Assignments for Monday's class:

Read up to (including) termination plus the sections entitled observations and criticisms

Read the chapter on Evolution as algorithm from "Darwin's Dangerous Idea" by Daniel C. Dennett, [available through WebCT]

Browse through

Explore the "what is life?" entry below.

Today's outline

Questions on Deep View

Questions on homology, similar function:

  • Are all / most proteins that have similar function homologs (old spelling homologues)?
  • Do most homologous proteins have similar functions?
  • Can can two proteins found in the same organism, encoded by distinct genes be homologs? Examples ... (ATP synthases, rho Termination factors, flagellar assembly ATPases) - Orthologs, Paralogs, Synologs, Xenologs
  • What could be the reason that two proteins that do not show any detectable sequence or structural similarity never-the-less have the same function?
  • Are most proteins with significant primary sequence similarity homologous?
  • Do most homologous proteins have significant sequence similarity?
  • If protein space were explored only through gene duplication and divergence, are all proteins homologs?
  • Why is protein space highly connected? (number of substitutions to convert one sequence into another.)
  • What other processes could create new proteins?
  • Do we mean detectable homology when we use the term homolog?
  • Regarding slides from class 2. What are the arguments in favor of the RNA world preceding a two or three polymer world?
  • Review Goals from class 2:

    How can molecular evolution help bioinformatics (discussion)

    Aside on recent Ebola outbreak Slides Here

    If time:

    Go through What is life box below.
    "What separates living from dead?"

    What does Bioinformatics have to do with Molecular Evolution? 

    Problem: Application of first principles does not (yet) work

    The following chain although (believed to be) mainly determined by the DNA sequence (plus other components of the cell which in turn are encoded by other parts of the genome) can at present not be simulated in a computer.  

    DNA sequence ->
    transcription ->
    translation ->
    protein folding ->
    protein function (catalytic and other properties) ->
    properties of the organism(s) ->
    ecology (taking also the non biological environment into account) ->

    ... .


    Most scientists believe that the principle of reductionism (plus new laws and relations emerging on each level) is true for this chain; however, this is clearly "in principle" only.
    Biology relies on this sequence to work more or less unambiguously (prions), but:

    At several steps along the way from DNA to function our understanding of the chemical and physical processes involved is so incomplete that prediction of protein function based on only a single DNA sequence is at present impossible (at least for a protein of reasonable size).

    Use evolutionary context:

    "Nothing in biology makes sense except in the light of evolution"

    Theodosius Dobzhansky

    Present day proteins evolved through substitution and selection from ancestral proteins. Related proteins have similar sequence AND similar structure AND similar function.

    In the above mantra "similar function" can refer to:

    • identical function,

    • similar function, e.g.:
      • identical reactions catalyzed in different organisms; or
      • same catalytic mechanism but different substrate (malic and lactic acid dehydrogenases);
      • similar subunits and domains that are brought together through a (hypothetical) process called domain shuffling, e.g. nucleotide binding domains in hexokinase, myosin, HSP70, and ATPsynthases.

    The Size of Protein Sequence Space (back of the envelope calculation):

    Consider a protein of 600 amino acids.
    Assume that for every position there could be any of the twenty possible amino acid.
    Then the total number of possibilities is
    20 choices for the first position times 20 for the second position times 20 to the third .... = 20 to the 600 = 4*10^780 different proteins possible with lengths of 600 amino acids.

    For comparison the universe contains only about 10^89 protons and has an age of about 5*10^17 seconds or 5*10^29 picoseconds.

    If every proton in the universe were a computer that explored one possible protein sequence per picosecond, we only would have explored 5*10^118 sequences, i.e. a negligible fraction of the possible sequences with length 600 (one in about 10^662).

    The following is based on observation and not on an a priori truth:

    If two proteins (not necessarily true for nucleotide sequences) show significant similarity in their primary sequence, they have shared ancestry, and probably similar function.

    To date there is no example known where convergent evolution has let to significant similarity of the primary sequence (although here are examples where similar selection pressures have resulted in similar convergent substitutions in homologous proteins).


    for one of two reasons:

    a)  they evolved independently
    (e.g. different types of nucleotide binding sites);


    b)   they underwent so many substitution events that there is no readily detectable similarity remaining.

    (reason: see B above); many recent advances concern the improved detection of similarity.

    What is life?

    Traditional criteria:

    • Uptake and dissipation of Energy
    • Metabolism
    • Responsiveness
    • Gestalt (distinctive shape, separate from environment)
    • Growth
    • Reproduction with variation - Ability to evolve

    See essay on definitions of life: The Seven Pillars of Life by Daniel E. Koshland
    (does not go much beyond the traditional multi-point characterization)

    NASA's working definition of life: "life is a self-sustaining system capable of Darwinian evolution"

    von Neumann's computers - alive? A-life?

    Turing machines and universal computers (Turing's biography)

    Cellular automata: A'life; John Conway's game of life. [rules: a cell survives if it has two or three living neighbors. A new cell is created on a "dead" square if it has exactly three living neighbors.] The game was popularized by Martin Gardner in Scientific American in 1970.


    More information on digital life is at Digital evolution homepage at MSU.
    Karl Sims' virtual creatures
    are worth a look, movie here. He describes his work as follows:

    "A population of several hundred creatures is created within a supercomputer, and each creature is tested for their ability to perform a given task, such the ability to swim in a simulated water environment. Those that are most successful survive, and their virtual genes containing coded instructions for their growth, are copied, combined, and mutated to make offspring for a new population. The new creatures are again tested, and some may be improvements on their parents. As this cycle of variation and selection continues, creatures with more and more successful behaviors can emerge. The creatures shown are results from many independent simulations in which they were selected for swimming, walking, jumping, following, and competing for control of a green cube."

    Genetic Algorithms in engineering: Ingo Rechenberg and others used "natural selection" in the computer to optimize aerodynamic profiles. Biased walk through "sequence" space. Finding optimal solutions. (To avoid local maxima: use demes with limited migration). For more information you can check a comprehensive collection of links on Evolutionary Computation and its application to art and design. It is amazing that GA work fine with rather small populations.

    Eigen_Rechenberg Sequence Space

    Can living systems be divided in smaller sub-systems that are themselves alive? Or, is life a property of the larger system? The ecosystem of the Sargasso sea that includes algae, bacteria and phage (viruses that live on bacteria). The cyanophage play an important role in the system as predators of the primary producers. They lyze the cells allowing for recycling of limiting elements. The phage are part of a living system, but usually are not cindered alive themselves.

    The Gaia hypothesis argues that the whole biosphere should be regarded as a single organism, with its own homeostatic feed back loops.
    Problems of the hypothesis:

    • Earth also includes many feed-forward loops (e.g., melting ice caps lower the albedo, which leads to more warming*). Relying on Gaia's regulatory mechanisms can provide a false sense of safety.
    • How does a single organism evolve? With only one Gaia, who would natural selection work?

    * Lovelock and Watson developed the Daisyworld model (simulation here), in which black and white Daisies stabilize the climate of the model planet. The release of DMS heat stressed algae creates a Daisyworld like feed back loop, because it acts as nucleating agent in cloud formation.

    Goals class 3: