 427 genes, 513 reactions, 457 metabolites, and 5 intracellular compartments, iMPMP427 is relatively streamlined and contains an abundance of transport reactions consistent with P. falciparum’s observed reliance on host nutrients. Flux Balance Analysis simulations reveal the model to be predictive in regards to nutrient transport requirements, amino acid efflux characteristics, and glycolytic flux calculation, which are validated by a wealth of experimental data. Furthermore, enzymes deemed to be essential for parasitic growth by iMPMP427 lend support to several previously computationally hypothesized metabolic drug targets, while discrepancies between essential enzymes and experimentally annotated drug targets highlight areas of malarial metabolism that could benefit from further research.

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CHANG, SHAO-CHUAN, and 張劭銓. "Flux balance analysis predicts Warburg-like effects of hepatocyte deficient." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4de5v8.

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碩士
國立中正大學
化學工程研究所
106
This study reconstructs the genome-scale metabolic model of hepatocytes. The reconstruction of this specific GEM is based on the generic human metabolic network model Recon2.2, and the proteomics data of hepatocytes from the HPA database. This study establishes a optimization formulation that integrates the Warburg hypothesis and the genome-scale metabolic model of hepatocytes to detect the oncogene that induce metabolic disorders in hepatocytes. A nested hybrid differential evolution (NHDE) algorithm is employed to solve the optimization problem. Minimum nutrients requirement analysis is used to calculate the minimum uptake of nutrients that required for the model. This study discusses the effect of nutrients on the incidence of cancer. The results predict that DDC (dopa decarboxylase) is overexpressed in HCC. DDC demonstrates high similarity ratio of 89% to the biological hypothesis of the Warburg effect. L-Dopa and 4-methyl-2-oxopentanoic acid are essential nutrients for DDC. The results show that genes have different expression under different nutrients.

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Xu, Xiaopeng. "Flux Balance Analysis of Escherichia coli under Temperature and pH Stress Conditions." Thesis, 2015. http://hdl.handle.net/10754/552665.

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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.

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Govindarajan, Srinath Garg. "Model Based Prediction of Physiology of G. sulfurreducens by Flux Balance and Thermodynamics Based Metabolic Flux Analysis Approaches." Thesis, 2009. http://hdl.handle.net/1807/18313.

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The development of genome scale metabolic models have been aided by the increasing availability of genome sequences of microorganisms such as Geobacter sulfurreducens, involved in environmentally relevant processes such as the in-situ bioremediation of U(VI). Since microbial activities are the major driving forces for geochemical changes in the sub-surface, understanding of microbial behavior under a given set of conditions can help predict the likely outcome of potential subsurface bioremediation strategies. Hence, a model based lookup table was created to capture the variation in physiology of G. sulfurreducens in response to environmental perturbations. Thermodynamically feasible flux distributions were generated by incorporating thermodynamic constraints in the model. These constraints together with the mass balance constraints formed the thermodynamics based metabolic flux analysis model (TMFA). Metabolomics experiments were performed to determine the concentration of intracellular metabolites. These concentrations were posed as constraints in the TMFA model to improve the model accuracy.

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Tang, Weng-Keong, and 鄧永強. "Flux Balance Analysis for Improving Product Bioethanol by Pichia Stipites CBS6054." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/6mxctf.

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碩士
國立中正大學
化學工程研究所
103
Bioethanol, ethanol, which is converted from the sugar in biomass by microbial fermentation. The raw material of bioethanol production is roughly divided into sugar, starch material and fibrous material. Pentose and hexose can be obtained by the hydrolysis of fibrous material (mostly xylose and glucose).Such as bagasse, which is the waste after sugar production, is composed of lignocellulose mainly, containing cellulose (about 38 to 50%), hemicellulose (about 23 to 32%), lignin (about 15 to 25%), and then cellulose and hemicellulose can be hydrolyzed to xylose and glucose.This study simulate Pichia stipitis CBS6054 ethanol production by metabolic network model iTL885, using Flux Balance Analysis(FBA) to calculate the ethanol productivity by P. stipitis utilizing glucose and xylose. As a result of simulation, the uptake flux of glucose and xylose are 15.37 mmol/g dry weight/h and 2.87 mmol/g dry weight/h respectively, the production flux of ethanol is 21.14 mmol/g dry weight/h when glucose and xylose coexisting, conform the property of P. stipitis that is first using glucose to ferment, until the glucose had been consumed, so we can trust the model iTL885 is collect.

