GreatMod - References

References

Papers on the Package:

• A computational framework for modeling and studying pertussis epidemiology and vaccination. P. Castagno, S. Pernice, G. Ghetti, M. Povero, L. Pradelli, D. Paolotti, G. Balbo, M. Sereno and M. Beccuti. BMC Bioinformatics, Volume 21, 16 September 2020, Page 344.

• Computational modeling of the immune response in multiple sclerosis using epimod framework. S. Pernice, L. Follia, A. Maglione, M. Pennisi, F. Pappalardo, F. Novelli, M. Clerico, M. Beccuti, F. Cordero, S. Rolla. BMC Bioinformatics, Volume 21, Article number: 550 (2020).

Papers on ESPN, ESSN and SSN formalisms:

• A Tool for the Automatic Derivation of Symbolic ODE from Symmetric Net Models. L. Capra, M. Beccuti, M. De Pierro, L. Follia, G. Franceschinis and S. Pernice. In 27th IEEE Int. Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS19), Rennes, France, October 22-25, 2019, Article number 8843193, Pages 36-48.

• Integrating Petri nets and Flux Balance methods in computationalbiology models: a methodological and computational practice. S. Pernice, L. Follia, G. Balbo, L. Milanesi, G. Sartini, N. Totis, P. Lio I. Merelli, F. Cordero and M. Beccuti. Fundamenta Informaticae, Volume 171, Issue 1-4, 2019, Pages 367-392.

• Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets without unfolding. M. Beccuti, L. Capra, M. De Pierro, G. Franceschinis and S. Pernice. In 15th European Performance Engineering Workshop (EPEW 2018), Spring LNCS, pp. 30-45, Octobre 29-30, 2018, Paris, France.

Case Studies:

• Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region. S. Pernice, P. Castagno, L. Marcotulli, M. M. Maule, L. Richiardi, G. Moirano, M. Sereno, F. Cordero and M. Beccuti. BMC Infectious Diseases, Volume 20, Article number: 798 (2020).

• Multiple Sclerosis disease: a computational approach for investigating its drug interactions. S. Pernice, M. Beccuti, G. Romano, M. Pennisi, A. Maglione, S. Cutrupi, F. Pappalardo, L. Capra, G. Franceschinis, M. De Pierro, G. Balbo, F.Cordero and R. Calogero. Proceeding of 16th Int. Conference Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB 2019), volume 12313 LNBI, pp. 299-308, Bergamo, Italy, September 4-6, 2019.

• Exploiting Stochastic Petri Net formalism to capture the Relapsing Remitting Multiple Sclerosis variability under Daclizumab administration. S. Pernice, G. Romano, G. Russo, M. Beccuti, M. Pennisi, and F. Pappalardo. In 3rd Int. Workshop on Computational Methods for the Immune System Function (CMISF 2019), San Diego, USA, November 2019.

• A computational approach based on the Colored Petri Net formalism for studying Multiple Sclerosis. S. Pernice, M. Pennisi, G. Romano, A. Maglione, S. Cutrupi, F. Pappalardo, G. Balbo, M. Beccuti, F. Cordero and R.A. Calogero. BMC Bioinformatics, Volume 20, 10 December 2019, Article number 623.

• Estimating Daclizumab effects in Multiple Sclerosis using Stochastic Symmetric Nets. S. Pernice, M. Beccuti, P. Do’, M. Pennisi, and F. Pappalardo. In 2nd Int. Workshop on Computational Methods for the Immune System Function (CMISF 2018), December 3 - 6, 2018, Madrid, Spain, Article number 8621259, Pages 1393-1400.