1 / 50

Computational Biology and Bioinformatics in Computer Science

Computational Biology and Bioinformatics in Computer Science. Lenwood S. Heath Department of Computer Science 2160J Torgersen Hall Virginia Tech. Department Seminar Series September 9, 2005. Overview. Computational biology and bioinformatics (CBB) What is it? History at VT

lada
Télécharger la présentation

Computational Biology and Bioinformatics in Computer Science

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computational Biology and Bioinformatics in Computer Science Lenwood S. Heath Department of Computer Science 2160J Torgersen Hall Virginia Tech Department Seminar Series September 9, 2005

  2. Overview • Computational biology and bioinformatics (CBB) • What is it? • History at VT • Some biological terminology • CBB faculty and projects • Education in CBB • Bioinformatics option • GBCB • Conclusion 9/9//2005 Computational Biology and Bioinformatics

  3. Computational Biology and Bioinformatics (CBB) • Computational biology— computational research inspired by biology • Bioinformatics — application of computational research (computer science, mathematics, statistics) to advance basic and applied research in the life sciences • Agriculture • Basic biological science • Medicine • Both ideally done within multidisciplinary collaborations 9/9//2005 Computational Biology and Bioinformatics

  4. CBB History (Part I) • Biological modeling (Tyson, Watson): > 20 years • Computational biology, genome rearrangements (Heath): > 10 years • Fralin Biotechnology sponsored faculty advisory committee centered on bioinformatics: 1998-2000 • Biochemistry; biology; CALS; computer science (Heath, Watson); statistics; VetMed • Provost provided $1 million seed money • First VT bioinformatics hire (Gibas, biology, 1999) 9/9//2005 Computational Biology and Bioinformatics

  5. CBB History (Part II) • Outside initiative submitted to VT for a campus bioinformatics center — 1998 • Discussions of bioinformatics advisory committee contributed to a proposal to the Gilmore administration — 1999 • Governor Gilmore puts plans and money for bioinformatics center in budget — 1999-2000 • Virginia Bioinformatics Institute (VBI) established July, 2000; housed in CRC 9/9//2005 Computational Biology and Bioinformatics

  6. Virginia Bioinformatics Institute (VBI) • Established by the state in July, 2000; high visibility • Applies computational and information technology in biological research • Research faculty (currently, about 18) expertise includes • Biochemistry • Comparative Genomics • Computer Science • Drug Discovery • Human and Plant Pathogens • More than $43 million funded research • Mathematics • Physics • Simulation • Statistics 9/9//2005 Computational Biology and Bioinformatics

  7. CBB History (Part III) • Bioinformatics course and curriculum development began with faculty subcommittee — 1999 • Courses supporting bioinformatics now in many life science and computational science departments, including: • Biology • Biochemistry • Computer Science • Plant Pathology, Physiology, and Weed Science (PPWS) • Mathematics • Statistics 9/9//2005 Computational Biology and Bioinformatics

  8. Some Molecular Biology • The encoded instruction set for an organism is kept in DNA molecules. • Each DNA molecule contains 100s or 1000s of genes. • A gene is transcribed to an mRNA molecule. • An mRNA molecule is translated to a protein (molecule). 9/9//2005 Computational Biology and Bioinformatics

  9. Elaborating Cellular Function Regulation Degradation Transcription Translation DNA mRNA Protein (Genetic Code) Reverse Transcription • Protein functions: • Structure • Catalyze chemical reactions • Regulate transcription Thousands of Genes! 9/9//2005 Computational Biology and Bioinformatics

  10. Chromosomes • Large molecules of DNA: 104 to 108 base pairs. • Human chromosomes: 22 matched pairs plus X and Y. • A gene is a subsequence of a chromosome that encodes a protein. • Proteins associated with regulation are present in chromosomes. • Every gene is present in every cell. • Only a fraction of the genes are in use (“expressed”) at any time. 9/9//2005 Computational Biology and Bioinformatics

