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.
- S. Evoli, D.L. Mobley, R. Guzzi, B. Rizzuti, Multiple binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations, bioRxiv, 8, 1-27, (2016) doi:10.1101/068502.
- Regularization and decimation pseudolikelihood approaches to statistical inference in XY-spin models, P Tyagi, A Marruzzo, A Pagnani, F Antenucci, L Leuzzi, Physical Review B 94, 024203 (2016), Doi: 10.1103/PhysRevB.94.024203.
- Multi-body quenched disordered XY and p-clock models on random graphs. A Marruzzo, L Leuzzi, Physical Review B 93, 094206 (2016) Doi: 10.1103/PhysRevB.93.094206.
- M Mori, T Hwa, OC Martin, A De Martino and E Marinari. Constrained Allocation Flux Balance Anal- ysis. PLoS Comp Biol 12:e1004913 (2016) Doi: 10.1371/journal.pcbi.1004913
- A Martirosyan, M Figliuzzi, E Marinari and A De Martino. Probing the limits to microRNA-mediated control of gene expression. PLoS Comp Biol 12: e1004715 (2016) Doi: 10.1371/journal.pcbi.1004715
- Nonlinear XY and p-clock models on sparse random graphs: Mode-locking transition of localized waves, A Marruzzo, L Leuzzi, Physical Review B 91, 054201 (2015) Doi: 10.1103/PhysRevB.91.054201
- Statistical mechanical theory of mode-locked multimode lasers in closed cavity: determination of thresholds, spectra, pulse phase delays and pulse correlations. F Antenucci, M Ibanez Berganza, L Leuzzi, Phys. Rev. A 91, 043811 (2014) Doi:.10.1103/PhysRevA.91.043811
- D De Martino, F Capuani and A De Martino. Inferring metabolic phenotypes from the exometabolome through a thermodynamic variational principle. New J Phys 16: 115018 (2014) Doi: 10.1088/1367-2630/16/11/115018
- A De Martino, D De Martino, R Mulet and A Pagnani. Identifying all moiety conservation laws in genome-scale metabolic networks, PLoS ONE 9:e100750 (2014) Doi: 10.1371/journal.pone.0100750
- A Seganti, F Ricci Tersenghi and A De Martino. Searching for feasible stationary states in reaction net- works by solving a Boolean constraint satisfaction problem. Phys Rev E 89:022139 (2014) Doi: 10.1103/PhysRevE.89.022139
Other selected publications
- FA Massucci, M DiNuzzo, F Giove, B Maraviglia, I Perez Castillo, E Marinari and A De Martino. Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective. BMC Sys Biol 7:103 (2013) Doi: 10.1186/1752-0509-7-103
- D De Martino, F Capuani, M Mori, A De Martino and E Marinari. Counting and correcting thermody- namically infeasible flux cycles in genome-scale metabolic networks. Metabolites 3:946 (2013) Doi: 10.3390/metabo3040946
- FA Massucci, F Font Clos, A De Martino and I Perez Castillo. A novel methodology to estimate metabolic flux distributions in constraint-based models. Metabolites 3:838 (2013) Doi: 10.3390/metabo3030838
- A Seganti, A De Martino and F Ricci Tersenghi. Boolean constraint satisfaction problems for reaction networks. J Stat Mech P09009 (2013) Doi:10.1088/1742-5468/2013/09/P09009
- D De Martino, M Figliuzzi, A De Martino and E Marinari. A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks. PLoS Comp Biol 8:e1002562 (2012) Doi: 10.1371/journal.pcbi.1002562