Low-Sample-Size Data-Driven Re-stabilization of Gene Network Systems

Abstract

During the progression of complex diseases caused by qualitative shifts in gene networks, the deteriorations may be abrupt and cause a critical transition from a healthy state to a disease state. We define a pre-disease state as a state just before the imminent critical transition. Medical treatment in the pre-disease state should be more efficient than in the disease state. This paper proposes a data-driven method to design an input assignment and a stabilizing controller to avoid this kind of qualitative shift. The proposed method only requires a small number of data samples on the steady pre-disease state. We show numerical examples to validate the effectiveness of the proposed method.