ArticleJournal of the Royal Society, Interface2017
Synthetic associative learning in engineered multicellular consortia.
Article in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Burden-aware feedback control of microbial consortia.Nature communications · 2026Article
- Engineering Basal Cognition: Minimal Genetic Circuits for Habituation, Sensitization, and Massed-Spaced Learning.ACS synthetic biology · 2026Article
- Basal Xenobot transcriptomics reveals changes and novel control modality in cells freed from organismal influence.Communications biology · 2025Article
- GINS2 promotes oral squamous cell carcinoma progression and immune evasion by recruiting PD-L1Frontiers in immunology · 2025Article
- Open problems in synthetic multicellularity.NPJ systems biology and applications · 2024Article
- Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine.Patterns (New York, N.Y.) · 2023Review
- Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology.Cellular and molecular life sciences : CMLS · 2023Review
- Closing the loop on morphogenesis: a mathematical model of morphogenesis by closed-loop reaction-diffusion.Frontiers in cell and developmental biology · 2023Article
- Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions.International journal of molecular sciences · 2022Article
- An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation.Sensors (Basel, Switzerland) · 2022Article
- Programming Molecular Systems To Emulate a Learning Spiking Neuron.ACS synthetic biology · 2022Article
- Synthetic living machines: A new window on life.iScience · 2021Review
- Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks.iScience · 2021Article
- Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering.Communicative & integrative biology · 2021Review
- From Microbial Communities to Distributed Computing Systems.Frontiers in bioengineering and biotechnology · 2020Review
- Pathways to cellular supremacy in biocomputing.Nature communications · 2019Review
- ParAlleL: A Novel Population-Based Approach to Biological Logic Gates.Frontiers in bioengineering and biotechnology · 2019Article
- Booting up the organism during development: Pre-behavioral functions of the vertebrate brain in guiding body morphogenesis.Communicative & integrative biology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Associative learning (AL) is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was recognized early as a general trait of complex multicellular organisms but is also found in 'simpler' ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell, and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display AL responses to two classes of stimuli and displaying long- and short-term memory (i.e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.
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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.