Evolutionary Inference of Biological Systems Accelerated on Graphics Processing Units

In silico analysis of biological systems represents a valuable alternative and complementary approach to experimental research. Computational methodologies, indeed, allow to mimic some conditions of cellular processes that might be difficult to dissect by exploiting traditional laboratory techniques, therefore potentially achieving a thorough comprehension of the molecular mechanisms that rule the functioning of cells and organisms. In spite of the benefits that it can bring about in biology, the computational approach still has two main limitations: first, there is often a lack of adequate knowledge on the biological system of interest, which prevents the creation of a proper mathematical model able to produce faithful and quantitative predictions; second, the analysis of the model can require a massive number of simulations and calculations, which are computationally burdensome. The goal of the present thesis is to develop novel computational methodologies to efficiently tackle these two issues, at multiple scales of biological complexity (from single molecular structures to networks of biochemical reactions). The inference of the missing data — related to the three-dimensional structures of proteins, the number and type of chemical species and their mutual interactions, the kinetic parameters — is performed by means of novel methods based on Evolutionary Computation and Swarm Intelligence techniques. General purpose GPU computing has been adopted to reduce the computational time, achieving a relevant speedup with respect to the sequential execution of the same algorithms. The results presented in this thesis show that these novel evolutionary-based and GPU-accelerated methodologies are indeed feasible and advantageous from both the points of view of inference quality and computational performances.