Development and application of novel model-driven and data-driven approaches to study metabolism in the framework of systems medicine

• Autores: Igor Bartolomé Marín de Mas
• Directores de la Tesis: Balázs Papp (dir. tes.), Marta Cascante Serratosa (dir. tes.)
• Lectura: En la Universitat de Barcelona ( España ) en 2015
• Idioma: inglés
• Tribunal Calificador de la Tesis: Jaime Rubio Martínez (presid.), Roser López Alemany (secret.), Ulrich L. Günther (voc.)
• Materias:
  • Química
    • Bioquímica
      • Biología molecular
  • Ciencias de la vida
    • Biomatemáticas
      • Bioestadística
• Enlaces
  • Tesis en acceso abierto en:  TDX  Dipòsit Digital de la UB 
• Resumen

  • The general aim of this thesis is to develop and apply new computational tools to overcome existing limitations in the analysis of metabolism. This thesis is focused on developing new computational strategies to overcome the following identified limitations: i) The existing metabolic flux analysis tools does not account for the existence of metabolic channeling: Here we developed a new computational tool based on non-stationary 13 C-FBA to evaluate different models reflecting different topologies of intracellular metabolism, using the channeling in hepatocytes as case of concep ii) Metabolic drug-target discovery based on GSMM does not consider the different cell subpopulations existing within the tumor: Here we develope a method that integrate trancriptomic data into a comparative genome-scale metabolic network reconstruction analysis in the context of intra-tumoral heterogeneity . We determined subpopulation-specific drug targets . Additionally we determined a metabolic gene signature associated to tumor progression in pc that was correlated with other types of cancer. Iii) Current mechanistic and probabilistic computational approaches are not suitable to study the complexity of the crosstalk between metabolic and gene regulatory networks.: Here we developed a novel computational method combining probabilistic and mechanistic approaches to integrate multi-level omic data into a discrete model-based analysis. This method allowed to analyze the mechanism underlying the crosstalk between metabolism and gene regulation, using as case of concept the study of the abnormal adaptation to training in COPD patients.