Skip to main content
SpringerLink
Log in

Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI

  • Conference paper
  • First Online:
Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13483))

Abstract

Synthetic Biology and Artificial Intelligence are two relevant fields in modern science. Together with Robotics, they have either practical scopes, or can be used for modeling organisms’ features and behaviors. The recent Synthetic Biology advancements in the so-called “synthetic cells” area allow the construction of cell-like systems with non trivial complexity, paving the way to a novel direction: the realization of chemical artificial intelligence. One possible path foresees the “installation” of chemical versions of artificial intelligence devices in synthetic cells. In this article we present this new scenario, focusing on chemical mechanisms and systems that are topologically organized as neural networks, highlighting their possible role in synthetic cell biotechnology. Future directions, challenges and requirements, as well as epistemological interpretations are also briefly discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Because current SCs still lie at a far lower complexity level when compared with living organisms (even the simplest ones), by “intelligent” SCs we mean systems that most resemble machines rather than organisms. It seems appropriate, for the moment, referring to SC “intelligence” in this narrow sense. See also Sect. 2.1.

  2. 2.

    Experimentally, it will not be easy to build SCs that produce, thanks to their internal metabolic processes, all components of the CNN. To start with, however, the SCs could be endowed with required components, according to the usual shortcut; or it could produce just a limited sub-set of CNN components.

References

  1. Simon, H.A.: The Sciences of the Artificial. MIT Press, Cambridge MA (1996)

    Google Scholar 

  2. Cordeschi, R.: The Discovery of the Artificial. Behavior, Mind and Machines Before and Beyond Cybernetics. Springer, Netherlands (2002)

    Google Scholar 

  3. Damiano, L., Stano, P.: A wetware embodied AI? towards an autopoietic organizational approach grounded in synthetic biology’. Front. Bioeng. Biotech. 9, 724023 (2021)

    Article  Google Scholar 

  4. Schwille, P., et al.: MaxSynBio: avenues towards creating cells from the bottom up. Angew. Chem. Int. Ed. Engl. 57, 13382–13392 (2018)

    Google Scholar 

  5. Frischmon, C., Sorenson, C., Winikoff, M., Adamala, K.P.: Build-a-Cell: Engineering a Synthetic Cell Community. Life 11, 1176 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  6. Cronin, L., et al.: The imitation game - a computational chemical approach to recognizing life’. Nature Biotech. 24, 1203–1206 (2006)

    Google Scholar 

  7. Damiano, L., Stano, P.: Synthetic biology and artificial intelligence. grounding a cross-disciplinary approach to the synthetic exploration of (Embodied) cognition. Complex Syst. 27, 199–228 (2018)

    Google Scholar 

  8. Damiano, L., Stano, P.: On the ‘Life-Likeness’ of synthetic cells. Front. Bioeng. Biotech. 8, 953 (2020)

    Article  Google Scholar 

  9. Oberholzer, T., Wick, R., Luisi, P.L., Biebricher, C.K.: Enzymatic RNA replication in self- reproducing vesicles: an approach to a minimal cell. Biochem. Biophys. Res. Comm. 207, 250–257 (1995)

    Article  CAS  PubMed  Google Scholar 

  10. Oberholzer, T., Albrizio, M., Luisi, P.L.: Polymerase chain reaction in liposomes. Chem. Biol. 2, 677–682 (1995)

    Article  CAS  PubMed  Google Scholar 

  11. Oberholzer, T., Nierhaus, K.H., Luisi, P.L.: Protein expression in liposomes. Biochem. Biophys. Res. Comm. 261, 238–241 (1999)

    Article  CAS  PubMed  Google Scholar 

  12. Szostak, J.W., Bartel, D.P., Luisi, P.L.: Synthesizing life. Nature 409, 387–390 (2001)

    Article  CAS  PubMed  Google Scholar 

  13. Stano, P.: Is research on ‘synthetic cells’ moving to the next level? Life 9, 3 (2019)

    Article  CAS  Google Scholar 

  14. Abil, Z., Danelon, C.: Roadmap to Building a Cell: An Evolutionary Approach. Front. Bioeng. Biotech. 8, 927 (2020)

    Article  Google Scholar 

  15. Varela, F.J., Maturana, H., Uribe, R.: Autopoiesis: the organization of living systems, its characterization and a model. BioSystems 5, 187–196 (1974)

    Article  CAS  Google Scholar 

  16. Stano, P., Luisi, P.L.: Achievements and open questions in the self-reproduction of vesicles and synthetic minimal cells. Chem. Commun. 46, 3639–3653 (2010)

    Article  CAS  Google Scholar 

  17. Berhanu, S., Ueda, T., Kuruma, Y.: Artificial photosynthetic cell producing energy for protein synthesis. Nat. Commun. 10, 1325 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  18. Altamura, E., et al.: Chromatophores efficiently promote light-driven ATP synthesis and DNA transcription inside hybrid multicompartment artificial cells. Proc. Natl. Acad. Sci. USA 118, e2012170118 (2021)

