Abstract Probabilistic Semantics for the Analysis of Bio Sys Models » książka
Abstract Probabilistic Semantics for the Analysis of Bio Sys Models
ISBN-13: 9783659291708 / Angielski / Miękka / 2012 / 176 str.
Abstract Probabilistic Semantics for the Analysis of Bio Sys Models
ISBN-13: 9783659291708 / Angielski / Miękka / 2012 / 176 str.
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This book concerns the development of probabilistic semantics tailored to model the dynamic behavior of biological systems in order to formally analyze them. More specifically, it attempts to overcome problems, related to uncertainty and to the state space explosion, inherent to models describing biological systems. Recently, many formalisms originated from Computer Science have been successfully applied to describe biological systems. Many of these formalisms include probabilistic aspects, and techniques like stochastic simulation and probabilistic model checking have been proposed to study biological systems properties. However, the practical application of formal analysis tools in this context is still limited. The size of state space associated with models is often prohibitively large. Moreover, the knowledge of biological processes is often incomplete, resulting in models with uncertain parameters. To overcome these problems, in this Thesis, we propose to apply abstraction techniques to probabilistic semantics of biological systems models.
This book concerns the development of probabilistic semantics tailored to model the dynamic behavior of biological systems in order to formally analyze them. More specifically, it attempts to overcome problems, related to uncertainty and to the state space explosion, inherent to models describing biological systems. Recently, many formalisms originated from Computer Science have been successfully applied to describe biological systems. Many of these formalisms include probabilistic aspects, and techniques like stochastic simulation and probabilistic model checking have been proposed to study biological systems properties. However, the practical application of formal analysis tools in this context is still limited. The size of state space associated with models is often prohibitively large. Moreover, the knowledge of biological processes is often incomplete, resulting in models with uncertain parameters. To overcome these problems, in this Thesis, we propose to apply abstraction techniques to probabilistic semantics of biological systems models.
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