---
title: "Generative Neuromorphic Programming of Mammalian Cells Using ERNs and Biomorphic Neural Networks"
id: "biorxiv-0-generative-neuromorphic-programming-of-mammalian-cells"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-0-generative-neuromorphic-programming-of-mammalian-cells"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.18.752793v1?rss=1"
published_at: "2026-09-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Generative Neuromorphic Programming of Mammalian Cells Using ERNs and Biomorphic Neural Networks
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-0-generative-neuromorphic-programming-of-mammalian-cells
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.18.752793v1?rss=1)
- **Published At:** 2026-09-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors present a framework for **generative neuromorphic programming** that compiles desired analog behaviors directly into DNA for mammalian cells. - They construct composable regulatory devices from **RNA-targeting endoribonucleases (ERNs)** that provide signed weights and nonlinear activation to enable multi-layer analog computation in cells. - To predict circuit behavior, the team developed **biomorphic neural networks (BMNs)**, a compositional modeling architecture that mirrors biological processes; each basic process (transcription, translation, cleavage) is represented by a reusable neural block learned from whole-circuit behavior. - BMNs can be recomposed into novel architectures not present in training and often predict responses with errors comparable to experimental repeat variability. - The authors created a software package, the **biocompiler**, that jointly optimizes topology, parts, and weights to realize specified target behaviors and inverts the modeling flow to generate designs. - Challenged with three target analog behaviors, the biocompiler proposed three previously unseen architectures; all three were built and each reproduced its target behavior in mammalian cells in a single design pass without manual tuning. - This approach addresses the mismatch between current synthetic biology abstractions and the analog, densely interconnected nature of cellular regulation, and offers a systematic route to engineering cellular analog computation. - Funding sources include DARPA, NSF, NIH, ARIA, US Air Force Office of Scientific Research, United States Army Research Office, Imperial College/Schrödinger Scholarship, and others. - A subset of the authors declared a competing interest: three authors are co-founders of an early-stage company developing related technologies; other authors declared no competing interests.
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Wachter, [ View ORCID Profile](http://orcid.org/0009-0004-2071-7356)Shuo-Hsiu Kuo, Andrew Moorman, [ View ORCID Profile](http://orcid.org/0000-0002-2906-4724)Wenlong Xu, [ View ORCID Profile](http://orcid.org/0000-0002-0783-9779)Eric Palanques Tost, Andres Buritica Monroy, [ View ORCID Profile](http://orcid.org/0000-0002-7141-2657)Calin Belta, [ View ORCID Profile](http://orcid.org/0000-0003-0396-2443)Ron Weiss doi: https://doi.org/10.64898/2026.09.18.752793 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. Jean Disset 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jean%2BDisset%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Disset%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJean%2BDisset%2B) * [ORCID record for Jean Disset](http://orcid.org/0009-0008-7047-2026 "Open in new tab") Charles Van De Mark 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Charles%2BVan%2BDe%2BMark%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Van%20De%20Mark%20C&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ACharles%2BVan%2BDe%2BMark%2B) Georg K.A. Wachter 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Georg%2BK.A.%2BWachter%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wachter%20GK&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AGeorg%2BK.A.%2BWachter%2B) * [ORCID record for Georg K.A. Wachter](http://orcid.org/0000-0002-5112-1836 "Open in new tab") Shuo-Hsiu Kuo 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Shuo-Hsiu%2BKuo%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Kuo%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AShuo-Hsiu%2BKuo%2B) * [ORCID record for Shuo-Hsiu Kuo](http://orcid.org/0009-0004-2071-7356 "Open in new tab") Andrew Moorman 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Andrew%2BMoorman%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Moorman%20A&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AAndrew%2BMoorman%2B) Wenlong Xu 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Wenlong%2BXu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Xu%20W&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AWenlong%2BXu%2B) * [ORCID record for Wenlong Xu](http://orcid.org/0000-0002-2906-4724 "Open in new tab") Eric Palanques Tost 2 Boston University; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Eric%2BPalanques%2BTost%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Palanques%20Tost%20E&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AEric%2BPalanques%2BTost%2B) * [ORCID record for Eric Palanques Tost](http://orcid.org/0000-0002-0783-9779 "Open in new tab") Andres Buritica Monroy 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Andres%2BBuritica%2BMonroy%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Buritica%20Monroy%20A&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AAndres%2BBuritica%2BMonroy%2B) Calin Belta 3 University of Maryland * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Calin%2BBelta%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Belta%20C&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ACalin%2BBelta%2B) * [ORCID record for Calin Belta](http://orcid.org/0000-0002-7141-2657 "Open in new tab") Ron Weiss 1 Massachusetts Institute of Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Ron%2BWeiss%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Weiss%20R&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ARon%2BWeiss%2B) * [ORCID record for Ron Weiss](http://orcid.org/0000-0003-0396-2443 "Open in new tab") * For correspondence: rweiss@mit.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.09.18.752793v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5800089/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.09.18.752793v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5800089/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.09.18.752793v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5800089/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.09.18.752793v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5800089/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.09.18.752793v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5800089/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Cells make decisions through densely interconnected regulatory networks that compute graded, analog responses. Synthetic biology has made cells programmable, but the field's dominant abstractions poorly match the analog nature of cells. Designing analog circuits that work in cells is challenging: predicting how components combine remains difficult, and the vast combinatorial space of parts, weights and topologies exceeds what experiments can explore. Here we establish generative programming of mammalian cells through a neuromorphic framework that compiles desired behavior into DNA. Composable regulatory devices built from RNA-targeting endoribonucleases (ERNs) supply signed weights and nonlinear activation, enabling multi-layer circuits with diverse multi-input analog responses. To predict and design such circuits, we develop biomorphic neural networks (BMNs): a compositional architecture that mirrors biological interactions, in which each basic process (transcription, translation, cleavage) is captured by a reusable neural block learned from the behavior of whole circuits that contain it. Recomposed into architectures absent from training, these models predict responses with errors often comparable to variability between experimental repeats. Inverting this process, our software package, the biocompiler, jointly optimizes topology, parts, and weights to realize specified target behaviors. Challenged with three target behaviors, the biocompiler proposed three previously unseen architectures; we built all three, and each reproduced its target behavior in mammalian cells in a single design pass without manual tuning. Generative neuromorphic programming thus offers a systematic route to engineering the analog computation native to living cells. ### Competing Interest Statement J.D., G.K.A.W., and R.W. are co-founders in an early-stage company founded to develop technologies related to this work. The remaining authors declare no competing interests. ## Funder Information Declared Imperial College London, https://ror.org/041kmwe10, Schrödinger Scholarship Defense Advanced Research Projects Agency, https://ror.org/02caytj08 U.S. National Science Foundation, https://ror.org/021nxhr62 U.S. National Institute of Health Advanced Research and Innovation Agency (ARIA) United States Air Force Office of Scientific Research, https://ror.org/011e9bt93 United States Army Research Office, https://ror.org/05epdh915 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY 4.0 International license](http://creativecommons.org/licenses/by/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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