The post-genome era proved that DNA sequence data [11, 26] with structural and functional analysis on genes archived in many data bases can support in developing new bio-engineering technologies and can drive systemic views for biological systems. However, the post-genome era also proved that sequence data alone is not sufficient, but revealed that higher knowledge of the function of proteins is indispensable. Personalized medicine required not only sequence data, but further knowledge such as SNP (single nucleotide polymorphism) and of functioning of proteins and its deployment to interacting systems such as gene networks, giving birth of a new territory called proteome.
This Chapter is organized as follows: Sect. 2 focuses on the preliminary problem of whether recognition is indeed needed, focusing on the specific task of abnormal state eradication on a simple network. Section 3 addresses the problem of networked recognition that involve action counterpart, hence agents can not only recognize but also be recognized. Section 4 further introduces adaptation by assuming agents can not only reproduce but also mutate in the receptor counterpart. Section 5 considers arrayed recognition, which is the very first step, even before networked recognition; however, it assumes specific recognition capability of antibody-antigen recognition.
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References
Adleman LM 2004 Molecular computation of solutions to combinatorial problems. Science,266(11): 1021-1024.
Balan MS, Krithivasan K 2004 Parallel computation of simple arithmetic using peptide-antibody interactions. Biosystems,76(1-3): 30-307.
Basu S, Gerchman Y, Collins CH, Arnold FH, Weiss R 2005 A synthetic multicellular system for programmed pattern formation. Nature,434: 1130-1134.
Burnet FM 1957 A modification of Jerne’s theory of antibody production using the concept of clonal selection. Australian J. Science,20: 67-69.
Elowitz MB, Leibler SA 2000 Synthetic oscillatory network of transcriptional regulators. Nature,403: 335-338.
Ernst G, Newell A 1969 GPS: A Case Study in Generality and Problem Solving. Academic Press, New York, NY.
Gale D, Shapley L 1962 College admissions and the stability of marriage. American Mathematical Monthly,69: 9-15.
Gusfield D, Irving RW 1989 The Stable Marriage Problem: Structure and Algorithms. MIT Press, Cambridge, MA.
Hood L, Galas D 2003 The digital code of DNA. Nature, 421: 444-448.
Hug H, Schuler R 2001 Strategies for the development of a peptide computer. Bioinformatics,17(4): 364-368.
International Human Genome Sequencing Consortium (2001) Initial sequencing and analysis of the human genome. Nature,409: 860-921.
Irving RW, Leather P, Gusfield D 1987 An efficient algorithm for the optimal stable marriage. J. ACM,34(3): 532-543.
Ishida Y (2004) Immunity-Based Systems: a Design Perspective Springer-Verlag, Berlin.
Ishida Y 2006 Designing an immunity-based sensor network for sensor-based diagnosis of automobile engines. In: Gabrys B, Howlett RJ, Jain LC (eds.) Proc. Knowledge-Based Intelligent Informaiton and Engineering Systems Conf. (KES’2006). Lecture Notes in Computer Science LNCS 4252. Springer-Verlag, Berlin: 146-153.
Ishida Y (2007) A constructive systems approach to understanding the immune system as a biological problem solver. In: Proc. 1st IEEE Symp. Foundations of Computational Intelligence (FOCI’07), 1-5 April, Honolulu, Hawaii. IEEE/Omnipress, Madison, WI: 646-650 (CD-ROM).
Ishida Y, Adachi N (1996) Active noise control by an immune algorithm: adaptation in immune system as an evolution. In: Proc. 3rd IEEE Intl. Conf. Evolutionary Computation (ICEC’96), 20-22 May, Nagoya, Japan. IEEE Press, Piscataway, NJ: 150-153.
Jerne NK 1973 The immune system. Scientific American, 229(1): 52-60.
Kollmann M, Lovdok L, Bartholome K, Timmer J, Sourjik V 2005 Design principles of a bacterial signalling network. Nature,438(24): 504-507.
Landsteiner K(1900) Zur kenntnis der antifermentativen, lytischen und agglutinierenden wirkungen des blutserums und der lymphe. Zentralblatt Bakteriologie, 27: 357-362.
Langman RE, Cohn M (eds.) Seminars in Immunology (available online at: http://www.ingentaconnect.com/content/ap/si/2000/00000012/00000003 -last accessed: April 2007).
Newell A, Simon H 1972 Human Problem Solving. Prentice Hall, Englewood Cliffs, NJ.
Pearl JJ 1988 Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Revised 2nd Printing). Morgan Kaufmann, San Mateo, CA.
Perelson AS, Oster GF 1979 Theoretical studies of clonal selection: min-imal antibody repertoire size and reliability of self-non-self discrimination. J. Theoretical Biology, 81: 645-670.
Sprinzak D, Elowitz MB 2005 Reconstruction of genetic circuits. Nature, 438(7067): 438-424.
Tonegawa S 1983 Somatic generation of antibody diversity. Nature,302: 575-581.
Venter JC, Adams MD, Myers EW, et al. 2001 The sequence of the human genome. Science,291: 1304-1351.
Volterra V 1931 Variations and fluctuations of the number of individuals in animal species living together. In: Chapman RN (ed.) Animal Ecology. McGraw-Hill, New York, NY.
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Ishida, Y. (2008). The Next Generation of Immunity-Based Systems: From Specific Recognition to Computational Intelligence. In: Fulcher, J., Jain, L.C. (eds) Computational Intelligence: A Compendium. Studies in Computational Intelligence, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78293-3_25
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