2019 № 2(en)

Details: Hits: 1071

ISSN 2410-776X (Online)
ISSN 2410-7751 (Print)

2 2019

"Biotechnologia Acta" V. 12, No 2, 2019
Р. 27-45, Bibliography 73, English
Universal Decimal Classification: 577.152.32
https://doi.org/10.15407/biotech12.02.027

KERATINOLYTIC ENZYMES: PRODUCERS, PHYSICAL AND CHEMICAL PROPERTIES. APPLICATION FOR BIOTECHNOLOGY

K. V. Avdiyuk, L. D. Varbanets

Zabolotny Institute of Microbiology and Virology of the National Academy of Sciences of Ukraine, Kyiv

The aim of the review was to analyze the current ideas on keratinases, a group of proteolytic enzymes that catalyse the cleavage of keratins, which are highly stable fibrous proteins. Representatives of various taxonomic groups of microorganisms, including fungi, actinomycetes and bacteria, are keratinase producers. Modern classification of keratinases according to the MEROPS database is given.

The studies of physical and chemical properties of keratinases indicate that the enzymes are active in a wide range of temperature and pH values, with the optimal action at neutral and alkaline pH and t = 40–70 ^oC. It was shown that microbial keratinases were predominantly the metallo-, serine- or metallo-serine proteases. They are usually extracellular, and their synthesis is induced by keratin substrates. The review discusses the practical use of keratinases. These enzymes have been successfully applied in bioconversion of keratin wastes to animal feed and nitrogenous fertilizer, as well as in leather, textile, detergent, cosmetic, pharmaceutical industries. Keratinases are also applicable as pesticides and in the production of nanoparticles, biofuel, biodegradable films, glues and foils. In addition, keratinases are used in the degradation of prion proteins which are able to cause a number of human and animal neurodegenerative diseases of spongiform encephalopathy.

Key words: keratinases, producers, regulation of synthesis, physical and chemical properties, application.

References

1. Sharma S., Gupta A. Sustainable management of keratin waste biomass: applications and future perspectives. Braz. Arch. Biol. Technol. 2016, V. 59, e16150684. https://doi.org/10.1590/1678-4324-2016150684

2. Gunes B. G., Akkoyun O., Demir T., Bozaci E., Demir A., Hames E. E. Microbial keratinase production and application to improve the properties of wool fabrics. Int. J. Textile Sci. 2018, 7 (2), 43–47. https://doi.org/10.5923/j.textile.20180702.02

3. Sahni N., Sahota P. P., Phutela U. G. Bacterial keratinases and their prospective applications: a review. Int. J. Curr. Microbiol. App. Sci. 2015, 4 (6), 768–783.

4. Brandelli A., Daroit J. D., Riffel A. Bioche mical features of microbial keratinases and their production and applications. Appl. Microbiol. Biotechnol. 2010, 85 (6), 1735–1750. https://doi.org/10.1007/s00253-009-2398-5

5. Vidmar В., Vodovnik М. Microbial keratinases: enzymes with promising biotechnological applications. Food Technol. Biotechnol. 2018, 56 (3). https://doi.org/10.17113/ftb.56.03.18.5658

6. Bragulla H. H., Homberger D. G. Structure and functions of keratin proteins in simple, stratified, keratinized and cornified epithelia. J. Anat. 2009, 214 (4), 516–559. https://doi.org/10.1111/j.1469-7580.2009.01066.x

7. Pupkova M. A. Definition of keratinolytic activity of fungi: (review). Probl. Med. Mycol. 2010, 12 (2), 53–58. (In Russian).

8. Sahoo D. K., Thatoi H. N., Mitra B., Mondal K. C., Mohapatra P. K. Microbial Biotechnology: Volume 1. Applications in Agriculture and Environment. Advances in microbial keratinase and its potential applications. Patra J. K., Vishnuprasad C. N., Das G. (Eds.). Springer Verlag, Singapore. 2018, P. 105–134. https://doi.org/10.1007/978-981-10-6847-8_5

9. Naleway S. E., Taylor J. R. A., Porter M. M., Meyers M. A., McKittrick J. Structure and mechanical properties of selected protective systems in marine organisms. Mater. Sci. Eng. C. 2016, V. 59, P. 1143–1167. https://doi.org/10.1016/j.msec.2015.10.033

10. Sharma R., Devi S. Versatility and commercial status of microbial keratinases: a review. Rev. Environ. Sci. Biotechnol. 2018, 17 (1), 19–45. https://doi.org/10.1007/s11157-017-9454-x

11. El-Ghonemy D. H., Ali T. H. Optimization of physico-chemical parameters for hyper keratinase production from a newly isolated Aspergillus sp. DHE7 using chicken feather as substrate-management of biowaste. J. Appl. Pharm. Sci. 2017, 7 (9), 171–178. https://doi.org/10.7324/JAPS.2017.70923

12. Gopinath C. B., Anbu P., Lakshmipriya T., Tang T. H., Chen Y., Hashim U., Ruslinda A. R., Arshad M. K. Biotechnological aspects and perspective of microbial keratinase production. Biomed. Res. Int. 2015, V. 2015, P. 140726–140736. https://doi.org/10.1155/2015/140726

13. Abdel-Naby M. A., El-Refai H. A., Ibrahim M. H. A. Structural characterization, catalytic, kinetic and thermodynamic properties of keratinase from Bacillus pumilus FH9. Int. J. Biol. Macromol. 2017, 105 (1), 973–980. https://doi.org/10.1016/j.ijbiomac.2017.07.118.

14. Purchase D. The handbook of microbial bioresources. Microbial keratinases: characteristics, biotechnological applications and potential. Gupta V. K., Sharma G. D., Tuohy M. G., Gaur R. (Eds.). CAB International Publishing, Wallingford. 2016, P. 634–674.

15. Langeveld J. P., Wang J. J., Van de Wiel D. F., Shih G. C., Garssen G. J., Bossers A., Shih J. C. Enzymatic degradation of prion protein in brain stem from infected cattle and sheep. J. Infect. Dis. 2003, 188 (11), 1782–1789. https://doi.org/10.1086/379664

16. Yue X. Y., Zhang B., Jiang D. D., Liu Y. J., Niu T. G. Separation and purification of a keratinase as pesticide against root-knot nematodes. World J. Microbiol. Biotechnol. 2011, 27 (9), 2147–2153. https://doi.org/10.1007/s11274-011-0680-z

17. Verma A., Singh H., Anwar S., Chattopadhyay A., Tiwari K. K., Kaur S., Dhilon G. S. Microbial keratinases: industrial enzymes with waste management potential. Crit. Rev. Biotechnol. 2017, 37 (4), 476–491. https://doi.org/10.1080/07388551.2016.1185388

18. Wu W.-L., Chen M.-Y., Tu I-F., Lin Y.-C., Kumar N. E., Chen M.-Y., Ho M.-C., Wu S.-H. The discovery of novel heat-stable keratinases from Meiothermus taiwanensis WR-220 and other extremophiles. Sci. Rep. 2017, 7 (1), 4658–4669. https://doi.org/10.1038/s41598-017-04723-4

19. G?dde C., Sahm K., Brouns S. J., Kluskens L. D., van der Oost J., de Vos W. M., Antranikian G. Cloning and expression of islandisin, a new thermostable subtilisin from Fervidobacterium islandicum, in Escherichia coli. Appl. Environ. Microbiol. 2005, 71 (7), 3951–3958. https://doi.org/10.1128/AEM.71.7.3951-3958.2005

20. Habbeche A., Saoudi B., Jaouadi B., Haberra S., Kerouaz B., Boudelaa M., Badis A., Ladjama A. Purification and biochemical characterization of a detergentstable keratinase from a newly thermophilic actinomycete Actinomadura keratinilytica strain Cpt29 isolated from poultry compost. J. Biosci. Bioeng. 2014, 117 (4), 413–421. https://doi.org/10.1016/j.jbiosc.2013.09.006

21. Ivanko O. V., Varbanets L. D. Purification and physico-chemical properties of Streptomyces sp. 1349 collagenase and Streptomyces sp. 1382 keratinase. Mikrobiol. Zh. 2004, 66 (2), 11–24. (In Ukrainian).

22. Ben Elhoul M., Zara? Jaouadi N., Rekik H., Omrane Benmrad M., Mechri S., Moujehed E., Kourdali S., El Hattab M., Badis A., Bejar S., Jaouadi B. Biochemical and molecular characterization of new keratinoytic protease from Actinomadura viridilutea DZ50. Int. J. Biol. Macromol. 2016, V. 92, P. 299–315. https://doi.org/10.1016/j.ijbiomac.2016.07.009

23. Cavello I. A., Hours R. A., Rojas N. L., Cavalitto S. F. Purification and characterization of a keratinolytic serine protease from Purpureocillium lilacinum LPS # 876. Proc. Biochem. 2013, 48 (5–6), 972–978. https://doi.org/10.1016/j.procbio.2013.03.012

24. Anitha T. S., Palanivelu P. Production and characterization of keratinolytic protease(s) from the fungus Aspergillus parasiticus. Int. J. Res. Biol. Sci. 2012, 2 (2), 87–93.

