A Computational Approach for Multi-Level Biological Complex Systems Analysis

Due to their intrinsic nature, biological entities are universally considered as complex systems. Over years, many different computational methods pertaining to the Systems Biology field, have been devised to unravel this complexity. However, when taken alone, most of times these methods are not able to provide a deep comprehension of structural, spatial and dynamical aspects of the systems under evaluation. For this reason, approaches exploiting different levels of analysis are today a hot research topic in different areas, such as the theoretical formalization of the method, and the development of computational tools for the integration of different modeling perspectives. In the present dissertation I developed a computational pipeline able to perform analyses exploiting, one after the other, the three main modeling frameworks for biological systems, gaining, from every level, a different type of information: i.e. identification of flux distributions and metabolic sub-phenotypes from the ensemble evolutionary FBA (a novel method inspired by Flux Balance Analysis); information on network structural properties and topological metrics from graph theory approaches; estimation of kinetic constants for mechanism-based modeling through the definition of an efficient version of the Particle Swarm Optimizer based on Fuzzy Logic. Moreover, I also redefined a network visualization strategy able to overlay flux values and topological metrics to network structure. In order to validate the proposed pipeline I also developed a “core model” of yeast metabolism from which I identified two ensembles of flux distributions (possible solutions) in agreement with the “Crabtree-positive” and “Crabtree-negative” metabolic phenotypes. Moreover, by means of a cluster analysis, devised methods were able to define groups inside each ensemble that I identified as putative “sub-phenotypes”. Lastly, I contributed to reconstruct four reduced metabolic “core models”, deriving from the Human Metabolic Atlas, and describing three tissue-specific cancer conditions and a reference state. From these models a relevant heterogeneity emerged between reference and cancer conditions in terms of metabolic flux values.