Evolutionary constraints in regulatory networks defined by partial order between phenotypes

Gene regulation networks allow organisms to adapt to diverse environmental niches. However, the constraints underlying the evolution of regulatory phenotypes remain ill-defined both theoretically and experimentally. Here, we show that the concept of partial order identifies such constraints, and test the predictions by experimentally evolving an engineered signal-integrating network in multiple environments. We find that populations: 1) expand in fitness space along the Pareto-optimal front predicted by conflicts in regulatory demands, by fine-tuning binding affinities within the network, 2) expand beyond this constraint by changes in the network structure, thus allowing access to new fitness domains. Strikingly, the constraint predictions are based on whether the network output increases or decreases in response to the different signals, and do not require information on the network architecture or underlying genetics. Overall, our findings show that limited knowledge on current regulatory phenotypes can provide predictions on future evolutionary constraints.