Publications

• P. Lecca, A. Re, Observability of Bacterial Growth Models in Bubble Column Bioreactors, Computational Intelligence Methods for Bioinformatics and Biostatistics, Springer International Publishing pp. 309-322,  Springer International Publishing, 2020. 

• A. Re, P. Lecca, On TD-WGcluster: Theoretical Foundations and Guidelines for the User. In: Canzar S., Ringeling F. (eds) Protein-Protein Interaction Networks. Methods in Molecular Biology, pp 233-262, vol. 2074. Humana, New York, NY, 2020 

• P. Lecca, A. Re, Network-Oriented Approaches to Anticancer Drug Response, Methods Mol. Biol., 1513:101-117, 2016.

• P. Lecca, A. Re, Module Detection in Dynamic Networks by Temporal Edge Weight Clustering, Computational Intelligence Methods for Bioinformatics and Biostatistics Volume 9874 of the series Lecture Notes in Computer Science pp 54-70, 2016.

• P. Lecca, M. Lecca, Mechanistic Models of Astrocytic Glucose Metabolism Calibrated on PET Images, Biomechanics of Cells and Tissues, Lecture Notes in Computational Vision and Biomechanics 9, pag. 131-155, Springer Science+Business Media, Dordrecht 2013.

• P. Lecca, G. Constantin, C. Laudanna and C. Priami, A Model Predicting Rolling Cells Percentage in Inflamed Brain Venules, Biomechanics of Cells and Tissues, Lecture Notes in Computational 131, Vision and Biomechanics 9, DOI: 10.1007/978-94-007-5890-2_6, Springer Science+Business Media Dordrecht 2013.

• P. Lecca, A. Palmisano, The present and the future perspectives of biological network inference, In Bioinformatics and Computational Systems Biology: Recent Advances and Applications, Eds. Paola Lecca, Dan Tulpan, Rajaraman Kanagasabai, IGI-Global 2012.

• P. Lecca, A process algebra model of the cell mechanics of autoreactive lymphocites recruitment, Technologies for Medical Sciences, Lecture Notes in Computational Vision and Biomechanics, Volume 1:311-333, 2012.

• F. Jordan, P. Lecca, C. Livi, Structural and dynamical heterogeneity in ecological networks, Bioinformatics and Computational Systems Biology: Recent Advances and Applications, Eds. Paola Lecca, Dan Tulpan, Rajaraman Kanagasabai, IGI-Global, 2012.

• P. Lecca, T-P. Nguyen, C. Priami, P. Quaglia, Network inference from Time-Dependent Omics Data, Bioinformatics for Omics data, Methods Mol Biol. 719:435-55, Springer, 2011.

Journals

• M. Lecca, P. Lecca, A dataset for illuminant- and device- invariant colour barcode decoding with cameras, Data in Brief, Volume 52, 109960, February 2024, doi: https://doi.org/10.1016/j.dib.2023.109960 . 

• Lecca P and Lecca M (2023) Graph embedding and geometric deep learning relevance to network biology and structural chemistry. Front. Artif. Intell. 6:1256352. doi: 10.3389/frai.2023.1256352 

• Lecca P, Lombardi G, Latorre RV and Sorio C (2023) How the latent geometry of a biological network provides information on its dynamics: the case of the gene network of chronic myeloid leukaemia. Front. Cell Dev. Biol. 11:1235116. doi: 10.3389/fcell.2023.1235116 .

• Lecca P, Editorial: Expert Opinions in Network bioinformatics: 2022. Front. Bioinform. 3:1254668, 2023, doi: 10.3389/fbinf.2023.1254668.

• Lecca P and Ihekwaba-Ndibe AEC (2022) Dynamic Modelling of DNA Repair Pathway at the Molecular Level: A New Perspective. Front. Mol. Biosci. 9:878148. doi: 10.3389/fmolb.2022.878148

• Lombardi, G.; Latorre, R.V.; Mosca, A.; Calvanese, D.; Tomasello, L.; Boni, C.; Ferracin, M.; Negrini, M.; Dewik, N.A.; Yassin, M.; Ismail, M.A.; Carpentieri, B.; Sorio, C.; Lecca, P. Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG. Int. J. Mol. Sci. 2022, 23, 9899. https://doi.org/10.3390/ijms23179899 

• Lecca P, Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge. Front. Bioinform. 1:746712., September 22, 2021. DOI: 10.3389/fbinf.2021.746712 

