ArticlePLoS computational biology2020
Deep reinforcement learning for the control of microbial co-cultures in bioreactors.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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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
37 citing papers in PubMed.
- From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode.PLoS computational biology · 2026Article
- Dynamic, Unconstrained Optimization of Secreted Enzyme Production in Fed-Batch Fermentation Using Reinforcement Learning.Biotechnology and bioengineering · 2026Article
- Synthetic Microbial Community Biosensors: From Engineered Ecosystems to Modular Detection Platforms with AI-Driven Intelligence.Biosensors · 2026Review
- Artificial intelligence for food innovation.Nature food · 2026Review
- Artificial Intelligence for Microbial Isolation and Cultivation: Progress and Challenges.Microorganisms · 2026Review
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights.Frontiers in microbiology · 2026Review
- Directing microbial co-culture composition using cybernetic control.Cell reports methods · 2025Article
- From genetic constraints to process hurdles: A review ofCurrent research in food science · 2025Review
- Biotechnology systems engineering: preparing the next generation of bioengineers.Frontiers in systems biology · 2025Article
- Machine learning: an advancement in biochemical engineering.Biotechnology letters · 2024Review
- Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- A molecular toolkit of cross-feeding strains for engineering synthetic yeast communities.Nature microbiology · 2024Article
- Modularized Design and Construction of Tunable Microbial Consortia with Flexible Topologies.ACS synthetic biology · 2024Article
- Steering and controlling evolution - from bioengineering to fighting pathogens.Nature reviews. Genetics · 2023Review
- Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.Nature communications · 2023Article
- Structural Identifiability and Observability of Microbial Community Models.Bioengineering (Basel, Switzerland) · 2023Article
- Biotechnological production of omega-3 fatty acids: current status and future perspectives.Frontiers in microbiology · 2023Review
- Artificial intelligence in microbial natural product drug discovery: current and emerging role.Natural product reports · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures within continuous bioreactors. We confirm that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.
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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.