25. Ramakrishna R. M., Sathi R. K., Ranjita C. Y., Bee H., Reddy G. Effective feather degradation and keratinase production by Bacillus pumilus GRK for its application as bio-detergent additive. Biores. Technol. 2017, V. 243, P. 254–263. https://doi.org/10.1016/j.biortech.2017.06.067

26. Fakhfakh N., Kanoun S., Manni L., Nasri M. Production and biochemical and molecular characterization of a keratinolytic serine protease from chicken featherdegrading Bacillus licheniformis RPk. Can. J. Microbiol. 2009, 55 (4), 427–436. https://doi.org/10.1139/w08-143

27. Wang L., Qian Y., Cao Y., Huang Y., Chang Z., Huang H. Production and characterization of keratinolytic proteases by a chicken feather-degrading thermophilic strain, Thermoactinomyces sp. YT06. J. Microbiol. Biotechnol. 2017, 27 (12), 2190–2198. https://doi.org/10.4014/jmb.1705.05082

28. Jaouadi N. Z., Rekik H., Badis A., Trabelsi S.,Belhoul M., Yahiaoui A. B., Ben Aicha H., Toumi A., Bejar S., Jaouadi B. Biochemical and molecular characterization of a serine keratinase from Brevibacillus brevis US575 with promising keratin-biodegradation and hide-dehairing activities. PLoS One. 2013, 8 (10), 1–17. https://doi.org/10.1371/journal.pone.0076722

29. Bouacem K., Bouanane-Darenfed A., Zara? Jaouadi N., Joseph M., Hacene H., Ollivier B., Fardeau M. L., Bejar S., Jaouadi B. Novel serine keratinase from Caldicoprobacter algeriensis exhibiting outstanding hide dehairing abilities. Int. J. Biol. Macromol. 2016, V. 86, P. 321–328. https://doi.org/10.1016/j.ijbiomac.2016.01.074

30. Zhang R. X., Gong J. S., Zhang D. D., Su C., Hou Y. S., Li H., Shi J. S., Xu Z. H. A metallo-keratinase from a newly isolated Acinetobacter sp. R-1 with low collagenase activity and its biotechnological application potential in leather industry. Bioproc. Biosyst. Eng. 2016, 39 (1), 193–204. https://doi.org/10.1007/s00449-015-1503-7

31. Balaji S., Senthil Kumar M., Karthikeyan R., Kumar Ramadhar, Kirubanandan S., Sridhar R., Sehgal P. K. Purification and characterization of an extracellular keratinase from a hornmeal-degrading Bacillus subtilis MTCC (9102). World J. Microbiol. Biotechnol. 2008, V. 24, P. 2741–2745. https://doi.org/10.1007/s11274-008-9782-7

32. Zhang R. X., Gong J. S., Su C., Zhang D. D., Tian H., Dou W. F., Li H., Shi J. S., Xu Z. H. Biochemical characterization of a novel surfactant-stable serine keratinase with no collagenase activity from Brevibacillus parabrevis CGMCC 1079. Int. J. Biol. Macromol. 2016, V. 93, P. 843–851. https://doi.org/10.1016/j.ijbiomac.2016.09.063

33. Prakash P., Jayalakshmi S. K., Sreeramulu K. Purification and characterization of extreme alkaline, thermostable keratinase, and keratin disulfide reductase produced by Bacillus halodurans PPKS-2. Appl. Microbiol. Biotechnol. 2010, 87 (2), P. 625–633. https://doi.org/10.1007/s00253-010-2499-1

34. Gordonova I. K., Nikitina Z. K. Cladosporium sphaerospermum keratinase biosynthesis regulation. Probl. Biol. Med. Pharm. Chem. 2015, N 8, P. 14–18. (In Russian).

35. Anitha T. S., Palanivelu P. Purification and characterization of an extracellular keratinolytic protease from a new isolate of Aspergillus parasiticus. Prot. Expr. Purif. 2013, 88 (2), 214–220. https://doi.org/10.1016/j.pep.2013.01.007

36. Gradisar H., Friedrich J., Krizaj I., Jerala R. Similarities and specificities of fungal keratinolytic proteases: comparison of keratinases of Paecilomyces marguandii and Doratomyces microsporus to some known proteases. Appl. Environ. Microbiol. 2005, 71 (7), 3420–3426. https://doi.org/10.1128/AEM.71.7.3420-3426.2005

37. Nikitina Z. K., Gordonova I. K. Exogenous regulation of productivity of Penicillium citrinum — keratinase producer. Probl. Biol. Med. Pharm. Chem. 2016, N 9, P. 8–11. (In Russian).

38. Barman N. C., Zohora F. T., Das K. C., Mow la M. G., Banu N. A., Salimullah M., Hashem A. Production, partial optimization and characterization of keratinase enzyme by Arthrobacter sp. NFH5 isolated from soil samples. AMB Express. 2017, 7 (1), 181–188. https://doi.org/10.1186/s13568-017-0462-6

39. ?aba W., ?arowska B., Chor??yk D., Pud?o A., Piegza M., Kancelista A., Kope? W. New keratinolytic bacteria in valorization of chicken feather waste. AMB Express. 2018, 8 (1), 9–22. https://doi.org/10.1186/s13568-018-0538-y

40. Pawar V. A., Prajapati A. S., Akhani R. C., Patel D. H., Subramanian R. B. Molecular and biochemical characterization of a thermostable keratinase from Bacillus altitudinis RBDV1. Biotechnology. 2018, 8 (2), 107–113. https://doi.org/10.1007/s13205-018-1130-5

41. Zara? Jaouadi N., Rekik H., Ben Elhoul M., Zohra Rahem F., Hila C. G., Ben Aicha H. S., Badis A., Toumi A., Bejar S., Jaouadi B. A novel keratinase from Bacillus tequilensis strain Q7 with promising potential for the leather bating process. Int. J. Biol. Macromol. 2015, V. 79, P. 952–964. https://doi.org/10.1016/j.ijbiomac.2015.05.038

42. Roohi Kuddus M. Keratin degrading microbial keratinase as a tool for bioremediation. J. Microbiol. Exp. 2017, 5 (4), 00154–00155. https://doi.org/10.15406/jmen.2017.05.00154

43. Yusuf I., Ahmad S. A., Phang L. Y., Syed M. A., Shamaan N. A., Abdul Khalil K., Dahalan F. A., Shukor M. Y. Keratinase production and biodegradation of polluted secondary chicken feather wastes by a newly isolated multi heavy metal tolerant bacterium Alcaligenes sp. AQ05-001. J. Environ. Manage. 2016, 183 (1), 182–195. https://doi.org/10.1016/j.jenvman.2016.08.059

44. Ranjit G. Gurav, Dhanashri B. Mirajkar, Akanksha V. Savardekar, Snehal M. Pisal. Microbial degradation of poultry feather biomass by Klebsiella sp. BTSUK isolated from poultry waste disposal site. RJLBPCS. 2016, 1 (6), 279–289. https://doi.org/10.26479/2016.0106.01

45. Singh I., Kushwaha R. K. S. Keratinases and microbial degradation of keratin. Adv. Appl. Sci. Res. 2015, 6 (2), 74–82.

46. Kanoksilapatham W., Intagun W. A Review: biodegradation and applications of keratin degrading microorganisms and keratinolytic enzymes, focusing on thermophiles and thermostable serine proteases. Amer. J. Appl. Sci. 2017, 14 (11), 1016–1023. https://doi.org/10.3844/ajassp.2017.1016.1023

47. Isiaka A. Adelere, Agbaje Lateef. Keratinases: emerging trends in production and applica tions as novel multifunctional biocatalysts. Kuwait J. Sci. 2016, 43 (3), 118–127.

48. Wawrzkiewicz K., Lobarzewski J., Wolski T. Intra cellular keratinase of Trichophyton gallinae. J. Med. Vet. Mycol. 1987, 25 (4), 261–268. https://doi.org/10.1080/02681218780000601

49. Uttangi V., Aruna K. Optimization of production and partial characterization of keratinase produced by Bacillus thuringiensis strain Bt407 isolated from poultry soil. Int. J. Curr. Microbiol. App. Sci. 2018, 7 (4), 596–626. https://doi.org/10.20546/ijcmas.2018.704.069

50. Kumar R., Balaji S., Uma T. S., Mandal A. B., Sehgal P. K. Optimization of influential parameters for extracellular keratinase production by Bacillus subtilis (MTCC9102) in solid state fermentation using horn meal — a biowaste management. Appl. Biochem. Biotechnol. 2010, 160 (1), 30–39. https://doi.org/10.1007/s12010-008-8452-4

51. Nigam V. K., Singhal P., Vidyarthi A. S., Mohan M. K., Ghosh P. Studies on kera tinolytic activity of alkaline proteases from halophilic bacteria. Int. J. Pharm. Bio. Sci. 2013, 4 (2), 389–399.

