Evolutionary Biology Concepts Molecular Evolution Phylogenetic Inference BIO520 BioinformaticsJim Lund Reading: Ch7

Evolution Evolution is a process that results in heritable changes in a population spread over many generations. "In fact, evolution can be precisely defined as any change in the frequency of alleles within a gene pool from one generation to the next." - Helena Curtis and N. Sue Barnes, Biology, 5th ed Worth Publishers, p.974

Levels of Evolution Changes in allele frequencies within a species. Speciation. Molecular changes: –Single bp changes. –Genomic changes (alterations in large DNA segments).

Branching Descent PopulationsIndividuals

Phylogeny Branching diagram showing the ancestral relations among species. “Tree of Life” History of evolutionary change FRAMEWORK for INFERENCE

The framework for phylogenetics How do we describe phylogenies? How do we infer phylogenies?

Inheritance DNA  RNA  Protein  Function

Ancestral Node or ROOT of the Tree Internal Nodes or Divergence Points (represent hypothetical ancestors of the taxa) Branches or Lineages Terminal Nodes A B C D E Represent the TAXA (genes, populations, species, etc.) used to infer the phylogeny Common Phylogenetic Tree Terminology

Phylogenetic trees diagram the evolutionary relationships between the taxa ((A,(B,C)),(D,E)) = The above phylogeny as nested parentheses Taxon A Taxon B Taxon C Taxon E Taxon D No meaning to the spacing between the taxa, or to the order in which they appear from top to bottom. This dimension either can have no scale (for ‘cladograms’), can be proportional to genetic distance or amount of change (for ‘phylograms’ or ‘additive’ trees). These say that B and C are more closely related to each other than either is to A, and that A, B, and C form a clade that is a sister group to the clade composed of D and E. If the tree has a time scale, then D and E are the most closely related.

Taxon A Taxon B Taxon C Taxon D genetic change Taxon A Taxon B Taxon C Taxon D no meaning Two types of trees Cladogram Phylogram or additive tree Meaning of branch length differs. All show the same evolutionary relationships, or branching orders, between the taxa.

Rooted vs Unrooted Trees

More Trees ABCDEF

Trees-3 ABCDEF

Extinction ABCDEF

Population Genetic Forces Natural Selection (fitness) Drift (homozygosity by chance) –much greater in small populations Mutation/Recombination (variation) Migration –homogenizes gene pools Hardy-Weinberg Paradigm p+q=1 p 2 + 2pq + q 2 =1

Modes of speciation Many ways speciation can occur, among the most common are: Geographic isolation. Reproductive isolation. –Sexual selection. –Behavioral isolation.

DNA, protein sequence change

Multiple Changes/No Change..CCU AUA GGG....CCC AUA GGG....CCC AUG GGG....CCC AUG GGC....CCU AUG GGC....CCU AUA GGC.. 5 mutations 1 DNA change 0 amino acid changes (net) Enumerating bp/aa changes underestimates evolutionary change

Mechanisms of DNA Sequence Change Neutral Drift vs Natural Selection Traditional selection model Neutral (Kimura/Jukes) Pan-neutralism

Rate of change (evolution) of hemoglobin protein Each point on the graph is for a pair of species, or groups of species. From Kimura (1983) by way of Evolution, Ridley, 3rd ed.

Mutation rate varies Gene-to-Gene

Rate varies Site-to-Site

From Evolution. Mark Rdley, 3rd Ed.

Constraints on “Silent” Changes Codon Biases-translation rates Transcription elongation rates –polymerase ‘pause’ sites “Silent” regulatory elements –select for or against presence/absence Overall genome structure

DNA, Protein Similarity Similarity by common descent –phylogenetic Similarity by convergence (rare) –functional importance Similarity by chance –random variation not limitless –particular problem in wide divergence

Homology-similar by common descent CCCAGG CCCAAG CCCAAA CCTAAA

Inferring Trees and Ancestors CCCAGG CCCAAG-> CCCAAG CCCAAA-> CCTAAA CCTAAA-> CCTAAC Not always straightforward. The data doesn’t always give a single, correct answer.

Homology, Orthology, Paralogy

Paralogy Trap

Improper Inference Garbage in, garbage out!

Our Goals Infer Phylogeny –Optimality criteria –Algorithm Phylogenetic inference –(interesting ones)

Watch Out “The danger of generating incorrect results is inherently greater in computational phylogenetics than in many other fields of science.” “…the limiting factor in phylogenetic analysis is not so much in the facility of software application as in the conceptual understanding of what the software is doing with the data.”