Systems biology approaches to evaluate disease modularity

  1. Reyes Palomares, Armando
Supervised by:
  1. Francisca Sánchez Jiménez Director
  2. Miguel Ángel Medina Torres Director

Defence university: Universidad de Málaga

Fecha de defensa: 07 April 2014

  1. Miguel Angel Andrade Navarro Chair
  2. Enrique Viguera Mínguez Secretary
  3. Aurelio A. Moya García Committee member
  4. Carlos Rodríguez Caso Committee member
  5. Manuel Corpas Committee member

Type: Thesis

Teseo: 361582 DIALNET lock_openRIUMA editor


Biological systems are in a constant process of innovation as an essential precondition to evolve. For this reason, the emergence of phenotypic variation is an inherent property of complex adaptive systems. Even though living systems acquire robustness to internal and external disturbances during the evolutionary process, pathological conditions entail impairments to their functionality. This is the rationale for studying biomedical issues according to the organizational properties of biological systems, with the aim of understanding the mechanisms of diseases. I first discuss the theoretical background that is suitable for the research included in this Thesis, such as my own interpretation of systems biology, the current theories about the origin of the biological modularity and some evolutionary considerations that concern in the genotype-phenotype relationships. In this section, I also argue the use and the development of integrative systems biology methods that should be addressed to evaluate disease modules: computational models (i.e. mathematical and network-based models) and other standardized efforts (ontologies and different databases with biological and biomedical data). Then, I enunciate the hypothesis and declare the objectives that motivated this research: i) mathematical modelling based on kinetic law formalism for studying the functional modularity of the metabolism; ii) the development of a workflow to integrate metabolic and kinetic data from different databases for metabolic modelling; iii) the evaluation of the functional coherence in phenotypic relationships between disease-causing genes by using network-based analysis; iv) the development of an integrative framework of biomedical information; v) the use of network medicine approaches to study the phenotypic and genotypic relationships in a heterogeneous group of patients with genetic syndromes. Finally, the results derived from the research carried out in this Thesis are included in the form of already published articles and manuscripts (either submitted or in preparation).