• Lecca P, Corchado JM and Besozzi D, Editorial: Network-Oriented Approaches to Anticancer Drug Response. Front. Bioeng. Biotechnol. 9:692369, 2021. DOI: 10.3389/fbioe.2021.692369

• Marco Pedrazzi, Silvia Vercellone , Elettra Barberis, Michela Capraro, Roberta De Tullio, Federico Cresta, Rosaria Casciaro, Carlo Castellani, Mauro Patrone, Emilio Marengo, Paola Lecca, Paola Melotti, Claudio Sorio, Marcello Manfredi and Monica Averna,  Identification of Potential Leukocyte Biomarkers Related to Drug Recovery of CFTR: Clinical Applications in Cystic Fibrosis,  Int. J. Mol. Sci. 22, no. 8: 3928. https://www.mdpi.com/1422-0067/22/8/3928. 

• Paola Lecca, Control Theory and Cancer Chemotherapy: How They Interact. Front. Bioeng. Biotechnol. 8:621269, 2021, doi: 10.3389/fbioe.2020.621269.

• Paola Lecca, Michela Lecca, Cecilia Ada Maestri, Marina Scarpa, Biexponential fitting for noisy data with error propagation, Mathematical Methods in the Applied Sciences, Volume 44, Issue13, Wiley, 2021 (here)

• Cecilia Ada Maestri , Antonella Motta, Lorenzo Moschini, Andreas Bernkop‐Schnürch, Randi Angela Baus, Paola Lecca, Marina Scarpa, Composite nanocellulose‐based hydrogels with spatially oriented degradation and retarded release of macromolecules, Journal of Bioemdical Research Part A, Volume 108, Number 7}, pp. 1509--1519. https://doi.org/10.1002/jbm.a.36922.

• P. Lecca, A. Re, Identifying necessary and sufficient conditions for the observability of models of biochemical processes, Biophysical Chemistry, 254: 106257, 2019 (here) .

• P. Lecca, A. Re, A reaction-based model of the state space of chemical reaction systems enables efficient simulations,  IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume: 17 , Issue: 2 , March-April 1 2020 (here) .

• P. Lecca, M. Lecca, S. Caldrer, F. Rescigno, A. Farinazzo, V. Esposito, L. Rodella, A. Cerofolini, F. Lombardo, F. Catalano, C. D'Orazio, Computing volumes of organoids from 3D confocal acquisition with CORVO. In Pediatric Pulmonology, Vol. 53, Issue S2, pp. 220-220. 111 River St, Hoboken 07030-5774, NJ USA: Wiley., 2018 (here).

• P.. Melotti, P. Lecca, V. Esposito, M. Bertini, R. Vicentini,E. Pintani, C. D’Orazio, C. Sorio, Measurements of Beta Adrinergic vs cholinerginc sweat rates in single human sweat glands, In Pediatric Pulmonology, Vol. 53, Issue S2, pp. 220-220. 111 River St, Hoboken 07030-5774, NJ USA: Wiley., 2018 (here).

• P. Lecca, How Monte Carlo heiristics aid to identify the physical processes of drug release kinetics, MethodsX 5:204-216, 2018.

• P. Lecca. F. Bagagiolo, M. Scarpa, Hybrid deterministic/stochastic simulation of complex biochemical systems, Molecular Biosystems, Mol. BioSyst., 13, 2672-2686, 2017.

• P. Lecca, C. Sorio, Accurate prediction of chronic myeloid leukemia with an improved two-mutation mathematical model, Integrative Biology, 8, 1261-1275, 2016, 2016.

• P. Lecca, I. Mura, A. Re, G. Barker, A. E: Ihekwaba, Time series analysis of the Bacillus subtilis sporulation network reveals low dimensional chaoti dynamics, Front. Microbiology 7:1760., 2016.

• P. Lecca, N. Casiraghi, F. Demichelis, Defining order and timing of mutations during cancer progression: the TO-DAG graphical model, Front. Genet. 6:309, 2015.

• P. Lecca, A. Re, Detecting modules in biological networks by edge weight clustering and entropy significance, Front Genet. 6:265, 2015.

