Assignments for Monday's class:

Read http://en.wikipedia.org/wiki/Genetic_algorithm 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 http://en.wikipedia.org/wiki/Natural_selection
Explore the "what is life?" entry below.
If you have time and are interested, listen to the recent RNA world discussion at the library of congress

Past Goals:

Class 1:

Know how to contact the instructor and TA.
Understand how your performance will be assessed and graded.
Understand that the take-home exams and computer lab assignments are an important part of this course, and that they will be graded.
Know at least three examples for the connection between bioinformatics and the study of molecular evolution.
Understand why significant sequence similarity implies likely shared ancestry and similar function, but that the reverse (non-significant sequence similarity implies independent origin) is not true.

Class 2:

Understand the homology concept used evolutionary biology
Know that most significantly similar complex sequences (in their primary sequence) are homologous, and that not all homologous sequences are significantly similar in a pairwise comparison of the primary sequence.
Know the difference between primary, secondary and tertiary structure
Recognise secondary structure elements
Know that the combinatoric protein space is huge, but highly connected
Be able to explain, how functional proteins might have evolved despite combinatoric protein being huge

Class 3:

Understand that RNA can be both genetic material and catalyst
Know what else supports the RNA-world concept
Know a few examples for the study of molecular evolution being helpful in other areas of biology.
Understand that different mechanisms (gene duplication, gene transfer, lineage fusion) can lead to many homologs coexisting in a single cell, or even in a single multi subunit enzyme.
Know about the different ways protein coding genes can evolve.
Understand how the origin of the recent ebola outbreak has been followed using molecular data.
Know that some proteins evolve so slowly that they remain recognizable homologs after more than 7 billion years of evolution.

Today's outline

Slides from class 2. What are the arguments in favor of the RNA world preceding a two or three polymer world?

Questions on Deep View

Review: ATP synthases

The F1-ATPase as three cylinder three-cycle engine

Nojiet al (1997) Direct observation of the rotation of F1-ATPase. Nature, 386, 299 - 302 tethered the head group of an ATPase to a cover slip and linked the gamma subunit to an actin filament that was fluorescently labeled:

ATPase tethered

The resulting movie after ATP addition is here .

Slides on Friday's DeepView Exercise

Questions on homology and 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?

What other processes could create new proteins?

Why is protein space considered highly connected?

Slides on ancient paralogs

How can the study of molecular evolution help biology (discussion)

Aside on recent Ebola outbreak Slides Here

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.

Example:

https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
http://www.mindspring.com/~alanh/life/ (this lets you specify living cells)

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.