52. Kublanov I. V., Tsiroulnikov K. B., Kaliber da E. N., Rumsh L. D., Haertl? T., Bonch-Osmolovskaya E. A. Keratinase of an anaerobic thermophilic bacterium Thermoanaerobacter strain 1004-09 isolated from a hot spring in the Baikal rift zone. Microbiol. 2009, 78 (1), 67–75. https://doi.org/10.1134/S0026261709010093

53. Cai C., Zheng X. Medium optimization for keratinase production in hair substrate by a new Bacillus subtilis KD-N2 using response surface methodology. J. Ind. Microbiol. Biotechnol. 2009, 36 (7), 875–883. https://doi.org/10.1007/s10295-009-0565-4

54. Nikitina Z. K., Gordonova I. K. Purification, clearining and some properties keratinolytic enzyme, secreting by Penicillium citrinum. Probl. Biol. Med. Pharm. Chem. 2013, 11 (9), 36–41. (In Russian).

55. Prakash P., Jayalakshmi S. K., Sreeramulu K. Production of keratinase by free and immobilized cells of Bacillus halodurans strain PPKS-2: partial characterization and its application in feather degradation and dehairing of the goat skin. Appl. Biochem. Biotechnol. 2010, 160 (7), 1909–1920. https://doi.org/10.1007/s12010-009-8702-0

56. Sahoo D. K., Halder S. K., Das A., Jana A., Paul T., Thatoi H. Keratinase production by Bacillus weihenstephanensis PKD5 in solid-state fermentation and its milk clotting potential. Ind. J. Biotechnol. 2015, V. 14, P. 200–207.

57. Abdel-Fattah A. M., El-Gamal M. S., Ismail S. A., Emran M. A., Hashem A. M. Biodegradation of feather waste by keratinase produced from newly isolated Bacillus licheniformis ALW1. Genet. Eng. Biotechnol. J. 2018. https://doi.org/10.1016/j.jgeb.2018.05.005

58. Fang Z., Zhang J., Du G., Chen J. Rational protein engineering approaches to further improve the keratinolytic activity and thermostability of engineered keratinase KerSMD. Biochem. Eng. J. 2017, V. 127, P. 147–153. https://doi.org/10.1016/j.bej.2017.08.010

59. Lin H. H., Yin L. J., Jiang S. T. Functional expression and characterization of keratinase from Pseudomonas aeruginosa in Pichia pastoris. J. Agric. Food Chem. 2009, 57 (12), 5321–5325. https://doi.org/10.1021/jf900417t

60. Liu B., Zhang J., Li B., Liao X., Du G., Chen J. Expression and characterization of extreme alkaline, oxidationresistant keratinase from from Bacillus licheniformis in recombinant Bacillus subtilis WB600 expression system and its application in wool fiber processing. World J. Microbiol. Biotechnol. 2013, 29 (5), 825–832. https://doi.org/10.1007/s11274-012-1237-5

61. Aly M. M., Tork S. High keratinase production and keratin degradation by a mutant strain KR II, from Streptomyces radiopugnans KR 12. JABS. 2018, 12 (1), 18–25.

62. Cai С., Lou В., Zheng X. Keratinase production and keratin degradation by a mutant strain of Bacillus subtilis. J. Zhejiang Univ. Sci. B. 2008, 9 (1), 60–67. https://doi.org/10.1631/jzus.B061620

63. Liu B., Zhang J., Fang Z., Gu L., Liao X., Du G., Chen J. Enhanced thermostability of keratinase by computational design and empirical mutation. J. Ind. Microbiol. Biotechnol. 2013, 40 (7), 697–704. https://doi.org/10.1007/s10295-013-1268-4

64. Bressollier P., Letourneau F., Urdaci M., Verneuil B. Purification and characterization of a keratinolytic serine proteinase from Streptomyces albidoflavus. Appl. Environ. Microbiol. 1999, 65 (6), 2570–2576.

65. Laba W., Choinska A., Rodziewicz A., Piegza M. Keratinolytic abilities of Micrococcus luteus from poultry waste. Braz. J. Microbiol. 2015, 46 (3), 691–700. https://doi.org/10.1590/S1517-838246320140098

66. Tiwary E., Gupta R. Medium optimization for a novel 58 kDa dimeric keratinase from Bacillus licheniformis ER-15: Biochemical characterization and application in feather degradation and dehairing of hides. Bioresours Technol. 2010, 101 (15), 6103–6110. https://doi.org/10.1016/j.biortech.2010.02.090

67. Fang Z., Zhang J., Liu B., Du G., Chen J. Biochemical characterization of three keratinolytic enzymes from Stenotrophomonas maltophilia BBE11-1 for biodegrading keratin wastes. Int. Biodeterior. Biodegradation. 2013, V. 82, P. 166–172. https://doi.org/10.1016/j.ibiod.2013.03.008

68. Daniel J. Daroit, Ana Paula F. Corr?a, J?ferson Segalin, Adriano Brandelli. Characterization of a keratinolytic protease produced by the feather-degrading Amazonian bacterium Bacillus sp. P45. Biocatal. Biotransform. 2010, 28 (5), 370–379. https://doi.org/10.3109/10242422.2010.532549

69. Jaouadi B., Abdelmalek B., Fodil D., Ferradji F. Z., Rekik H., Zara? N., Bejar S. Purification and characterization of a thermostable keratinolytic serine alkaline proteinase from Streptomyces sp. Strain AB1 with high stability in organic solvents. Bioresours Technol. 2010, 101 (21), 8361–8369. https://doi.org/10.1016/j.biortech.2010.05.066

70. Mitsuiki S., Ichikawa M., Oka T., Sakai M., Moriyama Y., Sameshima Y., Goto M., Furukawa K. Molecular characterization of a keratinolytic enzyme from an alkaliphilic Nocardiopsis sp. TOA-1. Enzyme Microb. Technol. 2004, 34 (5), 482–489. https://doi.org/10.1016/j.enzmictec.2003.12.011

71. Gupta S., Nigam A., Singh R. Purification and characterization of a Bacillus subtilis keratinase and its prospective application in feed industry. Acta Biologica Szegediensis. 2015, 59 (2), 197–204.

72. Gupta R., Sharma R., Beg Q. K. Revisiting microbial keratinases: next generation proteases for sustainable biotechnology. Crit. Rev. Biotechnol. 2013, 33 (2), 216–228. https://doi.org/10.3109/07388551.2012.685051

73. Fang Z., Yong Y. C., Zhang J., Du G., Chen J. Keratinolytic protease: a green biocatalyst for leather industry. Appl. Microbiol. Biotechnol. 2017, 101 (21), 7771–7779. https://doi.org/10.1007/s00253-017-8484-1

Details: Hits: 932

ISSN 2410-776X (Online)
ISSN 2410-7751 (Print)

2 2019

"Biotechnologia Acta" V. 12, No 2, 2019
Р. 5-26, Bibliography 179, English
Universal Decimal Classification: 004:591.5:612:616-006
https://doi.org/10.15407/biotech12.02.005

ELECTRONIC AUTOMATED WORK PLACES FOR BIOTECHNOLOGY

Klyuchko O.M. ¹, Aralova A.A.², Aralova N.I.²

¹Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology of the National Academy of Sciences of Ukraine, Kyiv
²Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Kyiv

The purpose of the work was to analyze data about the construction of electronic automated work places for organization of more perfect ones for biotechnology. Methods of program and mathematic simulation, imitation modeling were used for these works. The information about some prototypes of electronic automated work places constructed for biology and linked sciences in Ukraine during the last 25–30 years was discussed. The results of some automated work places constructed by the authors were presented. In conclusion the observed experience was summarized and the set of recommendations for its practical implementation were done.

Key words: electronic automated work places, bioinformatics, electronic information systems, databases.

References

1. Klyuchko O. M. Information and computer technologies in biology and medicine. Kyiv: NAU-druk. 2008, 252 p. (In Ukrainian).

2. Aralova N. I., Klyuchko O. M., Shakhlina L. Ya.-H. Parameters of athlete respiratory system dependence on organism hormonal status during hypoxic mixtures inhalation: research on mathematical models. SciFed J. Sports Med., USA. 2018, 1 (2). http://scifedpublishers.com/fulltext/parameters-of-athlete-respiratory-systemdependence-on-organism-hormonal-statusduring-hypoxic-mixtures-inhalation-researchon-mathematical-models/22385#Figures

3. Klyuchko O. М. On the mathematical methods in biology and medicine. Biotechnol. acta. 2017, 10 (3), 31-40. https://doi.org/10.15407/biotech10.03.031

4. Klyuchko O. М. Application of artificial neural networks method in biotechnology. Biotechnol. acta. 2017, 10 (4), 5-13. https://doi.org/10.15407/biotech10.04.005

5. Klyuchko O. М. Cluster analysis in biotechnology. Biotechnol. acta. 2017, 10 (5), 5-18. https://doi.org/10.15407/biotech10.05.005

6. Klyuchko O. М. Technologies of brain images processing. Biotechnol. acta. 2017, 10 (6), 5-17. https://doi.org/10.15407/biotech10.05.005

7. Klyuchko O. М., Onopchuk Yu. M. Some trends in mathematical modeling for biotechnology. Biotechnol. acta. 2018, 11 (1), 39-57. https://doi.org/10.15407/biotech11.01.039

8. Klyuchko O. М. Electronic information systems in biotechnology. Biotechnol. acta. 2018, 11 (2), 5-22. https://doi.org/10.15407/biotech11.05.005

9. Klyuchko O. М. Information computer technologies for using in biotechnology: electronic medical information systems. Biotechnol. acta. 2018, 11 (3), 5-26.
https://doi.org/10.15407/biotech11.03.005

10. Schnase J. L., Cushing J., Frame M. Information technology challenges of biodiversity and ecosystems informatics. Inform. Syst. 2003, 28 (4), 339-345.
https://doi.org/10.1016/S0306-4379(02)00070-4

11. Piatigorsky B. Ya., Zaytman G. A., Cherkassky V. L., Chinarov V. A. Automated electrophysiological experiment. Kyiv: Naukova dumka. 1985, 214 p. (In Russian).

