Daniela Besozzi
Daniela Besozzi
affiliation: Università di Milano
research area(s): Computational Biology
Course: Biomolecular Sciences
University/Istitution: Università di Milano
December 2002 to date: Assistant Professor, University of Milano, Department of Informatics and Communication

- 2000: Degree in Mathematics, University of Insubria (Como)
- 2004: Ph.D. in Computer Science, University of Milano

- February 2002: Fraunhofer Gesellschaft, BioMIP (BioMolecular Information Processing), Sankt Augustin (Bonn), Germany
- October 2003: Institute of Biology of the Romanian Academy, Centre of Microbiology, Bucharest, Romania
- September-October 2005: Leiden Center for Natural Computing, Leiden, The Netherlands
- January 2006: Department of Computer Science, University of Sevilla, Sevilla, Spain
- July 2006: Leiden Center for Natural Computing, Leiden, The Netherlands

2004: Best Italian Ph.D Thesis in the field of Theoretical Computer Science, conferred by the Italian Chapter of EATCS (European Association for Theoretical Computer Science)
- Computational Biology, Systems Biology
- Biological systems analysis, modelling and simulation
- Intracellular signaling pathways, intercellular communication mechanisms, ecological systems
- Stochastic simulation algorithms for (multivolume) biological systems
- Parameter estimation and reverse engineering of biochemical systems
- Evolutionary Algorithms (Genetic Algorithms, Particle Swarm Optimizers)
- Formal Languages, bio-inspired computational models
D. Pescini P. Cazzaniga, D. Besozzi, G. Mauri, L. Amigoni, S. Colombo, E. Martegani (2011) Simulation of the Ras/cAMP/PKA pathway in budding yeast highlights the establishment of stable oscillatory states. Biotechnology Advances, in press (doi:10.1016/j.biotechadv.2011.06.014)

D. Besozzi, P. Cazzaniga, D. Pescini, G. Mauri, S. Colombo, E. Martegani (2011) Investigating oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae: the role of feedback control mechanisms. Eigth International Workshop on Computational Systems Biology, WCSB 2011, June 6-8, 2011, Zürich, Switzerland (H. Koeppl, J. Acimovic, J. Kesseli, T. Maki-Marttunen, A. Larjo, O. Yli-Harja, eds.), TICSP Series #57:33-36.

D. Besozzi, P. Cazzaniga, S. Cocolo, G. Mauri, D. Pescini (2011) Modeling diffusion in a signal transduction pathway: the use of virtual volumes in P systems. International Journal of Foundations of Computer Science, Vol. 22, Issue 1:89-96.

D. Besozzi, P. Cazzaniga, G. Mauri, D. Pescini (2010) BioSimWare: a software for the modeling, simulation and analysis of biological systems. Membrane Computing, 11th International Conference, CMC 2010, Jena, Germany, August 24-27, 2010. Revised Selected Papers (M. Gheorghe, T. Hinze, G. Paun, G. Rozenberg, A. Salomaa, eds.), LNCS 6501:119-143.

D. Besozzi, P. Cazzaniga, D. Pescini, G. Mauri (2010) An analysis on the influence of network topologies on local and global dynamics of metapopulation systems. Applications of Membrane Computing, Concurrency and Agent-based Modelling in Population Biology (AMCA-POP 2010) (P. Milazzo, M.J. Perez-Jimenez, eds.), EPTCS 33:1-17.

D. Besozzi, N. Busi, P. Cazzaniga, C. Ferretti, A. Leporati, G. Mauri, D. Pescini, C. Zandron (2009) (Tissue) P systems with cell polarity. Mathematical Structures in Computer Science, Vol.19, Issue 6:1141-1160.

D. Besozzi, P. Cazzaniga, M. Dugo, D. Pescini, G. Mauri (2009) A study on the combined interplay between stochastic fluctuations and the number of flagella in bacterial chemotaxis. Proceedings of CompMod2009 - 2nd International Workshop on Computational Models for Cell Processes (R.J. Back, I. Petre, E. de Vink, eds.), EPTCS 6:47-62.

D. Besozzi, P. Cazzaniga, G.Mauri, D. Pescini, L. Vanneschi (2009) A comparison of genetic algorithms and particle swarm optimization for parameter estimation in stochastic biochemical systems. EvoBIO 2009 (C. Pizzuti, M.D. Ritchie, M. Giacobini, eds.), LNCS 5483:116-127.

