BioSystems Modelling Group
- Systems Biology
- Signalling and metabolic pathways, gene regulation networks
- Epidemic and population models
- Modelling and simulation
- ODEs and stochastic modelling
- Formal methods
- Model checking and static analyses

Web page
Members

Roberta Gori
Associate Professor

Francesca Levi
Associate Professor

Paolo Milazzo
Associate Professor

Lucia Nasti
PostDoc
Projects
PreMed2 Funder: Regione Toscana, Bando Salute, 2020. @misc{PreMed2, title = {PreMed2}, year = {2020}, date = {2020-12-29}, abstract = {The main purpose of this project is to identify the population of subjects at risk of developing type 2 diabetes based on eating habits (using validated questionnaires), physical activity (using wearable devices) and metabolic profile. The BioSystems Modeling group is involved in data analysis with the application of systems biology approaches. }, howpublished = {Funder: Regione Toscana, Bando Salute}, keywords = {}, pubstate = {published}, tppubtype = {misc} } The main purpose of this project is to identify the population of subjects at risk of developing type 2 diabetes based on eating habits (using validated questionnaires), physical activity (using wearable devices) and metabolic profile. The BioSystems Modeling group is involved in data analysis with the application of systems biology approaches. |
Metodi Informatici Integrati per la Biomedica PRA – Progetti di Ricerca di Ateneo (Institutional Research Grants) - Project no. PRA_2020-2021_26 , 2020. @misc{PRA_2020, title = {Metodi Informatici Integrati per la Biomedica}, year = {2020}, date = {2020-12-29}, abstract = {This project aims at integrating different computer methodologies that contribute to interdisciplinary research in bioinformatics and, in particular, to its applications in the biomedical field. This in order to experiment the co-existence of algorithmic, modeling, and machine learning methods in the analysis of biomedical data and, at the same time, to refine the synergy of the many research groups that have been active in the Department of Computer Science for decades.}, howpublished = {PRA – Progetti di Ricerca di Ateneo (Institutional Research Grants) - Project no. PRA_2020-2021_26 }, keywords = {}, pubstate = {published}, tppubtype = {misc} } This project aims at integrating different computer methodologies that contribute to interdisciplinary research in bioinformatics and, in particular, to its applications in the biomedical field. This in order to experiment the co-existence of algorithmic, modeling, and machine learning methods in the analysis of biomedical data and, at the same time, to refine the synergy of the many research groups that have been active in the Department of Computer Science for decades. |
Selected Publications
2020 |
Bove, Pasquale; Micheli, Alessio; Milazzo, Paolo; Podda, Marco Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks Inproceedings Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, SCITEPRESS - Science and Technology Publications, 2020. @inproceedings{Bove2020, title = {Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks}, author = {Pasquale Bove and Alessio Micheli and Paolo Milazzo and Marco Podda}, doi = {10.5220/0008964700320043}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies}, publisher = {SCITEPRESS - Science and Technology Publications}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Barbuti, Roberto; Gori, Roberta; Milazzo, Paolo Encoding Boolean networks into reaction systems for investigating causal dependencies in gene regulation Journal Article Theoretical Computer Science, 2020. @article{Barbuti2020, title = {Encoding Boolean networks into reaction systems for investigating causal dependencies in gene regulation}, author = {Roberto Barbuti and Roberta Gori and Paolo Milazzo}, doi = {10.1016/j.tcs.2020.07.031}, year = {2020}, date = {2020-01-01}, journal = {Theoretical Computer Science}, publisher = {Elsevier BV}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2016 |
Barbuti, Roberto; Gori, Roberta; Levi, Francesca; Milazzo, Paolo Investigating dynamic causalities in reaction systems Journal Article Theoretical Computer Science, 623 , pp. 114–145, 2016. @article{Barbuti2016, title = {Investigating dynamic causalities in reaction systems}, author = {Roberto Barbuti and Roberta Gori and Francesca Levi and Paolo Milazzo}, doi = {10.1016/j.tcs.2015.11.041}, year = {2016}, date = {2016-01-01}, journal = {Theoretical Computer Science}, volume = {623}, pages = {114--145}, publisher = {Elsevier BV}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Sameen, Sheema; Barbuti, Roberto; Milazzo, Paolo; Cerone, Antonio; Re, Marzia Del; Danesi, Romano Mathematical modeling of drug resistance due to KRAS mutation in colorectal cancer Journal Article Journal of Theoretical Biology, 389 , pp. 263–273, 2016. @article{Sameen2016, title = {Mathematical modeling of drug resistance due to KRAS mutation in colorectal cancer}, author = {Sheema Sameen and Roberto Barbuti and Paolo Milazzo and Antonio Cerone and Marzia Del Re and Romano Danesi}, doi = {10.1016/j.jtbi.2015.10.019}, year = {2016}, date = {2016-01-01}, journal = {Journal of Theoretical Biology}, volume = {389}, pages = {263--273}, publisher = {Elsevier BV}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2012 |
Barbuti, Roberto; Levi, Francesca; Milazzo, Paolo; Scatena, Guido Probabilistic model checking of biological systems with uncertain kinetic rates Journal Article Theoretical Computer Science, 419 , pp. 2–16, 2012. @article{Barbuti2012, title = {Probabilistic model checking of biological systems with uncertain kinetic rates}, author = {Roberto Barbuti and Francesca Levi and Paolo Milazzo and Guido Scatena}, doi = {10.1016/j.tcs.2011.10.022}, year = {2012}, date = {2012-02-01}, journal = {Theoretical Computer Science}, volume = {419}, pages = {2--16}, publisher = {Elsevier BV}, keywords = {}, pubstate = {published}, tppubtype = {article} } |