12. Patent 1370136 USSR, С12N 5/00. The method for dissociation of hippocampal cells. Klyuchko E. М., Tzyndrenko A. Ya. Priority: 31. 01. 1986; Issued: 30. 01. 1988, Bull. No 4, 3 p.

13. Hardy P. B., Sparks T. H., Isaak N. J. Specialism for larval and adult consumer resources among Brittish butterflies: implications for conservation. Biol. Conserv. 2007, 138 (3-4), 440-452.
https://doi.org/10.1016/j.biocon.2007.05.014

14. Dennis R. L. H., Shreeve T. G., Sparks T. H. A comparison of geographical and neighbourhood models for improving atlas databases. The case of the French butterfly atlas. Biol. Conserv, 2002, 108 (2), 143-159. https://doi.org/10.1016/S0006-3207(02)00101-5

15. Klyuchko O. M., Paskivsky A. O., Sheremet D. Y. Computer modeling of some nanoelements for radio and television systems. Electronics and control systems. 2012, 3 (33), 102-107. (In Ukrainian). https://doi.org/10.18372/1990-5548.33.5589

16. Klyuchko O. M., Hayrutdinov R. R. Modeling of electrical signals propagation in neurons and its nanostructures. Electronics and control systems. 2011, 2 (28), 120-124. (In Ukrainian).
https://doi.org/10.18372/1990-5548.28.870

17. Trinus K. F., Klyuchko E. M. Mediators influence on motoneurons retrogradly marked by primulin. Physiol. J. 1984. 30 (6), 730-733 (In Russian).

18. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Algorithmic and program support for optimization of interval hypoxic training modes selection of pilots. Electronics and control systems. 2017, 2 (52), 85-93. https://doi.org/10.18372/1990-5548.52.11882

19. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Mathematic and program models for investigation of reliability of operator professional activity in "Human-Machine" systems. Electronics and control systems. 2017, 1 (51), 105-113. https://doi.org/10.18372/1990-5548.51.11712

20. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Mathematical model for research of organism restoring for operators of continuously interacted systems. Electronics and control systems. 2016, 3 (49), 100-105. https://doi.org/10.18372/1990-5548.49.11245

21. Aralova N. I., Klyuchko O. M., Mashkin V. I., Mashkina I. V. Investigation of reliability of operators work at fluctuating temperature conditions. Electronics and control systems. 2016, 2 (48), 132-139. https://doi.org/10.18372/1990-5548.48.11227

22. Plakhotnij S. A., Klyuchko O. M., Krotino va M. V. Information support for automatic industrial environment monitoring systems. Electronics and control systems. 2016, 1 (47), 19-34.
https://doi.org/10.18372/1990-5548.47.10266

23. Onopchuk Yu. M., Aralova N. I., Klyuchko O. M., Beloshitsky P. V. Mathematic models and integral estimation of organism systems reliability in extreme conditions. Electronics and control systems. 2015, 4 (46), 109-115. https://doi.org/10.18372/1990-5548.46.9978

24. Onopchuk Yu. M., Aralova N. I., Klyuchko O. M.,Beloshitsky P. V. Integral estimations of human reliability and working capacity in sports wrestling. Journal of the Engineering Academy. 2015, V. 3, P. 145-148. (In Russian).

25. Klyuchko O. M., Shutko V. N., Navrotskyi D. O., Mikolushko A. M. The set of program models for ecological monitoring technical system based on principles of biophysics. Electronics and control systems. 2014, 4 (42), 135-142. https://doi.org/10.18372/1990-5548.42.8834

26. Klyuchko O. M., Sheremet D. Yu. Computer simulation of biological nanogenerator functions. Electronics and control systems. 2014, 2 (40), 103-111.
https://doi.org/10.18372/1990-5548.40.7447

27. Klyuchko O. M., Shutko V. N. Computer modeling of auto-oscillating phenomena in neuron complexes. Electronics and control systems. 2014, 1 (39), 127-132. https://doi.org/10.18372/1990-5548.39.7349

28. Klyuchko O. M., Sheremet D. Yu. Computer modeling of biologic voltage-activated nanostructures. Electronics and control systems. 2014, 1 (39), 133-139. https://doi.org/10.18372/1990-5548.39.7350

29. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. M. Radiation damage of organism and its correction in conditions of adaptation to highmountain meteorological factors. Bul. NAU. 2010, N 1, P. 224-231. (In Ukrainian).

30. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu., Makarenko M. V. Estimation of psychophysiological functions of a person and operator work in extreme conditions. Bul. NAU. 2009, N 3, P. 96-104. (In Ukrainian).

31. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu., Kolchinska A. Z. Results of research of higher nervous activity problems by Ukrainian scientists in Prielbrussie. Bul. NAU. 2009, N 2, P. 105-112. (In Ukrainian).

32. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of research of structural and functional interdependencies by Ukrainian scientists in Prielbrussie. Bul. NAU. 2009, N 1, P. 61-67. (In Ukrainian).

33. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of research of highlands factors influence on health and longevity by Ukrainian scientists in Prielbrussie. Bul. NAU. 2008, N 4, P. 108-117. (In Ukrainian).

34. Onopchuk Yu. M., Klyuchko O. M., Beloshitsky P. V. Development of mathematical models basing on researches of Ukrainian scientists at Elbrus. Bul. NAU. 2008, N 3, P. 146-155. (In Ukrainian). https://doi.org/10.18372/2306-1472.36.1609

35. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of research of adaptation problems by Ukrainian scientists in Prielbrussie. Bul. NAU. 2008, N 1, P. 102-108. (In Ukrainian).

36. Beloshitsky P. V., Klyuchko O. M., Onop chuk Yu. Results of research of hypoxia problems by Ukrainian scientists in Elbrus region. Bul. NAU. 2007, N 3-4, P. 44-50. (In Ukrainian).

37. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of medical and biological research of Ukrainian scientists at Elbrus. Bul. NAU. 2007, N 2, P. 10-16. (In Ukrainian).

38. Belan P. V., Gerasimenko O. V., Tepikin A. V., Petersen O. H. Localization of Ca++ extrusion sites in pancreatic acinar cells. J. Biol. Chem. 1996, V. 271, P. 7615-7619.
https://doi.org/10.1074/jbc.271.13.7615

39. Belan P., Gardner J., Gerasimenko O. Extracellular Ca++ spikes due to secretory events in salivary gland cells. J. Biol. Chem. 1998, V. 273, P. 4106-4111. https://doi.org/10.1074/jbc.273.7.4106

40. Jabs R., Pivneva T., Huttmann K. Synaptic transmission onto hyppocampal glial cells with hGFAP promoter activity. J. Cell Sci. 2005, V. 118, P. 3791-3803. https://doi.org/10.1242/jcs.02515

41. Linne C. Fauna Suecica. Stocholmiac. 1761, 578 p.

42. Klyuchko Z. F. Noctuidae of the Western regions of Ukraine. Kyiv: KyivUniv. Publ. 1963, 176 p. (In Russian).

43. Baert P., Meesen G., De Schynkel S., Poffijn A., Oostveldt P. V. Simultaneous in situ profiling of DNA lesion endpoints based on image cytometry and a single cell database approach. Micron. 2005, 36 (4), 321-330. https://doi.org/10.1016/j.micron.2005.01.005

44. Berks G., Ghassemi A., von Keyserlingk D. G. Spatial registration of digital brain atlases based on fuzzy set theory. Comp. Med. Imag. Graph. 2001, 25 (1), 1-10. https://doi.org/10.1016/S0895-6111(00)00038-0

45. Nowinski W. L., Belov D. The Cerefy Neuroradiology Atlas: a Talairach-Tournoux atlas-based tool for analysis of neuroimages available over the internet. NeuroImage. 2003, 20 (1), 50-57. https://doi.org/10.1016/S1053-8119(03)00252-0

46. Chaplot S., Patnaik L. M., Jagannathan N. R. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Proc. Contr. 2006, 1 (1), 86-92. https://doi.org/10.1016/j.bspc.2006.05.002

