ReviewCellular and molecular life sciences : CMLS2021
Characterization of effects of genetic variants via genome-scale metabolic modelling.
Review in Cellular and molecular life sciences : CMLS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Investigation of the transcriptional impact of rare germline JAK/STAT variants found in a Tyrolean alpine community.BMC genomics · 2025Article
- Genome-scale metabolic modelling identifies reactions mediated by SNP-SNP interactions associated with yeast sporulation.NPJ systems biology and applications · 2025Article
- Non-canonical plant metabolism.Nature plants · 2025Review
- Integrated morphophysiological, transcriptomic, and metabolomic data uncover the molecular mechanism of environmental adaptation ofFrontiers in plant science · 2025Article
- Toward mechanistic modeling and rational engineering of plant respiration.Plant physiology · 2023Article
- Genetic and Metabolite Variability among Commercial Varieties and Advanced Lines ofPlants (Basel, Switzerland) · 2023Article
- Systems-level analysis of the plasticity of the maize metabolic network reveals novel hypotheses in the nitrogen-use efficiency of maize roots.Journal of experimental botany · 2022Article
- Whither metabolic flux analysis in plants?Journal of experimental botany · 2021Article
- Data Management and Modeling in Plant Biology.Frontiers in plant science · 2021Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.
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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.