Computer Technologies in Medical and Biological Research

  • Ivan V. Sergienko
Part of the Springer Optimization and Its Applications book series (SOIA, volume 78)


The chapter highlights interesting results obtained in decoding the genetic information for models of DNA, proteins, and important processes in cells of the human body. The symmetry rules are established, and genetic information is shown to be encoded based on the optimal Watson–Crick model. The properties of the symmetry are analyzed for amino acid sequences of proteins along two opposite strands in genomes of higher organisms. A model is developed to identify functional regions of genes in DNA based on Markov models with hidden variables. Methods for modeling of biological processes in cells of living organisms using the apparatus of active particles for which transformations of random walk, composition of a complex particle from basic particles, and disintegration of complex particles are carried out. Intelligent technologies of telemedicine with the use of electronic medical advice are developed. New interdisciplinary research areas of bioecomedicine and digital medicine are considered. Microelectronic digital medicine devices for early diagnosis of cardiovascular disease, diabetes, and rehabilitation of patients with motor function disorder are described. Supersensitive magnetometric systems for research in cardiology (magnetocardiography) and for the distribution analysis of iron compounds in biological objects are created based on superconducting quantum interference device (SQUID). A feature of these SQUID magnetic systems is their noise immunity, which allows carrying out research in unshielded premises. Magnetocardiographic systems were used to develop a new screening information technology for early diagnosis of heart disease. It was shown for the first time that a harmful liquid introduced intravenously into a laboratory animal yields iron overload in the liver. Smart sensors were developed for biomedical purposes. These are noninvasive hemoglobin meter, a device for the assessment of the peripheral blood flow, a pulse wave recording device, gas analyzers, and biosensors based on surface plasmon resonance.


Magnetic Nanoparticles Hide Variable Medical Cybernetic Markov Chain Model Complex Particle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 2.
    Amosov, N.M.: Thinking About Health [in Russian]. Molodaya Gvardiya, Moscow (1978)Google Scholar
  2. 10.
    Beletskiy, B.A.: Collective processes modeling by means of particles with complex structure. J. Autom. Inform. Sci 43(9), 34–43 (2011)CrossRefGoogle Scholar
  3. 26.
    Vovk, M.I.: Bioinformation technology of human motion control. Kibern. Vych. Tekhnika, No. 161, 42–52 (2010)Google Scholar
  4. 52.
    Gritsenko, V.I., Vovk, M.I., Kotova, A.B., et al.: Bioecomedicine, Global Information Space [in Russian]. Naukova Dumka, Kyiv (2001)Google Scholar
  5. 53.
    Gritsenko, V.I., Vovk, M.I., Kotova, A.B., Gritsenko, V.I.: An Introduction to the Architectonics of Information Space [in Russian]. Naukova Dumka, Kyiv (2003)Google Scholar
  6. 54.
    Gupal, A.M., Sergienko, I.V.: Optimal Pattern Recognition Procedures [in Russian]. Naukova Dumka, Kyiv (2008)Google Scholar
  7. 81.
    Gritsenko, V.I., Kotova, A.B., Vovk, M.I., Kiforenko, S.I., et al.: Information Technologies in Biology and Medicine. Lecture Course: A Handbook [in Ukrainian]. Naukova Dumka, Kyiv (2007)Google Scholar
  8. 86.
    Kozak, L.M., Pezentsiali, A.A., et al.: Structure and principles of the development of medical advisory service via the Internet. Kibern. Vych. Tekhnika., No. 143, 14–22 (2004)Google Scholar
  9. 102.
    Lavrenyuk, N.V., Kiforenko, S.I., Kotova, A.B., Ivas’kiva, E.Yu.: Information–computer decision support in early diagnostics of diabetes. Kibern. Vych. Tekhnika, No. 157, 42–52 (2009)Google Scholar
  10. 142.
    Sergienko, I.V., Gupal, A.M., Vagis, A.A.: Symmetry and properties of encoding information in DNA. Dokl. Akad. Nauk 439(1), 30–32 (2011)MathSciNetGoogle Scholar
  11. 143.
    Sergienko, I.V., Gupal, A.M., Ostrovskiy, A.V.: Symmetry of the proteins synthesized by DNA strands. J. Autom. Inform. Sci 44(5), 1–9 (2012)CrossRefGoogle Scholar
  12. 144.
    Sergienko, I.V., Gupal, A.M., Ostrovsky, A.V.: Recognition of DNA gene fragments using hidden Markov models. Cybern. Syst. Anal 48(3), 369–377 (2012)CrossRefGoogle Scholar
  13. 159.
    Fainzil’berg, L.S.: Computer analysis and interpretation of electrocardiograms in the phase space. Sist. Issled. Inform. Tekhnol., No. 1, 32–46 (2004)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ivan V. Sergienko
    • 1
  1. 1.V. M. Glushkov Cybernetics InstituteNational Academy of Sciences of UkraineKievUkraine

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