Models and Methods for Biological Evolution : Mathematical Models and Algorithms to Study Evolution

• Autore: Didier Gilles
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (328 pages)
• Disciplina: 576.8015118
• Soggetto topico: Mathematical models; Bioinformatics
• Altri autori: GuindonStephane
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. Trees: Combinatorics and Models -- 1.1. Introduction -- 1.2. Preliminary definitions -- 1.3. Counting trees -- 1.3.1. Fully labeled non-rooted trees -- 1.3.2. Binary trees with labeled leaves -- 1.3.3. Binary trees with labeled leaves and ordered internal nodes -- 1.3.4. Number of orders of internal nodes of a given tree -- 1.3.5. Directed binary trees -- 1.4. Probabilities of trees resulting from branching processes -- 1.5. Birth-death processes -- 1.5.1. Probability density of a birth-death tree -- 1.6. The coalescent -- 1.6.1. Links with "classical" models in population genetics -- 1.6.2. Moran's model -- 1.6.3. The Wright-Fisher model -- 1.6.4. Generic model -- 1.6.5. Coalescent-generated tree probability density -- 1.7. Conclusion -- 1.8. References -- Chapter 2. Models of Sequences and Discrete Traits Evolution -- 2.1. Introduction -- 2.2. Discrete set-valued continuous-time Markov process -- 2.2.1. Poisson processes -- 2.2.2. Finite set-valued continuous-time Markov process -- 2.3. Models of DNA sequence evolution -- 2.3.1. The Jukes-Cantor model -- 2.3.2. The Kimura model -- 2.3.3. The Felsenstein model -- 2.3.4. The HKY model -- 2.3.5. The general time reversible model -- 2.4. Models of rate evolution along the sequence -- 2.4.1. Independent and identically distributed rates along the sequence -- 2.4.2. Hidden Markov model -- 2.5. Models of discrete trait evolution -- 2.6. References -- Chapter 3. Evolutionary Models of Continuous Traits -- 3.1. Motivations -- 3.1.1. Comparative methods -- 3.1.2. Studies of evolutionary phenomena -- 3.2. Brownian motion -- 3.2.1. Description -- 3.2.2. Phylogenetic regression and statistical transformations -- 3.2.3. Recursive algorithms for inference -- 3.3. Multivariate analysis -- 3.3.1. Description. ; 3.3.2. Phylogenetic contrasts -- 3.3.3. Phylogenetic PCA -- 3.4. Gaussian models -- 3.4.1. Some limits of the Brownian motion -- 3.4.2. Ornstein-Uhlenbeck process -- 3.4.3. Biological interpretations and caveats -- 3.4.4. Further Gaussian processes -- 3.4.5. Heterogeneous evolution -- 3.4.6. Observation models -- 3.4.7. Model selection -- 3.5. Extensions and generalizations -- 3.5.1. Non-Gaussian models -- 3.5.2. Tree-trait interactions -- 3.5.3. Interactions between species -- 3.5.4. Trait of high dimension -- 3.6. Useful references -- 3.7. Acknowledgements -- 3.8. References -- Chapter 4. Correlated Evolution: Models and Methods -- 4.1. Introduction -- 4.2. Correlated evolution between traits -- 4.2.1. Species are not independent -- 4.2.2. The phylogenetically independent contrasts -- 4.2.3. Extending the linear model to account for phylogeny -- 4.2.4. Correlation between discrete traits -- 4.2.5. Examples of correlated traits -- 4.2.6. Jointly modeling traits and sequences -- 4.3. Correlated evolution within genomes -- 4.3.1. Within genes, between nucleotides -- 4.3.2. Within proteins, between amino acids -- 4.3.3. Within genomes, between genes -- 4.4. Genetics is also correlated evolution -- 4.4.1. In individuals -- 4.4.2. In pedigrees -- 4.4.3. In the population -- 4.5. Conclusion -- 4.6. References -- Chapter 5. A Century of Genomic Rearrangements -- 5.1. Introduction -- 5.2. Orderings of genes and the rearrangements that act on them -- 5.2.1. Basic representations and definitions -- 5.2.2. DCJ operations and the breakpoint graph -- 5.3. Counting DCJ scenarios -- 5.3.1. Scenarios for a balanced cycle of length 2m -- 5.3.2. The (many) cycle decompositions of a breakpoint graph -- 5.4. Chromosomal contact data and weighted scenarios -- 5.4.1. A model incorporating chromosomal contacts -- 5.4.2. Planar trees and an algorithm for exploring them. ; 5.4.3. Planar trees -- 5.5. Conclusion -- 5.6. References -- Chapter 6. Phylogenetic Inference: Distance-Based Methods -- 6.1. Introduction -- 6.2. Mathematical basis -- 6.3. Distance estimation -- 6.3.1. Estimating distances from aligned sequences -- 6.3.2. Other approaches to estimate distances -- 6.4. Tree inference -- 6.4.1. Fitting branch lengths with least squares -- 6.4.2. Scoring trees: from least squares to minimum evolution -- 6.4.3. NJ and other agglomerative algorithms -- 6.4.4. Beyond distances -- 6.5. Conclusion -- 6.6. References -- Chapter 7. Computing Inference in Phylogenetic Trees -- 7.1. Inferences and modeling -- 7.1.1. Inferences -- 7.1.2. Parsimony and likelihood -- 7.1.3. Maximum parsimony -- 7.2. Dynamic programming -- 7.2.1. Over