1 Bioinformatics in the Department of Computer Science Lenwood S. Heath Department of Computer Science Blacksburg, VA College of Engineering Northern Virginia Engineering Showcase March 5, 2004

2 Bioinformatics Faculty Cliff Shaffer Adrian Sandu Alexey Onufriev Lenny HeathT. M. Murali Naren Ramakrishnan Eunice Santos Layne Watson Roger Ehrich Chris North Joao Setubal, CS and VBI

3/5/2004 Bioinformatics in Computer Science 3 Relevant Expertise Algorithms — Heath, Santos, Setubal, Shaffer, Watson Computational structural biology — Onufriev, Sandu Computational systems biology — Murali Data mining — Ramakrishnan Genomics — Heath, Murali, Ramakrishnan Human-omputer interaction, visualization — North Image processing — Ehrich, Watson High performance computing — Sandu, Santos, Watson Numerical analysis — Onufriev, Watson Optimization — Watson Problem solving environments — Ramakrishnan, Shaffer

3/5/2004 Bioinformatics in Computer Science 4 Selected Collaborations Virginia Tech: Biochemistry, Biology, Fralin Biotechnology Center, Plant Physiology, Veterinary Medicine, Virginia Bioinformatics Institute (VBI), Wood Science North Carolina State University: Forest Biotechnology Center Duke: Biology University of Illinois: Plant Biology

5 Selected Funding NSF IBN : ITR: Understanding Stress Resistance Mechanisms in Plants: Multimodal Models Integrating Experimental Data, Databases, and the Literature. L. S. Heath; R. Grene, B. I. Chevone, N. Ramakrishnan, L. T. Watson. $499,973. NSF EIA : A Microarray Experiment Management System. N. Ramakrishnan, L. S. Heath, L. T. Watson, R. Grene, J. W. Weller (VBI). $600,000. DARPA N : Dryophile Genes to Engineer Stasis-Recovery of Human Cells. M. Potts, L. S. Heath, R. F. Helm, N. Ramakrishnan, T. O. Sitz, F. Bloom, P. Price (Life Technologies), J. Battista (LSU). $4,532,622. NSF MCB : Biocomplexity---Incubation Activity: A Collaborative Problem Solving Environment for Computational Modeling of Eukaryotic Cell Cycle Controls. J. J. Tyson, L. T. Watson, N. Ramakrishnan, C. A. Shaffer, J. C. Sible. $99,965. NIH 1 R01 GM : ``Problem Solving Environment for Modeling the Cell Cycle. J. J. Tyson, J. Sible, K. Chen, L. T. Watson, C. A. Shaffer, N. Ramakrishnan, P. Mendes (VBI). 211,038. Air Force Research Laboratory F : The Eukaryotic Cell Cycle as a Test Case for Modeling Cellular Regulation in a Collaborative Problem Solving Environment. J. J. Tyson, J. C. Sible, K. C. Chen, L. T. Watson, C. A. Shaffer, N. Ramakrishnan. $1,650,000.

3/5/2004 Bioinformatics in Computer Science 6 Research Resources System X Third fastest computer on the planet Laboratory for Advanced Scientific Computing & Applications (LASCA) Parallel algorithms & math software Anantham Cluster Grid computing Bioinformatics Research LAN Linux, Mac OS X, Windows Bioinformatics databases and analysis

3/5/2004 Bioinformatics in Computer Science 7 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

3/5/2004 Bioinformatics in Computer Science 8 Computational Molecular Biology DNA mRNA Protein Enzyme Reaction Network Cell Physiology …TACCCGATGGCGAAATGC... …AUGGGCUACCGCUUUACG... …Met - Gly - Tyr - Arg - Phe - Thr... ATP ADP -P XYZ E1E1 E2E2 E3E3 E4E4

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

3/5/2004 Bioinformatics in Computer Science 10 JigCell Problem-Solving Environment Experimental Database Wiring Diagram Differential EquationsParameter Values Analysis Simulation Visualization Automatic Parameter Estimation

3/5/2004 Bioinformatics in Computer Science 11 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.

3/5/2004 Bioinformatics in Computer Science 12 Expresso: A Next Generation Software System for Microarray Experiment Management and Data Analysis

3/5/2004 Bioinformatics in Computer Science 13 Integration of design, experimentation, and analysis Data mining; inductive logic programming (ILP) Closing the loop Drought stress experiments with pine trees and Arabidopsis Expresso: A Problem Solving Environment (PSE) for Microarray Experiment Design and Analysis

3/5/2004 Bioinformatics in Computer Science 14 Scenarios for Effects of Abiotic Stress on Gene Expression in Plants

3/5/2004 Bioinformatics in Computer Science 15 Data Mining with ILP ILP (inductive logic programming) is a data mining algorithm for inferring relationships or rules. ILP groups related data and chooses in favor of relationships having short descriptions. ILP can also flexibly incorporate a priori biological knowledge (e.g., categories and alternate classifications). Hybrid reasoning: Information Integration “Is there a relationship between genes in a given functional category and genes in a particular expression cluster?” ILP mines this information in a single step

3/5/2004 Bioinformatics in Computer Science 16 Rule Inference in ILP Infers rules relating gene expression levels to categories, both within a probe pair and across probe pairs, without explicit direction Example Rule: [Rule 142] [Pos cover = 69 Neg cover = 3] level(A,moist_vs_severe,not positive) :- level(A,moist_vs_mild,positive). Interpretation: “If the moist versus mild stress comparison was positive for some clone named A, it was negative or unchanged in the moist versus severe comparison for A, with a confidence of 95.8%.”

3/5/2004 Bioinformatics in Computer Science 17 ILP in the Expresso Pipeline Expresso is a next generation software system for microarray experiments that provides a database interface to ILP functionality.

3/5/2004 Bioinformatics in Computer Science 18 Status of Expresso Capabilities –Data capture and storage –Statistical analysis –Data mining by ILP –Microarray experiment design — GeneSieve –Expresso-assisted experiment composition –Closing the experimental loop Successful microarray experiment analysis –Pine, Norway spruce, yeast, Deinococcus radiodurans (an extremophile microorganism), human cell lines Planned microarray experiment analysis –Potato, Arabidopsis thaliana, tomato, rice, corn

3/5/2004 Bioinformatics in Computer Science 19 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?

3/5/2004 Bioinformatics in Computer Science 20 Reconciling Networks Munnik and Meijer, FEBS Letters, 2001 Shinozaki and Yamaguchi- Shinozaki, Current Opinion in Plant Biology, 2000

3/5/2004 Bioinformatics in Computer Science 21 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

3/5/2004 Bioinformatics in Computer Science 22 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

3/5/2004 Bioinformatics in Computer Science 23 Conclusions Engaged faculty with the right expertise Numerous life science collaborations Federal research funding First-class computational resources A variety of cutting-edge bioinformatics research projects

3/5/2004 Bioinformatics in Computer Science 24 Bioinformatics Education Courses in Computer Science Courses in the Life Sciences Bioinformatics Option Doctoral Program in Genetics, Bioinformatics, and Computational Biology

3/5/2004 Bioinformatics in Computer Science 25 Doctoral Program in Genetics, Bioinformatics, and Computational Biology Multidisciplinary: biology, biochemistry, crop science, plant physiology, computer science, mathematics, statistics, veterinary medicine

3/5/2004 Bioinformatics in Computer Science 26 Anantham Cluster Previous cluster specs 200 AMD 1 GHz processors 1 GB RAM per processor 2 TB disk space 2.56 Gb/s Myrinet network Previous 200 processor cluster