  11. Genomics Genomics: Discovery of genetic sequences and the ordering of those sequences into individual genes, into gene families, and into chromosomes. Identification of sequences that code for gene products/proteins and sequences that act as regulatory elements. 9/9//2005 Computational Biology and Bioinformatics

  12. Functional Genomics Functional Genomics: The biological role of individual genes, mechanisms underlying the regulation of their expression, and regulatory interactions among them. 9/9//2005 Computational Biology and Bioinformatics

  13. Challenges for Computer Science • Analyzing and synthesizing complex experimental data • Representing and accessing vast quantities of information • Pattern matching • Data mining • Gene discovery • Function discovery • Modeling the dynamics of cell function 9/9//2005 Computational Biology and Bioinformatics

  14. CBB Faculty in CS • Chris Barrett (VBI, CS) • Vicky Choi • Roger Ehrich • Edward A. Fox • Lenny Heath • Madhav Marathe (VBI, CS) • T. M. Murali • Chris North • Alexey Onufriev • Naren Ramakrishnan • Adrian Sandu • Eunice Santos • João Setubal (VBI, CS) • Cliff Shaffer • Anil Vullikanti (VBI, CS) • Layne Watson • Liqing Zhang 9/9//2005 Computational Biology and Bioinformatics

  15. Established CBB Faculty • Layne Watson • Lenny Heath • Cliff Shaffer • Naren Ramakrishnan • Eunice Santos 9/9//2005 Computational Biology and Bioinformatics

  16. Layne Watson • Professor of Computer Science and Mathematics • Expertise: algorithms; image processing; high performance computing; optimization; scientific computing • Computational biology: has worked with John Tyson (biology) for over 20 years • JigCell: cell-cycle modeling environment; with Tyson, Shaffer, Ramakrishnan, Pedro Mendes of VBI • Expresso: microarray experimentation; with Heath, Ramakrishnan 9/9//2005 Computational Biology and Bioinformatics

  17. Lenny Heath • Professor of Computer Science • Expertise: algorithms; theoretical computer science; graph theory • Computational biology: worked in genome rearrangements 10 years ago • Bioinformatics: concentration in past 5 years • Expresso: microarray experimentation; with Ramakrishnan, Watson • Multimodal networks • Computational models of gene silencing 9/9//2005 Computational Biology and Bioinformatics

  18. Cliff Shaffer • Associate Professor of Computer Science • Expertise: algorithms; problem solving environments; spatial data structures; • JigCell: cell-cycle modeling environment; with Ramakrishnan, Tyson, Watson 9/9//2005 Computational Biology and Bioinformatics

  19. Naren Ramakrishnan • Associate Professor of Computer Science • Expertise: data mining; machine learning; problem solving environments • JigCell: cell-cycle modeling problem solving environment; with Shaffer, Watson • Expresso: microarray experimentation; with Heath, Watson • Proteus — inductive logic programming system for biological applications • Computational models of gene silencing 9/9//2005 Computational Biology and Bioinformatics

  20. Eunice Santos • Associate Professor of Computer Science • Expertise: Algorithms;computational biology;computational complexity; parallel and distributed processing; scientific computing • Relevant bioinformatics project: modeling progress of breast cancer 9/9//2005 Computational Biology and Bioinformatics

  21. New CBB Faculty • T. M. Murali (2003) CS bioinformatics hire • Alexey Onufriev (2003) CS bioinformatics hire • Adrian Sandu(2004) CS hire • João Setubal (Early 2004) VBI and CS • Vicky Choi (2004) CS bioinformatics hire • Liqing Zhang (2004) CS bioinformatics hire • Chris Barrett, Madhav Marathe (Fall 2004) VBI and CS • Anil Vullikanti (Fall 2004) VBI and CS • Yang Cao (January, 2006) CS bioinformatics hire 9/9//2005 Computational Biology and Bioinformatics

  22. T. M. Murali • Assistant Professor of Computer Science • Hired in 2003 for bioinformatics group • Expertise: algorithms; computational geometry; computational systems biology • Projects: • Functional gene annotation • xMotif — find patterns of coexpression among subsets of genes • RankGene — rank genes according to predictive power for disease 9/9//2005 Computational Biology and Bioinformatics

