OVium Bioinformatic Solutions

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OVium Bio-Information Solutions use forefront algorithms to analyze key data resources such NCBI, EBLM and PDB to develop cell signal pathways.

OVium employs cloud and MPP computing solutions with homology and signal network mapping to develop chemical and protein pathways for discovery research.

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OVium Bioinformatic Solutions

  1. 1. Computational Bio-Discovery Wolfgang Kraske PhD EE A scientific paradigm Bio-Information Networking Community Tool Services Model Characterization
  2. 2. Wolfgang F. Kraske, PhD PhD Electrical Engineering University of Southern California, December 1995 Advisor- Irving S. Reed, PhD, Charles E. Powell Professor Emeritus of E.E./C.S. Research- Geometric Algebra for Topological Information Representation MS Electrical Engineering University of Southern California, December 1986 Research- Microwave Imaging and Signal Processing BS Physics/Mathematics-University of Maryland, UMCP, 1982
  3. 3. Computational Bio-Discovery An Awkward Situation Cells in Vivo? Bioinformatic Research System
  4. 4. Computational Bio-Discovery Overview <ul><li>Emerging Environment for Bio-Discovery
  5. 5. Community Enterprise and Tools
  6. 6. Model Characterization </li></ul>Vector/ Small Molecule Sequence Structure Pathway
  7. 7. Computational Bio-Discovery Goal <ul><li>Establish a breakthrough network environment to promote significant drug discoveries
  8. 8. Integrate tools over network to promote community analysis and discovery </li><ul><ul><li>Integration of Research communities
  9. 9. Integration of internal with external scientific communities
  10. 10. Integration with external patient and lay communities </li></ul></ul></ul>
  11. 11. Computational Bio-Discovery Network Communities Clinical Applications & Research Internal Biological Informatics Laboratory <ul><li>Algorithmic Definition of Structural Model Characteristics
  12. 12. Wet Laboratory Sequencing and Molecular Data Acquisition </li></ul>Pharmaceutical Research & Development Mathematics and Computer Science Research & Development External Application Computing Resources Community Bio-Information Network Sources
  13. 13. PCR Mass Spectrometry Magnetic Resonance Spectroscopy Micro-Array Gel Electrophoresis Protein Rapid Translation Southern Blot X-ray crystallography Computational Bio-Discovery Challenge: Tool Integration Centrifugation Titration Electron Microscopy Light Microscopy IVC Emulsion Phage Display
  14. 14. Computational Bio-Discovery Service Architecture Solution Legend Campus Laboratories Firewall REST Server Campus Switch Load Balancing Internet Load Balancing Firewall Entity Servers Database Servers Mass Storage Network Switch Web Segment Data Segment Application Segment External Services Web/Session/Tool Servers Mass Storage Database Server Entity Server Tool Server Web Content Server HTTP Server Social Server Ethernet/ 100/1000 Base T Network Switch Load Balance Server Firewall Server Fiber Channel Rest Server/
  15. 15. Scalable Algorithm Solutions Parallel Language & Algorithm Library Research Application of Parallel Algorithms Parallel Algorithm & OS Development Parallel Algorithm & OS Layer MPP Cluster w/Algorithm & OS Layer SMP w/Algorithm Mass Storage Cloud: <ul><li>App Engine
  16. 16. EC2/3
  17. 17. Oracle Metro
  18. 18. MS Azure
  19. 19. Web Services </li></ul>WF Kraske, Voxar-All ATM Distributed Biomedical Visualization:T3D MPP, 8 th IEEE Symposium on Computer Based Med Systems, 249, 1995 Dell Xeon Cluster, Message Passing Innterface
  20. 20. MVC Architecture Fi bers REST-Representation State Transfer Zend Framework
  21. 21. Ajax – XMLHttpRequest
  22. 22. Iterated Software Lifecycle Requirements Design Development Test Implementation Analysis Deployment Upgrade or New Iteration ... Iterated Weekly Functional Roll out Agile and eXtreme programming processes I Jacobson, G Booch, J Rumbaugh, Unified Software Development Process, Addison Wesley 1999
  23. 23. Use Case Management Programmer Program Test & Validation Evaluator Project Manager Report Report Planning Stakeholder Control
  24. 24. Conceptual Model Sequence: Linear Small Molecule: Node Structure: Spatial Pathway: Time & Space
  25. 25. Allometric Scaling <ul><li>The 4 th dimension of life (Evolution/Temporal Development)
