ReviewFrontiers in bioengineering and biotechnology2020
Opportunities at the Interface of Network Science and Metabolic Modeling.
Review in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Unraveling the glycosphingolipid metabolism by leveraging transcriptome-weighted network analysis on neuroblastic tumors.Cancer & metabolism · 2024Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.PloS one · 2024Article
- Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design.iScience · 2023Article
- Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism?PloS one · 2023Article
- Review
- Toward modeling metabolic state from single-cell transcriptomics.Molecular metabolism · 2022Review
- Review
- Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacteriumSTAR protocols · 2021Article
- Modelling microbial communities: Harnessing consortia for biotechnological applications.Computational and structural biotechnology journal · 2021Review
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
No grant is acknowledged in the PubMed record.
Abstract
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
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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.