Grid Computing The Illinois Bio-Grid

1. Grid ComputingThe Illinois Bio-Grid Alexander B. Schilling, Ph.D. University of Chicago Proteomics Core Lab aschilli@uchicago.edu

2. Outline • Bio-Medical Informatics • Show how computability is growing exponentially • Illinois Bio-Grid • Describe this Grid founded at DePaul • IBG Workbench • Describe these grid enabled BioInformatics tools • Mass Spec Toolkit in Cactus • Describe plans to implement tools for spectral interpretation in Cactus

3. BioInformatics and Computability • Growth of data in GenBank is exponential and doesn't show signs of slowing down yet. • Source GenBank/NCBI • Compute time to process data growing equivalently • Twice Moore's law • Biologists don't have access to supercomputers for everyday work • Grid computing gives Biologists more computing power affordably

4. Illinois Bio-Grid • A consortium of • Educational Institutions • National Labs • Private Industry • City & State entities • Museums

5. Goals • Provide an infrastructure of computational (and other) resources to Biological and Medical researchers • Provide an infrastructure of computational (and other) resources to Computer Scientists working on BioMedical problems • Provide a tool suite of BioMedical software for BioMedical researchers to use on the IBG computational resources • Also for open source distribution worldwide • Provide an environment for CS researchers to work with BioMedical researchers • Try to solve some computationally intense BioMedical Informatics problems • Create a workbench of BioMedical software modules in open source distribution to facilitate more rapid BioMedical Informatics research by researchers worldwide

6. Illinois Bio-Grid Infrastructure DePaul Chicago TechnologyPark (Supercomputing Center Of Chicago) Canadian NRC Field Museum ArgonneMCS U Chicago IIT

7. Bio-Grid Workbench • Consists of many applications important to Biological and Medical Researchers • All Grid enabled to provide enhanced computational power • Genomics • Proteomics • Phylogenetics • Computational Fluid Dynamics / Medical Imaging • Cell membrane modeling • Data Modeling LSG-RG in GGF Reference Implementation

8. Genomics and Proteomics 1 • Homology Searching • Searching for proteins with the same evolutionary "ancestor" • Smith-Waterman / Blast / FastA • Database against database searches (instead of single sequence against database searches) • Allow groups of input sequences to search for homologous sequences to all in the set • Mass Spec Data Interpretation • Ionize peptides and fragment them inside mass spectrometer • Measure charge/mass ratio of peptide ions and fragments • Interpret resulting spectra

9. Genomics and Proteomics 2 • Mass Spec Based Protein Identification • Conduct “In Silico” Digestion of protein database • Predict charge/mass ratio of all possible peptide ions resulting from database • Search actual ions in spectra against predicted ions • Return identifications of proteins based on scoring match

10. Genomics and Proteomics 3 • Predict 3D Protein folding given sequence of amino acids • Solution to Schrödinger equation is intractable • Search space of possible folds is immense • Current methods of searching • ab-initio • AI • Lego • Monte Carlo • Lattice • On Grids can runmultiple searches • In parallel • In series • On Grids can run athigher resolutions

11. Phylogenetics • Sequence various taxa (individuals or species) • Frequently sequence mitochondrial DNA • Mitochondrial DNA much like prokaryote DNA • Compare sequences • Form hypothetical evolutionary tree • Each branch is a mutation • Shows mutations from hypothetical ancestor • Search space is immense • Runs for 6 months on a single processor • Then crashes!

12. Computational Fluid Dynamics / Medical Imaging • Monitor and collect real time CAT scan data • Arterial blood flow • Use Grid to interpret data • Use Computational Fluid Dynamics to model blood flow • Produce real time imaging • Locate aneurisms and other anomalies • Aid in diagnosis and decision making forsurgical procedures • Non-invasive

13. Cell membrane modeling • Run simulations using both • Configurational Bias Monte Carlo Method (CBMC) • Molecular Dynamics (MD) • Current simulations being done involve the properties of cholesterol in lipid membranes • Cholesterol is known to be an essential component of mammalian cell membranes • Its exact role is not well understood • Previous simulations have been run • Up to 1600 lipid or cholesterol molecules • And 52,000 water molecules • We're increasing these simulations by • An order of magnitude in the physical dimensions • And 2 to 3 orders of magnitude in time

14. Data Modeling • Data Modeling LSG-RG in GGF Reference Implementation • Automatic Data Synchronization • Flagging "dirty" data • Flagging data sources (including versioning)

15. IBG Workbench Phylogenetic Trees Mass Spec Proteomics Membrane Modeling CFD Homology Searching DB Access Grid Services (Middleware) Grid Fabric (Resources)

16. The Purpose of Mass Spectrometry in Proteomics • Identify and sequence all proteins involved in an organism’s biology. • Use this knowledge to identify proteins (or peptides) that can be used to study and understand different biological states. • Correlate protein expression levels to biological function. Use protein or peptide biomarkers to identify disease states in patients. • Use the structure of the relevant proteins as targets for developing new therapeutic techniques (drugs etc..).

