Computational Biology From The Perspective Of A Physical Scientist
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1 Computational Biology From The Perspective Of A Physical Scientist Dr. Arthur Dong PP1@TUM 26 November 2013
2 Bioinformatics Education Curriculum Math, Physics, Computer Science (Statistics and Programming) Life Sciences (Biochemistry and Molecular Biology) Bioinformatics (sequence, structure, system) Three pillars roughly equal weight; adjustment possible. Synergistic collaboration among SEM faculties / schools / universities. Appreciate complementary strengths + Acquire complementary ways of thinking. Technical competence + Ability to ask the right questions. Recruitment Bioinformatics students from first year on Marketing! Converts from physical sciences: Where else can you do cutting-edge research that matters so early?! Risk not taking the time to truly understand biology. Converts from life sciences: How about finishing an experiment in days rather than months?! Could be a steep learning curve at the beginning. Women You can excel in this!
3 How To Read A Paper Focus: Technical details or the big picture? Within the paper: What's the whole point, the take-home lesson? Why did they do what they did? (historical perspective) Any parts problematic and could be improved? Expected versus unexpected Go beyond the paper: Observation Question Hypothesis Investigation Application What's the next obvious step? Can I apply the same ideas/techniques in other areas? Turn any question into a project (and possibly a paper)!
4 Classical Biology (up to 1950s) Anatomy Organs, tissues, cells Mendelian Genetics Evolution of species Then came triumph of reductionism...
5 Modern Biology Molecular Biology of The Cell Cell Biology Biochemistry Molecular Genetics Molecular Evolution Where does bioinformatics come into the picture? Classically: Protein structure prediction Genomics: Sequence search and comparison Functional genomics and proteomics: Networks and systems
6 Biology/Chemistry Information AGAGCATGTTGGCCTGGTCCTTT GCTAGGTACTGTAGAGCAGGTGA GAGAGTGAGGGGGAAGGACTCC AAATTAGACCAGTTCTTAGCCATG AAGCAGAGACTCTGAAGCCAGAC TACCTGGGTCCCAATCTTGGGCTT GGTATTTCCTCGCTGTGTGACTCT GGGTAAGTTACTTAACTTCTCTGT GCCTCAGTTCTCTCAAGTGTAAAG TGACGCTTGTAAAAGTGTCTCCTG CAAAAGAAAGGGCTGCTGGGAGG AGGGGTGTCCCTGGTGTGCACTA AGTACAATATGAGTTTGT Genetic Code MGLSDGEWQLVLNVWGKVEADIP GHGQEVLIRLFKGHPETLEKFDKFK HLKSEDEMKASEDLKKHGATVLTAL GGILKKKGHHEAEIKPLAQSHATKH KIPVKYLEFISECIIQVLQSKHPGDF GADAQGAMNKALELFRKDMASNY KELGFQG
7 Protein Folding and Structure Prediction Sequence determines structure and structure determines function (roughly!) Challenge: Given target sequence, predict target structure Homology Modelling: Target sequence has a homologous sequence with solved structure 1. Align the two sequences (crucial step) 2. Put target sequence onto homologous structure and massage Need at least 40% homology Target Template
8 What if no close homologous structure? QNVERLSLRKNHLTSLPASFKRLSRLQYLDLHNNNFKEIPYILT?? Threading: Inverse folding problem 3D profile and pairwise contact potential Difficulty with multi-domain proteins or those with no clear domain structures
9 Ab Initio Prediction Molecular Dynamics Physics throw in electric charge, solvent etc. and minimize the energy function Logo Method Assemble library fragments Bradley et al, Science 2005 Partial success on small proteins In general computationally prohibitive How does nature work?
10 (Traditional) Tenets of Molecular Biology One gene, one protein, one function (or disease) Protein sequence determines structure, structure determines function 2nd. str. pred. Success! Protein Folding Partial success on small proteins, but dead end? How does nature work?? Coiled coils Beta barrels Intrinsic disorders
11 Genomics Producing the Parts List Large-scale sequencing of genomes and the resulting data explosion Sequence Comparison: Given a query, find similar sequences among tens of millions in databases fast! Align a group of related sequences; identify conserved residues or regions for structure or function prediction. Cluster sequences according to different features. String comparison algorithms and machine learning (regression, clustering, hidden Markov models, neural networks, etc.)
12 Functional Genomics and Proteomics Understanding How Parts Work Individually and Together Genome-wide mrna expression profiling; synthetic lethal screening Proteome-wide Yeast-2-hybrid screening and co-ap/ms
13 Protein-Protein Interactions: Stable Complexes
14 PPI network is not static! Different time Different space Different conditions
15 Beyond Physical Interactions Genetic Landscape Of A Cell
16 From Chimpanzees to Neandertals to...
17 Humans 1000 genomes Common vs rare, disease-causing SNPs Interconnections among diseases
18 Systems Biology in Medicine Disease Networks Goh et al, PNAS 2007
19
20 Cancer Classification Set Marker (Leukemia) Network Marker (Breast Cancer)
21 Looking Ahead Classical Biology Whole Molecular Biology Parts Systems Biology Whole Understand how life works Mechanism (and hopefully) treatment for cancer and other diseases Synthetic biology, new materials, new energies Genome Computational Systems Biology What questions to ask? What stories to tell? Phenome
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