  23. Alexey Onufriev • Assistant Professor of Computer Science • Hired in 2003 for bioinformatics group • Expertise: Computational and theoretical biophysics and chemistry; structural bioinformatics; numerical methods; scientific programming • Projects: • Biomolecular electrostatics • Theory of cooperative ligand binding • Protein folding • Protein dynamics — how does myoglobin uptake oxygen? • Computational models of gene silencing 9/9//2005 Computational Biology and Bioinformatics

  24. Adrian Sandu • Associate Professor of Computer Science • Hired in 2003 • Expertise: Computational science; numerical methods; parallel computing; scientific and engineering applications • Computational science: • New generation of air quality models • computational tools for assimilation of atmospheric chemical and optical measurements into atmospheric chemical transport models 9/9//2005 Computational Biology and Bioinformatics

  25. João Setubal • Research Associate Professor at VBI • Associate Professor of Computer Science • Joined in early 2004 • Expertise: algorithms; computational biology; bacterial genomes • Comparative genomics 9/9//2005 Computational Biology and Bioinformatics

  26. Vicky Choi • Assistant Professor of Computer Science • Hired in 2004 for bioinformatics group • Expertise: computational biology; algorithms • Projects: • Algorithms for genome assembly • Protein docking • Biological pathways 9/9//2005 Computational Biology and Bioinformatics

  27. Liqing Zhang • Assistant Professor of Computer Science • Hired in 2004 for bioinformatics group • Expertise: evolutionary biology; bioinformatics • Research interests: • Comparative evolutionary genomics • Functional genomics • Multi-scale models of bacterial evolution 9/9//2005 Computational Biology and Bioinformatics

  28. Selected CBB Research Projects • JigCell • Expresso • Multimodal Networks • Computational Modeling of Gene Silencing 9/9//2005 Computational Biology and Bioinformatics

  29. JigCell: A PSE for Eukaryotic Cell Cycle Controls Marc Vass, Nick Allen, Jason Zwolak, Dan Moisa, Clifford A. Shaffer, Layne T. Watson, Naren Ramakrishnan, and John J. Tyson Departments of Computer Science and Biology 9/9//2005 Computational Biology and Bioinformatics

  30. Cell Cycle of Budding Yeast Cln2 Clb2 Clb5 Sic1 Sic1 P Sister chromatid separation Cdc20 PPX Lte1 Esp1 Budding Pds1 Tem1 Esp1 Net1P Esp1 Bub2 Cdc15 Cln2 SBF Unaligned chromosomes Pds1 SBF Net1 RENT Mcm1 Unaligned chromosomes Cdh1 Mcm1 Cdc20 Mad2 Cdc20 Cdc14 Cln3 Cdc15 and Bck2 Cdh1 Mcm1 APC Clb2 Cdc14 growth CDKs Swi5 SCF Cdc14 ? Cdc20 MBF Clb5 Esp1 DNA synthesis 9/9//2005 Computational Biology and Bioinformatics

  31. Experimental Database WiringDiagram DifferentialEquations ParameterValues Simulation Analysis Visualization Automatic Parameter Estimation JigCell Problem-Solving Environment 9/9//2005 Computational Biology and Bioinformatics

  32. Why do these calculations? • Is the model “yeast-shaped”? • Bioinformatics role: the model organizes experimental information. • New science: prediction, insight JigCell is part of the DARPA BioSPICE suite of software tools for computational cell biology. 9/9//2005 Computational Biology and Bioinformatics

  33. Expresso: A Next Generation Software System for Microarray Experiment Management and Data Analysis 9/9//2005 Computational Biology and Bioinformatics

  34. Scenarios for Effects of Abiotic Stress on Gene Expression in Plants 9/9//2005 Computational Biology and Bioinformatics

