Development of model-driven approaches for metabolic flux analysis and anticancer drug discovery

dc.contributor
Universitat de Barcelona. Departament de Bioquímica i Biomedicina Molecular
dc.contributor.author
Foguet Coll, Carles
dc.date.accessioned
2020-02-14T12:19:20Z
dc.date.available
2020-12-19T01:00:13Z
dc.date.issued
2019-12-20
dc.identifier.uri
http://hdl.handle.net/10803/668644
dc.description
Programa de Doctorat: Biotecnologia
dc.description.abstract
Metabolism is a hallmark of life and underlies most biological processes in both health and disease. For instance, dysregulation of liver metabolism underlies multifactorial disorders such as diabetes or obesity. Similarly, cancer progression involves a reprogramming of metabolism to support unchecked proliferation, metastatic spread and other facets of the cancer phenotype. Hence, the study of metabolism is of great biomedical interest. The metabolic phenotype emerges from the complex interactions of metabolites, enzymes, and the signaling cascades regulating their expression and thus must be studied following a holistic approach. With this aim, Systems Biology formulates the interactions between the molecular components of metabolism as a set of mathematical expressions, termed metabolic models, and uses them as a framework to integrate multiple layers of data (e.g., transcriptomics, proteomics and metabolomics) and simulate the emergent metabolic phenotype. The Systems Biology toolbox for the analysis of metabolism consists of several complementary model-based approaches, each with its strengths and limitations. For instance, constraint-based modeling can predict flux distributions at a genome-scale, whereas kinetic modeling and 13C metabolic flux analysis (13C MFA) can more accurately model central carbon metabolism. As part of this Ph.D. thesis, we have expanded this toolbox through the development of new model-based approaches for computing both detailed metabolic maps of central carbon metabolism and genome-scale flux maps. With this aim, we developed HepatoDyn, a highly detailed kinetic model of hepatocyte metabolism capable of dynamic 13C MFA and used it to characterize the negative effects of fructose in hepatic metabolic function. Similarly, we also developed Iso2Flux, a novel steady-state 13C MFA software, and parsimonious 13C MFA, a new 13C MFA algorithm that can integrate transcriptomics to trace flux through large metabolic networks. Even more, we developed r2MTA a constraint-based modeling algorithm to robustly identify the optimal interventions to induce a transition towards a therapeutically desirable metabolic state. Finally, we also developed a workflow for integrating transcriptomics, metabolomics, gene dependencies, and 13C MFA to predict genome-scale flux maps. Furthermore, we apply the systems biology toolbox, using both newly developed and existing tools, to the genome-scale analysis of the molecular drivers underlying cancer stem cells and metastasis in prostate and colorectal cancer, respectively. We identify putative therapeutic interventions against both phenotypes paving the way for a new generation of anticancer drugs.
dc.format.extent
355 p.
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Universitat de Barcelona
dc.rights.license
ADVERTIMENT. Tots els drets reservats. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.
dc.source
TDX (Tesis Doctorals en Xarxa)
dc.subject
Biologia de sistemes
dc.subject
Biología de sistemas
dc.subject
Systems biology
dc.subject
Càncer
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Cáncer
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Cancer
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Metabolisme
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Metabolismo
dc.subject
Metabolism
dc.subject.other
Ciències Experimentals i Matemàtiques
dc.title
Development of model-driven approaches for metabolic flux analysis and anticancer drug discovery
dc.type
info:eu-repo/semantics/doctoralThesis
dc.type
info:eu-repo/semantics/publishedVersion
dc.subject.udc
577
dc.contributor.director
Cascante i Serratosa, Marta
dc.contributor.director
Atauri Carulla, Ramón de
dc.contributor.tutor
Cascante i Serratosa, Marta
dc.embargo.terms
12 mesos
dc.rights.accessLevel
info:eu-repo/semantics/openAccess


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