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Related Experiment Video

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Quantitative Comparison of cis-Regulatory Element (CRE) Activities in Transgenic Drosophila melanogaster

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Published on: December 19, 2011

Modeling the evolution of a classic genetic switch.

Christos Josephides¹, Alan M Moses

¹Department of Cell & Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5 S 3B2, Canada.

BMC Systems Biology

|February 8, 2011

Summary

We developed a computational model to study the evolution of the yeast galactose-use (GAL) regulatory network. Our findings show that the order of evolutionary changes significantly impacts network function and that not all features can be optimized simultaneously.

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Published on: February 3, 2023

Area of Science:

Evolutionary biology
Systems biology
Genomics

Background:

The yeast galactose-use (GAL) network is a model for studying regulatory network evolution.
Adaptive evolution in the GAL network followed a whole genome duplication event.
An ancestral bi-functional gene subfunctionalized into GAL1 and GAL3 in Saccharomyces cerevisiae, with cis-regulatory changes driving fitness gains.

Purpose of the Study:

To develop a modeling framework for analyzing the evolution of the GAL regulatory network.
To translate molecular changes into quantitative network function.
To computationally reconstruct ancestral networks and trace evolutionary paths.

Main Methods:

Computational modeling framework development.
Reconstruction of an inferred ancestral GAL regulatory network.
Tracing evolutionary paths in the lineage leading to Saccharomyces cerevisiae.
Exploration of the evolutionary landscape and optimization trade-offs.

Main Results:

The order of accumulating evolutionary changes substantially alters the function of intermediate regulatory networks.
Some network features cannot be simultaneously optimized, revealing evolutionary constraints.
The developed model translates molecular changes to quantitative network function.

Conclusions:

Computational modeling is effective for analyzing the evolution of complex regulatory networks.
Results align with experimental findings on GAL network evolution, including fitness increases and regulatory divergence post-duplication.
The developed tools can be applied to future studies of regulatory network evolution.