47. Kovalev V. A., Petrou M., Suckling J. Detection of structural differences between the brains of schizophrenic patients and controls. Psych. Res.: Neuroimaging. 2003, 124 (3), 177-189. https://doi.org/10.1016/S0925-4927(03)00070-2

48. Ara?jo T. Classification of breast cancer histology images using Convolutional Neural Networks. PloS One. 2017, 12 (6), e0177544.
https://doi.org/10.1371/journal.pone.0177544

49. Vecht-Lifshitz S. E., Ison A. P. Biotechnological applications of image analysis: present and future prospects. J. Biotechnol. 1992, 23 (1), 1-18. https://doi.org/10.1016/0168-1656(92)90096-R

50. Toga A. W., Thompson P. M. The role of image registration in brain mapping. Image Vis. Comput. 2001, 19 (1-2), 3-24. https://doi.org/10.1016/S0262-8856(00)00055-X

51. Carro S. A., Scharcanski J. A framework for medical visual information exchange on the WEB. Computers in Biology and Medicine. 2006, V. 4, P. 327-338. https://doi.org/10.1016/j.compbiomed.2004.10.004

52. Chakravarty M. M., Bertrand G., Hodge C. P., Sadikot A. F., Collins D. L. The creation of a brain atlas for image guided neurosurgery using serial histological data. NeuroImage. 2006, 30 (2), 359-376. https://doi.org/10.1016/j.neuroimage.2005.09.041

53. Dikshit A., Wu D., Wu C., Zhao W. An online interactive simulation system for medical imaging education. Comp. Med. Imag. Graph. 2005, 29 (6), 395-404. https://doi.org/10.1016/j.compmedimag.2005.02.001

54. Singh R., Schwarz N., Taesombut N., Lee D., Jeong B., Renambot L., Lin A. W., West R., Otsuka H., Naito S., Peltier S. T., Martone M. E., Nozaki K., Leigh J., Ellisman M. H. Real-time multi-scale brain data acquisition, assembly, and analysis using an end-to-end. OptIPuter Future Generation Computer Systems. 2006, V. 22, P. 1032-1039.
https://doi.org/10.1016/j.future.2006.03.017

55. Stefanescu R., Pennec X., Ayache N. Grid powered nonlinear image registration with locally adaptive regularization. Med. Image Anal. 2004, 8 (3), 325-342.
https://doi.org/10.1016/j.media.2004.06.010

56. Ma Y., Hof P. R., Grant S. C., Blackband S. J., Bennett R., Slatest L., McGuigan M. D., Benveniste H. A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience. 2005, 135 (4), 1203-1215. https://doi.org/10.1016/j.neuroscience.2005.07.014

57. Yu-Len Huang. Computer-aided Diagnosis Using Neural Networks and Support Vector Machines for Breast Ultrasonography. J. Med. Ultrasound. 2009, 17 (1), 17-24.
https://doi.org/10.1016/S0929-6441(09)60011-4

58. Prachi Damodhar Shahare, Ram Nivas Giri. Comparative Analysis of Artificial Neural Network and Support Vector Machine Classification for Breast Cancer Detection. International Research Journal of Engineering and Technology (IRJET). 2015, 2 (9).

59. Natrajan R., Sailem H., Mardakheh F. K., Garcia M. F., Tape C. G., Dowsett M., Bakal C., Yuan Y. Microenvironmental heterogeneity parallels breast cancer progression: a histology-genomic integration analysis. PLoS Med. 2016. 13 (2), e1001961. https://doi.org/10.1371/journal.pmed.1001961

60. Klyuchko O. M. Brain images in information systems for neurosurgery and neurophysiology. Electronics and control systems. 2009, 3 (21), 152-156. (In Ukrainian).
https://doi.org/10.18372/1990-5548.21.733

61. Klyuchko O. M. Using of images' databases for diagnostics of pathological changes in organism tissues. Electronics and control systems. 2009, 2 (20), 62-68. (In Ukrainian).
https://doi.org/10.18372/1990-5548.20.687

62. Klyuchko O. M. Elements of different level organization of the brain as material for electronic databases with images. Electronics and control systems. 2009, 1 (19), 69-75. (In Ukrainian). https://doi.org/10.18372/1990-5548.19.665

63. Steimann F. On the representation of roles in object-oriented and conceptual modelling. Data& Knowedge Engin. 2000, 35 (1), 83-106. https://doi.org/10.1016/S0169-023X(00)00023-9

64. Klyuchko O. M., Managadze Yu. L., Pashkivsky A. O. Program models of 2D neuronal matrix for ecological monitoring and images' coding. Bulletin of the Engineering Academy. 2013, No 3-4, P. 77-82. (In Ukrainian).

65. Klyuchko O. M., Piatchanina T. V., Mazur M. G. Combined use of relation databases of images for diagnostics, therapy and prognosis of oncology diseases. "Integrated robototechnic complexes". Х ІІRTC-2017 Conference Proceedings. P. 275-276. (In Ukrainian).

66. Shutko V. M., Shutko O. M., Kolganova O. O. Methods and means of compression of information. Kyiv: Nauk. dumka. 2012, 168 p. (In Ukrainian).

67. Jecheva V., Nikolova E. Some clusteringbased methodology applications to anomaly intrusion detection systems. Int. J. Secur. Appl. 2016, 10 (1), 215-228. https://doi.org/10.14257/ijsia.2016.10.1.20

68. Iakovidis D. K., Maroulis D. E., Karkanis S. A. Texture multichannel measurements for cancer precursors' identification using support vector machines. Measurement. 2004, V. 36, P. 297-313. https://doi.org/10.1016/j.measurement.2004.09.010

69. Nguyen H. Q., Carrieri-Kohlman V., Rankin S. H., Slaughter R., Stulbarg M. S. Internet-based patient education and support interventions: a review of evaluation studies and directions for future research. Comp. Biol. Med. 2004, 34 (2), 95-112. https://doi.org/10.1016/S0010-4825(03)00046-5

70. J?z?quel P., Loussouarn L., Gu?rin-Charbonnel C., Campion L., Vanier A., Gouraud W., Lasla H., Guette C., Valo I., Verri?le V. Campone M. Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response. Breast Cancer Res. 2015, 17 (1), 43. https://doi.org/10.1186/s13058-015-0550-y

71. Bozhenko V. K. Multivariable analysis of laboratory blood parameters for obtaining diagnostic information in experimental and clinical oncology. The dissertation author's abstract on scientific degree editions. Dc. Med. Study. Moscow. 2004. (In Russian).

72. Ko J. H., Ko E. A., Gu W., Lim I., Bang H., Zhou T. Expression profiling of ion channel genes predicts clinical outcome in breast cancer. Mol. Cancer. 2013, 12 (1), 106. https://doi.org/10.1186/1476-4598-12-106

73. Kawai M., Nakashima A., Kamada S., Kikkawa U. Midostaurin preferentially attenuates proliferation of triple-negative breast cancer cell lines through inhibition of Aurora kinase family. J. Biomed. Sci. 2015, 22 (1), 48. https://doi.org/10.1186/s12929-015-0150-2

74. Uhr K., Wendy J. C., Prager-van der Smissen, Anouk A. J. Heine, Bahar Ozturk, Marcel Smid, Hinrich W. H. G?hlmann, Agnes Jager, John A. Foekens, John W. M. Martens. Understanding drugs in breast cancer through drug sensitivity screening. SpringerPlus. 2015, 4 (1), 611. https://doi.org/10.1186/s40064-015-1406-8

75. Onopchuk Yu. M., Biloshitsky P. V., Klyuchko O. M. Development of mathematical models based on the results of researches of Ukrainian scientists at Elbrus. Bul. NAU. 2008, N 3, P. 146-155. (In Ukrainian). https://doi.org/10.18372/2306-1472.36.1609

76. Ankur Poudel, Dhruba Bahadur Thapa, Manoj Sapkota. Cluster Analysis of Wheat (Triticum aestivum L.) Genotypes Based Upon Response to Terminal Heat Stress. Int. J. Appl. Sci. Biotechnol. 2017, 5 (2), 188-193. https://doi.org/10.3126/ijasbt.v5i2.17614

77. Zaslavsky L., Ciufo S., Fedorov B., Tatusova T. Clustering analysis of proteins from microbial genomes at multiple levels of resolution. BMC Bioinform. 2016, 17 (8), 276. Published online 2016 Aug 31. https://doi.org/10.1186/s12859-016-1112-8

78. Zhou J., Richardson A. J., Rudd K. E. EcoGene-RefSeq: EcoGene tools applied to the RefSeq prokaryotic genomes. Bioinformatics. 2013, 29 (15), 1917-1918. Published: 04 June 2013. https://doi.org/10.1093/bioinformatics/btt302

79. Zhang J., Wu G., Hu X., Li S., Hao S. A Parallel Clustering Algorithm with MPI - MKmeans. J. Comput. 2013, 8 (1), 10-17. https://doi.org/10.1109/PAAP.2011.17

