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A Quote…. There is no spirit-driven life force, no throbbing, heaving, pullulating, protoplasmic, mystic jelly. Life is just bytes and bytes and bytes of digital information.” --Richard Dawkins. History, Theory, and Historical Contingency in Biology. Jay Snoddy v8v@ornl.gov
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A Quote… There is no spirit-driven life force, no throbbing, heaving, pullulating, protoplasmic, mystic jelly. Life is just bytes and bytes and bytes of digital information.” --Richard Dawkins
History, Theory, and Historical Contingency in Biology Jay Snoddy v8v@ornl.gov (or jsnoddy@tennessee.edu) http://genome.ornl.gov/~v8v
Ira and the Worm: Similar Information in Genes and Gene Products Complexity of Form & Function Despite Deep Similarity and Unity, Deep Population Variation within Species Diversity of Forms and Function
Now Now Every biological fact has a “here and now” aspect of how it “works” and a historical aspect of how it got to work that way. Past
History… • History of Biological Thought… A history of “Applied bioinformatics” “Through the Louvre on Roller Skates” view of some conceptual views of biology, with an occasional homage to computation in biology. • History of Biological Information… Pure Bioinformatics Nothing in Biology makes any sense, except when viewed in the light of evolution.
A Computational/Conceptual Biology Timeline 1920's 1950's 1970's 1850's Darwin (& Malthus) Population growth; intrinsic rate of increase, r: Carrying capacity, K The Modern Synthesis: The Neo-Darwinian Synthesis between genetics and evolution. Populations under selection. Molecular Evolution: Molecular Biology, Protein Structure, Biophysics; Sequence Analysis Mendel “Quantitative” Genetics statistics in populations
Applied bioinformatics history: A beginning with Aristotle. • Observe Diversity • What is out there?
Applied bioinformatics history: A beginning with Aristotle. • Observe Diversity • What is out there? • Compare and Classify Diversity • Organized what is out there by similarity….
Applied bioinformatics history: A beginning with Aristotle. • Observe Diversity • Compare and Classify Diversity • Explain Origin of Complexity, Diversity, Similarity • How is biological form and function created in new individual? • Recognized two possibilities • Preformed structures. (“Russian Dolls”) • Information on the “possible” from parents somehow transformed into the “actual” observed phenotype: Aristotle favored the latter as most likely…most like the current idea that the possibleof genotypic information somehow gets transformed each generation into a phenotypicactual.
Applied Bioinformatics:Getting Data to Classify by“Voyages of Discovery” • Voyages of Discovery build upon the maps created by past Voyages of Discovery… • Examples • Species Diversity Voyages of Humboldt, Darwin, Alfred Russel Wallace, Bates, etc. • Other voyages have ventured below level of organisms (Anatomy, Development, Physiology, Cell Biology, Biochemistry, etc. )
Malthus: Rate of Increase • Elephant –19,000,000 in 750 years • Staphulococcus aureus—enough to cover the earth 7 feet deep in 48 hours • Progeny is far in excess of what is actually capable of living in an environment. • r – Intrinsic Rate of Increase. • K – Carrying capacity.
HOMOLOGY “The same organ in all its varieties of form” Serial Homology Derived forms within the same organism Special Homology Derived forms between different species Versus Analogy Sir Richard Owen Forms similar due to same function
Be careful here….importance of “historical contingency” • Owen was—for a time, at least a proponent of what could be called “Rational Morphogenesis” The similarity of shape indicated, in their view, constraints and drivers that were the cause of similarity. (simplistically put, leg bones are more like crystals that grow from common rules, and they are NOT similar due to common origin.) • While there are some biophysical constraints and drivers—esp. at more molecular level—it should be clear that a lot of similarity is due to a common origin and the historically-contingent events along the lines of descent.
MECHANISM of DARWINIAN NATURAL SELECTION • Variation exists in the population • Competition for survival, dying before leaving offspring often • Survival of those most fit for the environment (or genetic drift) • Offspring are from the survivors • Offspring tend to have the genes that made their parents fit for the environment.
Darwin Finches: Geospiza fortis Microevolution happens
CLADOGRAM ( partial) of VERTEBRATES: DESCENT WITH MODIFICATION: Macroevolution happens
Continuity of the Germ Plasm; Soma vs. Germ Cells; Differentiated somatic cells share same genome information (but use it differently). Cell Biology, and Genomics Germ Plasm Weismann
DNA coiled in chromosomes DNA passed in special cells (germ cells) from a generation to next. DNA helps direct a developmental program to create a new individual (soma) during embryogenesis from the fused germ cells. Modern Understanding: Source of Genotypic informationDNA in chromosomes
Rediscovery of Mendelism Adaptive landscape Statistical Genetics Mathematical biology
THE MODERN SYNTHESIS “Evolution is a change in the genetic composition of populations. The study of the mechanisms of evolution falls within the province of population genetics.” --Theodosius Dobzhansky. 1951
Hardy-Weinberg equilibrium: • Assumes organisms are • diploid • sexually reproducing • randomly mating • And have • no drift (i.e. an "infinite" population) • no selection • no mutation • no migration (gene flow) • Two allele case for a gene: • allele A allele has frequency, p, The allele a has a frequency, q • p + q =1 • AAhomozygote is p2, the Aaheterozygote 2pq, the aa homozygote is q2.
Now add “fitness” functions and adaptive landscapes…to these population genetics equations…. • Adaptive Landscape : An adaptive landscape is a surface in multidimensional space (analogous to a mountain range) that represents the mean fitness of a population (not the fitness of a genotype). An individual is represented as a point on the surface (mountain) and a population is represented as a cloud of points. “Adaptive landscape is probably the most common metaphor in evolutionary genetic[s]” Futuyma (1998) Evolutionary Biology pg. 403 Evolution may be envisioned as the movement of a population of points (individuals) on the w surface (adaptive landscape). The points move up-slope until it arrives at the peak (mountain top).
