ReviewProteomics2020
Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models.
Review in Proteomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Polyphenism Through RNA Chemical Modifications.Molecular ecology · 2026Review
- A systems-level view of metabolic impact of copper and methane valorization potential inMetabolic engineering communications · 2026Article
- Novel African American Colorectal CancerHuman mutation · 2026Article
- Metabolic Engineering of Terpenoid Biosynthesis in Medicinal Plants: From Genomic Insights to Biotechnological Applications.Current issues in molecular biology · 2025Review
- Personalized Genome-Scale Modeling Reveals Metabolic Perturbations in Fibroblasts of Methylmalonic Aciduria Patients.Journal of inherited metabolic disease · 2025Article
- Engineering Useful Microbial Species for Pharmaceutical Applications.Microorganisms · 2025Review
- Digital psychiatry: concepts, framework, and implications.Frontiers in psychiatry · 2025Review
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Basal State Calibration of a Chemical Reaction Network Model for Autophagy.International journal of molecular sciences · 2024Article
- Enhancing Colorectal Cancer Immunotherapy: The Pivotal Role of Ferroptosis in Modulating the Tumor Microenvironment.International journal of molecular sciences · 2024Review
- Life at the borderlands: microbiomes of interfaces critical to One Health.FEMS microbiology reviews · 2024Review
- Probing interspecies metabolic interactions within a synthetic binary microbiome using genome-scale modeling.Microbiome research reports · 2024Article
- A High-Quality Genome-Scale Model forACS synthetic biology · 2023Article
- A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.PNAS nexus · 2022Article
- Review
- Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms.Metabolites · 2021Review
- Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies.Frontiers in microbiology · 2021Review
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering.Frontiers in bioengineering and biotechnology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.
Indexed as
Identifiers
What OpenQuestion holds
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.