80. Tatusova T., Zaslavsky L., Fedorov B., Haddad D., Vatsan A., Ako-adjei D., Blinkova O., Ghazal H. Protein Clusters. The NCBI Handbook [Internet]. 2nd edition. Available at https://www.ncbi.nlm.nih.gov/books/NBK242632

81. Anderson J. G. Evaluation in health informatics: computer simulation. Computers in Biology and Medicine. 2002, 32 (3), 151-164. https://doi.org/10.1016/S0010-4825(02)00012-4

82. Aruna P., Puviarasan N., Palaniappan B. An investigation of neuro-fuzzy systems in psychosomatic disorders. Exp. Syst. Appl. 2005, 28 (4), 673-679. https://doi.org/10.1016/j.eswa.2004.12.024

83. Bange M. P., Deutscher S. A., Larsen D., Linsley D., Whiteside S. A handheld decision support system to facilitate improved insect pest management in Australian cotton systems. Comp. Electron. Agricult. 2004, 43 (2), 131-147. https://doi.org/10.1016/j.compag.2003.12.003

84. Beaulieu A. From brainbank to database: the informational turn in the study of the brain. Stud. Hist. Phil. Biol. Biomed. Sci. 2004, V. 35, P. 367-390. https://doi.org/10.1016/j.shpsc.2004.03.011

85. Horn W. AI in medicine on its way from knowledge-intensive to data-intensive systems. Artificial Intelligence in Medicine. Elsevier. 2001, 23 (1), 512. https://doi.org/10.1016/S0933-3657(01)00072-0

86. Brake I. Unifying revisionary taxonomy: insect exemplar groups. Abstr. XV SEL Congr. Berlin (Germany). 2007.

87. Braxton S. M., Onstad D. W., Dockter D. E., Giordano R., Larsson R., Humber R. A. Description and analysis of two internet-based databases of insect pathogens: EDWIP and VIDIL. J. Invertebr. Pathol. 2003, 83 (3), 185-195. https://doi.org/10.1016/S0022-2011(03)00089-2

88. Breaux A., Cochrane S., Evens J., Martindaled M., Pavlike B., Suera L., Benner D. Wetland ecological and compliance assessments in the San Francisco Bay Region, California, USA. J. Environm. Manag. 2005, 74 (3), 217-237. https://doi.org/10.1016/j.jenvman.2004.08.017

89. Budura A., Cudr?-Mauroux P., Aberer K. From bioinformatic web portals to semantically integrated Data Grid networks. Fut. Gener.Comput. Syst. 2007, 23 (3), 281-522. https://doi.org/10.1016/j.jenvman.2004.08.017

90. Burns G., Stephan K. E., Lud?scher B., Gupta A., K?tter R. Towards a federated neuroscientific knowledge management system using brain atlases. Neurocomputing. 2001, V. 3840, P. 1633-1641. https://doi.org/10.1016/S0925-2312(01)00520-3

91. Butenko S., Wilhelm W. E. Clique-detection models in computational biochemistry and genomics. Eur. J. Oper. Res. 2006, 173 (1), 117. https://doi.org/10.1016/j.ejor.2005.05.026

92. Carro S. A., Scharcanski J. Framework for medical visual information exchange on the WEB. Comp. Biol. Med. 2006, 36 (4), 327-338.
https://doi.org/10.1016/j.compbiomed.2004.10.004

93. Chau M., Huang Z., Qin J., Zhou Y., Chen H. Building a scientific knowledge web portal: The NanoPort experience. Decision Support Systems. 2006. https://doi.org/10.1016/j.dss.2006.01.004

94. Chen M., Hofest?dt R. A medical bioinformatics approach for metabolic disorders: Biomedical data prediction, modeling, and systematic analysis. J. Biomed. Inform. 2006, 39 (2), 147-159. https://doi.org/10.1016/j.jbi.2005.05.005

95. Chli M., De Wilde P. Internet search: Subdivisionbased interactive query expansion and the soft semantic web. Appl. Soft Comput. 2006. https://doi.org/10.1016/j.asoc.2005.11.003

96. Despont-Gros C., Mueller H., Lovis C. Evaluating user interactions with clinical information systems: A model based on human - computer interaction models. J. Biomed. Inform. 2005, 38 (3), 244-255. https://doi.org/10.1016/j.jbi.2004.12.004

97. Marios D., Dikaiakos M. D. Intermediary infrastructures for the World Wide Web. Comp. Networks. 2004, V. 45, P. 421-447. https://doi.org/10.1016/j.comnet.2004.02.008

98. Dimitrov S. D., Mekenyan O. G., Sinks G. D., Schultz T. W. Global modeling of narcotic chemicals: ciliate and fish toxicity. J. Mol. Struc.: Theochem. 2003, 622 (12), 63-70. https://doi.org/10.1016/S0166-1280(02)00618-8

99. Dong Y., Zhuang Y., Chen K., Tai X. A hierarchical clustering algorithm based on fuzzy graph connectedness. Fuzzy Sets. Syst. 2006, V. 157, P. 1760-1774. https://doi.org/10.1016/j.fss.2006.01.001

100. Duan Y., Edwards J. S., Xu M. X. Web-based expert systems: benefits and challenges. Inf. Manag. 2005, 42 (6), 799-811. https://doi.org/10.1016/j.im.2004.08.005

101. Essen van D. C. Windows on the brain: the emerging role of atlases and databases in neuroscience. Curr. Opin. Neurobiol. 2002, 12 (5), 574-579. https://doi.org/10.1016/S0959-4388(02)00361-6

102. Fellbaum C., Hahn U., Smith B. Towards new information resources for public health From Word Net to Medical Word Net. J. Biomed. Inform. 2006, 39 (3), 321-332. https://doi.org/10.1016/j.jbi.2005.09.004

103. Ferraris M., Frixione P., Squarcia S. Network oriented radiological and medical archive. Comp. Physics Commun. 2001, V. 140, P. 226-232. https://doi.org/10.1016/S0959-4388(02)00361-6

104. Flower D. R., Attwood T. K. Integrative bioinformatics for functional genome annotation: trawling for G protein-coupled receptors. Reviews 55 Semin. Cell. Dev. Biol. 2004, 15 (6), 693-701. https://doi.org/10.1016/j.semcdb.2004.09.008

105. Fink E., Kokku P. K., Nikiforou S., Hall L. O., Goldgof D. B., Krischer J. P. Selection of patients for clinical trials: an interactive webbased system. Art. Intell. Med. 2004, 31 (3), 241-254. https://doi.org/10.1016/j.artmed.2004.01.017

106. Fitzpatrick M. J., Ben-Shahar Y., Smid H. M., Vet L. E., Robinson G. E., Sokolowski M. B. Candidate genes for behavioural ecology. Trend Ecol. Evol. 2005, 20 (2), 96-104. https://doi.org/10.1016/j.tree.2004.11.017

107. Fox J., Alabassi A., Patkar V., Rose T., Black E. An ontological approach to modeling tasks and goals. Comp. Biol. Med. 2006, V. 36, P. 837-856. https://doi.org/10.1016/j.compbiomed.2005.04.011

108. Fu Zetian, Xu Feng, Zhou Yun, Shuan X. Z. Pig-vet: a web-based expert system for pig disease diagnosis. 2006. https://doi.org/10.1016/j.eswa.2005.01.011

109. Gaulton A., Attwood T. K. Bioinformatics approaches for the classification of G-protein-coupled receptors. Curr. Opin. Pharmacol. 2003, 3 (2), 114-120. https://doi.org/10.1016/S1471-4892(03)00005-5

110. Palla K., Ghahramani Z., Knowles D. A. A nonparametric variable clustering model. Adv. Neural Inform. Proc. Syst. 2012, P. 2987-2995

111. Curtis C., Shah S. P., Chin S. F., Turashvili G., Rueda O. M., Dunning M. J., Speed D., Lynch A. G., Samarajiwa S., Yuan Y., Gr?f S., Ha G., Haffari G., Bashashati A., Russell R., McKinney S., Langer?d A., Green A., Provenzano E., Wishart G., Pinder S., Watson P., Markowetz F., Murphy L., Ellis I., Purushotham A., Dale A. L., Brenton J. D., Tavar? S., Caldas C., Aparicio S. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012, 486 (7403), 346-352.
https://doi.org/10.1038/nature10983

112. Proceedings Of the second EuroLOOP Workshop, Amsterdam, 27-30 March 1995.

113. Graham C. H., Ferrier S., Huettman F., Moritz C., Peterson A. T. New developments in museum-based informatics and applications in biodiversity analysis. Trend. Ecol. Evol. 2004, 19 (9), 497-503. https://doi.org/10.1016/j.tree.2004.07.006

114. Anokhin B., Hemmrich G. Zoology www guide. Zoology. 2005, 108 (4), 673-679.
https://doi.org/10.1016/j.zool.2005.01.001

115. Hirano S., Sun X., Tsumoto S. Comparison of clustering methods for clinical databases. Inform. Sci. 2004, 159 (34), 155-165. https://doi.org/10.1016/j.ins.2003.03.011

