Computational Biology

1. Computational Biology June 24, 2004 MUPGRET Workshop

2. Overview • Math • Statistics • Computer Models • Bioinformatics

3. Math and Science • Mathematics are an integral part of science. • Used everyday by bench scientists to perform experiments, interpret data, and make predictions.

4. Math Examples • Making solutions • Plotting graphs • Calculating area

5. Area calculations • NIH Image Software • http://rsb.info.nih.gov/nih-image/Default.html • Allows you to measure length, width, area, density on objects in a picture. • Free

6. Statistics and Science • Necessity for analyzing datasets. • Experiment must be well designed to be meaningful. • Ex. replications and controls • Should know how you’ll analyze data before you start the experiment. • Means, standard deviations, and linear regression are often used.

7. Probability • Tests the likelihood that something will or will not occur. • Used extensively in everyday life. • Las Vegas type gaming • Lotto • Insurance amortization • Decisions regarding medical treatment

8. Everyday examples • Rolling the dice • 1 in 6 chance that you will roll a one with a single die. • (1/6)2 = 1/36 chance you will roll snake eyes. • Playing cards • 4 in 52 chance (1/13) of drawing an ace at random from a deck. • What’s the chance of a full house?

9. Biology examples • Punnett square • Nucleotide frequencies along a gene are used to examine evolutionary forces. • Mutation rates • Testing limits and sample sizes for transgenics. • DNA forensics

10. Computers • Data quality • Data storage • Data analysis • Data validation • Data manipulation

11. Barcode systems

12. The “ics” • Genomics • Proteomics • Metabolomics • Bioinformatics

13. Bioinformatics • Revolutionized our ability to do biology in much the same way as PCR and robotics changed the bench science. • “the computational branch of molecular biology” (Bioinformatics for Dummies). • a merger of computer science and biology (Introduction to Bioinformatics)

14. Before bioinformatics • In vivo experiments • In the living organism • In vitro experiments • In a test tube

15. Manhattan Project • Space Program • Human Genome Project

16. Progress towards the HGP • 1953-DNA structure • 1975-Maxim and Gilbert DNA sequencing • 1977- First genome sequenced (x174) • 1981-Human mitochondrial genome sequenced • 1984-Epstein Barr virus sequenced

17. Progress towards the HGP • 1990- Human genome project launched • 1992-TIGR formed • 1996-High resolution map of the human genome • 1998-C. elegans genome sequenced • 1999-Drosophila genome sequenced • 2000-Draft sequence of human genome completed.

18. Bioinformatics • Integration of computer science and biology • Applied field • Inference • Connection • Prediction

19. The basics • DNA sequence  protein sequence • protein sequence  protein structure • protein structure  protein function

20. Bioinformatics • Computer simulation • Data management and retrieval • Pattern recognition • Artificial intelligence

21. Data management/retrieval • Database design and implementation • Data entry tools • Distributed computing • Querying tools www.mgdb.org

22. Pattern Recognition • DNA sequence analysis • www.ncbi.nlm.nih • Geneology • Disease diagnosis

23. Artificial intelligence • Software learns from the data it is given and modifies its programs to be more efficient or to be more accurate. • Proteomics software • Disease diagnostic imaging

24. Computer Science • Algorithm-program that specifies how to solve a problem • Data structure and information retrieval • Software engineering

25. The human side • Curation • Annotation • Quality control design

26. Examples of utility • Determining phylogenetic relationships • Sequence similarities • Protein structure prediction • Disease diagnosis • Pharmacogenomics

27. Detailed structure information • Requires crystallization of the protein. • Large amount of protein required. • Often time consuming. • Limiting step to high throughput. • Followed by X-ray crystallography or NMR. • Determines position of each atom in the molecule.

28. A Rational Approach • Christendat et al. 2000. Nat. Struct. Biol. 7:903-908. • Determine structure of all proteins in Methanobacterium thermoautotrophicum. • 1871 ORFs

29. The dilemma • Cell membrane is “semipermeable” and comprised of phospholipids. • Only hydrophobic molecules can pass through cell membranes. • Conversely, no charged (polar) molecules. • Water can pass through membranes. • Water is a polar molecule.

30. Aquaporin-1 • First water channel protein cloned. • Water travels through aquaporin rather than phospholipid bilayer. • Water can pass through but protons can’t. • Membrane potential • Hydrogen gradients

31. Aquaporin • But protons can move along a column of water so how does aquaporin prevent this? • Monomer has 269 aa with 6 membrane spanning domains. • Heterotetramer is the functional molecule.

32. Aquaporin • Protein has a hourglass shape. • The narrowest place is 3.0 A wide (water is 2.8 A). • Passage is lined with hydrophobic aa that help exclude other small charged molecules. • Predicts one water molecule passes through at a time. • Hydrogen bond between molecules is transferred to two asparagine molecules. Fig. 6.11

33. Prions • Proteins that can change shape. • And make other proteins change their shape! • As number of changed proteins increases a phenotype is observed. • Causal agent of mad cow disease, scrapie in sheep and Creutzfeldt-Jakob disease in humans.

34. Prions II • Previously thought only nucleic acid encoded changes caused disease. • Stanley Prusiner discovered prion’s ability to change other protein’s structure and won the Nobel Prize. • Sup35 is a prion-like protein in yeast.

35. Sup35 • Translation termination factor • Carboxyl end binds to the ribosomal complex to terminate translation. • If Sup35 is converted to an alternate conformation (infectious prion conformation) the shape change spreads throughout the cell and is passed to daughter cells.

36. Sup35 • In prion conformation causes ribosomes to read through stop codons altering shape and function of proteins. • Fig. 6.13 • Not adaptively advantageous so why is it maintained?

37. Why? • True et al. 2000. Nature 407: 477-483. • Reduced translation fidelity, extends proteins. • Some of these are antibiotic resistant. • Could lead to stabilization of new phenotype under correct environment.

38. Introduction to Bioinformatics • www.oup.com/uk/lesk/bioinf