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Models of dispersal in biological systems

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TLDR
Two stochastic processes that model the major modes of dispersal that are observed in nature are introduced, and explicit expressions for the mean squared displacement and other experimentally observable quantities are derived.
Abstract
In order to provide a general framework within which the dispersal of cells or organisms can be studied, we introduce two stochastic processes that model the major modes of dispersal that are observed in nature. In the first type of movement, which we call the position jump or kangaroo process, the process comprises a sequence of alternating pauses and jumps. The duration of a pause is governed by a waiting time distribution, and the direction and distance traveled during a jump is fixed by the kernel of an integral operator that governs the spatial redistribution. Under certain assumptions concerning the existence of limits as the mean step size goes to zero and the frequency of stepping goes to infinity the process is governed by a diffusion equation, but other partial differential equations may result under different assumptions. The second major type of movement leads to what we call a velocity jump process. In this case the motion consists of a sequence of "runs" separated by reorientations, during which a new velocity is chosen. We show that under certain assumptions this process leads to a damped wave equation called the telegrapher's equation. We derive explicit expressions for the mean squared displacement and other experimentally observable quantities. Several generalizations, including the incorporation of a resting time between movements, are also studied. The available data on the motion of cells and other organisms is reviewed, and it is shown how the analysis of such data within the framework provided here can be carried out.

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Citations
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A user’s guide to PDE models for chemotaxis

TL;DR: This paper explores in detail a number of variations of the original Keller–Segel model of chemotaxis from a biological perspective, contrast their patterning properties, summarise key results on their analytical properties and classify their solution form.
Journal ArticleDOI

Random walk models in biology.

TL;DR: The mathematical theory behind the simple random walk is introduced and how this relates to Brownian motion and diffusive processes in general and a reinforced random walk can be used to model movement where the individual changes its environment.

From 1970 until present: the Keller-Segel model in chemotaxis and its consequences

TL;DR: This article summarizes various aspects and results for some general formulations of the classical chemotaxis models also known as Keller-Segel models and offers possible generalizations of these results to more universal models.
Journal ArticleDOI

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

TL;DR: In this paper, the authors focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics.
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Toward a mathematical theory of Keller–Segel models of pattern formation in biological tissues

TL;DR: In this article, a survey and critical analysis focused on a variety of chemotaxis models in biology, namely the classical Keller-Segel model and its subsequent modifications, which, in several cases, have been developed to obtain models that prevent the non-physical blow up of solutions.
References
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Journal ArticleDOI

Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen

Albert Einstein
- 01 Jan 1905 - 
TL;DR: In el marco del Proyecto subvencionado by the Fundación Antorchas (FAN) as discussed by the authors, el material was digitalizado, e.g., en la Biblioteca del Departamento de Fisica de la Facultad de Ciencias Exactas de la Universidad Nacional de La Plata.
Book

A first course in stochastic processes

TL;DR: In this paper, the Basic Limit Theorem of Markov Chains and its applications are discussed and examples of continuous time Markov chains are presented. But they do not cover the application of continuous-time Markov chain in matrix analysis.
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