ArticleNature communications2016
Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
- Topological Environment in Genetic and Metabolic Networks.bioRxiv : the preprint server for biology · 2026Article
- Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.International journal of molecular sciences · 2025Review
- Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida.NPJ systems biology and applications · 2025Article
- Feed-forward loop improves the transient dynamics of an antithetic biological controller.Journal of the Royal Society, Interface · 2025Article
- Biotechnology systems engineering: preparing the next generation of bioengineers.Frontiers in systems biology · 2025Article
- MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.Computational and structural biotechnology journal · 2025Article
- Omics data analysis reveals the system-level constraint on cellular amino acid composition.Synthetic and systems biotechnology · 2024Article
- A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.BMC medical research methodology · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design.Synthetic and systems biotechnology · 2024Article
- Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.Frontiers in oncology · 2024Article
- A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models.Nature communications · 2023Article
- Supplementation of Sulfide or Acetate and 2-Mercaptoethane Sulfonate Restores Growth of theMicroorganisms · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- Multi-scale models of whole cells: progress and challenges.Frontiers in cell and developmental biology · 2023Review
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
- Multi-omic integration by machine learning (MIMaL).Bioinformatics (Oxford, England) · 2022Article
- Impaired glucose metabolism by deleting the operon of hydrogenase 2 in Escherichia coli.Archives of microbiology · 2022Article
- Understanding the Formation and Mechanism of Anticipatory Responses inInternational journal of molecular sciences · 2022Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery.
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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.