17. Mass Spectrometers in Proteomics • Mass spectrometers measure the masses of proteins and peptides by moving their ions through the instrument in a controlled way. • Proteins can be degraded using enzymes and the peptides produced can be analyzed by the mass spectrometer. • A MS/MS instrument can cause the peptide ions to fragment into smaller pieces which can be used to deduce the peptide’s sequence. • Once the sequence of the peptides has been determined, the protein’s complete sequence can be reassembled from the peptide sequences. • The intensity of peaks can be used to determine the expression level of a protein in a sample. • Samples from healthy and diseased tissue can be compared to locate biomarkers for disease.

18. The MS/MS Experiment Produces Multidimensional Data +MS, 4.7min (#36) +MS2(1535.8), 5.6min (#44) Intens. 38. MS 1+ 1479.8 MS TIC 1000 500000 1+ 800 1+ 1640.0 646.4 400000 600 1+ 1305.8 300000 400 1+ 1+ 1578.7 562.2 1163.6 200000 200 1710.0 1074.6 1+ 417.1 927.7 476.1 2076.8 2169.4 725.2 845.1 0 38. 100000 MS/MS of m/z 1535.8 150 1153.1 125 y7 1+ y11 1516.6 100 b6 842.5 75 1389.3 y6 y5 50 1304.2 659.9 964.4 599.3 727.2 25 0 400 600 800 1000 1200 1400 1600 1800 2000 m/z • Chromatograms (Time vs Intensity) • Precursor Ion Spectra of Peptides (Mass vs Intensity) • Product Ion Spectra of Peptides(*(Precursor Mass), Mass vs Intensity)

19. Y-ions from C to N terminus Y ion Y ion Y ion 3 1 2 O O O O R R R R 1 2 3 4 C C C C OH NH 2 C N C N C N C H H H H H H H B ion B ion B ion 1 3 2 B-ions from N to C terminus What the tandem mass spectrum of a peptide looks like.

20. Important Issues In Computation for Proteomics • DeNovo Sequencing • Many computationally efficient algorithms exist • Many times algorithms produce incorrect results very quickly! • Issue of posttranslational modifications introduces complexity into interpretations • Much data must be discarded to accommodate workstation based computational capacity • A strong desire exists to use intensity data as well as mass data in interpretations • Database Search (Protein ID) • Most packages are commercial, few open source (BLAST based only) • The more posttranslational modifications you allow for, the longer the searches take. Area is ripe for parallelism. • Serious problems with false positive identifications • Many active in research to address this problem • Could be reduced by more front end interpretation before search • Could combine spectra from multiple MS types before search instead of correlating ID results after searches • Datamining • What do you do with all the identifications? Systems Biology! • Create models for signal pathways using protein id and expression data

21. GridProt: A Cactus Based Proteomics Tool Kit Thorns: GridMass – handles basic data extraction, chromatographic peak integration, mass detection GridTAG - partial sequence mass tag extraction GridID - grid based database search using mass spec data GridDeNS - grid based denovo sequencing Visualization – OpenDX Data Storage – mzXML andHDF-5

22. Conclusions • Illinois Bio-Grid • Excellent resource for Biological and Medical researchers • IBG Workbench • Excellent software architecture for compute intensive applications • Will be source of BioMedical Informatics software sharing for a plethora of different research areas • Will be source of workbench tools for researchers in other related Informatics software creation • Cactus is an ideal platform for HPC of Mass Spec data • Modular thorns allow generalization for MS, specialization for Proteomics • Ideal base for open source, extendable software ready for HPC as Proteomics data sets grow. • http://facweb.cs.depaul.edu/bioinformatics • http://facweb1.cs.depaul.edu/~dangulo

23. Acknowledgements Illinois Biogrid: Dave Angulo, DePaul University Gregor von Laszewski, ANL Kevin Drew, Tim Freeman University of Chicago Howard Hughes Medical Institute Ben May Cancer Center Pfizer Inc.