Stability analysis of genetic regulatory network with additive noises

Yufang Jin, Merry L Lindsey

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Background: Genetic regulatory networks (GRN) can be described by differential equations with SUM logic which has been found in many natural systems. Identification of the network components and transcriptional rates are critical to the output behavior of the system. Though transcriptional rates cannot be measured in vivo, biologists have shown that they are alterable through artificial factors in vitro. Results: This study presents the theoretical research work on a novel nonlinear control and stability analysis of genetic regulatory networks. The proposed control scheme can drive the genetic regulatory network to desired levels by adjusting transcriptional rates. Asymptotic stability proof is conducted with Lyapunov argument for both noise-free and additive noises cases. Computer simulation results show the effectiveness of the control design and robustness of the regulation scheme with additive noises. Conclusions: With the knowledge of interaction between transcriptional factors and gene products, the research results can be applied in the design of model-based experiments to regulate gene expression profiles.

Original languageEnglish (US)
Article numberS21
JournalBMC Genomics
Volume9
Issue numberSUPPL. 1
DOIs
StatePublished - Mar 20 2008

Fingerprint

Noise
Gene Regulatory Networks
Transcriptome
Research
Computer Simulation
Theoretical Models
Genes
In Vitro Techniques
Genetic Background

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

Stability analysis of genetic regulatory network with additive noises. / Jin, Yufang; Lindsey, Merry L.

In: BMC Genomics, Vol. 9, No. SUPPL. 1, S21, 20.03.2008.

Research output: Contribution to journalArticle

@article{0d3448884240441a97e8a53b35c26460,
title = "Stability analysis of genetic regulatory network with additive noises",
abstract = "Background: Genetic regulatory networks (GRN) can be described by differential equations with SUM logic which has been found in many natural systems. Identification of the network components and transcriptional rates are critical to the output behavior of the system. Though transcriptional rates cannot be measured in vivo, biologists have shown that they are alterable through artificial factors in vitro. Results: This study presents the theoretical research work on a novel nonlinear control and stability analysis of genetic regulatory networks. The proposed control scheme can drive the genetic regulatory network to desired levels by adjusting transcriptional rates. Asymptotic stability proof is conducted with Lyapunov argument for both noise-free and additive noises cases. Computer simulation results show the effectiveness of the control design and robustness of the regulation scheme with additive noises. Conclusions: With the knowledge of interaction between transcriptional factors and gene products, the research results can be applied in the design of model-based experiments to regulate gene expression profiles.",
author = "Yufang Jin and Lindsey, {Merry L}",
year = "2008",
month = "3",
day = "20",
doi = "10.1186/1471-2164-9-S1-S21",
language = "English (US)",
volume = "9",
journal = "BMC Genomics",
issn = "1471-2164",
publisher = "BioMed Central",
number = "SUPPL. 1",

}

TY - JOUR

T1 - Stability analysis of genetic regulatory network with additive noises

AU - Jin, Yufang

AU - Lindsey, Merry L

PY - 2008/3/20

Y1 - 2008/3/20

N2 - Background: Genetic regulatory networks (GRN) can be described by differential equations with SUM logic which has been found in many natural systems. Identification of the network components and transcriptional rates are critical to the output behavior of the system. Though transcriptional rates cannot be measured in vivo, biologists have shown that they are alterable through artificial factors in vitro. Results: This study presents the theoretical research work on a novel nonlinear control and stability analysis of genetic regulatory networks. The proposed control scheme can drive the genetic regulatory network to desired levels by adjusting transcriptional rates. Asymptotic stability proof is conducted with Lyapunov argument for both noise-free and additive noises cases. Computer simulation results show the effectiveness of the control design and robustness of the regulation scheme with additive noises. Conclusions: With the knowledge of interaction between transcriptional factors and gene products, the research results can be applied in the design of model-based experiments to regulate gene expression profiles.

AB - Background: Genetic regulatory networks (GRN) can be described by differential equations with SUM logic which has been found in many natural systems. Identification of the network components and transcriptional rates are critical to the output behavior of the system. Though transcriptional rates cannot be measured in vivo, biologists have shown that they are alterable through artificial factors in vitro. Results: This study presents the theoretical research work on a novel nonlinear control and stability analysis of genetic regulatory networks. The proposed control scheme can drive the genetic regulatory network to desired levels by adjusting transcriptional rates. Asymptotic stability proof is conducted with Lyapunov argument for both noise-free and additive noises cases. Computer simulation results show the effectiveness of the control design and robustness of the regulation scheme with additive noises. Conclusions: With the knowledge of interaction between transcriptional factors and gene products, the research results can be applied in the design of model-based experiments to regulate gene expression profiles.

UR - http://www.scopus.com/inward/record.url?scp=44449094175&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44449094175&partnerID=8YFLogxK

U2 - 10.1186/1471-2164-9-S1-S21

DO - 10.1186/1471-2164-9-S1-S21

M3 - Article

VL - 9

JO - BMC Genomics

JF - BMC Genomics

SN - 1471-2164

IS - SUPPL. 1

M1 - S21

ER -