ArticleNature communications2019
Metabolic perceptrons for neural computing in biological systems.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
40 citing papers in PubMed.
- Engineering Basal Cognition: Minimal Genetic Circuits for Habituation, Sensitization, and Massed-Spaced Learning.ACS synthetic biology · 2026Article
- A recursive enzymatic competition network capable of multitask molecular information processing.Nature chemistry · 2026Article
- RetroRules 2026: an expanded database combining biochemical and organic reaction templates for pathway discovery.Nucleic acids research · 2026Article
- Reconstituting alternative life using the test-bed of cell-free systems.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Supervised learning in DNA neural networks.Nature · 2025Article
- Why cellular computations challenge our design principles.Seminars in cell & developmental biology · 2025Review
- Self-evolving cognitive substrates through metabolic data processing and recursive self-representation with autonomous memory prioritization mechanisms.Frontiers in artificial intelligence · 2025Article
- How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression.Applied sciences (Basel, Switzerland) · 2024Article
- Engineering Sequestration-Based Biomolecular Classifiers with Shared Resources.ACS synthetic biology · 2024Article
- A Microbial Cocaine Bioreporter.Sensors (Basel, Switzerland) · 2024Article
- SemiSynBio: A new era for neuromorphic computing.Synthetic and systems biotechnology · 2024Review
- Article
- Ground Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks.Applied sciences (Basel, Switzerland) · 2024Article
- Biocomputation: Moving Beyond Turing with Living Cellular Computers.Communications of the ACM · 2024Article
- Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems.Chemical reviews · 2024Review
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Intelligent computation in cancer gene therapy.Frontiers in genetics · 2024Review
- Article
- Customizing cellular signal processing by synthetic multi-level regulatory circuits.Nature communications · 2023Review
- Revealing gene regulation-based neural network computing in bacteria.Biophysical reports · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.