Evolutionary biology concepts
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Evolutionary Biology Concepts. Evolutionary Biology “What is behind is not important!”- or is it? Phylogenetic Inference. Evolution. Change in living organisms via reproduction. Branching Descent. Evolutionary Tree. Family Tree. Phylogeny.

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Evolutionary Biology Concepts

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Evolutionary Biology Concepts

Evolutionary Biology

“What is behind is not important!”-

or is it?

Phylogenetic Inference

Chuck Staben


Evolution

Change in living organisms

via

reproduction

Chuck Staben


Branching Descent

Evolutionary Tree

Family Tree

Chuck Staben


Phylogeny

Branching diagram showing the ancestral relations among species.

“Tree of Life”

History of evolutionary change

Chuck Staben


Phylogenetic Inference

Mom's hair color?

Which form of reproduction?

Chuck Staben


Mom-Inferred vs Real

  • “Blonde” Phenotype

  • Recombination

    • Male/hair color

  • Multigenic hair color?

  • Potential Asexual Propagation

    • Date of vasectomy?

courtesy, Ms. J. Rae Staben

Chuck Staben


Classical Phylogeny

Molecular Phylogeny

Inheritance

RNA Protein Function

DNA

Chuck Staben


A

B

C

D

E

F

Node

Internode

Phylogenetic Trees

Sister Taxa

Terminal Taxa

Ancestor

Root

Chuck Staben


A

B

C

D

E

F

Paraphyletic Group

Clade

Monophyletic Group

More Trees

Chuck Staben


A

B

C

D

E

F

Trees-3

Polyphyletic Group

Chuck Staben


Add Root

Rooted vs Unrooted Trees

Chuck Staben


A

B

C

D

E

F

Extinction

Chuck Staben


Evolutionary Genetic Forces

Hardy-Weinberg Paradigm

p+q=1

p2 + 2pq + q2 =1

  • Natural Selection (fitness)

  • Drift (homozygosity by chance)

    • much greater in small populations

  • Mutation/Recombination (variation)

  • Migration

    • homogenizes gene pools

Chuck Staben


DNA, protein sequence change

Rate=1 change/6 aa sites per 108 yrs

Rate=0.16 x 10-9 yrs

(normal ~ 1 per 10-9 yrs per site)

Chuck Staben


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)

Underestimate Evolution

Chuck Staben


Mechanisms of DNA Sequence Change

Neutral Drift vs Natural Selection

  • For a 1000 base gene, 41000 sequences!

Selection

(Jukes)

Neutral

(Kimura)

Chuck Staben


Rate varies Gene-to-Gene

Chuck Staben


Rate varies Site-to-Site

Chuck Staben


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

Chuck Staben


Neutralism in Eukaryotes vs Prokaryotes-“Slightly deleterious mutations” Models

Most non-coding sites

are neutral?

Coding/noncoding can be flexible?

Reconsider evolutionary mechanisms?

Chuck Staben


Evolutionary Genetic Forces

Hardy-Weinberg Paradigm

p+q=1

p2 + 2pq + q2 =1

  • Natural Selection (fitness)

  • Drift (homozygosity by chance)

    • much greater in small populations

  • Mutation/Recombination (variation)

  • Migration

    • homogenizes gene pools

Genome

Recombination?

Chuck Staben


DNA, Protein Similarity

  • Similarity by common descent

    • phylogenetic

  • Similarity by convergence

    • functional importance

  • Similarity by chance

    • random variation not limitless

    • particular problem in wide divergence

Chuck Staben


Paralogs

Homology, Orthology, Paralogy

Orthologs

Chuck Staben


Paralogy Trap

Chuck Staben


Improper Inference

Man is a mouse,

not a rat!

Chuck Staben


Convergence

Globin

Common Ancestor

Convergence

Leghemoglobin

Chuck Staben


Our Goals

  • Infer Phylogeny

    • Optimality criteria

    • Algorithm

  • Phylogenetic inference

    • (interesting ones)

Chuck Staben


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 applicaition as in the conceptual understanding of what the software is doing with the data.”

Chuck Staben


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