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Parameter tuning of a stochastic biological simulator by metaheuristics
AI*IA 2009: Emergent Perspectives in Artificial Intelligence – XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia, Italy, December 9-12, 2009 Proceedings, pp. 466–475
Lecture Notes in Computer Science 5883
Springer Berlin / Heidelberg
2009
In this paper we address the problem of tuning parameters of a biological model, in particular a simulator of stochastic processes. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. We tackle the problem with a metaheuristic algorithm for continuous variables, Particle swarm optimisation, and show the effectiveness of the method in a prominent case-study, namely the mitogen-activated protein kinase cascade.
progetto finanziatore
NETSCALE — Ricostruzione e modellazione delle dinamiche molecolari e genetiche alla base della precoce regionalizzazione degli embrioni di zebrafish e di seaurchin (01/07/2008–30/06/2010)
AI*IA 2009: Emergent Perspectives in Artificial Intelligence – XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia, Italy, December 9-12, 2009 Proceedings
NETSCALE — Ricostruzione e modellazione delle dinamiche molecolari e genetiche alla base della precoce regionalizzazione degli embrioni di zebrafish e di seaurchin (01/07/2008–30/06/2010)