    Google Scholar 

  19. Chang, T.M.: Applications of artificial cells in medicine and biotechnology. Biomater. Artif. Cells Artif. Organs 15, 1–20 (1987)

    Article  PubMed  Google Scholar 

  20. Leduc, P.R., et al.: Towards an in vivo biologically inspired nanofactory. Nat. Nanotechnol. 2, 3–7 (2007)

    Google Scholar 

  21. Varela, F.J.: Principles of Biological Autonomy. Elsevier North Holland, New York (1979)

    Google Scholar 

  22. Wooldridge, M.: The Road to Conscious Machines. The Story of AI. Penguin Books, London (2020)

    Google Scholar 

  23. Okamoto, M., Sakai, T., Hayashi, K.: Switching mechanism of a cyclic enzyme system: role as a ‘chemical diode’. BioSystems 21, 1–11 (1987)

    Article  CAS  PubMed  Google Scholar 

  24. Hjelmfelt, A., Weinberger, E.D., Ross, J.: Chemical implementation of neural networks and Turing machines. Proc. Natl. Acad. Sci. USA 88, 10983–10987 (1991)

    Google Scholar 

  25. Lakin, M.R., Stefanovic, D.: Supervised learning in adaptive DNA strand displacement networks. ACS Synth. Biol. 5, 885–897 (2016)

    Article  CAS  PubMed  Google Scholar 

  26. Fernando, C.T., et al.: Molecular circuits for associative learning in single-celled organisms. J. R. Soc. Interface 6, 463–469 (2009)

    Google Scholar 

  27. Kim, J., Hopfield, J., Winfree, E.: Neural network computation by in vitro transcriptional circuits. In: Saul, L., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17 (NIPS 2004), pp. 681–688. Vancouver, Canada (2004)

    Google Scholar 

  28. Blount, D., Banda, P., Teuscher, C., Stefanovic, D.: Feedforward chemical neural network: an in silico chemical system that learns XOR. Artif. Life 23, 295–317 (2017)

    Article  PubMed  Google Scholar 

  29. Hellingwerf, K.J., Postma, P.W., Tommassen, J., Westerhoff, H.V.: Signal transduction in bacteria: phospho-neural network(s) in Escherichia coli? FEMS Microbiol. Rev. 16, 309–321 (1995)

    Article  CAS  PubMed  Google Scholar 

  30. Laub, M.T., Goulian, M.: Specificity in two-component signal transduction pathways. Annu. Rev. Genet. 41, 121–145 (2007)

    Article  CAS  PubMed  Google Scholar 

  31. Agrawal, R., Sahoo, B.K., Saini, D.K.: Cross-talk and specificity in two-component signal transduction pathways. Future Microbiol. 11, 685–697 (2016)

    Article  CAS  PubMed  Google Scholar 

  32. Skerker, J.M., Prasol, M.S., Perchuk, B.S., Biondi, E.G., Laub, M.T.: Two-component signal transduction pathways regulating growth and cell cycle progression in a bacterium: a system-level analysis. PLoS Biol. 3, e334 (2005)

    Article  PubMed  PubMed Central  Google Scholar 

  33. Magarini, M., Stano, P.: Synthetic cells engaged in molecular communication: an opportunity for modelling shannon- and semantic-information in the chemical domain. Front. Commun. Networks 2, 48 (2021)

    Article  Google Scholar 

  34. MacKay, D.M.: Information. Mechanism and Meaning. MIT Press, Cambridge MA (1969)

    Google Scholar 

  35. Kolchinsky, A., Wolpert, D.H.: Semantic information, autonomous agency and non-equilibrium statistical physics. Interface Focus 8, 20180041 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  36. Damiano, L., Stano, P.: a wetware embodied AI? towards an autopoietic organizational approach grounded in synthetic biology. Front Bioeng. Biotech. 9, 873 (2021)

    Article  Google Scholar 

Download references

Acknowledgments

I am indebted to Luisa Damiano (IULM-Milan, Italy) and to Maurizio Magarini (Politecnico di Milano, Milan, Italy) for inspiring discussions.

Author information

Authors and Affiliations

  1. Department of Biological and Environmental Sciences and Technologies (DiSTeBA), University of Salento, 73100, Lecce, Italy

    Pasquale Stano

Authors
  1. Pasquale Stano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pasquale Stano .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Davide Chicco

  2. Consiglio Nazionale delle Ricerche, Avellino, Italy

    Angelo Facchiano

  3. Università di Padova, Padua, Italy

    Erica Tavazzi

  4. Università di Padova, Padua, Italy

    Enrico Longato

  5. Università di Padova, Padua, Italy

    Martina Vettoretti

  6. Politecnico di Milano, Milan, Italy

    Anna Bernasconi

  7. Università di Verona, Verona, Italy

    Simone Avesani

  8. Università di Bergamo, Bergamo, Italy

    Paolo Cazzaniga

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stano, P. (2022). Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20837-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20836-2

  • Online ISBN: 978-3-031-20837-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation