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OVium Bioinformatic Solutions


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

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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  • 1. Computational Bio-Discovery Wolfgang Kraske PhD EE A scientific paradigm Bio-Information Networking Community Tool Services Model Characterization
  • 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. Computational Bio-Discovery An Awkward Situation Cells in Vivo? Bioinformatic Research System
  • 4. Computational Bio-Discovery Overview
    • Emerging Environment for Bio-Discovery
    • 5. Community Enterprise and Tools
    • 6. Model Characterization
    Vector/ Small Molecule Sequence Structure Pathway
  • 7. Computational Bio-Discovery Goal
    • Establish a breakthrough network environment to promote significant drug discoveries
    • 8. Integrate tools over network to promote community analysis and discovery
        • Integration of Research communities
        • 9. Integration of internal with external scientific communities
        • 10. Integration with external patient and lay communities
  • 11. Computational Bio-Discovery Network Communities Clinical Applications & Research Internal Biological Informatics Laboratory
    • Algorithmic Definition of Structural Model Characteristics
    • 12. Wet Laboratory Sequencing and Molecular Data Acquisition
    Pharmaceutical Research & Development Mathematics and Computer Science Research & Development External Application Computing Resources Community Bio-Information Network Sources
  • 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. 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. 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: 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. MVC Architecture Fi bers REST-Representation State Transfer Zend Framework
  • 21. Ajax – XMLHttpRequest
  • 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. Use Case Management Programmer Program Test & Validation Evaluator Project Manager Report Report Planning Stakeholder Control
  • 24. Conceptual Model Sequence: Linear Small Molecule: Node Structure: Spatial Pathway: Time & Space
  • 25. Allometric Scaling
    • The 4 th dimension of life (Evolution/Temporal Development)
    • 26. Evolution maximizes external surface area versus internal efficiency to yield a one quarter scaling of fractal dimension(size invariant property)
      • ie Protein folding, capillary & mitochondria formation
    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. Computational Bio-Discovery Scale Free Versus Random Scale Free Interaction characterizes the natural (Organic) behavior of :
    • Metabolic signaling pathways
    • 28. Internet links
    • 29. Bioinformatic Analysis Algorithms
    Random Network Scale-Free Network
  • 30. Protein Interactome Knockout of Proteins Eliminates Temporal Communication between Processes Knockout of Scaffold Proteins Eliminate Structure NATURE | doi:10.1038/nature02555 | NATURE | doi:10.1038/nature02555 | 2004, pp 1-6
  • 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. Model Based Algorithms
    • Algorithms are easily programmed and distributed on parallel computing architectures
      • Trivially distributed on an SMP architecture
      • 33. Optimally distributed on an MPP architecture
    • Use fractional dimension models to analyze biomedical data sets
      • Markov field analysis
      • 34. Cluster measures to establish hierarchical structure
      • 35. Regular statistical characterization
    MEJ Neuman, SH Strogatz, DJ Watt, “Random Graphs with arbitrary degree distributions & applications”, Physical Review E, 64, 026118-1, 2003
  • 36. Kinase Pathway Analysis Receptor Tyrosine Kinases
    • EGFR(ErbB1/HER1-4)
      • Inhibited by Irressa/Tarceva
      • 37. Internalized by Herceptin
    • VEGFR (Tumor)
    • 38. FGFR
    • 39. Insulin Receptor (CD220)
    Nonreceptor Tyrosine Kinases
    • Bcr-Abl
    • 40. Inhibited by Gleevac
    • 41. Serine Threonine Kinases
    • 42. P38- α MAPK
      Cyclin dependent Kinases
    • Flavopinridol HIV Treatment
    • 43. Indirubin Chinese Herbal
  • 44. Transcription & Translation Hox Algorithm Varieties Hox Clock
  • 45. Protein Micro-Array Analysis Pathway Studio
    • Interpret gene expression and other high throughput data
    • 46. Build, expand and analyze pathways
    • 47. Find relationships among genes, proteins,
    • 48. cell processes and diseases
    • 49. Draw publication-quality pathway diagrams
    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
    • BIND: Bio-molecular Interaction Network
    • KEGG
    • 50. Science Signaling
    • 51. Prolexys HyNet protein-protein
    • 52. Interaction database
    Probe-target hybridization
  • 53. Development up to present
    • Multidimensional Markov field analysis is a standard algorithm for sequence analysis, signal processing and multidimensional image processing
    • 54. I have programmed a variety of multi-scale and multi-dimensional algorithms on Cloud and MPP architecture with great success
    A Gelman,J Hill, “Data Analysis Using Regression & Multilevel/Hierarchical Models”, Cambridge University Press, 2008
  • 55. Further Topics for Algorithms
    • Incorporation of RNAi Regulation of Transcription/ Translation, Epigenic Activation and Translational Coding Cycles
    • 56. Agent Based Organization and Processing to Develop and Execute Processing Systems
      • Neural Networks
      • 57. Genetic Algorithms
      • 58. Swarm Algorithms
    • Applications in Phylogeny, Developmental Biology, Social Psychology
    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. Translation Codon
  • 60. Human Kinome
  • 61. Montage Pictures
  • 62. Montage Pictures