D. Besozzi, I.I. Ardelean (2009) Cell biology for Membrane Computing. In: The Oxford Handbook of Membrane Computing (G. Paun, G. Rozenberg, A. Salomaa eds.), Oxford University Press.

P. Cazzaniga, D. Pescini, D. Besozzi, G. Mauri, S. Colombo, E. Martegani (2008) Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in the yeast Saccharomyces cerevisiae evidences a key regulatory function for intracellular guanine nucleotides pools. Journal of Biotechnology, Vol. 133, Issue 3:377-385.

D. Besozzi, P. Cazzaniga, D. Pescini, G. Mauri (2008) Modelling metapopulations with stochastic membrane systems. BioSystems, Vol. 91, Issue 3:499-514.

D. Besozzi, P. Cazzaniga, D. Pescini, G. Mauri (2007) Seasonal variance in P system models for metapopulations. Progress in Natural Science, Vol. 17, No. 4:392-400.

M. Muskulus, D. Besozzi, R. Brijder, P. Cazzaniga, S. Houweling, D. Pescini, G. Rozenberg (2007) Cycles and communicating classes in membrane systems and molecular dynamics. Theoretical Computer Science, Vol. 372, Issues 2-3:242-266.

D. Besozzi, G. Rozenberg (2006) Formalizing spherical membrane structures and membrane proteins populations. Membrane Computing, 7th International Workshop (WMC 2006) (H.J. Hoogeboom, G. Paun, G. Rozenberg, A. Salomaa, eds.), LNCS 4361:18-41.
Project Title:
Dissecting the Post-Replication Repair pathway in S. cerevisiae by integrating experimental and computational biology approaches
To maintain genome stability, cells have developed a number of repair and tolerance mechanisms to face the effects of DNA damage. Post-Replication Repair (PRR) is the main tolerance pathway acting to bypass DNA lesions in S phase. The current model of PRR involves a complex and still largely uncharacterized cross-talk between two sub-pathways: the first one requires the action of translesion (TLS) DNA polymerases which, differently from replicative polymerases, are able to bypass lesions in the DNA template and are, therefore, error-prone. The second sub-pathway is error-free and likely acts through recombination mechanisms, such as template switching and/or fork reversal, to bypass the DNA damage. The choice among these sub-pathways is based, in a still unknown way, on the covalent modifications of a key protein, the Proliferating Cell Nuclear Antigen (PCNA), which acts as a molecular switch. In particular, the mono-ubiquitylation of PCNA by the Rad6-Rad18 complex drives the PRR pathway to TLS, while the K63-linked poly-ubiquitylation of PCNA, catalyzed by the Ubc13-Mms2-Rad5 complex, directs PRR to the error-free sub-pathway. Many aspects of the PRR response are still unclear: for example, it is unknown how the number of lesions caused by a specific damaging agent (such as UV irradiation) influences the balance between PCNA mono- and poly-ubiquitylation, what is the role of poly-ubiquitylated PCNA, or whether exists a damage-threshold regulating the cross-talk between the two sub-pathways, and so on.
The PhD research project is focused on the synergistic integration of computational, mathematical and experimental analysis to study the PRR, using a methodology based on the iterative cross-talk between model-driven experiments and data-driven modeling. In this context, we are exploiting this Systems Biology approach to better understand the cellular response to UV-induced DNA damage in the Saccharomyces cerevisiae model system, using PCNA mono- and poly-ubiquitylation as a biochemical readout at different UV doses. Our preliminary data revealed that PCNA mono- and poly-ubiquitylated forms are switched off rapidly at low UV doses, while at high doses both signals are still present after several hours. On the basis of these quantitative data and kinetics analysis, we have developed a computational model of PCNA ubiquitylation that allows to distinguish between the behaviour of the system at various UV doses and times.
The final goal of the project is to elucidate the spatio-temporal cascade of protein-protein interactions involved in the two PRR sub-pathways and to clarify the cross-talk between PRR and other DNA damage repair mechanisms (e.g. Nucleotide Excision Repair), and to develop a computational model which will be able to predict the PRR behaviour in different experimental conditions.