ReviewFrontiers in bioengineering and biotechnology2015
Analysis of genetic variation and potential applications in genome-scale metabolic modeling.
Review in Frontiers in bioengineering and biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
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Who cites it
17 citing papers in PubMed.
- Article
- Provenance and family variations in early growth of Manchurian walnut (Juglans mandshurica Maxim.) and selection of superior families.PloS one · 2024Article
- Evaluation of salinity resistance and combining ability analysis in the seedlings of mulberry hybrids (Physiology and molecular biology of plants : an international journal of functional plant biology · 2023Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.iScience · 2022Article
- Systems Biology on Acetogenic Bacteria for Utilizing C1 Feedstocks.Advances in biochemical engineering/biotechnology · 2022Article
- Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.Frontiers in oncology · 2022Review
- Reconstructing organisms in silico: genome-scale models and their emerging applications.Nature reviews. Microbiology · 2020Review
- A biochemically-interpretable machine learning classifier for microbial GWAS.Nature communications · 2020Article
- CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.Human mutation · 2019Article
- Constraint-based modeling in microbial food biotechnology.Biochemical Society transactions · 2018Review
- Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches.Frontiers in microbiology · 2018Review
- Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests.Human mutation · 2017Review
- A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism.Cell systems · 2016Article
- Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction.PLoS computational biology · 2016Article
- Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases.Briefings in bioinformatics · 2016Article
- Editorial: Current Challenges in Modeling Cellular Metabolism.Frontiers in bioengineering and biotechnology · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.
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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.