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Individual gene effects were measured through essentiality (if the gene is essential for growth and development) and single mutant fitness (fitness after perturbing a single gene).
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Despite the precedence for successful drugs that act through agonist relationships with non-essential molecules, essentiality is a desirable character of genes to be pursued by a target-centric approach.
Metabolic networks have also been constructed for a number of genomes such as E. coli [5] and Staphylococcus aureus [6] which have been used to study the metabolic capabilities of organisms and gene essentiality through flux balance analyses.
Based on this network, we re-calculated gene essentiality through FBA and compared the predictions with the experimental essentiality data in mice [ 34, 36].
Development of a strategy for identifying drug targets by combined genome-scale metabolic network and essentiality analyses through in silico approaches provides tantalizing opportunity to investigate the role of protein/substrate metabolism.
Selecting features associated with gene essentiality is necessary to identify essential genes through machine learning approaches.
Synthetic accessibility [ 22] is a parameter-free method to predict the essentiality of genes through their deletions in metabolic networks.
Our general aim is to predict the essentiality of signaling components through structural (graph theoretical) analysis.
Here, we have attempted to define general characters that are predictive of essentiality of eukaryotic genes through orthology.
DoT had the best performance in SPO SCE prediction because, relative to that in bacteria, gene essentiality is more conserved through the function of protein domains or domain combinations rather than through the conservation of the entire genes in fungi [ 28].
Previous experience suggests that as the model is expanded and refined through successive iterations, the accuracy of the essentiality predictions will improve [ 3, 57- 59].
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