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2017 | Book

Piecewise Deterministic Processes in Biological Models

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About this book

This book presents a concise introduction to piecewise deterministic Markov processes (PDMPs), with particular emphasis on their applications to biological models. Further, it presents examples of biological phenomena, such as gene activity and population growth, where different types of PDMPs appear: continuous time Markov chains, deterministic processes with jumps, processes with switching dynamics, and point processes. Subsequent chapters present the necessary tools from the theory of stochastic processes and semigroups of linear operators, as well as theoretical results concerning the long-time behaviour of stochastic semigroups induced by PDMPs and their applications to biological models.

As such, the book offers a valuable resource for mathematicians and biologists alike. The first group will find new biological models that lead to interesting and often new mathematical questions, while the second can observe how to include seemingly disparate biological processes into a unified mathematical theory, and to arrive at revealing biological conclusions. The target audience primarily comprises of researchers in these two fields, but the book will also benefit graduate students.

Table of Contents

Frontmatter
Chapter 1. Biological Models
Abstract
A special role in biological applications is played by piecewise deterministic Markov processes. It is a large family of different stochastic processes which includes continuous-time Markov chains, deterministic processes with jumps, dynamical systems with random switching and some point processes. In this chapter, we present a number of biological models to illustrate possible applications of such processes.
Ryszard Rudnicki, Marta Tyran-Kamińska
Chapter 2. Markov Processes
Abstract
In this chapter, we provide a background material that is needed to define and study Markov processes in discrete and continuous time. We start by giving basic examples of transition probabilities and the corresponding operators on the spaces of functions and measures. An emphasis is put on stochastic operators on the spaces of integrable functions. The importance of transition probabilities is that the distribution of a stochastic process with Markov property is completely determined by transition probabilities and initial distributions. The Markov property simply states that the past and the future are independent given the present. We refer the reader to Appendix A for the required theory on measure, integration, and basic concepts of probability theory.
Ryszard Rudnicki, Marta Tyran-Kamińska
Chapter 3. Operator Semigroups
Abstract
Semigroups of linear operators provide the primary tools in the study of continuous-time Markov processes. They arise as the solutions of the initial value problem for the differential equation \(u'(t)=Au(t)\), where A is a linear operator acting on a Banach space. We describe what is generally regarded as the basic theory. We provide basic definitions, examples and theorems characterizing the operators as being the generators of semigroups. The aim here is to provide necessary foundations for studying semigroups on \(L^1\) spaces in the next chapter.
Ryszard Rudnicki, Marta Tyran-Kamińska
Chapter 4. Stochastic Semigroups
Abstract
In this chapter, we introduce stochastic semigroups as strongly continuous semigroups of stochastic operators on \(L^1\) spaces. We provide characterizations of their generators and we explain their connection with PDMPs as defined in the previous chapters. We give examples of such semigroups which correspond to pure jump-type processes, to deterministic processes, to semiflows with jumps and to randomly switched dynamical systems.
Ryszard Rudnicki, Marta Tyran-Kamińska
Chapter 5. Asymptotic Properties of Stochastic Semigroups—General Results
Abstract
In the theory of stochastic processes a special role is played by results concerning the existence of invariant densities and the long-time behaviour of their distributions. These results can be formulated and proved in terms of stochastic semigroups induced by these processes. We consider two properties: asymptotic stability and sweeping. A stochastic semigroup induced by a stochastic process is asymptotically stable if the densities of one-dimensional distributions of this process converge to a unique invariant density. Sweeping is an opposite property to asymptotic stability and it means that the probability that trajectories of the process are in a set Z goes to zero. The main result presented here shows that under some conditions a substochastic semigroup can be decomposed into asymptotically stable parts and a sweeping part. This result and some irreducibility conditions allow us to formulate theorems concerning asymptotic stability, sweeping and the Foguel alternative. This alternative says that under suitable conditions a stochastic semigroup is either asymptotically stable or sweeping.
Ryszard Rudnicki, Marta Tyran-Kamińska
Chapter 6. Asymptotic Properties of Stochastic Semigroups—Applications
Abstract
Most of time-homogeneous piecewise deterministic Markov processes induce stochastic semigroups. Therefore, general analytic tools used in the description of long-time behaviour of stochastic semigroups can be useful to study asymptotic and ergodic properties of piecewise deterministic Markov processes. In this chapter we give applications of some general results concerning stochastic semigroups to PDMPs considered in the first chapter.
Ryszard Rudnicki, Marta Tyran-Kamińska
Backmatter
Metadata
Title
Piecewise Deterministic Processes in Biological Models
Authors
Prof. Ryszard Rudnicki
Prof. Marta Tyran-Kamińska
Copyright Year
2017
Electronic ISBN
978-3-319-61295-9
Print ISBN
978-3-319-61293-5
DOI
https://doi.org/10.1007/978-3-319-61295-9

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