A Computational Framework for Autonomous Self-repair Systems

  • Tran Nguyen Minh-ThaiEmail author
  • Jagannath Aryal
  • Sandhya Samarasinghe
  • Michael Levin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)


This paper describes a novel computational framework for damage detection and regeneration in an artificial tissue of cells resembling living systems. We represent the tissue as an Auto-Associative Neural Network (AANN) consisting of a single layer of perceptron neurons (cells) with local feedback loops. This allows the system to recognise its state and geometry in a form of collective intelligence. Signalling entropy is used as a global (emergent) property characterising the state of the system. The repair system has two submodels - global sensing and local sensing. Global sensing is used to sense the change in whole system state and detect general damage region based on system entropy change. Then, local sensing is applied with AANN to find the exact damage locations and repair the damage. The results show that the method allows robust and efficient damage detection and accurate regeneration.


Self-repair Multi-cellular structures Regeneration Auto-Associative Neural Network Perceptron Signalling entropy Modeling 



Authors gratefully acknowledge support of the the following: TNM - Doctoral Scholarship from VIED, Vietnam; J.A.- Sabbatical at Lincoln University, New Zealand; S.S.- Lincoln University Research Fund; M.L.- DARPA (#HR0011-18-2-0022), the Allen Discovery Center award from the Paul G Allen Frontiers Group, and the Templeton World Charity Foundation (TWCF0089/AB55 and TWCF0140).


  1. 1.
    Arbuckle, D.J., Requicha, A.A.G.: Self-assembly and self-repair of arbitrary shapes by a swarm of reactive robots: algorithms and simulations. Auton. Robots 28(2), 197–211 (2010)CrossRefGoogle Scholar
  2. 2.
    Bessonov, N., Levin, M., Morozova, N., Reinberg, N., Tosenberger, A., Volpert, V.: On a model of pattern regeneration based on cell memory. PLOS ONE 10(2), e0118091 (2015)CrossRefGoogle Scholar
  3. 3.
    De, A., Chakravarthy, V.S., Levin, M.: A computational model of planarian regeneration. Int. J. Parallel Emerg. Distrib. Syst. 32(4), 331–347 (2017)CrossRefGoogle Scholar
  4. 4.
    Dinsmore, C.E.: A history of regeneration research: milestones in the evolution of a science. J. Hist. Biol. 26(1), 156–158 (1993)Google Scholar
  5. 5.
    Edwards, C.: Self-repair techniques point to robots that design themselves. Commun. ACM 59(2), 15–17 (2016)CrossRefGoogle Scholar
  6. 6.
    Ferreira, G.B.S., Smiley, M., Scheutz, M., Levin, M.: Dynamic structure discovery and repair for 3D cell assemblages. In: Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFEXV) (2016)Google Scholar
  7. 7.
    Frei, R., McWilliam, R., Derrick, B., Purvis, A., Tiwari, A., Di Marzo Serugendo, G.: Self-healing and self-repairing technologies. Int. J. Adv. Manuf. Technol. 69(5), 1033–1061 (2013)CrossRefGoogle Scholar
  8. 8.
    Gerlee, P., Basanta, D., Anderson, A.R.A.: Evolving homeostatic tissue using genetic algorithms. Prog. Biophys. Mole. Biol. 106(2), 414–425 (2011)CrossRefGoogle Scholar
  9. 9.
    Levin, M.: The wisdom of the body: future techniques and approaches to morphogenetic fields in regenerative medicine, developmental biology and cancer. Regen. Med. 6(6), 667–673 (2011). pMID: 22050517CrossRefGoogle Scholar
  10. 10.
    Levin, M.: Morphogenetic fields in embryogenesis, regeneration, and cancer: non-local control of complex patterning. Biosystems 109(3), 243–261 (2012). Biological Morphogenesis: Theory and ComputationCrossRefGoogle Scholar
  11. 11.
    Lobo, D., Solano, M., Bubenik, G.A., Levin, M.: A linear-encoding model explains the variability of the target morphology in regeneration. J. Roy. Soc. Interface 11(92), 20130918 (2014)CrossRefGoogle Scholar
  12. 12.
    Mustard, J., Levin, M.: Bioelectrical mechanisms for programming growth and form: taming physiological networks for soft body robotics. Soft Robot. 1(3), 169–191 (2014). Biological Morphogenesis: Theory and ComputationCrossRefGoogle Scholar
  13. 13.
    Rubenstein, M., Sai, Y., Chuong, C., Shen, W.: Regenerative patterning in swarm robots: mutual benefits of research in robotics and stem cell biology. Int. J. Dev. Biol. 53(5–6), 869–881 (2009)CrossRefGoogle Scholar
  14. 14.
    Teschendorff, A.E., Enver, T.: Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat. Commun. 8, 15599 (2017)CrossRefGoogle Scholar
  15. 15.
    Tosenberger, A., Bessonov, N., Levin, M., Reinberg, N., Volpert, V., Morozova, N.: A conceptual model of morphogenesis and regeneration. Acta Biotheor. 63(3), 283–294 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tran Nguyen Minh-Thai
    • 1
    • 4
    Email author
  • Jagannath Aryal
    • 2
  • Sandhya Samarasinghe
    • 1
  • Michael Levin
    • 3
  1. 1.Complex Systems, Big Data and Informatics Initiative (CSBII)Lincoln UniversityCanterburyNew Zealand
  2. 2.Discipline of Geography and Spatial SciencesUniversity of TasmaniaHobartAustralia
  3. 3.Allen Discovery CenterTufts UniversityBostonUSA
  4. 4.College of ICTCan Tho UniversityCan Tho CityVietnam

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