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Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty

Ozmen, Ayse
Kropat, Erik
Weber, Gerhard Wilhelm
In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model's reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the target-environment interaction, based on the expression values of all targets and environmental factors.