Data-Driven Re-stabilization of Gene Regulatory Network Towards Early Medical Treatment

Abstract

The Dynamical Network Biomarkers (DNBs) theory has been proposed to detect early-warning signals of critical transitions in gene regulatory networks only with High-Dimension Low-Sample-Size (HDLSS) data of the system state. Towards giving a theoretical foundation for early medical treatment, this paper proposes a data-driven approach for the re-stabilization of gene regulatory networks based on HDLSS data. In the proposed re-stabilization method, only the diagonal elements of the system matrix need to be adjusted. Namely, only the self-feedback loops of mRNA expression for genes are intervened in, which reduces the complexity of the early medical treatment based on gene regulation and makes it practical to be implemented. The proposed re-stabilization method is generalized to the systems with either saddle-node bifurcation or Hopf bifurcation. Numerical simulations have been implemented to validate the effectiveness of the proposed method.