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Ramos, João Rodrigues Correia. "Analysis of metabolic flux distributions in relation to the extracellular environment in Avian cells." Master's thesis, 2015. http://hdl.handle.net/10362/15449.

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Continuous cell lines that proliferate in chemically defined and simple media have been highly regarded as suitable alternatives for vaccine production. One such cell line is the AG1.CR.pIX avian cell line developed by PROBIOGEN. This cell line can be cultivated in a fully scalable suspension culture and adapted to grow in chemically defined, calf serum free, medium [1]–[5]. The medium composition and cultivation strategy are important factors for reaching high virus titers. In this project, a series of computational methods was used to simulate the cell’s response to different environments. The study is based on the metabolic model of the central metabolism proposed in [1]. In a first step, Metabolic Flux Analysis (MFA) was used along with measured uptake and secretion fluxes to estimate intracellular flux values. The network and data were found to be consistent. In a second step, Flux Balance Analysis (FBA) was performed to access the cell’s biological objective. The objective that resulted in the best predicted results fit to the experimental data was the minimization of oxidative phosphorylation. Employing this objective, in the next step Flux Variability Analysis (FVA) was used to characterize the flux solution space. Furthermore, various scenarios, where a reaction deletion (elimination of the compound from the media) was simulated, were performed and the flux solution space for each scenario was calculated. Growth restrictions caused by essential and non-essential amino acids were accurately predicted. Fluxes related to the essential amino acids uptake and catabolism, the lipid synthesis and ATP production via TCA were found to be essential to exponential growth. Finally, the data gathered during the previous steps were analyzed using principal component analysis (PCA), in order to assess potential changes in the physiological state of the cell. Three metabolic states were found, which correspond to zero, partial and maximum biomass growth rate. Elimination of non-essential amino acids or pyruvate from the media showed no impact on the cell’s assumed normal metabolic state.

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Song, Carl Yulun. "Genome-scale Metabolic Network Reconstruction and Constraint-based Flux Balance Analysis of Toxoplasma gondii." Thesis, 2012. http://hdl.handle.net/1807/33538.

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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.

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Chi, Pei-wen, and 紀佩文. "Application of Flux Balance Analysis on Ethanol Fermentation Process Utilizing both Glucose and Xylose." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/e583pd.

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碩士
國立臺灣科技大學
化學工程系
99
Flux balance analysis (FBA) is a popular approach which is used extensively for predicting cellular growth and product secretion patterns in microbial systems. A dynamic flux balance model based on a genome-scale metabolic network reconstruction is developed for in silico analysis of Saccharomyces cerevisiae fermentation. The model couples a detailed steady-state description of primary carbon metabolism with dynamic mass balances on key extracellular species. In addition, model-based dynamic optimization is performed to determine fed-batch operation policies that maximize ethanol productivity on glucose and xylose. Therefore, A optimized fed-batch process designed to maintain a low level of glucose throughout the course of xylose conversion provide ethanol productivity of 1.3281 g ethanol/L/hr and 1.9526 g ethanol/L/hr in anaerobic and aerobic conditions, respectively. The analysis results suggest that enhancements in biomass yield are most beneficial for the increase of fed-batch ethanol productivity in aerobic fermentation.

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Correia, Gonçalo dos Santos 1989. "Coupling metabolic footprinting and flux balance analysis to predict how single gene knockouts perturb microbial metabolism." Master's thesis, 2012. http://hdl.handle.net/10451/7524.