• Gunther Boysen, Christopher E Barbieri, Davide Prandi, Mirjam Blattner , Sung-Suk Chae, Arun Dahija, Srilakshmi Nataraj, Dennis Huang, Clarisse Marotz, Limei Xu, Julie Huang, Paola Lecca, Sagar Chhangawala, Deli Liu, Pengbo Zhou, Andrea Sboner, Johann S de Bono, Francesca Demichelis, Yariv Houvras, Mark A Rubin, SPOP mutation leads to genomic instability in prostate cancer, Elife 2015 Sep 16;4:e09207. doi: 10.7554/eLife.09207.

• P. Meyer, T. Cokelaer, D. Chandran, K. Hyuk Kim, P-R Loh, G. Tucker, M. Lipson, B. Berger, C. Kreutz, A. Raue, B. Steiert, J. Timmer, E. Bilal, DREAM 6&7 Parameter Estimation consortium, H. M. Sauro, G. Stolovitzky and J. Saez-Rodriguez, Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach, BMC Systems Biology 8(13): 2014.

• P. Lecca, Methods of biological network inference for reverse engineering cancer chemoresistance mechanisms, Drug Discovery 19(2):151–163, 2014.

• P. Lecca, Stochastic chemical kinetics. A review of the modelling and simulation approaches, Biophysical Reviews, 5(4):323-345., 2013.

• P. Lecca, An integrative network inference approach to predict mechanisms of cancer chemoresistance, Integr. Biol., 5 (3), 458 - 473, 2013.

• C. Damiani, A. Filisetti, A. Graudenzi, P. Lecca, Parameter sensitivity analysis of stochastic models: an application to catalytic reaction networks, Computational Biology and Chemistry, 42:5-17, 2013.

• P. Lecca, C. Priami, Biological network inference for drug discovery, Drug Discovery Today,Volume 18, Issues 5–6, 256–264, 2013.

• O. Kahramanogullari, G. Fantaccini, P. Lecca, D. Morpurgo, C. Priami, Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy, PloS ONE 7(12): e50176. doi:10.1371/journal.pone.0050176, 2012.

• P. Lecca, D. Morpurgo, Modelling non-homogeneous stochastic reaction-diffusion systems: the case study of gemcitabine-treated non-small cell lung cancer growth, BMC Bioinformatics, 13(Suppl 14):S14, 2012.

• P. Lecca, D. Morpurgo, G. Fantaccini, A. Casagrande, C. Priami, Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics, BMC Systems Biology,6:51, 2012.

• C. Livi, F. Jordan, P. Lecca, T. Okey, Identifying key species in ecosystems with stochastic sensitivity analysis, Ecological Modelling, 222:2542-2551, 2011.

• P. Lecca, BlenX models of alpha-synuclein and parkin kinetics in neuropathology of Parkinson's disease, Journal of Biological Systems 19(2): 149-181, 2011.

• P. Lecca, A. Palmisano, A. Ihekwaba, C. Priami, Calibration of dynamic models of biological systems with KInfer, Eur. Biophysics J., 39:1019-1039, 2010.

• P. Lecca, A. Palmisano, Identification of biochemical chaotic systems with KInfer, Int. Journal of Simulation: Systems, Science and Technology, 11(3), 2010.

• P. Lecca, A. Palmisano, A. Ihekwaba, Inferring the kinetic constants of cyclin-triggered expression of Cdc20 gene in eukaryotic cell cycle, Online Journal of Bioinformatics, 11:177-216, 2010.

• P. Lecca, A. Ihekwaba, L. Dematté, C. Priami, Stochastic simulation of the spatio-temporal dynamics of reaction-diffusion systems: the case for the bicoid gradient, Journal of Integrative Bioinformatics,7, 2010.

• P. Lecca and L. Dematté, Stochastic Simulation of Reaction-diffusion Systems, Int. Journal of Medical and Biological Engineering, 1(4):211-231, WASET, December, 2008.

• P. Lecca, In silico experiments on a faulty ubiquitin-proteaosome system in the pathogenesis of Parkinson's disease, Online Journal of Bioinformatics, 9 (1): 30-43, 2008.

• P. Lecca and M. Lecca, Molecular mechanism of glutamate-triggered brain glucose metabolism: a parametric model from FDG-PET-scans, Advances in Brain, Vision, and Artificial Intelligence, Lecture Notes in Computer Science 4729:350-359, 2007.

• P. Lecca, Simulating the cellular passive transport of glucose using a time-dependent extension of Gillespie algorithm for stochastic pi - calculus, accepted as upcoming special issue in Int. Journal of Data Mining and Bioinformatics, vol. 1. no. 4, pp. 315-336, 2007.

