Achieving Optimal Tradeoff Adaptation Functionality for the Minimal Gene Regulatory Network

Abstract

Adaptation functionality is an essential property for gene regulatory network (GRN). Understanding the relationship between optimal tradeoff adaptation performance and GRN parameters remains an open question. In this paper, a minimal three-node GRN with 12 parameters is modeled by the Michaelis-Menten rate equations. The NSGA-III algorithm is used to find the ‘best’ parameter sets as many as possible, which make GRN achieve optimal tradeoff adaptation: high sensitivity, high precision, short peak time and short settle down time. Further statistical analysis is performed to obtain reliable rules of the ‘best’ parameter sets. The results show that 11 out of 12 GRN parameters have preferred value. The proposed methodology can provide the guidance to design GRN with optimal tradeoff adaptation, or with other biological functionalities.