Deterministic Stochastic And Thermodynamic Modelling Of Some Interacting Species

This book presents the understanding of how the different forms of regulatory mechanisms, like birth and death, competition, consumption and the like, result in changes in the stability and dynamics of ecological systems. It deals with a profound and unique insight into the mathematical richness of basic ecological models. Organised into eight chapters, the book discusses the models of mathematical ecology, the dynamical models of single-species system in a polluted environment, the dynamical behaviour of different nonautonomous two species systems in a polluted environment, the influence of environmental noise in Gompertzian and logistic growth models, stability behaviour in randomly fluctuating versus deterministic environments of two interacting species, stochastic analysis of a demographic model of urbanization and stability behaviour of a social group by means of loop analysis, thermodynamic criteria of stability and stochastic criteria of stability. The book will be useful to the researchers and graduate students who wish to pursue research in mathematical ecology.

Stochastic Models in Biology describes the usefulness of the theory of stochastic process in studying biological phenomena. The book describes analysis of biological systems and experiments though probabilistic models rather than deterministic methods. The text reviews the mathematical analyses for modeling different biological systems such as the random processes continuous in time and discrete in state space. The book also discusses population growth and extinction through Malthus' law and the work of MacArthur and Wilson. The text then explains the dynamics of a population of interacting species. The book also addresses population genetics under systematic evolutionary pressures known as deterministic equations and genetic changes in a finite population known as stochastic equations. The text then turns to stochastic modeling of biological systems at the molecular level, particularly the kinetics of biochemical reactions. The book also presents various useful equations such as the differential equation for generating functions for birth and death processes. The text can prove valuable for biochemists, cellular biologists, and researchers in the medical and chemical field who are tasked to perform data analysis.

In this contribution, several probabilistic tools to study population dynamics are developed. The focus is on scaling limits of qualitatively different stochastic individual based models and the long time behavior of some classes of limiting processes. Structured population dynamics are modeled by measure-valued processes describing the individual behaviors and taking into account the demographic and mutational parameters, and possible interactions between individuals. Many quantitative parameters appear in these models and several relevant normalizations are considered, leading to infinite-dimensional deterministic or stochastic large-population approximations. Biologically relevant questions are considered, such as extinction criteria, the effect of large birth events, the impact of environmental catastrophes, the mutation-selection trade-off, recovery criteria in parasite infections, genealogical properties of a sample of individuals. These notes originated from a lecture series on Structured Population Dynamics at Ecole polytechnique (France). Vincent Bansaye and Sylvie Méléard are Professors at Ecole Polytechnique (France). They are a specialists of branching processes and random particle systems in biology. Most of their research concerns the applications of probability to biodiversity, ecology and evolution.

This book aims to provide a unified treatment of input/output modelling and of control for discrete-time dynamical systems subject to random disturbances. The results presented are of wide applica bility in control engineering, operations research, econometric modelling and many other areas. There are two distinct approaches to mathematical modelling of physical systems: a direct analysis of the physical mechanisms that comprise the process, or a 'black box' approach based on analysis of input/output data. The second approach is adopted here, although of course the properties ofthe models we study, which within the limits of linearity are very general, are also relevant to the behaviour of systems represented by such models, however they are arrived at. The type of system we are interested in is a discrete-time or sampled-data system where the relation between input and output is (at least approximately) linear and where additive random dis turbances are also present, so that the behaviour of the system must be investigated by statistical methods. After a preliminary chapter summarizing elements of probability and linear system theory, we introduce in Chapter 2 some general linear stochastic models, both in input/output and state-space form. Chapter 3 concerns filtering theory: estimation of the state of a dynamical system from noisy observations. As well as being an important topic in its own right, filtering theory provides the link, via the so-called innovations representation, between input/output models (as identified by data analysis) and state-space models, as required for much contemporary control theory.

Survival of the fittest” is a tautology, because those that are “fit” are the ones that survive, but to survive, a species must be “fit”. Modern evolutionary theory avoids the problem by defining fitness as reproductive success, but the complexity of life that we see today could not have evolved based on selection that favors only reproductive ability. There is nothing inherent in reproductive success alone that could result in higher forms of life. Evolution from a Thermodynamic Perspective presents a non-circular definition of fitness and a thermodynamic definition of evolution. Fitness means maximization of power output, necessary to survive in a competitive world. Evolution is the “storage of entropy”. “Entropy storage” means that solar energy, instead of dissipating as heat in the Earth, is stored in the structure of living organisms and ecosystems. Part one explains this in terms comprehensible to a scientific audience beyond biophysicists and ecosystem modelers. Part two applies thermodynamic theory in non-esoteric language to sustainability of agriculture, and to conservation of endangered species. While natural systems are stabilized by feedback, agricultural systems remain in a mode of perpetual growth, pressured by balance of trade and by a swelling population. The constraints imposed by thermodynamic laws are being increasingly felt as economic expansion destabilizes resource systems on which expansion depends.

Single-species growth; Pedration and parasitism; Predador-prey systems; Lotka-volterra systems for predator-prey interactions; Intermediate predator-prey models; Continous models; Discrete models; The kolmogorov model; Related topics and applications; Related topics; Aplications; competition and cooperation (symbiosis); Lotka-volterra competition models; Higher-oder competition models; cooperation (symbiosis); Pertubation theory; The implicit function theorem; Existence and Uniqueness of solutions of ordinary differential equations; Stability and periodicity; The poincare-bendixon theorem; The hopf bifurcation theorem.

The aim of this book is to provide a well-structured and coherent overview of existing mathematical modeling approaches for biochemical reaction systems, investigating relations between both the conventional models and several types of deterministic-stochastic hybrid model recombinations. Another main objective is to illustrate and compare diverse numerical simulation schemes and their computational effort. Unlike related works, this book presents a broad scope in its applications, from offering a detailed introduction to hybrid approaches for the case of multiple population scales to discussing the setting of time-scale separation resulting from widely varying firing rates of reaction channels. Additionally, it also addresses modeling approaches for non well-mixed reaction-diffusion dynamics, including deterministic and stochastic PDEs and spatiotemporal master equations. Finally, by translating and incorporating complex theory to a level accessible to non-mathematicians, this book effectively bridges the gap between mathematical research in computational biology and its practical use in biological, biochemical, and biomedical systems.