ArticlePLoS computational biology2023
Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Deciphering microbial community dynamics using cross-sectional data-informed NeuralODE.Microbiome · 2026Article
- Designing fiber-gut microbiome interactions with active learning.Nature chemical biology · 2026Article
- A Guide to Bayesian Optimization in Bioprocess Engineering.Biotechnology and bioengineering · 2026Review
- Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Statistical design of a synthetic microbiome that suppresses diverse gut pathogens.bioRxiv : the preprint server for biology · 2025Article
- Systems biology and microbiome innovations for personalized diabetic retinopathy management.NPJ systems biology and applications · 2025Review
- Predicting microbial community structure and temporal dynamics by using graph neural network models.Nature communications · 2025Article
- Enhanced l-serine synthesis inSynthetic and systems biotechnology · 2025Article
- Bayesian Optimization in Bioprocess Engineering-Where Do We Stand Today?Biotechnology and bioengineering · 2025Review
- Decoding the role of the arginine dihydrolase pathway in shaping human gut community assembly and health-relevant metabolites.Cell systems · 2025Article
- Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information.Nature communications · 2025Article
- A data-driven modeling framework for mapping genotypes to synthetic microbial community functions.bioRxiv : the preprint server for biology · 2025Article
- Oral pathogens meet the gut microbiome: new mechanistic insights on systemic disease.Frontiers in cellular and infection microbiology · 2025Review
- Proteomic study ofFrontiers in microbiology · 2025Article
- Wheels turning: CHO cell modeling moves into a digital biomanufacturing era: Subtitle: CHO Metabolic Modeling.Computational and structural biotechnology journal · 2025Review
- The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations.Microorganisms · 2024Review
- Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes.Nature communications · 2024Article
- Gradient boosted regression as a tool to reveal key drivers of temporal dynamics in a synthetic yeast community.FEMS microbiology ecology · 2024Article
- mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics.PLoS computational biology · 2024Article
- Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro.ACS synthetic biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically-constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it can outperform existing machine learning methods in the prediction of certain experimentally measured species abundance and metabolite concentrations. Further, we present a closed-loop, Bayesian experimental design algorithm to guide data collection by selecting experimental conditions that simultaneously maximize information gain and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions.
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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.