  35. The Expresso Pipeline 9/9//2005 Computational Biology and Bioinformatics

  36. Proteus — Data Mining with ILP • ILP (inductive logic programming) — a data mining algorithm for inferring relationships or rules • Proteus — efficient system for ILP in bioinformatics context • Flexibly incorporates a priori biological knowledge (e.g., gene function) and experimental data (e.g., gene expression) • Infers rules without explicit direction 9/9//2005 Computational Biology and Bioinformatics

  37. Fusion — Chris North • “Snap together” visualization environment • Interactively linked data from multiple sources • Data mining in the background 9/9//2005 Computational Biology and Bioinformatics

  38. Sequence Analysis • Evolution implies changes in genomic sequence through mutations and other mechanisms • Genomic or protein sequences that are similar are called homologous • Algorithms to detect homology provide access to evolutionary relationships and perhaps function conservation through genomic data. 9/9//2005 Computational Biology and Bioinformatics

  39. Networks in Bioinformatics • Mathematical Model(s) for Biological Networks • Representation: What biological entities and parameters to represent and at what level of granularity? • Operations and Computations: What manipulations and transformations are supported? • Presentation: How can biologists visualize and explore networks? 9/9//2005 Computational Biology and Bioinformatics

  40. Reconciling Networks Munnik and Meijer, FEBS Letters, 2001 Shinozaki and Yamaguchi-Shinozaki, Current Opinion in Plant Biology, 2000 9/9//2005 Computational Biology and Bioinformatics

  41. Multimodal Networks • Nodes and edges have flexible semantics to represent: • Time • Uncertainty • Cellular decision making; process regulation • Cell topology and compartmentalization • Rate constants • Phylogeny • Hierarchical 9/9//2005 Computational Biology and Bioinformatics

  42. Using Multimodal Networks • Help biologists find new biological knowledge • Visualize and explore • Generating hypotheses and experiments • Predict regulatory phenomena • Predict responses to stress • Incorporate into Expresso as part of closing the loop 9/9//2005 Computational Biology and Bioinformatics

  43. Computational Modeling of Gene Silencing (CMGS) Lenwood S. Heath, Richard Helm, Alexey Onufriev, Naren Ramakrishnan, and Malcolm Potts Departments of Computer Science and Biochemistry 9/9//2005 Computational Biology and Bioinformatics

  44. RNA Interference (RNAi) 9/9//2005 Computational Biology and Bioinformatics

  45. CMGS System 9/9//2005 Computational Biology and Bioinformatics

  46. Other CBB Research Projects • Bacterial genomics —Setubal • xMotif —Murali • Plant Orthologs and Paralogs (POPS) • Heath, Murali, Setubal, Zhang, Ruth Grene (plant physiology) • Protein structure and docking —Choi • Whole-genome functional annotation —Murali • Modeling biomolecular systems —Onufriev 9/9//2005 Computational Biology and Bioinformatics

  47. CBB Education at VT • CS has been training CS graduate students in CBB since 2000 • Graduate bioinformatics option established in a number of participating departments — 2003 • Ph.D. program in Genetics, Bioinformatics, and Computational Biology (GBCB) — 2003 • First GBCB students arrived, Fall, 2003; now in third year 9/9//2005 Computational Biology and Bioinformatics

  48. CBB Education in CS • A key department of the Ph.D. program in Genetics, Bioinformatics, and Computational Biology (GBCB) • Computation for the Life Sciences I, II • Algorithms in Bioinformatics • Systems Biology • Structural Bioinformatics and Computational Biophysics • Databases for Bioinformatics 9/9//2005 Computational Biology and Bioinformatics

  49. Conclusions • Important research area in department • Close collaboration between life scientists and computational scientists from the beginning of CBB research at VT • Educational approach insists on adequate multidisciplinary background • Multidisciplinary collaborators work closely on a regular basis • Contributions to biology or medicine essential outcomes 9/9//2005 Computational Biology and Bioinformatics

  50. Supported by:Next Generation SoftwareInformation Technology ResearchNSF 9/9//2005 Computational Biology and Bioinformatics

More Related