• P. Lecca and C. Priami, Cell cycle control in Eukaryotes: a BioSpi model, Electronic Notes in Theoretical Computer Science, 180(3):51-63, 2007.

• C. Eccher and P. Lecca Translating SBML models to stochastic pi-calculus for stochastic simulation in Transactions on Computational Systems Biology (Springer LNCS journal), 2005.

• P. Lecca, C. Priami, P. Quaglia and B. Rossi, C. Laudanna and G. Costantin, A stochastic process algebra approach to simulation of autoreactive lymphocyte recruitment. SIMULATION: Transactions of the society for modelling and simulation international, vol. 80: 273-288, 2004.

• D. D'Ambrosio, P. Lecca, C. Priami and C. Laudanna, Concurrency in leukocyte vascular recognition: developing the tools for a predictive computer model, Trends in Immunology, vol. 25: 411-416, 2004.

• C. Furlanello, P. Lecca and S. Zamboni, Servizi GIS e metodi statistici per la gestione della tutela e della ricerca archeologica in Trentino, In Universit\`a degli studi di Trento, Dip. di Scienze Filologiche e Storiche Editor, Archeologia del territorio: metodi materiali e prospettive. A cura di M. De Vos, Labirinti 73, 2004.

• G. Cognola and P. Lecca, Electromagnetic fields in Schwarzschild and Reissner-Noerdstrom geometry: quantum corrections to the black hole entropy. Physical Review. D, Particles and fields, vol. 57: 1108-1111, 1998.

Conference papers

• P. Lecca, Learning systems of ordinary differential equations with Physics-Informed Neural Networks: the case study of enzyme kinetics, Journal of Physics: Conference Series 2701 (1), 012107. DOI 10.1088/1742-6596/2701/1/012107.

• P. Lecca, "Modelling a network as a spring system to estimate edge vulnerability," 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3466-3472, doi: 10.1109/BIBM58861.2023.10385910

• P. Lecca, A. Re, G. Lombardi, R. V. Latorre, C. Sorio, Graph Embedding of Chronic Myeloid Leukaemia K562 Cells Gene Network Reveals a Hyperbolic Latent Geometry, X.-S. Yang et al. (eds.), Proceedings of Eighth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 694, https://doi.org/10.1007/978-981-99-3091-3_80, 2023.

• P. Lecca and A. Re, Checking for non-Euclidean latent geometry of biological networks, in 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 2022 pp. 2526-2535. doi: 10.1109/BIBM55620.2022.9995274 

• P. Lecca, Uncovering the geometry of protein interaction network: the case of SARS-CoV-2 protein interactome, 11th International Conference on Mathematical Modeling in Physical Sciences, September 5-8, 2022.

• P. Lecca, A. Re, A New Proposal of Power Series Method to Solve The Navier-Stokes Equations: Application Contexts And Perspectives, Proccedings of  2022 5th International Conference on Mathematics and Statistics ([ICoMS 2022), June 17--19, 2022, Paris, France (jount to 4th International Conference of Applied Mathematics, Modelling and Simulations, Paris, France, June 17-19, 2022) DOI: 10.1145/3545839.3545844 . (In press)

• Lecca P. , Carpentieri B., Sylos Labini P., Vella F., Troiani E., Cavezzi A., Analysis of SARS-CoV-2 protein interactome map, IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021) - Workshop on Integrative Data Analysis in Systems Biology (IDASB 2021), 9-12 December 2021 - Virtual online conference, pp. 2429-2436, doi: https://doi.org/10.1109/BIBM52615.2021.9669641 

• P. Lecca, The effects of gravitational potential on chemical reaction rates, J. Phys.: Conf. Ser. 2090 012034.

• P. Lecca, A. Re, On the asymptotic stability of advection-diffusion equations of mass transport in a bubble column bioreactor, J. Phys.: Conf. Ser. 2090 012035.

• P. Lecca, A. Re, The theory of active agents for simulating dynamical networks and its π-calculus specification, ICoMS 2021: 2021 4th International Conference on Mathematics and Statistics, June 2021 Pages 13–21, DOI:  https://doi.org/10.1145/3475827.3475830.

• P. Lecca, A. Re, Stiffness estimate of information propagation in biological systems modelled as spring networks, Proceedings of 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020.

• P. Lecca, M. Lecca, P. Melotti, V. Lotti, S. Preato, K. Kleinfelder, A. Farinazzo, C. Sorio, Computing organoids’ volume in medical images: the case study of cystic fibrosis, 2020 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2020), pp. 1-8, Viña del Mar, October 27-29, 2020 (here).

