MEDICA Project

@article{BBF24,

title = {A framework for monitored dynamic slicing of Reaction Systems},

author = {Linda Brodo and Roberto Bruni and Moreno Falaschi},

doi = {https://doi.org/10.1007/s11047-024-09976-3},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Natural Computing},

abstract = {Reaction systems (RSs) are a computational framework inspired by biochemical mechanisms. A RS defines a finite set of reactions over a finite set of entities. Typically each reaction has a local scope, because it is concerned with a small set of entities, but complex models can involve a large number of reactions and entities, and their computation can manifest unforeseen emerging behaviours. When a deviation is detected, like the unexpected production of some entities, it is often difficult to establish its causes, e.g., which entities were directly responsible or if some reaction was misconceived. Slicing is a well-known technique for debugging, which can point out the program lines containing the faulty code. In this paper, we define the first dynamic slicer for RSs and show that it can help to detect the causes of erroneous behaviour and highlight the involved reactions for a closer inspection. To fully automate the debugging process, we propose to distil monitors for starting the slicing whenever a violation from a safety specification is detected. We have integrated our slicer in BioResolve, written in Prolog which provides many useful features for the formal analysis of RSs. We define the slicing algorithm for basic RSs and then enhance it for dealing with quantitative extensions of RSs, where timed processes and linear processes can be represented. Our framework is shown at work on suitable biologically inspired RS models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PERNICE2023104546,

title = {A new computational workflow to guide personalized drug therapy},

author = {Simone Pernice and Alessandro Maglione and Dora Tortarolo and Roberta Sirovich and Marinella Clerico and Simona Rolla and Marco Beccuti and Francesca Cordero},

url = {https://www.sciencedirect.com/science/article/pii/S1532046423002678},

doi = {https://doi.org/10.1016/j.jbi.2023.104546},

issn = {1532-0464},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Journal of Biomedical Informatics},

volume = {148},

pages = {104546},

abstract = {Objective: 

Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics. 

Methods: 

GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations. 

Results: 

To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months. 

Conclusion: 

GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}