Prof. Jesús A. Izaguirre Department of Computer Science and Engineering Computational Biology and Bioinformatics at Notre Dame

Overview u Systems approach to computational biology and bioinformatics n Integrate multiple levels of information n Distinguish noise from signal: analysis of large data sets n Mathematical modeling of biological complexity n Computer assisted analysis of integrated data u Research in CSE n Biomedical engineering n Biocomplexity simulations n Computational biology and biochemistry u Challenges n Right biological problem and team n Obstacles to interdisciplinary research (publications, learning curve) n Computational resources u Opportunities n Indiana Biocomplexity Consortium n Interdisciplinary Center for the Study of Biocomplexity n Collaborative frameworks n Systems biology approach n Approximate multi-scale, multi-level models n Nanoscale genomics

Background on computational biology and bioinformatics I How to integrate data from many levels? 1. Genomic sequencing data 2. Proteomics data (global analysis of proteins) 3. Gene expression data (DNA microarrays) 4. Protein-protein, metabolic networks 5. Genetic regulatory networks 6. Morphogenesis: cellular and genetic

Background on computational biology and bioinformatics II How to distinguish noise from signal? 1. Growth in number of base pair sequences has surpassed Moore’s law about the number of transistors in a chip 2. Dr. Leroy Hood predicts that in 10 years, using nanotechnology to analyze single cells and molecules, one will sequence human genome in 1 day

Background on computational biology and bioinformatics III Mathematical modeling of biological complexity 1. Multiscale in nature 2. Several qualitative models 1. Rule based – intuitive for biologist 2. Continuum based – PDE – can be linked to quantitative experiments 3. Discrete – Monte Carlo or Molecular Dynamics 3. Need to relate more to digital code

Background on computational biology and bioinformatics IV Computer assisted analysis and visualization of integrated data 1. Multi-tool in nature 2. Geographically distributed, collection of cooperating tools 3. Visualization and analysis should integrate both simulation and experimental data, to facilitate cross validation and comparisons 4. Dissemination via web services

Current Research at ND CSE I Danny Chen and students are developing algorithms for radiosurgery treatment planning Find a set of beams to destroy a tumor without harming surrounding healthy tissues. Considerations: –Desired dose –Constraints of the device delivering the radiation –Treatment time Collaboration with Univ. of Maryland radiologists

Current Research at ND CSE II Greg Madey and Patricia Maurice (CE) are developing a collaboratory for biocomplexity simulation of environmental chemistry Try to model the life cycle of pollutants in a soil – swamp ecosystem Validated through experiments Web based interface using Java and SWARM simulation package Simulation results are stored in an Oracle database (“E-science”) Multi-institutional collaboration funded through NSF Biocomplexity grant

Jesús Izaguirre and collaborators are working on: Computer-aided drug design, human genome proteomics, and understanding of morphogenesis by: u Enabling simulations of biomolecules (Proteins, DNA, etc.) u Permitting simulations of cells and organ growth CHALLENGES: Large systems--millions of particles Long time scales--billions of time steps =weeks and months of simulations in supercomputers with hundreds of nodes! Research in Izaguirre’s group I

Research in Izaguirre’s group II We have released CompuCell, a multi-model software for simulation of morphogenesis 1. Models interaction of genetic regulatory system with cellular dynamics 2. Uses knowledge-base, stochastic, discrete and continuum model 3. NSF Biocomplexity grant to model chicken limb development

Molecular dynamics of biological molecules: Faster algorithms, up to an order of magnitude faster for molecular dynamics and sampling of conformational space of proteins Parallel program ProtoMol, a software framework for molecular dynamics and related-applications, which is open source ( IN PROGRESS: MULTISCALE approximate methods in biology and MEMS (with Paolucci) Applications in real problems: Brian Baker in Chemistry (immune system) Ruhong Zhou at IBM T. J. Watson (BLUE GENE project) Grant application to NSF Nanoscale. Army in preparation Research in Izaguirre’s group III

Opportunities I Indiana Biocomplexity Consortium OBJECTIVE: Make ProtoMol and CompuCell into web services u Web service for molecular and cellular simulations u Component that provides data and simulation capabilities through the web u NIH Center of Excellence proposal (Indiana University and Notre Dame) APPROACH: Integrated tools for simulation, visualization and analysis Use distributed grids Disseminate using web services Simulation Request CompuCell Biocomplexity Computing Environment Server Results via XML

Opportunities II Research areas of interest u Collaboratory frameworks: recommender systems, assistants, not just algorithms u Data mining in high performance environment: clustering, pattern recognition, distributed databases u Modeling: constrained optimization, stochastic techniques, geometry u Simulation: qualitative/quantitative models

I would like to thank the following-- NSF Biocomplexity grant IBN NSF CAREER award ACI Department of Computer Science and Engineering, Univ. of Notre Dame u u u u u u u u u u