Recurrent neural chemical reaction networks that approximate arbitrary dynamics

[2406.03456] Recurrent neural chemical reaction networks that approximate arbitrary dynamics Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Molecular Networks arXiv:2406.03456 (q-bio) [Submitted on 5 Jun 2024 ( v1 ), last revised 5 Jun 2025 (this version, v2)] Title: Recurrent neural chemical reaction networks that approximate arbitrary dynamics Authors: Alexander Dack , Benjamin Qureshi , Thomas E. Ouldridge , Tomislav Plesa View a PDF of the paper titled Recurrent neural chemical reaction networks that approximate arbitrary dynamics, by Alexander Dack and 3 other authors View PDF HTML (experimental) Abstract: Many important phenomena in biochemistry and biology exploit dynamical features such as multi-stability, oscillations, and chaos. Construction of novel chemical systems with such rich dynamics is a challenging problem central to the fields of synthetic biology and molecular nanotechnology. In this paper, we address this problem by putting forward a molecular version of a recurrent artificial neural network, which we call recurrent neural chemical reaction network (RNCRN). The RNCRN uses a modular architecture - a network of chemical neurons - to approximate arbitrary dynamics. We first prove that with sufficiently many chemical neurons and suitably fast reactions, the RNCRN can be systematically trained to achieve any dynamics. RNCRNs with relatively small number of chemical neurons and a moderate range of reaction rates are then trained to display a variety of biologically-important dynamical features. We also demonstrate that such RNCRNs are experimentally implementable with DNA-strand-displacement technologies. Comments: Major revision: rewritten Introduction and Discussion; added DNA implementation; and added robustness investigation Subjects: Molecular Networks (q-bio.MN) ; Dynamical Systems (math.DS) Cite as: arXiv:2406.03456 [q-bio.MN] (or arXiv:2406.03456v2 [q-bio.MN] for this version) https://doi.org/10.48550/arXiv.2406.03456 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1016/j.cels.2026.101572 Focus to learn more DOI(s) linking to related resources Submission history From: Alexander Dack [ view email ] [v1] Wed, 5 Jun 2024 17:00:16 UTC (412 KB) [v2] Thu, 5 Jun 2025 09:50:10 UTC (850 KB) Full-text links: Access Paper: View a PDF of the paper titled Recurrent neural chemical reaction networks that approximate arbitrary dynamics, by Alexander Dack and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.MN < prev | next > new | recent | 2024-06 Change to browse by: math math.DS q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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