116. Hong Yu., Hatzivassiloglou V., Rzhetsky A., Wilbur W. J. Automatically identifying gene/protein terms in MEDLINE abstracts. J. Biomed. Inform. 2002, 35 (56), 322-330. https://doi.org/10.1016/S1532-0464(03)00032-7

117. Bedathur S. J., Haritsa J. R., Sen U. S. The building of BODHI, a bio-diversity database system. Inform. Syst. 2003, 28 (4), 347-367. https://doi.org/10.1016/S0306-4379(02)00073-X

118. Hsi-Chieh Lee, Szu-Wei Huang, Li E. Y. Mining protein-protein interaction information on the internet. Exp. Syst. Appl. Elsevier. 2006, 30 (1), 142-148. https://doi.org/10.1016/j.eswa.2005.09.083

119. Young R. Genetic toxicology: web-resources. Toxicology. 2002, 173 (1-2), 103-121.
https://doi.org/10.1016/S0300-483X(02)00026-4

120. Johnson S. B., Friedman R. Bridging the gap between biological and clinical informatics in a graduate training program. J. Biomed. Inform. 2007, 40 (1), 59-66. Epub. 2006 Mar 15. https://doi.org/10.1016/j.jbi.2006.02.011

121. Kaiser M., Hilgetag C. C. Modelling the development of cortical systems networks. Neurocomputing. 2004, V. 5860, P. 297-302. https://doi.org/10.1016/j.neucom.2004.01.059

122. Kane M. D., Brewer J. L. An information technology emphasis in biomedical informatics education. J. Biomed. Inform. 2007, 40 (1), 67-72. https://doi.org/10.1016/j.jbi.2006.02.006

123. Kannathal N., Acharya U. R., Lim C. M., Sadasivan P. K. Characterization of EEG. A comparative study. Comp. Meth. Progr. Biomed. 2005, 80 (1), 17-23. https://doi.org/10.1016/j.cmpb.2005.06.005

124. Koh W., McCormick B. H. Brain micro structure database system: an exoskeleton to 3D reconstruction and modelling. Neurocomputing. 2002, V. 4446, 1099-1105. https://doi.org/10.1016/S0925-2312(02)00426-5

125. Koh W., McCormick B. H. Registration of a 3D mouse brain atlas with brain microstructure data. Neurocomputing. 2003, V. 5254, P. 307-312. https://doi.org/10.1016/S0925-2312(02)00793-2

126. Kulish V., Sourin A., Sourina O. Human electro encephalograms seen as fractal time series: Mathematical analysis and visualization. Comp. Biol. Med. 2006, 36 (3), 291-302. https://doi.org/10.1016/j.compbiomed.2004.12.003

127. Lubitz von D., Wickramasinghe N. Networkcentric healthcare and bioinformatics: Unified operations within three domains of knowledge. Exp. Syst. Appl. 2006, 30 (1), 11-23. https://doi.org/10.1016/j.eswa.2005.09.069

128. Martin-Sanchez F., Iakovidis I., Norager S., Maojo V., de Groen P., Van der Lei J., Jones T., Abraham-Fuchs K., Apweiler R., Babic A., Baud R., Breton V., Cinquin P., Doupi P., Dugas M., Eils R., Engelbrecht R., Ghazal P., Jehenson P., Kulikowski C., Lampe K., De Moor G., Orphanoudakis S., Rossing N., Sarachan B., Sousa A., Spekowius G., Thireos G., Zahlmann G., Zv?rov? J., Hermosilla I., Vicente F. J. Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. J. Biomed. Inform. 2004, 37 (1), 30-42. https://doi.org/10.1016/j.jbi.2003.09.003

129. Masseroli M., Visconti A., Bano S. G. Pinciroli F. He@lthCo-op: a web-based system to support distributed healthcare co-operative work. Comp. Biol. Med. 2006, 36 (2), 109-127. https://doi.org/10.1016/j.compbiomed.2004.09.005

130. Moon S., Byun Y., Han K. FSDB: A frameshift signal database. Comp. Biol. Chem. 2007, 31 (4), 298-302. https://doi.org/10.1016/j.compbiolchem.2007.05.004

131. Orgun B., Vu J. HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Comp. Biol. Med. 2006, 36 (78), 817-836. https://doi.org/10.1016/j.compbiomed.2005.04.010

132. P?rez-Rey D., Maojo V., Garc?a-Remesal M., Alonso-Calvo R., Billhardt H., Martin-S?nchez F., Sousa A. Ontofusion: Ontologybased integration of genomic and clinical databases. Comp. Biol. Med. 2006, 36 (78), 712-730. https://doi.org/10.1016/j.compbiomed.2005.02.004

133. Krishtal O. A., Kiskin N. I., Tsyndrenko A. Ya., Klyuchko E. M. Pharmacological properties of amino acid receptors in isolated hippocampal neurons. In: Receptors and ion channels. Ed. by Ovchinnikov Y. A., Hucho F. Berlin-New York: Walter de Gruyter. 1987, P. 127-137.

134. Klyuchko E. M., Klyuchko Z. F., Beloshitsky P. V. Some adaptation characteristics of insects in mountains of Prielbrussie. Nalchik (Russia), "Hypoxia: automatic analysis of hypoxic states of healthy people and sick ones". 2005, V. 1, P. 137-140. (In Russian).

135. Klyuchko Z. F., Klyuchko E. M. Analysis of taxonomic structure of moth fauna (Lepidoptera: Noctuidae s. l.) of Ukraine according to monitoring data. Eversmannia. 2012, 3 (33), 41-45. (In Russian).

136. Klyuchko Z. F., Klyuchko E. M. Moth (Lepidoptera: Noctuidae s. l.) of Chercasska region of Ukraine according to results of many-year monitoring. Eversmannia. 2014, V. 37, P. 32-49. (In Russian).

137. Gonchar O., Klyuchko O., Seredenko M., Oliynyk B. Correction of metabolic disorders at hypoxia by new pharmacological preparations. Mater. 3 FEPS Congress. Nice (France). 2003, P. 228.

138. Seredenko M., Gonchar O., Klyuchko O., Oliynyk S. Peculiarities of prooxidant - antioxidant balance of organism under hypoxia of different genesis and its corrections by new pharmacological preparations. Acta Physiologica Hungarica. Budapest (Hungary). 2002, 89 (1-3), 292.

139. Klyuchko O., Klyuchko Z., Lizunova A. Electronic Noctuidae database: some problems and solutions. Proceed. 16th European Congress of Lepidopterology. Cluj (Romania). 2009, P. 31-32.

140. Klyuchko O., Klyuchko Z., Lizunova A. Noctuidae fauna of Ukrainian Karpathy: results of monitoring (1956-2008). Proceed. 16th European Congress of Lepidopterology. Cluj (Romania). 2009, P. 31.

141. Klyuchko O. M., Beloshitsky P. V. Investigation of insect adaptation characteristics in Prielbrussie in 2004-2005. Mater. VIII World Congress of International Society for Adaptive Medicine (ISAM). Moskva (Russia). 2006, Р. 165-166.

142. Beloshitsky P. V., Klyuchko O. M. Contribution of Sirotinin's school into adaptation medicine. Mater. VIII World Congress of International Society for Adaptive Medicine (ISAM). Moskva (Russia). 2006, Р. 158.

143. Klyuchko O. M., Klyuchko Z. F. Ukrainian Noctuidae Database. Mater. of XIV SEL Congress. Roma (Italy). 2005, P. 49.

144. Klyuchko Z. F., Klyuchko O. M. Noctuidae (Lepidoptera) of Donbass, Ukraine. Mater. of XIV SEL Congress. Roma (Italy). 2005, P. 41-42.

145. Beloshitsky P., Klyuchko O., Onopchuck Yu., Onopchuck G. Mathematic model for hypoxic states development for healthy people and ones with ischemic heart disease. High altitude medicine and biology: Mater. ISMM Congress. Beijing (China). 2004, V. 5, P. 251.

146. Beloshitsky P., Klyuchko O., Kostyuk O., Beloshitsky S. Peculiarities of high mountain factors influence on organism. High altitude medicine and biology: Mater. ISMM Congress. Beijing (China). 2004, V. 5, P. 250.

147. Klyuchko O. M., Kiskin N. I., Krishtal O. A., Tsyndrenko A. Ya. Araneidae toxins as antagonists of excitatory amino acid responses in isolated hippocampal neurons. X School on biophysics of membrane transport. Szczyrk (Poland). 1990, V. 2, P. 271.

148. Akaike N., Kawai N., Kiskin N. I., Krishtal O. A., Tsyndrenko A. Ya., Klyuchko O. M. Spider toxin blocks excitatory amino acid responses in isolated hippocampal pyramidal neurons. Neurosci. Lett. 1987, V. 79, P. 326-330. https://doi.org/10.1016/0304-3940(87)90453-8

149. Gonchar O., Klyuchko O., Beloshitsky P. Ways of myocardial metabolic disorders correction at hypoxia by new pharmacological preparations. High altitude medicine and biology: Mater. ISMM Congress. Beijing (China). 2004, V. 5, P. 249.

