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
Funder: Regione Toscana, Bando Salute, 2020.
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.
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.
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.
Encoding Boolean networks into reaction systems for investigating causal dependencies in gene regulation Journal Article
Theoretical Computer Science, 2020.
Investigating dynamic causalities in reaction systems Journal Article
Theoretical Computer Science, 623 , pp. 114–145, 2016.
Mathematical modeling of drug resistance due to KRAS mutation in colorectal cancer Journal Article
Journal of Theoretical Biology, 389 , pp. 263–273, 2016.
Probabilistic model checking of biological systems with uncertain kinetic rates Journal Article
Theoretical Computer Science, 419 , pp. 2–16, 2012.