Enhanced phenotyping of complex traits with a circadian clock model.

Models of biological systems are increasingly used to generate and test predictions in silico. This article explores the basic workings of a multifeedback network model of a circadian clock. In a series of in silico experiments, we investigated the influence of the number of feedbacks by adding and removing one or more. We further explore the possibilities of testing in silico models in classic "circadian" protocols. In addition, we performed an in silico mutagenesis screen (by altering parameters throughout the network), creating a library of mutants (based on "phenotype," not "genotype"), and subjected them to a variety of straightforward "circadian" protocols. The results of this mutant "taxonomy" are surprising. While most mutants can be identified (separated) using a limited set of experimental protocols, some resist such a separation, even when "mutations" are at vastly different locations within the complex model. Furthermore, some protocols distinguish similar alleles of the same component, which would be counterproductive. The described taxonomy invites experimental verification, in vivo, and may ultimately streamline genotyping of complex traits, which may have been based previously on imprecise phenotypes.