150. Gonchar O., Klyuchko E., cMankovskaya I. Role of complex nucleosides in the reversal of oxidative stress and metabolic disorders induced by acute nitrite poisoning. Ind. J. Pharmacol. 2006, 38 (6), 414-418. https://doi.org/10.4103/0253-7613.28208

151. Gonchar O., Klyuchko E., Seredenko M., Oliynik S. Corrections of prooxidant - antioxidant homeostasis of organism under hypoxia of different genesis by yackton, new pharmacological preparation. Acta Physiol. Pharmacol. Bulg. 2003, V. 27, P. 53-58.

152. Klyuchko O. M., Tzal-Tzalko V. I. Elaboration of new monitoring system for Ukrainian Polissia conditions with data defence. Bulletin of Engineering Academy. 2014, V. 2, P. 239-246. (In Ukrainian).

153. Klyuchko O. M., Piatchanina T. V., Mazur M. G., Basarak O. V. Ontological methods in the development of biomedical information systems. "Integrated intellectual robototechnical complexes" - "IIRTC-2018": Materials of ХІ Intl. Scient. Tech. Conference. 2018, P. 270-272. (In Ukrainian).

154. Klyuchko O. M., Piatchanina T. V., Mazur M. G., Basarak O. V. Elaboration of networkbased biomedical systems with databases. "Integrated intellectual robototechnical complexes" - "IIRTC-2018": Materials of ХІ Intl. Scient. Tech. Conference. 2018, P. 273-274. (In Ukrainian). https://doi.org/10.1515/9783110442922-009

155. Aralova N. I., Klyuchko O. M. Mathematic modeling of functional self-organization of pilots' respiratory systems. "Integrated intellectual robototechnical complexes" - "IIRTC-2018": Materials of ХІ Intl. Scient. Tech. Conference. 2018, P. 268-269. (In Ukrainian).

156. Klyuchko Z. F., Klyuchko O. M. Monitoring of the diversity of Noctuidae (Lepidoptera) fauna in Ukrainian Polissia. "Nature of Polissia: investigation and protection": Materials of Intl. Scient. Conference. 2014, P. 498-502. (In Ukrainian).

157. Klyuchko Z. F., Klyuchko O. M., Lizunova A. G. Monitoring of Noctuidae fauna of Ukraine using electronic information systems. "Nature of Polissia: investigation and protection": Materials of Intl. Scient. Conference. 2014, P. 261-265. (In Ukrainian).

158. Biloshitsky P. V., Klyuchko O. M. Studying of sport medicine problems at Elbrus Medical and Biological Station. World Congress in Mountain Medicine. Chronic Hypoxia Symposium. LaPaz, Bolivia. Feb 23-Mar 2, 2019. P. 18.

159. Aralova N. I., Biloshitsky P. V., Klyuchko O. M. Mathematical models of system mechanisms of organism adaptation to hypoxia. World Congress in Mountain Medicine. Chronic Hypoxia Symposium. LaPaz, Bolivia. Feb 23-Mar 2, 2019. P. 17. https://doi.org/10.18372/1990-5548.56.12944

160. Klyuchko O. М., Klyuchko Z. F. Electronic databases for Arthropods: methods and applications. Biotechnol. acta. 2018, 11 (4), 28-49. https://doi.org/10.15407/biotech11.04.028

161. Klyuchko O. М., Klyuchko Z. F. Electronic information systems for monitoring of populations and migrations of insects. Biotechnol. acta. 2018, 11 (5), 5-25. https://doi.org/10.15407/biotech11.05.005

162. Klyuchko O. М. Expert systems for biology and medicine. Biotechnol. аcta. 2018, 11 (6), 5-28. https://doi.org/10.15407/biotech11.06.005

163. Klyuchko O. М. Biotechnical information systems for monitoring of chemicals in environment: biophysical approach. Biotechnol. аcta. 2019, 12 (1), 5-28. https://doi.org/10.15407/biotech

164. Klyuchko O. M., Biletsky A. Ya., Navrotskyi D. O. Method of bio-sensor test system application. Patent UA 129923 U, G01N33/00, G01N33/50, C12Q 1/02. Priority: 22.03.2018, u201802896, Issued: 26.11.2018, Bull. 22, 7 p. (In Ukrainian)

165. Klyuchko O. M., Biletsky A. Ya., Navrotskyi D. O. Method of application of biotechnical monitoring system with biosensor (biosensor test system). Patent UA 132245 U; G01N33/50. Priority: 22. 03. 2018 u201802893, Issued: 25.02. 2019, Bull. 4, 7 p. (In Ukrainian).

166. Klyuchko O. M., Biletsky A. Ya., Navrotskyi D. O. Method of application of biotechnical monitoring system with biosensor and subsystem for optical registration. Patent UA 129922 U, G01N33/50. Priority: 22. 03. 2018, u201802894 Issued: 26. 11. 2018, Bull. 22, 10 p. (In Ukrainian).

167. Klyuchko O. M. Method of application of biotechnical monitoring system for bioindicators' accounting with biosensor and sub-system for optical registration. Patent UA 129987 U, G01N33/00, C12Q 1/02, C12N 15/00. Priority: 27. 04. 2018, u201804662: Issued: 26.11.2018, Bull. 22, 11 p. (In Ukrainian).

168. Klyuchko O. M. Method for cells' dissociation. Patent UA 130672 U, G01N 33/00, C12Q 1/02, C12N 15/00. Priority: 27. 04. 18, u201804668, Issued: 26. 12. 2018, Bull. 24, 7 p. (In Ukrainian).

169. Klyuchko O. M., Biletsky A. Ya., Navrotskyi D. Method of application of biotechnical monitoring system with expert subsystem and biosensor. Patent UA 131863 U; G01N33/00, C12Q 1/02, C12N 15/00. Priority: 27. 04. 18, u201804663, Issued: 11. 02. 2019, Bull. 3, 7 p. (In Ukrainian).

170. Klyuchko O. M. Method of qualitative analysis of chemical substances. Patent UA 131016 U, G01N33/50, G01N21/78, C12Q 1/60. Priority: 11. 05. 2018, u201805174, Issued: 10. 01. 2019, Bull. 1. (In Ukrainian).

171. Klyuchko O. M., Biletsky A. Ya., Navrotskyi D. A. Method of quantitative analysis of chemical substances. Patent UA 131524 U; G01N33/50, G01N21/78, C12Q 1/60. Priority: 11. 05. 2018, u201805175, Issued: 25. 01. 2019, Bull. 2, 10 p. (In Ukrainian).

172. Klyuchko O. M., Biletsky A. Ya. Method of qualitative analysis of chemical substances for the influence on electrical currents in bioobjects. Patent UA; G01N 33/50, G01N 21/78, C12Q 1/60. Priority: May 2018, u201806345. (In Ukrainian)

173. Klyuchko O. M., Biletsky A. Ya. Method of qualitative analysis of hydrocarbons with a high and toxic effect on bioobjects. Patent UA 133676 U; G01N 33/50, G01N 21/78. Priority: 06.06.2018, u201806342, Issued: 25.04.2019, Bull. 8, 10 p. (In Ukrainian)

174. Klyuchko O. M., Biletsky A. Ya., Navrots kyi D. O. Method of monitoring of chemical substances' infuences on organisms in few time intervals. Patent UA; G01N33/00, C12Q 1/02. Priority: 14. 12. 2018, u201812443. (In Ukrainian).

175. Klyuchko O. M., Biletsky A. Ya., Shutko V. N. Method for constructing and testing of physical molecular memory in anisotropic media with molecules - derivatives of phenol. Patent UA; H01B1/121, G01N33/00, C12Q 1/02. Priority: 14. 12. 2018, u201812430. (In Ukrainian).

176. Klyuchko O. M., Biletsky A. Ya., Lizunov G. V., Shutko V. N. Method of electrical signals generating by bio-elements in the technical hybrid system. Patent UA; A61N1/326, G01N33/00, C12Q 1/02. Priority: 14. 12. 2018, u201812442. (In Ukrainian).

177. Klyuchko O. M., Biletsky A. Ya., Lizunov G. V., Shutko V. N. Method for application of the system of large-scale monitoring of bioobjects with possibility of their radar control. Patent UA; G01N33/00, G01S13/88, C12N 15/00. Priority: 14. 12. 2018, u201812444. (In Ukrainian).

178. Klyuchko O. M., Biletsky A. Ya., Lizunov G. V., Piankova O. V. Method of application of biosensor test-system with databases. Patent UA; G01N33/00, C12Q 1/02. Priority: 17. 01. 2019, u201900476. (In Ukrainian).

179. Klyuchko O. M., Biletsky A. Ya., Lizunov G. V., Piankova O. V. Method of application of monitoring system with biosensor and databases. Patent UA; G01N33/00, C12Q 1/02, G06F11/20. Priority: 17. 01. 2019, u201900476. (In Ukrainian).

Page 2 of 2