Computational Techniques for the Structural and Dynamic Analysis of Biological Networks

The analysis of biological systems involves the study of networks from different omics such as genomics, transcriptomics, metabolomics and proteomics. In general, the computational techniques used in the analysis of biological networks can be divided into those that perform (i) structural analysis, (ii) dynamic analysis of structural prop- erties and (iii) dynamic simulation. Structural analysis is related to the study of the topology or stoichiometry of the biological network such as important nodes of the net- work, network motifs and the analysis of the flux distribution within the network. Dy- namic analysis of structural properties, generally, takes advantage from the availability of interaction and expression datasets in order to analyze the structural properties of a biological network in different conditions or time points. Dynamic simulation is useful to study those changes of the biological system in time that cannot be derived from a structural analysis because it is required to have additional information on the dynamics of the system. This thesis addresses each of these topics proposing three computational techniques useful to study different types of biological networks in which the structural and dynamic analysis is crucial to answer to specific biological questions. In particu- lar, the thesis proposes computational techniques for the analysis of the network motifs of a biological network through the design of heuristics useful to efficiently solve the subgraph isomorphism problem, the construction of a new analysis workflow able to integrate interaction and expression datasets to extract information about the chromo- somal connectivity of miRNA-mRNA interaction networks and, finally, the design of a methodology that applies techniques coming from the Electronic Design Automation (EDA) field that allows the dynamic simulation of biochemical interaction networks and the parameter estimation