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Tese de mestrado. Biologia (Bioinformática e Biologia Computacional). Universidade de Lisboa, Faculdade de Ciências, 2012
The model organisms Caenorhabditis elegans and E. coli form one of the simplest gut microbe host interaction models. Interventions in the microbe that increase the host longevity including inhibition of folate synthesis have been reported previously. To find novel single gene knockouts with an effect on lifespan, a screen of the Keio collection of E. coli was undertaken, and some of the genes found are directly involved in metabolism. The next step in those specific cases is to understand how these mutations perturb metabolism systematically, so that hypotheses can be generated. For that, I employed dynamic Flux Balance Analysis (dFBA), a constraint-based modeling technique capable of simulating the dynamics of metabolism in a batch culture and making predictions about changes in intracellular flux distribution. Since the specificities of the C. elegans lifespan experiments demand us to culture microbes in conditions differing from most of the published literature on E. coli physiology, novel data must be acquired to characterize and make dFBA simulations as realistic as possible. To do this exchange fluxes were measured using quantitative H NMR Time-Resolved Metabolic Footprinting. Furthermore, I also investigate the combination of TReF and dFBA as a tool in microbial metabolism studies. These approaches were tested by comparing wild type E. coli with one of the knockout strains found, ΔmetL, a knockout of the metL gene which encodes a byfunctional enzyme involved in aspartate and threonine metabolism. I found that the strain exhibits a slower growth rate than the wild type. Model simulation results revealed that reduced homoserine and methionine synthesis, as well as impaired sulfur and folate metabolism are the main effects of this knockout and the reasons for the growth deficiency. These results indicate that there are common mechanisms of the lifespan extension between ΔmetL and inhibition of folate biosynthesis and that the flux balance analysis/metabolic footprinting approach can help us understand the nature of these mechanisms.
Os organismos modelo Caenorhabditis elegans e E. coli formam um dos modelos mais simples de interacções entre micróbio do tracto digestivo e hospedeiro. Intervenções no micróbio capazes de aumentar a longevidade do hospedeiro, incluindo inibição de síntese de folatos, foram reportadas previamente. Para encontrar novas delecções génicas do micróbio capazes de aumentar a longevidade do hospedeiro, a colecção Keio de deleções génicas de E. coli foi rastreada. Alguns dos genes encontrados participam em processos metabólicos, e nesses casos, o próximpo passo é perceber como as deleções perturbam o metabolismo sistémicamente, para gerar hipóteses. Para isso, utilizo dynamic Flux Balance Analysis (dFBA), uma técnica de modelação metabólica capaz de fazer previsões sobre alterações na distribuição intracelular de fluxos. As especificidades das experiências de tempo de vida em C.elegans obrigam-nos a trabalhar em condições diferentes das usadas na maioria da literatura publicada em fisiologia de E. coli, e para dar o máximo realismo às simulações de dFBA novos dados foram adquiridos, utilizando H NMR Time-Resolved Metabolic Footprinting para medir fluxos de troca de metabolitos entre microorganismo e meio de cultura. A combinação de TReF e dFBA como ferramenta de estudo do metabolism microbiano é também investigada. Estas abordagens foram testadas ao comparar E. coli wild-type com uma das estirpes encontradas no rastreio, ΔmetL, knockout do gene metL, que codifica um enzima bifunctional participante no metabolismo de aspartato e treonina, e que exibe uma taxa de crescimento reduzida comparativamente ao wild-type. Os resultados das simulações revelaram que os principais efeitos da deleção deste gene, e as razões para a menor taxa de crescimento observada, são a produção reduzida de homoserina e metionina e os efeitos que provoca no metabolismo de folatos e enxofre. Estes resultados indicam que há mecanismos comuns na extensão da longevidade causada por esta deleção e inibição de síntese de folatos, e que a combinação metabolic footprinting/flux balance analysis pode ajudar-nos a compreender a natureza desses mecanismos.

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Khazaei, Tahmineh. "Ensemble Modeling of Cancer Metabolism." Thesis, 2011. http://hdl.handle.net/1807/30649.