Evolution is a change in the gene pool of a population over time. Evolution is the cornerstone of modern biology. It unites all the fields of biology under one theoretical umbrella. It is not a difficult concept, but very few people -- the majority of biologists included -- have a satisfactory grasp of it. One common mistake is believing that species can be arranged on an evolutionary ladder from bacteria through "lower" animals, to "higher" animals and, finally, up to man. Mistakes permeate popular science expositions of evolutionary biology. Mistakes even filter into biology journals and texts. For example, Lodish, et. al., in their cell biology text, proclaim, "It was Charles Darwin's great insight that organisms are all related in a great chain of being..." In fact, the idea of a great chain of being, which traces to Linnaeus, was overturned by Darwin's idea of common descent. • Misunderstandings about evolution are damaging to the study of evolution and biology as a whole. People who have a general interest in science are likely to dismiss evolution as a soft science after absorbing the pop science nonsense that abounds. The impression of it being a soft science is reinforced when biologists in unrelated fields speculate publicly about evolution. http://www.talkorigins.org/faqs/faq-intro-to-biology.html
Evolution of Biological Sequences; Methods to compare sequences and find patterns. Gene Duplication & Divergence Molecular Evolution Margaret Dayhoff Molecular evolution • When looked at molecular sequences in 1960 and 70s … • Recognized that these changes are result of • Mutation • Selection • Primarily purifying selection or near-neutral mutations • Not primarily result of directional selection!!! • Genetic Drift
Networked Molecular Regulators (e.g. hormones, morphogens) communicate across cells; intracellular regulation; signal transduction Cell Regulatory Networks Bernard Physiology and Cell-Cell Communication Cell Type B Cell Type A A cell type produces an informational molecule (e.g. hormone) Another cell type is capable of sensing this informational molecule. It has a receptor “lock” for the “key” produced by the other cell.
Cells as receiving/integrator of different environmental signals. Cell Membranes restrict access to environmental information; Promote “modularity” (information hiding) Internal MethodsExternal Methods Receptors ( a type of protein) sit in the membrane and allow only some external information to be received and transduced.
Emergence of heterogeneous phenotypes from apparent homogeneity; complexity from homogenity Developmental Biology Driesch Developmental biology and Evolution • Long standing interest and problem, but not really part of the first Modern Synthesis…yet.
KARL ERNST von BAER: (Not Haeckel!) “The general features of a large group of animals appear earlier in development than do the specialized features of a smaller group…The early embryo is never like a lower animal, but only like its early embryo.”
CHARLES DARWIN Studied Barnacle classification and development ON THE ORIGIN OF SPECIES 1859 “Community of embryonic structure reveals community of descent.” “Embryology rises greatly in interest, when we look at the embryo as a picture, more or less obscured, of the progenitor, either in its class or larval state, of all the members of the same great class.”
COMPLEXITY How do Genomes build up phenotypic Complexity during development? Phenotypic complexity is created during development of an embryo; Development a) [Gene Regulation]/t &b) [Cell communication]/t
How Do we explain this emergence of complexity? • Over developmental time… • Largely by a change in gene regulation and cell-cell communication…. • The same genes in the body, but different expression of those genes in different cells and different types of cells..
Control & Regulatory sites for Gene Expression ‘Regulatory Sites and proteins that promote or prevent making RNA and proteins What is the important information encoded in the genome? OR Gene ProductSequence “Gene” B C D A hnRNA mRNA protein: 1D, 2D, 3D
Transcription--Boolean operation--Sums Regulatory Signals--Combinatorial Complexity
Think of logical gates!!! • While there are subtleties (and things may not work as well in very simple systems as predicted)……repressors and activators of gene expression can be thought of as acting as AND, NOT, OR, NAND gates…
The Sea Urchin Endo 16 gene Coding region Regulatory region 6 Modules F E D C B A Unique binding sites within each module BP 1st EXON Binding sites that define a specific module Circa 50 binding site motifs Redrawn from Yuh et al., Science 279, 1998
Cell Differentiation—Stem Cells Pluripotent Restricted Fate From: Cells, Embryos, and Evolution: Toward a Cellular and Developmental Understanding of Phenotypic Variation and Evolutionary Adaptability
Different cell types have different GRNS and use only a fraction of the same genome information. Cell Type 2 Cell Type 1
Cell Type 1 What should I do? Cell Differentiation—Sensing Environments via Receptors A signal produced by other cells in a region or position in body that is received by the cell helps decide on path. From: Cells, Embryos, and Evolution: Toward a Cellular and Developmental Understanding of Phenotypic Variation and Evolutionary Adaptability
Different Cell Types: Different GRNs A, B, C, D, E A different cell type (even in the same lineage) can have a different set of GRNs that allow it to respond to different signals A cell will have a set of GRNs that allow it to respond to signals (in this case, GRNs A, B, C, D, E,) D, E, F, X, Y A Cartoon…
Cascades of Different GRNs A, B, C, D, E A, C, D, E, G B, C, D, E, F D, E, F, X, Y A Cartoon…
How does biology build up phenotypic complexity, diversity, and variability over EVOLUTIONARY time?
A Bioinformatics Timeline 3800 MYA 0.1 MYA 600 MYA
Metazoa circa 700 million years or so of multicellular animal life… A Bioinformatics Timeline 3800 MYA 0.1 MYA 600 MYA
Similar gene products lay down the radically different body plans! Key Genome Data & Idea
Very Different Body Plans, yet remarkably similar protein-coding `11