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

Xun Shen; Hampei Sasahara; Jun Ichi Imura; Makito Oku; Kazuyuki Aihara

doi:10.1016/j.ifacol.2023.10.738

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

Xun Shen^*, Hampei Sasahara^*, Jun Ichi Imura^*, Makito Oku, Kazuyuki Aihara

^*Corresponding author for this work

Research Center for Pre-Disease Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	IFAC-PapersOnLine
Editors	Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
Publisher	Elsevier B.V.
Pages	6200-6205
Number of pages	6
Edition	2
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.738
State	Published - 2023/07/01
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 2023/07/09 → 2023/07/14

Publication series

Name	IFAC-PapersOnLine
Number	2
Volume	56
ISSN (Electronic)	2405-8963

Conference

Conference	22nd IFAC World Congress
Country/Territory	Japan
City	Yokohama
Period	2023/07/09 → 2023/07/14

Keywords

Early medical treatment
bifurcation
data-driven control
high-dimension low-sample-size data
pole placement

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.1016/j.ifacol.2023.10.738

Cite this

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title = "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.",

keywords = "Early medical treatment, bifurcation, data-driven control, high-dimension low-sample-size data, pole placement",

author = "Xun Shen and Hampei Sasahara and Imura, {Jun Ichi} and Makito Oku and Kazuyuki Aihara",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",

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Shen, X, Sasahara, H, Imura, JI, Oku, M & Aihara, K 2023, Data-Driven Re-stabilization of Gene Regulatory Network Towards Early Medical Treatment. in H Ishii, Y Ebihara, J Imura & M Yamakita (eds), IFAC-PapersOnLine. 2 edn, IFAC-PapersOnLine, no. 2, vol. 56, Elsevier B.V., pp. 6200-6205, 22nd IFAC World Congress, Yokohama, Japan, 2023/07/09. https://doi.org/10.1016/j.ifacol.2023.10.738

Data-Driven Re-stabilization of Gene Regulatory Network Towards Early Medical Treatment. / Shen, Xun; Sasahara, Hampei; Imura, Jun Ichi et al.
IFAC-PapersOnLine. ed. / Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita. 2. ed. Elsevier B.V., 2023. p. 6200-6205 (IFAC-PapersOnLine; Vol. 56, No. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data-Driven Re-stabilization of Gene Regulatory Network Towards Early Medical Treatment

AU - Shen, Xun

AU - Sasahara, Hampei

AU - Imura, Jun Ichi

AU - Oku, Makito

AU - Aihara, Kazuyuki

PY - 2023/7/1

Y1 - 2023/7/1

N2 - 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.

AB - 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.

KW - Early medical treatment

KW - bifurcation

KW - data-driven control

KW - high-dimension low-sample-size data

KW - pole placement

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A2 - Ebihara, Yoshio

A2 - Imura, Jun-ichi

A2 - Yamakita, Masaki

PB - Elsevier B.V.

T2 - 22nd IFAC World Congress

Y2 - 9 July 2023 through 14 July 2023

ER -

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

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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