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Metabolism in cancer cells is adapted to meet the proliferative needs of these cells, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. A metabolic network consisting of 58 reactions is considered which accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation. Experimentally measured metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinate-CoA ligase (SUCOAS1m) to display a significant reduction in growth rate when repressed relative to currently known drug targets. Furthermore, the synergetic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) showed a three fold decrease in growth rate compared to the repression of single enzyme targets.

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Yang, Laurence. "A Bilevel Optimization Algorithm to Identify Enzymatic Capacity Constraints in Metabolic Networks - Development and Application." Thesis, 2008. http://hdl.handle.net/1807/10443.

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Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1,155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. Capacity constraints identified using experimental fitness profiles with OCCI generated novel hypotheses, while integrating thermodynamics-based metabolic flux analysis allowed prediction of metabolite concentrations.

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Santos, Cíntia Dione Carvalho dos. "In silico culture media costumization for Human Embrionic Kidney 293 Cells." Master's thesis, 2020. http://hdl.handle.net/10362/100218.

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Human Embryonic Kidney (HEK-293) cells have gained substantial interest in cell biology research and biotechnology. Still, there are not many studies in the literature on the specificities of the nutritional requirements and culture media composition to grow HEK-293 cells in comparison to other mammalian cells. As the development of a custom culture media for a specific cell line is, among many factors, dependent on the complexity of cellular physiology, a better understanding of the cellular metabolism can help to predict the response on a particular culture medium composition. With the goal to determine the culture medium composition on cellular expansion of HEK293 cells and develop a rational in silico design methodology of culture medium composition, a metabolic network for HEK-293 cells was established, based on the human genome scale model Recon-2. The metabolic model comprises 327 biochemical reactions and 341 metabolites.In order to validate the HEK-293 metabolic model, flux balance analysis (FBA) and flux variability analysis (FVA) methods were performed in MATLAB based on a constrained nonlinear program. The computed flux distributions and respective 95% confidence intervals were compared/validated with experimental data from the literature. The computed flux distributions for the first and second metabolic phases were highly concordant with literature values (explained variance > 85%). In a few cases, significative differences were observed. These exceptions were analyzed in detail. An hypothetical minimum consumption medium scenario was computed, where substrates uptake are minimized concomitantly to the minimization of toxic by-products formation. Results demonstrate that 100% of lactate and ammonia production can be ceased as a consequence of glucose and non-essential amino-acids (AA) intake reduction. However, reduction of byproduct formation did not necessarily result in an improved cell’s energetic efficiency, as ATP synthase did not increase with regards of lactate dehydrogenase reduction.

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Pandit, Aditya. "An in silico Characterization of Microbial Electrosynthesis for Metabolic Engineering of Biochemicals." Thesis, 2012. http://hdl.handle.net/1807/32616.

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A critical concern in metabolic engineering is the need to balance the demand and supply of redox intermediates. Bioelectrochemical techniques offer a promising method to alleviate redox imbalances during the synthesis of biochemicals. Broadly, these techniques reduce intracellular NAD+ to NADH and therefore manipulate the cell’s redox balance. The cellular response to such redox changes and the additional reducing can be harnessed to produce desired metabolites. In the context of microbial fermentation, these bioelectrochemical techniques can improve product yields and/or productivity. We have developed a method to characterize the role of bioelectrosynthesis in chemical production using the genome-scale metabolic model of E. coli. The results elucidate the role of bioelectrosynthesis and its impact on biomass growth, cellular ATP yields and biochemical production. The results also suggest that strain design strategies can change for fermentation processes that employ microbial electrosynthesis and suggest that dynamic operating strategies lead to maximizing productivity.

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Chang, Yi-Chien. "Systematic approaches to mine, predict and visualize biological functions." Thesis, 2016. https://hdl.handle.net/2144/14501.

