Reaction Systems (RSs) were introduced in the field of natural computing as a qualitative model inspired by biological systems. In a RS, each reaction comprises a set of reactants that generate products unless inhibited by reaction inhibitors. We propose a method for analyzing the attractors of a RS model (the states to which the system converges) and for identifying the entities responsible for their attainment. Our approach builds on (i) the construction of a Labeled Transition System (LTS) based on a formal SOS semantics of RSs, and (ii) the application of a slicing method to the trajectories within the LTS. Our model analysis provides new insights with respect to previous studies. We illustrate our methodology on a case study that demonstrates its capability to identify stimulus combinations that lead to specific phenotypes, but also to elucidate the involvement of proteins within T cells in each scenario. These findings allow for a better understanding of the phenomenon and for the identification of potential drug targets for diseases.
Brodo, L., Bruni, R., Falaschi, M., Gori, R., Milazzo, P. (2025). Attractor and Slicing Analysis of a T Cell Differentiation Model Based on Reaction Systems. In From Data to Models and Back (DataMod 2023) (pp.69-89). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-87217-4_4].
Attractor and Slicing Analysis of a T Cell Differentiation Model Based on Reaction Systems
Falaschi, M.;
2025-01-01
Abstract
Reaction Systems (RSs) were introduced in the field of natural computing as a qualitative model inspired by biological systems. In a RS, each reaction comprises a set of reactants that generate products unless inhibited by reaction inhibitors. We propose a method for analyzing the attractors of a RS model (the states to which the system converges) and for identifying the entities responsible for their attainment. Our approach builds on (i) the construction of a Labeled Transition System (LTS) based on a formal SOS semantics of RSs, and (ii) the application of a slicing method to the trajectories within the LTS. Our model analysis provides new insights with respect to previous studies. We illustrate our methodology on a case study that demonstrates its capability to identify stimulus combinations that lead to specific phenotypes, but also to elucidate the involvement of proteins within T cells in each scenario. These findings allow for a better understanding of the phenomenon and for the identification of potential drug targets for diseases.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/1301535
