Abstract
BackgroundAn exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task.ResultsUsing yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes.ConclusionsThis case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.
Highlights
An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations
Predicting the consequences of environmental cues or genetic perturbations based on genetic network is becoming a powerful tool to understand biological phenomena or gene functions from a systems point of view
Can one predict a phenotype that is quantitatively measured using a genetic network that consists of physical interactions? A quantitative phenotype may provide a rigorous assessment of the prediction accuracy and physical-interaction network often shed light on understanding the molecular mechanism of phenotype formation
Summary
An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. De novo prediction for quantitative phenotypes based on network topology is always a challenging task. Predicting the consequences of environmental cues or genetic perturbations based on genetic network is becoming a powerful tool to understand biological phenomena or gene functions from a systems point of view. In the present study, we aim to address the following issues in predicting phenotypes based on genetic network. Can one predict a phenotype that is quantitatively measured using a genetic network that consists of physical interactions? A quantitative phenotype may provide a rigorous assessment of the prediction accuracy and physical-interaction network often shed light on understanding the molecular mechanism of phenotype formation. Can genomic analysis capture the most prominent features, which may form the major regulatory interactions, of such network? Is this “scaffold” of the network still able to predict the quantitative phenotypes?
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