the branches -- 7.2.2. Over the nodes -- 7.2.3. Over the tree -- 7.2.4. At the root -- 7.2.5. Recursion relations -- 7.2.6. Complexity reduction -- 7.2.7. Root management -- 7.3. Maximum parsimony -- 7.3.1. Ancestral interference -- 7.4. Likelihood -- 7.4.1. Root management -- 7.4.2. Computation at the nodes -- 7.4.3. Maximization, differentiation -- 7.4.4. Ancestral interference -- 7.5. References -- Chapter 8. The Bayesian Paradigm in Molecular Phylogeny -- 8.1. Introduction -- 8.2. General principles of the Bayesian approach in phylogeny -- 8.2.1. Markov chain Monte Carlo sampling -- 8.2.2. Summary of posterior distribution and sampling -- 8.3. Demarginalization of the likelihood function -- 8.3.1. Parameter expansion -- 8.3.2. Data augmentation -- 8.4. Bayesian selection of substitution models -- 8.4.1. Relative model comparison via the Bayes factor -- 8.4.2. Absolute evaluation of models via predictive posterior simulation -- 8.5. Impacts and future directions -- 8.6. References -- Chapter 9. Measures of Branch Support in Phylogenetics -- 9.1. Introduction. ; 9.2. Local supports: parametric and non-parametric aLRT -- 9.2.1. Null branch test and its limitations -- 9.2.2. Local aLRT test, parametric version -- 9.2.3. Local aLRT test, SH-like nonparametric version -- 9.2.4. Comparison with an example of aLRT support and bootstrap -- 9.3. Phylogenetic bootstrap -- 9.3.1. Statistic bootstrap -- 9.3.2. The Felsenstein bootstrap -- 9.3.3. Transfer bootstrap -- 9.3.4. Comparison with an example of bootstrap supports -- 9.4. Bayesian supports -- 9.4.1. Principle, use of Markov Monte Carlo chains -- 9.4.2. Local Bayesian support -- 9.4.3. Comparison of Bayesian supports with an example -- 9.5. Discussion -- 9.6. References -- Chapter 10. Fossils and Phylogeny -- 10.1. Inferences on topology -- 10.1.1. First approaches -- 10.1.2. Traits usable in paleontology -- 10.1.3. First quantitative approach: phenetics -- 10.1.4. Stratophenetics -- 10.1.5. Cladistics -- 10.1.6. Model-based approaches: likelihood, Bayesian approaches -- 10.1.7. Fossils and molecular data -- 10.2. Dating the tree of life -- 10.2.1. First qualitative approaches -- 10.2.2. First statistical approaches -- 10.2.3. Molecular dating -- 10.2.4. Tip dating -- 10.2.5. Birth-death model-based dating -- 10.3. Conclusion -- 10.4. References -- Chapter 11. Phylodynamics -- 11.1. Reconciling ecology, evolution and mathematics -- 11.2. Data and processors -- 11.2.1. New generation sequencing -- 11.2.2. PCR and capture -- 11.3. Infection phylogenies -- 11.3.1. Link to transmission chains -- 11.3.2. Dating and evolutionary rates -- 11.3.3. Biological applications of time calibration -- 11.4. Phylodynamics -- 11.4.1. A field in search of definition -- 11.4.2. As closely as possible to epidemiology -- 11.4.3. Coalescent -- 11.4.4. Birth-death models -- 11.4.5. Limitations of likelihood approaches -- 11.4.6. ABC phylodynamics -- 11.5. Infection phylogeography. ; 11.6. Infection and viral life history traits -- 11.7. Perspectives and challenges -- 11.8. References -- Chapter 12. Inference of Demographic Processes in Human Populations -- 12.1. Introduction -- 12.2. Demographic inferences from population genetics data -- 12.2.1. Reconstruction of the history of Central African Pygmies -- 12.2.2. Inference of the history of populations in Central Asia -- 12.2.3. Impact of lifestyle on population growth dynamics -- 12.3. Inferring human expansions from next-generation sequence data -- 12.4. Reconstructing population dynamics from genetic and cultural data -- 12.4.1. Simultaneous analysis of genetic and linguistic diversity -- 12.4.2. Detecting the intergenerational transmission of reproductive success -- 12.5. Conclusion -- 12.6. References -- List of Authors -- Index -- EULA.
• Sommario/riassunto: This scholarly book, coordinated by Gilles Didier and Stéphane Guindon, delves into mathematical models and algorithms to study biological evolution. It provides a comprehensive exploration of various models used in evolutionary biology, including trees, sequences, and traits evolution. The book discusses combinatorial models, branching processes, and probabilistic models such as birth-death processes and coalescent theory. It also covers models of DNA sequence evolution and the evolutionary models of continuous and discrete traits. Aimed at researchers and students in the fields of computer science, biology, and bioinformatics, it aims to enhance understanding of evolutionary processes through mathematical and computational approaches.
• Titolo autorizzato: Models and Methods for Biological Evolution
• ISBN: 9781394284252; 139428425X; 9781394284238; 1394284233
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911020004203321
• Lo trovi qui: Univ. Federico II