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With advances in high-throughput technologies and next-generation sequencing, the amount of genomic and proteomic data is dramatically increasing in the post-genomic era. One of the biggest challenges that has arisen is the connection of sequences to their activities and the understanding of their cellular functions and interactions. In this dissertation, I present three different strategies for mining, predicting and visualizing biological functions. In the first part, I present the COMputational Bridges to Experiments (COMBREX) project, which facilitates the functional annotation of microbial proteins by leveraging the power of scientific community. The goal is to bring computational biologists and biochemists together to expand our knowledge. A database-driven web portal has been built to serve as a hub for the community. Predicted annotations will be deposited into the database and the recommendation system will guide biologists to the predictions whose experimental validation will be more beneficial to our knowledge of microbial proteins. In addition, by taking advantage of the rich content, we develop a web service to help community members enrich their genome annotations. In the second part, I focus on identifying the genes for enzyme activities that lack genetic details in the major biological databases. Protein sequences are unknown for about one-third of the characterized enzyme activities listed in the EC system, the so-called orphan enzymes. Our approach considers the similarities between enzyme activities, enabling us to deal with broad types of orphan enzymes in eukaryotes. I apply our framework to human orphan enzymes and show that we can successfully fill the knowledge gaps in the human metabolic network. In the last part, I construct a platform for visually analyzing the eco-system level metabolic network. Most microbes live in a multiple-species environment. The underlying nutrient exchange can be seen as a dynamic eco-system level metabolic network. The complexity of the network poses new visualization challenges. Using the data predicted by Computation Of Microbial Ecosystems in Time and Space (COMETS), I demonstrate that our platform is a powerful tool for investigating the interactions of the microbial community. We apply it to the exploration of a simulated microbial eco-system in the human gut. The result reflects both known knowledge and novel mutualistic interactions, such as the nutrients exchanges between E. coli, C. difficile and L. acidophilus.

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Collins, Sara Baldwin. "The interdependence between environment and metabolism in microbes and their ecosystems." Thesis, 2014. https://hdl.handle.net/2144/14311.

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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.

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Nogiec, Christopher Domenic. "Modeling human muscle metabolism: using constraint-based modeling to investigate nutrition supplements, insulin resistance, and type 2 diabetes." Thesis, 2014. https://hdl.handle.net/2144/15223.

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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.

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Wang, Taiyao. "Data analytics and optimization methods in biomedical systems: from microbes to humans." Thesis, 2020. https://hdl.handle.net/2144/41007.

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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.

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Leone, Lisa M. "Metabolic Modeling of Secondary Metabolism in Plant Systems." 2014. https://scholarworks.umass.edu/masters_theses_2/27.

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In the first part of this research, we constructed a Genome scale Metabolic Model (GEM) of Taxus cuspidata, a medicinal plant used to produce paclitaxel (Taxol®). The construction of the T. cuspidata GEM was predicated on recent acquisition of a transcriptome of T. cuspidata metabolism under methyl jasmonate (MJ) elicited conditions (when paclitaxel is produced) and unelicited conditions (when paclitaxel is not produced). Construction of the draft model, in which transcriptomic data from elicited and unelicited conditions were included, utilized tools including the ModelSEED developed by Argonne National Laboratory. Although a model was successfully created and gapfilled by ModelSEED using their software, we were not able to reproduce their results using COBRA, a widely accepted FBA software package. Further work needs to be done to figure out how to run ModelSEED models on commonly available software. In the second part of this research, we modeled the MJ elicited/defense response phenotype in Arabidopsis thaliana. Previously published models of A. thaliana were tested for suitability in modeling the MJ elicited phenotype using publicly available computation tools. MJ elicited and unelicited datasets were compared to ascertain differences in metabolism between these two phenotypes. The MJ elicited and unelicited datasets were significantly different in many respects, including the expression levels of many genes associated with secondary metabolism. However, it was found that the expression of genes related to growth and central metabolism were not generally significantly different for the MJ+ and MJ- datasets, the pathways associated with secondary metabolism were incomplete and could not be modeled, and FBA methods did not show the difference in growth that was expected. These results suggest that behavior associated with the MJ+ phenotype such as slow growth and secondary metabolite production may be controlled by factors not easily modeled with transcriptome data alone. Additional research was performed in the area of cryosectioning and immunostaining of fixed Taxus aggregates. Protocols developed for this work can be found in Appendix B.

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