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Open Access Highly Accessed Research article

Modeling the evolution of a classic genetic switch

Christos Josephides and Alan M Moses*

Author Affiliations

Departments of Cell & Systems Biology and Ecology & Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5 S 3B2, Canada

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BMC Systems Biology 2011, 5:24  doi:10.1186/1752-0509-5-24

Published: 5 February 2011



The regulatory network underlying the yeast galactose-use pathway has emerged as a model system for the study of regulatory network evolution. Evidence has recently been provided for adaptive evolution in this network following a whole genome duplication event. An ancestral gene encoding a bi-functional galactokinase and co-inducer protein molecule has become subfunctionalized as paralogous genes (GAL1 and GAL3) in Saccharomyces cerevisiae, with most fitness gains being attributable to changes in cis-regulatory elements. However, the quantitative functional implications of the evolutionary changes in this regulatory network remain unexplored.


We develop a modeling framework to examine the evolution of the GAL regulatory network. This enables us to translate molecular changes in the regulatory network to changes in quantitative network function. We computationally reconstruct an inferred ancestral version of the network and trace the evolutionary paths in the lineage leading to S. cerevisiae. We explore the evolutionary landscape of possible regulatory networks and find that the operation of intermediate networks leading to S. cerevisiae differs substantially depending on the order in which evolutionary changes accumulate; in particular, we systematically explore evolutionary paths and find that some network features cannot be optimized simultaneously.


We find that a computational modeling approach can be used to analyze the evolution of a well-studied regulatory network. Our results are consistent with several experimental studies of the evolutionary of the GAL regulatory network, including increased fitness in Saccharomyces due to duplication and adaptive regulatory divergence. The conceptual and computational tools that we have developed may be applicable in further studies of regulatory network evolution.