![]() Furthermore, with panYeast8 and coreYeast8, 1011 strain-specific GEMs were reconstructed and compared. Through ecYeast8 and proYeast8 DB, multiple parameters related to protein kinetics and 3D structures could be integrated based on gene–protein-reaction relations. Yeast8 is a consensus GEM maintained in an open and version-controlled way. ![]() This model ecosystem has the ability to meet wide application demands from the large scientific yeast community in systems and synthetic biology of yeast. cerevisiae strains and proYeast8 DB, a database containing 3D structures of metabolic proteins. We also introduce a model ecosystem around this GEM, including ecYeast8, a model incorporating enzyme constraints panYeast8 and coreYeast8, representing the pan and core metabolic networks of 1011 S. This study presents Yeast8, the latest release of the consensus GEM of S. Advanced functional mutation cluster analysis constrained with protein 3D structures is therefore needed to integrate knowledge on protein structures into GEMs. However, it remains challenging to directly predict cellular metabolism based on changes in protein sequences with current GEMs. GEMs with protein 3D structures connect the structure-related parameters and genetic variation 27 with cellular metabolism 6, thus enlarging the prediction scope of GEMs. GEMs constrained with k cat values and enzyme abundances have been able to directly integrate proteomics data and correctly predict cellular phenotypes under conditions of stress 26. Recently, enzyme constraints 26 and protein 3D structures 27 have been integrated into GEMs, thereby expanding their scope of application and laying the foundation for whole cell modelling. ![]() simulating the impact of temperature on growth rates 25. Traditional GEMs only consist of reactions and their related gene and protein identifiers, and therefore cannot accurately predict cellular phenotypes under varied environmental conditions other than nutritional conditions, e.g. Metabolism is complex and regulated at several different levels 23, 24. ![]() However, the hitherto latest version, Yeast7 22, with only 909 genes, falling behind the latest genome annotation, presents a bottleneck for the use of yeast GEMs as a scaffold for integrating omics datasets. These GEMs have contributed significantly to systems biology studies of yeast including their use as platforms for multi-omics integration 18, 19, and use for in silico strain design 20, 21. Consistent with strong research interests in yeast, the relevant GEMs have also undergone numerous rounds of curation since the first published version in 2003 17. Recently, the emergence of technologies, such as CRISPR 15 and single cell omics data generation 16, have accelerated the developments in systems biology. cerevisiae is a widely used cell factory 11, 12 and is extensively used as a model organism in basic biological and medical research 13, 14. Approaches to record updates of a GEM exist for this purpose 9, 10, albeit with some limitations on their simplicity and flexibility. Therefore, it is important to keep track of changes as new knowledge is added to GEMs, in order to make model development recorded, repeatable, free and open to the community. With their thousands of reactions, genes and proteins, GEMs also represent valuable organism-specific databases. The quality and scope of GEMs have improved as demonstrated by the models for human 6, yeast 7, and E. As a bottom-up systems biology tool, genome scale metabolic models (GEMs) connect genes, proteins and reactions, enabling metabolic and phenotypic predictions based on specified constraints 4, 5. In the era of big data, computational models are instrumental for turning different sources of data into valuable knowledge for e.g. ![]()
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