ArticleBMC bioinformatics2019
Adapting machine-learning algorithms to design gene circuits.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- GenAI-Net: A generative AI framework for automated biomolecular network design.Science advances · 2026Article
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- GCAD: A Computational Framework for Mammalian Genetic Program Computer-Aided Design.ACS synthetic biology · 2026Article
- Aging as a Loss of Goal-Directedness: An Evolutionary Simulation and Analysis Unifying Regeneration with Anatomical Rejuvenation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- GCAD: a Computational Framework for Mammalian Genetic Program Computer-Aided Design.bioRxiv : the preprint server for biology · 2025Article
- Genetic "expiry-date" circuits control lifespan of synthetic scavenger bacteria for safe bioremediation.Nucleic acids research · 2025Article
- Article
- AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.eLife · 2025Article
- Emerging trends in genome integration tools for precision engineering of diverse bacterial species.Synthetic biology (Oxford, England) · 2025Review
- Review on Advancement of AI in Synthetic Biology.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Feed-forward loop improves the transient dynamics of an antithetic biological controller.Journal of the Royal Society, Interface · 2025Article
- Computational Synthetic Biology Enabled through JAX: A Showcase.ACS synthetic biology · 2024Article
- Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales.Entropy (Basel, Switzerland) · 2024Article
- Automatic design of gene regulatory mechanisms for spatial pattern formation.NPJ systems biology and applications · 2024Article
- Applications of artificial intelligence and machine learning in dynamic pathway engineering.Biochemical Society transactions · 2023Review
- Bayesian Optimization for Design of Multiscale Biological Circuits.ACS synthetic biology · 2023Article
- Optimizing differential equations to fit data and predict outcomes.Ecology and evolution · 2023Article
- Optimization of Transcription Factor Genetic Circuits.Biology · 2022Article
- Sequence-to-function deep learning frameworks for engineered riboregulators.Nature communications · 2020Article
- Predictive biology: modelling, understanding and harnessing microbial complexity.Nature reviews. Microbiology · 2020Review
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1 author.
Funding
Abstract
backgroundGene circuits are important in many aspects of biology, and perform a wide variety of different functions. For example, some circuits oscillate (e.g. the cell cycle), some are bistable (e.g. as cells differentiate), some respond sharply to environmental signals (e.g. ultrasensitivity), and some pattern multicellular tissues (e.g. Turing's model). Often, one starts from a given circuit, and using simulations, asks what functions it can perform. Here we want to do the opposite: starting from a prescribed function, can we find a circuit that executes this function? Whilst simple in principle, this task is challenging from a computational perspective, since gene circuit models are complex systems with many parameters. In this work, we adapted machine-learning algorithms to significantly accelerate gene circuit discovery.
resultsWe use gradient-descent optimization algorithms from machine learning to rapidly screen and design gene circuits. With this approach, we found that we could rapidly design circuits capable of executing a range of different functions, including those that: (1) recapitulate important in vivo phenomena, such as oscillators, and (2) perform complex tasks for synthetic biology, such as counting noisy biological events.
conclusionsOur computational pipeline will facilitate the systematic study of natural circuits in a range of contexts, and allow the automatic design of circuits for synthetic biology. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.
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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.