A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks

Shu Qin ZHANG; Wai Ki CHING; Yue JIAO; Ling Yun WU; Raymond H. CHAN

doi:10.1109/ICBBE.2008.138

A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks

Shu Qin ZHANG^*, Wai Ki CHING, Yue JIAO, Ling Yun WU, Raymond H. CHAN

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

The construction and control of genetic regulatory networks using gene expression data is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN. The new model can preserve the strength of PBNs and at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are then given to demonstrate the effectiveness of our proposed model and control policy.

Original language	English
Title of host publication	The 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008
Publisher	IEEE
Pages	569-572
Number of pages	4
ISBN (Print)	9781424417483
DOIs	https://doi.org/10.1109/ICBBE.2008.138
Publication status	Published - May 2008
Externally published	Yes
Event	2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008 - Shanghai, China Duration: 16 May 2008 → 18 May 2008

Conference

Conference	2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008
Country/Territory	China
City	Shanghai
Period	16/05/08 → 18/05/08

Access to Document

10.1109/ICBBE.2008.138

Cite this

@inproceedings{bdfe48a61ee34207922702186e2c261f,

title = "A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks",

abstract = "The construction and control of genetic regulatory networks using gene expression data is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN. The new model can preserve the strength of PBNs and at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are then given to demonstrate the effectiveness of our proposed model and control policy.",

author = "ZHANG, {Shu Qin} and CHING, {Wai Ki} and Yue JIAO and WU, {Ling Yun} and CHAN, {Raymond H.}",

year = "2008",

month = may,

doi = "10.1109/ICBBE.2008.138",

language = "English",

isbn = "9781424417483",

pages = "569--572",

booktitle = "The 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008",

publisher = "IEEE",

note = "2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008 ; Conference date: 16-05-2008 Through 18-05-2008",

}

ZHANG, SQ, CHING, WK, JIAO, Y, WU, LY & CHAN, RH 2008, A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks. in The 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008., 4535018, IEEE, pp. 569-572, 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008, Shanghai, China, 16/05/08. https://doi.org/10.1109/ICBBE.2008.138

A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks. / ZHANG, Shu Qin; CHING, Wai Ki; JIAO, Yue et al.
The 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008. IEEE, 2008. p. 569-572 4535018.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks

AU - ZHANG, Shu Qin

AU - CHING, Wai Ki

AU - JIAO, Yue

AU - WU, Ling Yun

AU - CHAN, Raymond H.

PY - 2008/5

Y1 - 2008/5

N2 - The construction and control of genetic regulatory networks using gene expression data is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN. The new model can preserve the strength of PBNs and at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are then given to demonstrate the effectiveness of our proposed model and control policy.

AB - The construction and control of genetic regulatory networks using gene expression data is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN. The new model can preserve the strength of PBNs and at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are then given to demonstrate the effectiveness of our proposed model and control policy.

UR - http://www.scopus.com/inward/record.url?scp=50949098687&partnerID=8YFLogxK

U2 - 10.1109/ICBBE.2008.138

DO - 10.1109/ICBBE.2008.138

M3 - Conference paper (refereed)

AN - SCOPUS:50949098687

SN - 9781424417483

SP - 569

EP - 572

BT - The 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008

PB - IEEE

T2 - 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008

Y2 - 16 May 2008 through 18 May 2008

ER -

A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this