ArticleNPJ systems biology and applications2018
A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.
Article in NPJ systems biology and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
- AI-empowered human microbiome research.Gut · 2026Review
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- Removal of Cr (VI) from wastewater using bentonite as adsorbent: Experimental and Machine Learning investigations.Scientific reports · 2026Article
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation.Current environmental health reports · 2025Review
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- Integrating artificial intelligence with kinetic studies for Cr(vi) removal using young durian fruit biochar: a random forest regressor approach.RSC advances · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Sensitivity analysis on protein-protein interaction networks through deep graph networks.BMC bioinformatics · 2025Article
- Article
- A Guide to Metabolic Network Modeling for Plant Biology.Plants (Basel, Switzerland) · 2025Review
- A decade of advances in human gut microbiome-derived biotherapeutics.Nature microbiology · 2025Review
- Decoding the mystery: AI-assisted bioinformatics and functional genomics technologies in medicinal plants.Frontiers in plant science · 2025Article
- Metabolic Fluxes Using Deep Learning Based on Enzyme Variations:International journal of molecular sciences · 2024Article
- Review
- Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises.BMC biology · 2024Article
- Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli.NPJ systems biology and applications · 2024Article
- Enhancing Withanolide Production in thePlants (Basel, Switzerland) · 2024Review
- Machine learning: an advancement in biochemical engineering.Biotechnology letters · 2024Review
22 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.
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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.