Computational Biology and Bioinformatics at Notre Dame

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

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

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Background on computational biology and
bioinformatics I

• How to integrate data from many levels?
• Genomic sequencing data
• Proteomics data (global analysis of proteins)
• Gene expression data (DNA microarrays)
• Protein-protein, metabolic networks
• Genetic regulatory networks
• Morphogenesis cellular and genetic

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Background on computational biology and
bioinformatics II

• How to distinguish noise from signal?
• Growth in number of base pair sequences has
  surpassed Moores law about the number of
  transistors in a chip
• Dr. Leroy Hood predicts that in 10 years, using
  nanotechnology to analyze single cells and
  molecules, one will sequence human genome in 1 day

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Background on computational biology and
bioinformatics III

• Mathematical modeling of biological complexity
• Multiscale in nature
• Several qualitative models
• Rule based intuitive for biologist
• Continuum based PDE can be linked to
  quantitative experiments
• Discrete Monte Carlo or Molecular Dynamics
• Need to relate more to digital code

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Background on computational biology and
bioinformatics IV

• Computer assisted analysis and visualization of
  integrated data
• Multi-tool in nature
• Geographically distributed, collection of
  cooperating tools
• Visualization and analysis should integrate both
  simulation and experimental data, to facilitate
  cross validation and comparisons
• Dissemination via web services

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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.
• ConsiderationsDesired dose
• Constraints of the device delivering the
  radiationTreatment time
• Collaboration with Univ. of Maryland radiologists

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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

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Research in Izaguirres group I

• Jesús Izaguirre and collaborators are working on
  
• Computer-aided drug design, human genome
  proteomics, and understanding of morphogenesis
  by
• Enabling simulations of biomolecules (Proteins,
  DNA, etc.)
• 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!

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Research in Izaguirres group II

• We have released CompuCell, a multi-model
  software for simulation of morphogenesis
• Models interaction of genetic regulatory system
  with cellular dynamics
• Uses knowledge-base, stochastic, discrete and
  continuum model
• NSF Biocomplexity grant to model chicken limb
  development

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Research in Izaguirres group III

• 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 (http//www.nd.edu/lcls/prot
  omol)

• 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

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Opportunities IIndiana Biocomplexity Consortium
Simulation Request
Results via XML
CompuCell Biocomplexity Computing Environment
Server

• OBJECTIVE Make ProtoMol and CompuCell into web
  services
• Web service for molecular and cellular
  simulations
• Component that provides data and simulation
  capabilities through the web
• 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

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Opportunities IIResearch areas of interest

• Collaboratory frameworks recommender systems,
  assistants, not just algorithms
• Data mining in high performance environment
  clustering, pattern recognition, distributed
  databases
• Modeling constrained optimization, stochastic
  techniques, geometry
• Simulation qualitative/quantitative models

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References

• I would like to thank the following--
• NSF Biocomplexity grant IBN-0083653
• NSF CAREER award ACI-0135195
• Department of Computer Science and Engineering,
• Univ. of Notre Dame
• http//conferences.computer.org/bioinformatics
• http//smi-web.stanford.edu/people/altman
• http//geneticcircuits.ucsd.edu
• http//www.systemsbiology.org
• http//www.geneontology.org
• http//www.cytoscape.org
• http//www.genomatica.com
• http//www.nd.edu/lcls/compucell
• http//www.nd.edu/lcls/protomol
• http//www.nd.edu/xwu/uiowa_files/v3_document.htm