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Evolution (Biology) --- Evolution (Biologie) --- Mathematical models --- Modèles mathématiques --- Mathematical models. --- Modèles mathématiques --- Évolution (biologie) --- Modèles mathématiques. --- Evolution (Biology) - Mathematical models --- Évolution (biologie) --- Modèles mathématiques.

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The metaphor of the adaptive landscape - that evolution via the process of natural selection can be visualized as a journey across adaptive hills and valleys, mountains and ravines - permeates both evolutionary biology and the philosophy of science. The focus of this 2006 book is to demonstrate to the reader that the adaptive landscape concept can be put into actual analytical practice through the usage of theoretical morphospaces - geometric spaces of both existent and non-existent biological form - and to demonstrate the power of the adaptive landscape concept in understanding the process of evolution. The adaptive landscape concept further allows us to take a spatial approach to the concepts of natural selection, evolutionary constraint and evolutionary development. For that reason, this book relies heavily on spatial graphics to convey the concepts developed within these pages, and less so on formal mathematics.

Evolution (Biology) --- Adaptation (Biology) --- Morphology --- Mathematical models --- Mathematical models. --- Biological form --- Biological structure --- Comparative morphology --- Form in biology --- Structure in biology --- Anatomy, Comparative --- Morphogenesis --- Environment --- Biology --- Self-organizing systems --- Variation (Biology) --- Biological fitness --- Genetics --- Environmental adaptation --- Adaptation, Environmental --- Evolution (Biology) - Mathematical models --- Adaptation (Biology) - Mathematical models --- Morphology - Mathematical models --- Grammar, Comparative and general Morphology

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Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own. The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction. Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists., A how-to guide for developing new mathematical models in biology, Provides step-by-step recipes for constructing and analyzing models, Interesting biological applications, Explores classical models in ecology and evolution, Questions at the end of every chapter, Primers cover important mathematical topics, Exercises with answers, Appendixes summarize useful rules, Labs and advanced material available

Analytical biochemistry --- Ecology --- Evolution (Biology) --- Ecologie --- Evolution (Biologie) --- Mathematical models --- Modèles mathématiques --- 575 --- 51-76 --- General genetics. General cytogenetics. Immunogenetics. Evolution. Speciation. Phylogeny --- Mathematical biology --- Mathematical models. --- 575 General genetics. General cytogenetics. Immunogenetics. Evolution. Speciation. Phylogeny --- Modèles mathématiques --- 57.087 --- 57.087 Methods and techniques for parameter estimation. Recording of biological data --- Methods and techniques for parameter estimation. Recording of biological data --- Ecology - Mathematical models --- Evolution (Biology) - Mathematical models

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population dynamics --- Mathematical models --- Dynamic models --- biology --- genetics --- ecology --- Evolution (Biology) --- Game theory --- Population biology --- Volterra equations --- 519.83 --- 519.83 Theory of games --- Theory of games --- Equations, Volterra --- Integral equations --- Games, Theory of --- Mathematics --- General ecology and biosociology --- Game theory. --- Volterra equations. --- Mathematical models. --- Evolution --- Game Theory. --- Models, Theoretical. --- Population Dynamics. --- Evolution (Biologie) --- Biologie des populations --- Théorie des jeux --- Volterra, Equations de --- Modèles mathématiques --- Evolution (Biology) - Mathematical models --- Population biology - Mathematical models --- Equation lotka-volterra

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Esistono ormai da tempo molti articoli, in particolar modo su riviste di biomatematica, di (bio)fisica e di biologia, che presentano proposte e risultati di modellistica matematica relativi direttamente ed indirettamente alla teoria dell’evoluzione. Sicuramente questi studi sono da considerarsi cruciali per l’istituzione della biologia teorica. I temi da prendere in esame sono dapprima le convinzioni che i biologi hanno in merito. Quindi un’analisi dei precedenti tentativi di formulare una teoria matematica dell’evoluzione, nonché i relativi sviluppi e insuccessi a cui abbiamo assistito nell’ambito della "teoria della complessità". La nostra proposta consiste dunque nel realizzare una teoria matematicamente formulata e biologicamente ben fondata dell’evoluzione con specifico e giustificato riferimento a quella fenotipica. Quindi su questa base costruiamo sia di un modello geometrico sia un modello dinamico stocastico. In questo modo, pur tenendo presente l’intrinseca insufficienza dell’approccio riduzionista in biologia, si tenta di dare alcune risposte che hanno una corrispondenza biologica significativa.

Adaptation (Biology) -- Mathematical models. --- Biodiversity -- Mathematical models. --- Evolution (Biology) -- Mathematical models. --- Biology --- Health & Biological Sciences --- Biophysics --- Evolution (Biology) --- Evolution --- Statistical methods. --- Mathematics. --- Animal evolution --- Animals --- Biological evolution --- Darwinism --- Evolutionary biology --- Evolutionary science --- Origin of species --- Physics. --- Evolutionary biology. --- Probabilities. --- Biomathematics. --- Biophysics. --- Biological physics. --- Biophysics and Biological Physics. --- Evolutionary Biology. --- Mathematical and Computational Biology. --- Probability Theory and Stochastic Processes. --- Philosophy --- Creation --- Emergence (Philosophy) --- Teleology --- Biological fitness --- Homoplasy --- Natural selection --- Phylogeny --- Evolution (Biology). --- Distribution (Probability theory. --- Biological and Medical Physics, Biophysics. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Biological physics --- Medical sciences --- Physics