Instructor: J.Peter Gogarten Dept. Molecular and Cell Biology BP404 University of Connecticut Storrs, CT 06269-3125 Phone: 486-4061 Email: gogarten@uconn.edu Teaching Assistant Sean Gosselin Dept. Molecular and Cell Biology University of Connecticut Storrs, CT 06269-3125 sean.gosselin@uconn.edu Office hours  MW 12.15-2pm, or after arrangement. For fast response send an email! For questions of general interest, use the bulletin board on HuskyCT. You also can post anonymously on the bulletin board. Basis for grading and expectation see Syllabus Study and reading assignments, goals, and slides will be posted at the end of the individual classs pages. HuskyCT If you do not have access to the course site on huskyCT, send an email with your netID number to gogarten@uconn.edu. Textbook: We will not use a printed book for reading assignments this year! Reading assignments will use texts available on the www, including Wikipedia, and through WebCT.   Recommended books: Introduction to Bioinformatics (Paperback) by Arthur M Lesk, 4th edition at Amazon 3rd edition is available online at UConn Essential Bioinformatics (Paperback) by Jin Xiong Excellent book, it provides a very readable and concise overview of the most important tools and concepts in Bioinformatics Link to Amazon.com   Bioinformatics for Dummies by Jean-Michel Claverie Excellent introductory bioinformatics book. Link to Amazon.com Inferring Phylogenies by Joe Felsenstein ISBN: 0-87893-177-5;   $61.95 paper Excellent book on phylogenetics and many aspects of population genetics (e.g., gene coalescence in populations, a topic that is rather relevant to species phylogenies in microorganisms :)). For most MCBler this is not exactly bedtime reading, but if you need a well founded thorough explanation, this is a good book to consult. Link to publisher, link to amazon.com Molecular Evolution : A Phylogenetic Approach by Roderic D. M. Page, Edward C. Holmes Price: $63.95 Paperback - 352 pages (October 1998) Blackwell Science Inc; ISBN: 0865428891 This book gives an excellent introduction to terms, methods, and problems in molecular evolution. It does not contain too many details on individual algorithm, but it provides a very readable overview. Rather expensive, UConn provides access to an online version   Other recommended book: From Gaia to Selfish Genes, Selected Writings in the Life Sciences Edited by Connie Barlow MIT Press, isbn 0 262-52178-4 Topics (incomplete: open to student input)      Bioinformatics (general definition):    Area between Computer Sciences (Informatics) and Biology (genomics)     (or application of the tools of informatics to biology) Bioinformatics took off only with the availability of large amounts of genome information, thus a more narrow delineation might be:        Area between Informatics and Genomics Related areas: Computational biology, Cybernetics  Typically bioinformatics is considered to include: management of biological databanks, access to biological data, and extracting useful information from biological data. For more detailed discussion see Mark Gerstein's introduction slides 53ff   Bioinformatics at UConn: Courses relevant for students of bioinformatics are offered through a variety of different departments, colleges and schools at UConn. There is at present no Major in BIOINFORMATICS; however, UConn offers a Minor in Bioinformatics that is suitable for students in MCB, EEB, PNB and CSE. For information click here. An updated audit sheet is here. You should be aware that Bioinformatics is a field in its infancy. Many schools have rushed to attach the name Bioinformatics to a program, but upon closer inspection one realizes that this is not what one would hope for in a Bioinformatics program. E.g., often a single databank course attached to a normal biochemistry curriculum. Everything considered, the offering at UConn could be more streamlined for CSE and Biology students, respectively, but regarding the content UConn's offering is pretty good. Discussion How might the study of molecular evolution help bioinformatics? Items to consider: recognizing important parts of the genome, structure and function predictions, genomic islands.

What does Bioinformatics have to do with Molecular Evolution?  Problem: Application of first principles does not work (yet):  The following chain processes are (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). But at present it 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) -> properties of the population and community 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). Solution: 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. (although some proteins acquired radically new functional assignments, lysozyme -> lense crystalline).  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). THE REVERSE IS NOT TRUE: PROTEINS WITH THE SAME OR SIMILAR FUNCTION DO NOT ALWAYS SHOW SIGNIFICANT SEQUENCE SIMILARITY for one of two reasons: a)  they evolved independently (e.g. different types of nucleotide binding sites); or b)   they underwent so many substitution events that there is no readily detectable similarity remaining. In particular, PROTEINS WITH SHARED ANCESTRY DO NOT ALWAYS SHOW SIGNIFICANT SIMILARITY (reason: see B above); many recent advances concern the improved detection of similarity.