Advertisement

Introducing Biomimomics: Combining Biomimetics and Comparative Genomics for Constraining Organismal and Technological Complexity

  • Claudio L. Flores MartinezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10384)

Abstract

Integrated genomics and transcriptomics data, together with the analysis of total protein and metabolite content of a given cell, is providing the basis for complex, multi-scale and dynamic models of cellular metabolism in health and disease. Accordingly, the functional triad of genomics, transcriptomics and metabolomics is regarded as a foundational methodology in systems biology. Opening up a never-before seen vista into the organization and dynamical evolution of cellular life at multiple scales of complexity, Omics-approaches are poised to facilitate discoveries in biomimetic design processes. In the following, the proposed merger of biomimetics with Omics-techniques will be called “Biomimomics”. Focusing on comparative genomics, this paper will outline how ongoing work in the field is revising our understanding of early nervous system and synapse evolution in animals and, at the same time, promises to give insights into truly, i.e. evolutionarily-based, biomimetic neuromorphic computing architectures. We will show how a new kind of modular workflow based on a “Biomimomic Traceability Matrix” (BTM) can structure and facilitate both biomimetic design solutions and the discovery of universal principles underlying complexifying biological and technological systems.

Keywords

Comparative genomics Nervous system evolution Chemical computation 

References

  1. 1.
    Walker, S.I., Davies, P.C.W., Ellis, G.F.R. (eds.): From Matter to Life: Information and Causality, p. 1. Cambridge University Press, Cambridge (2017)CrossRefGoogle Scholar
  2. 2.
    Huerta-Cepas, J., et al.: eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44(D1), D286–D293 (2016)CrossRefGoogle Scholar
  3. 3.
    Koonin, E.V.: Comparative genomics, minimal gene-sets and the last universal common ancestor. Nat. Rev. Microbiol. 1(2), 127–136 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Csete, M.E., Doyle, J.C.: Reverse engineering of biological complexity. Science 295(5560), 1664–1669 (2002)CrossRefGoogle Scholar
  5. 5.
    Koonin, E.V.: The logic of chance: the nature and origin of biological evolution, xii edn, p. 516. Pearson Education, Upper Saddle River (2012)Google Scholar
  6. 6.
    Liebeskind, B.J., et al.: Complex homology and the evolution of nervous systems. Trends Ecol. Evol. 31(2), 127–135 (2016)CrossRefGoogle Scholar
  7. 7.
    Weiss, J.R., Smythe, W.D., Wenwen, L.: Science traceability. In: 2005 IEEE Aerospace Conference (2005)Google Scholar
  8. 8.
    Flores Martinez, C.L.: Convergent evolution and the search for biosignatures within the solar system and beyond. Acta Astronaut. 116, 394–402 (2015)CrossRefGoogle Scholar
  9. 9.
    Konstantinidis, K., et al.: A lander mission to probe subglacial water on Saturn׳s moon enceladus for life. Acta Astronaut. 106, 63–89 (2015)CrossRefGoogle Scholar
  10. 10.
    Moroz, L.L., et al.: The ctenophore genome and the evolutionary origins of neural systems. Nature 510(7503), 109–114 (2014)CrossRefGoogle Scholar
  11. 11.
    Albertin, C.B., et al.: The octopus genome and the evolution of cephalopod neural and morphological novelties. Nature 524(7564), 220–224 (2015)CrossRefGoogle Scholar
  12. 12.
    Kapheim, K.M., et al.: Social evolution. Genomic signatures of evolutionary transitions from solitary to group living. Science 348(6239), 1139–1143 (2015)CrossRefGoogle Scholar
  13. 13.
    Verschure, P.F.: Synthetic consciousness: the distributed adaptive control perspective. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371(1701), 20150448 (2016)CrossRefGoogle Scholar
  14. 14.
    Feinberg, T.E., Mallatt, J.: The evolutionary and genetic origins of consciousness in the Cambrian period over 500 million years ago. Frontiers Psychol. 4, 667 (2013)CrossRefGoogle Scholar
  15. 15.
    Bronfman, Z.Z., Ginsburg, S., Jablonka, E.: The transition to minimal consciousness through the evolution of associative learning. Frontiers Psychol. 7, 1954 (2016)CrossRefGoogle Scholar
  16. 16.
    Petralia, R.S., et al.: The diversity of spine synapses in animals. NeuroMol. Med. 18, 497 (2016)CrossRefGoogle Scholar
  17. 17.
    Greer, D.S.: Neurotransmitter fields. In: Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 19–28. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-74695-9_3 CrossRefGoogle Scholar
  18. 18.
    Greer, D.S., Tuceryan, M.: Neurotransmitter Field Theory: A Composite Continuous and Discrete Model 2010. Department of Computer and Information Science, Purdue University. http://cs.iupui.edu/~tuceryan/tech-reports/TR-CIS-0315-10.pdf
  19. 19.
    Greer, D.S.: Images as symbols: an associative neurotransmitter-field model of the brodmann areas. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds.) Transactions on Computational Science V. LNCS, vol. 5540, pp. 38–68. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02097-1_3 CrossRefGoogle Scholar
  20. 20.
    Aur, D.: From neuroelectrodynamics to thinking machines. Cogn. Comput. 4(1), 4–12 (2012)CrossRefGoogle Scholar
  21. 21.
    Sole, R.: The major synthetic evolutionary transitions. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371(1701), 20160175 (2016)CrossRefGoogle Scholar
  22. 22.
    Moses, M., et al.: Energy and time determine scaling in biological and computer designs. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371(1701), 20150446 (2016)CrossRefGoogle Scholar
  23. 23.
    Newman, S.A.: Form and function remixed: developmental physiology in the evolution of vertebrate body plans. J. Physiol. 592(11), 2403–2412 (2014)CrossRefGoogle Scholar
  24. 24.
    Baluška, F., Levin, M.: On having no head: cognition throughout biological systems. Frontiers Psychol. 7, 902 (2016)Google Scholar
  25. 25.
    Vincent, J.F., et al.: Biomimetics: its practice and theory. J. R. Soc. Interface 3(9), 471–482 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Biozentrum Grindel, University of HamburgHamburgGermany

Personalised recommendations