• P. Lecca, A. Re, Analysis of observability of bacterial growth models, Proceedings of  CIBB 2019 - 16th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, pp.  293-298, 2019.

• A. Re, P. Lecca, Determining structural parameter identifiability in biological dynamical models by analysing the statistical properties of the likelihood behaviour, 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology  (here).

•  P. Lecca, M. Bertini, R. Vicentini, E. Pintani, C. D’Orazio, V. Esposito, K. Kleinfelder, C. Sorio, P. Melotti, Multilinear Regression Analysis of Sweat Secretion Volumes in Cystic Fibrosis, Proceedings of FRUCT 23, Bologna (Italy), 13-16 November 2018 (here). 

• P. Lecca, A. Re, A. E. C. Ihekwaba, G. C. Barker, A hierarchical model to predict the probability of germination of bacterial spores, 2017 In Procs of IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2017), 2017.

• P. Lecca, A. Re, Identifying modules in biological networks with WG-Cluster, Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), Naples 10-12, September, 2015.

• P. Lecca and C. Priami, Inference and Modelling of the Gemcitabine Pharmakokinetics and Resistance, XXXVI NATIONAL CONGRESS OF THE ITALIAN ASSOCIATION FOR THE STUDY OF THE PANCREAS (AISP), 2012.

• P. Lecca, Identification of models of brain glucose metabolism from 18F-deoxyglucose PET images, In Procs of CompIMAGE2012 (Computational Modeling of Objects Presented in Images), 2012.

• P. Lecca, O. Kahramanogullari, D. Morpurgo, C. Priami, R. Soo, Modelling the tumor shrinkage pharmacodynamics with BlenX, In Procs of the 1st IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS), pp: 251, 2011.

• P. Lecca, O. Kahramanogullari, D. Morpurgo, C. Priami, R. Soo, Modelling and estimating dynamics of tumor shrinkage with BlenX and KInfer, In Procs of UKSim 2011 - 13th International Conference on Modelling and Simulation, pp. 75-80, 2011.

• P. Lecca, Calibration of process algebra models of discretely observed stochastic biochemical systems, In Procs of The 23rd European Modeling and Simulation Symposium, 2011.

• C. Damiani, P. Lecca,  Model identification using correlation-based inference and transfer entropy estimation, In Procs of EMS 2011 UKSim 5th European Modelling Symposium on Mathematical Modelling and Computer Simulation, pp: 129-134, 2011.

• P. Lecca, A. Palmisano, and A. E. C. Ihekwaba, Correlation-based network inference and modelling in systems biology: the NF-kB signalling network case study, In Procs of Int. Conf. on Intelligent Systems, Modelling and Simulation, IEEE CPS, Liverpool, England, January 27-29, 2010.

• Attila Csikász-Nagy, Adrien Fauré, Roberto Larcher, Paola Lecca, Ivan Mura, Ferenc Jordán, Alida Palmisano, Alessandro Romanel, Sean Sedwards, Heike Siebert, Sylvain Soliman, Denis Thieffry, Judit Zámborszky, Tommaso Mazza, Paolo Ballarini:, Analyzing various models of Circadian Clock and Cell Cycle coupling. winner paper at the competion Formal Methods in Molecular Biology 2009.

• A. B. M. Rubaiyat Islam Sadat and P. Lecca, On the performances in simulation of parallel databases: an overview on the most recent techniques for query optimization, In Procs of High Performance Computational Systems Biology (HiBi '09), Trento, Italy.

• P. Lecca, L. Dematté, M. Lecca, and C. Priami, Stochastic modelling of diffusion systems. Video image simulation of tubulin diffusion in cytoplasm: a case study, II Eccomas Thematic Conference on Computaional Vision and Medical Image Processing, Porto, Portugal, October, 2009.

• P. Lecca and A. Palmisano Estimating the parameters of cyclin-triggered gene expression in cell cycle control network, In Procs of International Conference on Computer Modelling and Simulation (CSSim 2009), IEEE Digital Library, Brno, Czech Republic, September, 2009.

• P. Lecca, A. Palmisano, C. Priami, and G. Sanguinetti A new probabilistic generative model of parameter inference in biochemical networks, In Proceedings of the 2009 ACM Symposium on Applied Computing, pp. 758-765, 2009.

