Scientific Computing & Big Data

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.

Facilities and Labs

S.Li.M. Lab @ Roma

People

Andrea_DeMArtinoAndrea

De Martino

CNR Researcher

Bruno_RizzutiBruno

Rizzuti

CNR Researcher

leuzziLuca

Leuzzi

CNR Researcher

fabrizioantenucci_postdocFabrizio

Antenucci

Associate PostDoc

alessiamarruzzo_postdocAlessia

Marruzzo

Associate PostDoc

Publications

  1. 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.
  2. 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.
  3.  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.
  4.  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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

Latest News

Technology Trasfer in Nanotechnology

Technology Transfer in Nanotechnology: Challenges and Opportunity

Lecce, 18/19 ottobre 2018

CNR NANOTEC c/o Campus Ecotekne

JRC in collaboration with the National Research Council (Cnr) is organising a workshop on Technology Transfer in Nanotechnology,

which will take place in CNR Nanotec (Lecce, Italy) on 18 and 19 October. This workshop is organised in the framework of the TTO-CIRCLE initiatives.   The aim of this event is to explore how technology transfer activities can be used as a mechanism to help EU industry, particularly Start-ups and SMEs, and Government in deploying and adopting Nano-technology. Practical examples will be presented to illustrate the potential of technology transfer in this area.   The workshop will gather technology providers, industry executives, technology transfer officers, policy makers and financial intermediaries to share experiences and lessons learned. One of the key objectives is to discuss policy implications at all levels that could help accelerating the adoption of Nanotechnology by the European manufacturing industry. More informations: https://ec.europa.eu/jrc/communities/community/european-tto-circle/event/technology-transfer-nanotechnology Download Locandina

Nanotechnology Transfer Day

26 Luglio 2018 - Lecce

CNR NANOTEC c/o Campus Ecotekne Siglato l’accordo lo scorso maggio tra CNR NANOTEC e Pairstech Capital Management, ha preso il via la collaborazione con PhD TT per la valutazione della ricerca

E’partita la collaborazione con PhD TT per la valorizzazione della ricerca sulla base dell’accordo siglato lo scorso Maggio tra CNR NANOTEC e Pairstech Capital Management, società di gestione patrimoniale che fornisce agli investitori istituzionali e privati un insieme di veicoli di investimento, al fine di valorizzare i risultati della ricerca svolta all'interno dell'Istituto.

Giovedì 19 Luglio dalle ore 11 alle ore 14 nella sede del CNR Nanotec di Lecce si è tenuto un incontro sul trasferimento tecnologico nel settore delle nanotecnologie applicate al settore biomedicale.

L’evento è stato organizzato dall’ufficio di Trasferimento Tecnologico del CNR Nanotec che ha inaugurato con questa giornata un ciclo di eventi mirato a presentare agli attori dell’ecosistema dell’innovazione nel settore delle nanotecnologie i vari modelli e alcune best practice di trasferimento tecnologico. In questa prima giornata il dott. Heber Verri e la dott.ssa Paola Urbani hanno presentato il nuovo modello di trasferimento tecnologico PhD TTãIndex Model.

PhD TT è una realtà italiana completamente indipendente specializzata in trasferimento tecnologico, è un acceleratore organizzato per il Go to Venture Practice, orientata al mondo delle Lifes Sciences.

PhD TT ha sviluppato un nuovo modello di trasferimento tecnologico: il PhD TT©INDEX MODEL dedicato alla generazione di valore dell'innovazione, focalizzato alla riduzione dei rischi delle opportunità di investimento a sostegno della ricerca.

I ricercatori intervengono attivamente nell'analisi iniziale di fattibilità e nella costituzione della futura società (start-up), con l'obiettivo di attrarre capitale di rischio utile a sostenere la fase del trasferimento tecnologico nella visione della "Research for go-to-market".

Il modello PhD TT nasce da un bisogno del mercato, quello di far dialogare due mondi estremamente diversi tra loro: il mondo della ricerca e il mondo degli investimenti.

PhD TT supporta tutte le attività in collaborazione con il TTO - CNR Nanotec con un team di lavoro esperto e grazie a un comitato scientifico-economico qualificato.

In occasione dell'evento del 19/7 u.s. al CNR Nanotec di Lecce, PHD TT ha presentato il proprio track record, dove si sono potuti valutare in dettaglio i casi di successo di intervento del PhD TT©INDEX MODEL.

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Disordered serendipity: a glassy path to discovery

A workshop in honour of Giorgio Parisi’s 70th birthday

September 19-21, 2018 - Roma

Sapienza University

With the occasion of celebrating Giorgio Parisi 70th birthday, the conference "Disordered serendipity: a glassy path to discovery" brings to Rome many among the world-leading experts in the field of complex systems. In order to properly represent the many fields of research where Giorgio Parisi gave a relevant contribution in his studies of disordered systems, the conference covers a broad spectrum of topics: from  fundamental and rigorous analysis of the statistical mechanics of disorder systems to applications in biology and computer science. These subjects are deeply interconnected since they are characterized by the presence of glassy behavior.

 

https://sites.google.com/site/disorderedserendipity/