We exploit parallel computing on single and multi Graphic Processing Units (GPU’s) for several problems. In particular, we develop optimised parallel codes for continuous variables Monte Carlo dynamics, Population dynamics for Belief propagation and Cavity method in random graphs, and Pseudolikelihood maximisation.
Algorithms. We are interested in the development of efficient computational techniques for the study of inference and optimization problems in large experimental data bases, mostly for complex biological systems. Among the key application domains are the analysis of gene expression at single cell resolution, the study of kinetic and/or thermodynamical conservation laws in cellular metabolic networks, the analysis of evolutionary variability in protein sequences, the characterization of cell-to-cell variability in microbial populations (at both the physiological and the molecular level), and the inference of complex interaction networks (protein-protein, protein-DNA, RNA-RNA) from genomic and/or thermodynamic data. In addition, we work on the development of multi-scale models for metabolic engineering of unicellular organisms and large-scale simulation of human tissues.
Biophysical simulations: Molecular competition on receptors. Many biological processes are based on the interaction between a receptor and various partner molecules that can bind it. To clarify these interactions, ‘in vitro’ experiments usually analyze the receptor in the presence of another single molecule. Nevertheless, ‘in vivo’ mechanisms are much more complex, and there can be competition phenomena among different molecular partners for the same receptor, or among different molecules of the same type that could associate to distinct regions of the same receptors through various binding modes. Computer simulations can help in a systematical mapping of the various possible combinations.