ReviewBioMed research international2019
Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine.
Review in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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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
43 citing papers in PubMed, 90 citations in OpenAlex.
- Multi-omics graph attention network reveals neuro-immune-tumor pathways in breast cancer liver depression syndrome.Frontiers in immunology · 2026Trial
- Type 2 Diabetes Mellitus as a Multisystem Disease: From Insulin Resistance to Organ Crosstalk-A Narrative Review.Biomedicines · 2026Review
- Novel African American Colorectal CancerHuman mutation · 2026Article
- Emerging pathological mechanisms of Alzheimer's disease pathogenesis: from neuroimmune interactions to intercellular communication.Frontiers in aging neuroscience · 2026Review
- Artificial Intelligence with Robotics for Metabolic Rehabilitation and Enhanced Patient Recovery in Critical Care.Research (Washington, D.C.) · 2026Review
- How should we teach homeostasis? Filling the gaps and envisioning the future.Frontiers in physiology · 2026Review
- ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.iScience · 2025Article
- Review
- Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.Machine learning · 2025Article
- Intratumoral Heterogeneity and Metabolic Cross-Feeding in a Three-Dimensional Breast Cancer Culture: An In Silico Perspective.International journal of molecular sciences · 2024Article
- Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.Cell reports methods · 2024Article
- Rational strain design with minimal phenotype perturbation.Nature communications · 2024Article
- Multiomics Research: Principles and Challenges in Integrated Analysis.Biodesign research · 2024Review
- Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients.Health information science and systems · 2023Article
- Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes.FEBS open bio · 2023Article
- Predicting weighted unobserved nodes in a regulatory network using answer set programming.BMC bioinformatics · 2023Article
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Ten quick tips for avoiding pitfalls in multi-omics data integration analyses.PLoS computational biology · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and
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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.