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

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2021)

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

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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.

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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.

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Acknowledgments

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

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Authors and Affiliations

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

    Pasquale Stano

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  1. Pasquale Stano
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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

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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

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

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  • Online ISBN: 978-3-031-20837-9

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