  26. 26. Evolution maximizes external surface area versus internal efficiency to yield a one quarter scaling of fractal dimension(size invariant property) </li><ul><li>ie Protein folding, capillary & mitochondria formation </li></ul></ul>Conventional Euclidean Biological (Fractal) Length L A 1/2 V 1/3 M 1/3 l a 1/3 v 1/4 M 1/4 Area L 2 A V 2/3 M 2/3 l 3 a V 3/4 M 3/4 Volume L 3 A 3/2 V M l 4 a 3/2 v M GB west, RH Brown, BJ Enquist, The Forth Dimension of Life, Science 1677, 284, 1999 WF Kraske, Analysis & Segmentation of Higher Dimensional Data Sets, SPIE Press 264, IS12, 1995
  27. 27. Computational Bio-Discovery Scale Free Versus Random Scale Free Interaction characterizes the natural (Organic) behavior of : <ul><li>Metabolic signaling pathways
  28. 28. Internet links
  29. 29. Bioinformatic Analysis Algorithms </li></ul>Random Network Scale-Free Network
  30. 30. Protein Interactome Knockout of Proteins Eliminates Temporal Communication between Processes Knockout of Scaffold Proteins Eliminate Structure NATURE | doi:10.1038/nature02555 |www.nature.com/nature NATURE | doi:10.1038/nature02555 | www.nature.com/nature 2004, pp 1-6
  31. 31. Iterative Construction of Deterministic Scale-Free Networks Peripheral Nodes with 4 Links Hub Nodes with 6 Links A. Barbasi, “Linked”, Penquin Books, pp. 232-237, 2003 Level 0 Level 1 Level 2 Hub Nodes with 15 Links
  32. 32. Model Based Algorithms <ul><li>Algorithms are easily programmed and distributed on parallel computing architectures </li><ul><li>Trivially distributed on an SMP architecture
  33. 33. Optimally distributed on an MPP architecture </li></ul><li>Use fractional dimension models to analyze biomedical data sets </li><ul><li>Markov field analysis
  34. 34. Cluster measures to establish hierarchical structure
  35. 35. Regular statistical characterization </li></ul></ul>MEJ Neuman, SH Strogatz, DJ Watt, “Random Graphs with arbitrary degree distributions & applications”, Physical Review E, 64, 026118-1, 2003
  36. 36. Kinase Pathway Analysis Receptor Tyrosine Kinases <ul><li>EGFR(ErbB1/HER1-4) </li><ul><li>Inhibited by Irressa/Tarceva
  37. 37. Internalized by Herceptin </li></ul><li>VEGFR (Tumor)
  38. 38. FGFR
  39. 39. Insulin Receptor (CD220) </li></ul>Nonreceptor Tyrosine Kinases <ul><li>Bcr-Abl
  40. 40. Inhibited by Gleevac
  41. 41. Serine Threonine Kinases
  42. 42. P38- α MAPK </li></ul><ul>Cyclin dependent Kinases </ul><ul><li>Flavopinridol HIV Treatment
  43. 43. Indirubin Chinese Herbal </li></ul>
  44. 44. Transcription & Translation Hox Algorithm Varieties Hox Clock
  45. 45. Protein Micro-Array Analysis Pathway Studio <ul><li>Interpret gene expression and other high throughput data
  46. 46. Build, expand and analyze pathways
  47. 47. Find relationships among genes, proteins,
  48. 48. cell processes and diseases
  49. 49. Draw publication-quality pathway diagrams </li></ul>BioConductor- analysis of single channel Affymetrix and two or more channel cDNA/Oligo micro-arrays based on the R statistical package Pathway analysis for model organisms <ul><li>BIND: Bio-molecular Interaction Network </li></ul>Database <ul><li>KEGG
  50. 50. Science Signaling
  51. 51. Prolexys HyNet protein-protein
  52. 52. Interaction database </li></ul>Probe-target hybridization
  53. 53. Development up to present <ul><li>Multidimensional Markov field analysis is a standard algorithm for sequence analysis, signal processing and multidimensional image processing
  54. 54. I have programmed a variety of multi-scale and multi-dimensional algorithms on Cloud and MPP architecture with great success </li></ul>A Gelman,J Hill, “Data Analysis Using Regression & Multilevel/Hierarchical Models”, Cambridge University Press, 2008
  55. 55. Further Topics for Algorithms <ul><li>Incorporation of RNAi Regulation of Transcription/ Translation, Epigenic Activation and Translational Coding Cycles
  56. 56. Agent Based Organization and Processing to Develop and Execute Processing Systems </li><ul><li>Neural Networks
  57. 57. Genetic Algorithms
  58. 58. Swarm Algorithms </li></ul><li>Applications in Phylogeny, Developmental Biology, Social Psychology </li></ul>CD Manning,P Ragahavan, H Shutze, “Introduction to Information Retrieval”, Cambridge University Press, 2008 J Augen, “Bioinformatics in the Post Genomic Era”, Addison-Wesley, 2005 P Baldi, S Brunak, “Bioinformatics The Machine Learning Approach”, MIT, 2001
  59. 59. Translation Codon
  60. 60. Human Kinome
  61. 61. Montage Pictures
  62. 62. Montage Pictures

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