Reference: Yu R and Nielsen J (2019) Big data in yeast systems biology. FEMS Yeast Res 19(7)

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Abstract


Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

Reference Type
Journal Article | Review
Authors
Yu R, Nielsen J
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Gene Ontology Annotations


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Gene/Complex Qualifier Gene Ontology Term Aspect Annotation Extension Evidence Method Source Assigned On Reference

Phenotype Annotations


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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

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Gene Disease Ontology Term Qualifier Evidence Method Source Assigned On Reference

Regulation Annotations


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Regulator Target Direction Regulation Of Happens During Method Evidence

Post-translational Modifications


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Site Modification Modifier Reference

Interaction Annotations


Genetic Interactions

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Interactor Interactor Allele Assay Annotation Action Phenotype SGA score P-value Source Reference

Physical Interactions

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Interactor Interactor Assay Annotation Action Modification Source Reference

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Gene Species Gene ID Strain background Direction Details Source Reference