• P. Lecca, On the mathematical structure and algorithmic implementation of biochemical network models, In Procs of Int. Conference on Cellular and Molecular Biology, vol. 40 pp. 345-368, World Academy of Science, Engineering and Technol, Rome, April, 2009.

• P. Lecca, Deterministic chemical chaos identification in models and experiments, In Procs of The 2009 International Conference on Bioinformatics \& Computational Biology, CSREA Press, Las Vegas, Nevada (USA), July, 2009.

• P. Lecca, A. Palmisano and C. Priami, Deducing chemical reaction rate constants and their regions of confidence from noisy measurements of time series of concentration 11th Int, Conference on Computer Modelling and Simulation (UKSim 2009), pp. 200-205, IEEE Computer Society, Cambridge - England, March, 2009.

• P. Lecca, L. Dematte', C. Priami Modeling and simulating reaction-diffusion systems with state-dependent diffusion coefficients In Proceedings of World Academy of Science, Engineering and Technology Vol. 35 - Int. Conference on Bioinformatics and Biomedicine, 2008.

• P. Lecca, Stochastic pi-calculus models of the molecular bases of Parkinson's disease. In Procs of The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP), CSREA Press, Las Vegas, Nevada (USA), July, 2008.

• P. Lecca and M. Lecca, A model of the Ca2+ and Na+ waves kinetics in astrocytes and its relevance to functional brain imaging, In Proceedings of the International Association for Computational Mechanics (IACM) and the European Community on Computational Methods in Applied Sciences, 2008.

• P. Lecca, G. Sanguinetti, A. Palmisano, and C. Priami, A new model for inferring rate coefficients from experimental time-consecutive measurements of reactant concentrations, Proceeding of International Conference on Systems Biology, 2007.

• P. Lecca, Adiabatic FRM to simulate voltage gating of ion channels, Poster paper in Procs. of German Conference of Bioinformatics, Tuebingen, Germany, 2006.

• P. Lecca, A stochastic description of the molecular mechanisms of hereditary parkinsonism, In Poster Procs, RECOMB, Venice 2006.

• J. Himmelspach, P. Lecca, D. Prandi, P. Quaglia and A. Uhrmacher, Developing A Hierarchical Simulator for Beta-binders, In Procs of 20th ACM/IEEE/SCS Workshop on Priciples of Advanced and Distributes Simulation (PADS) 2006.

• P. Lecca, A time-dependent extension of Gillespie algorithm for biochemical stochastic pi-calculus. In Proceeding of ACM Symposium on Applied Computing 2006 (SAC 2006, April 23-27, Dijon, France).

• P. Lecca and C. Priami and C. Laudanna and G. Constantin, A BioSpi model of lymphocyte-endothelial interaction in inflamed brain venules. Biocomputing 2004, Proceedings of the Pacific Symposium, Hawaii, USA, 6-10 January 2004 2004.

• P. Lecca and C. Priami and C. Laudanna and G. Constantin. Predicting cell adhesion probability via the biochemical stochastic pi-calculus. In Proceedings of "ACM Symposium on Applied Computing", Cyprus, pp. 211-212, 2004.

• P. Lecca and C. Priami and C. Laudanna and G. Constantin, Computer modeling of lymphocyte behavior in inflamed brain venules by using stochastic pi-calculus, Poster, 7th Int. Congress of neuroimmunology, Venice, September 27 - October 02, 2004.

• P. Lecca, EXPLODET Project: methods of automatic data processing and analysis for the detection of hidden explosive, In the Proceedings Book of International Workshop on Detecting Environmental, Industrial & Bio-Medical Signals, Editor M. de Palma, 2003.

• G. Viesti, M. Lunardon, G. Nebbia, M. Barbui, M. Cinausero, E. Fioretto, G. Prete, A. Pantaleo, G. D'Erasmo, M. Palomba, M. Abbrescia, G. Iaselli, F. Loddo, V. Paticchio, T. Ranieri, R. Trentadue, A. Colla, A. Musso, A. Piccotti, F. Poggio, G. Dellacasa, I. Lazzizzera, P. Lecca, J. Obhodas, D. Sudac, K. Nad, V. Valkovic, A. Brusegan, J. Lobo and S. Hlavac, The DIAMINE Landmine Detection System, In the Proceedings Book of International Workshop on Detecting Environmental, Industrial & Bio-Medical Signals, M. de Palma Editor, (http://www.pd.infn.it/explodet/diamine.html) 2003.