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Immunocomputing for Bioarrays Alexander O. Tarakanov, et al. Proceedings of ICARIS 2002, 32 -40 Immunocomputing for Bioarrays Alexander O. Tarakanov, et al. Proceedings of ICARIS 2002, 32 -40 MEC Seminar Su Dong Kim 2003. 4. 17. (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 1

Introduction Biosensor : antibodies, enzymes, etc. l Biochips vs. Computer microchips l ¨ Miniaturization Introduction Biosensor : antibodies, enzymes, etc. l Biochips vs. Computer microchips l ¨ Miniaturization ¨ Integration ¨ Parallel processing l Proteomics technologies ¨ 2 -D polyacrylamide gel electrophoresis with mass spectrometry ¨ Surface-enhanced laser desorption and ionization Immune diagnostic arrays : Ig. G with p. G l Immunocomputing approach : Pattern Recognition l (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 2

Bioarrays l General Approach ¨ p. G-Ig. G p. G binds to Fc-fragment of Bioarrays l General Approach ¨ p. G-Ig. G p. G binds to Fc-fragment of Ig. G ¨ p. G-HRP HRP: horseradish peroxidase ¨ p. G-CP CP : carbon particles l Materials and Methods ¨ Nitrocellulose membranes ¨ Human Ig. G (Fractions II, III) ¨ Recombinant p. G l Preparation of Bioarrays ¨ Spot : 5 μl solution of Ig. G ¨ Solid-phase immunoassay ¨ Experimental results (see left) (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 3

Software (1) : Immunochip Emulator MS Windows 2000 l MS Visual C++ 6. 0 Software (1) : Immunochip Emulator MS Windows 2000 l MS Visual C++ 6. 0 l Open. GL graphic tools l (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 4

Software (2) : Mathematical Bases (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. Software (2) : Mathematical Bases (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 5

Software (3) : Recognition Algorithms Processing of “Paratope” (C) 2002, SNU Biointelligence Lab, http: Software (3) : Recognition Algorithms Processing of “Paratope” (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 6

Software (4) : Recognition Results Example of Undetected Spots Detection of All Spots by Software (4) : Recognition Results Example of Undetected Spots Detection of All Spots by Training Example of Training the Emulator (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 7

Discussion l Artificial Immune System ¨ Direct immunoassay method ¨ Immunocomputing method l Biochip Discussion l Artificial Immune System ¨ Direct immunoassay method ¨ Immunocomputing method l Biochip ¨ ¨ l BCS (Biochip Controlling System) Precision motorized two coordinate stage Auto-manual or automated dispense module Precision optical electronic detection module Biocomputer ¨ An immunochip-based controller ¨ Controlling of biomolecules in the microwells of the biochip (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 8

Conclusion Immunocomputing approach to immunoassay-based diagnostic arrays l It recognizes more than 96% spots Conclusion Immunocomputing approach to immunoassay-based diagnostic arrays l It recognizes more than 96% spots as “non-self” codes l It works almost 10 times faster than traditional “MASK” method l Another immunological application had been also shown advantageous l (C) 2002, SNU Biointelligence Lab, http: //bi. snu. ac. kr/ 9