Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks

Junbai Wang; Qian Qian Wu; Tianhai Tian

doi:10.1109/BIBM.2015.7359692

Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks

Junbai Wang, Qian Qian Wu, Tianhai Tian

School of Mathematics

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

Abstract

Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is very important but challenging. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this problem, a new integrated method is proposed by combining both the top-down and bottom-up approaches. Firstly, a top-down approach, using probability graphical models, is employed to predict the network structure of DNA repair pathway that involves p53 regulation. Then, a bottom-up approach, using differential equation models, is applied to study the detailed genetic regulations based on either a fully-connected regulatory network or gene networks inferred with the top-down approach. Optimal network is selected based on model simulation error and robustness property. Overall, the proposed new integrated method is efficient for studying large dynamical genetic regulations.

Original language	English
Title of host publication	Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine
Editors	Jun (Luke) Huan, Satoru Miyano, Amarda Shehu, Xiaohua Hu, Bin Ma, Sanguthevar Rajasekaran, Vijay K Gombar, Ing Matthieu, Illhoi Yoo, Jiayu Zhou, Brian Chen, Vinay Pai, Brian Pierce
Place of Publication	New York NY USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	273-276
Number of pages	4
ISBN (Print)	9781467367981
DOIs	https://doi.org/10.1109/BIBM.2015.7359692
Publication status	Published - 2015
Event	IEEE International Conference on Bioinformatics and Biomedicine 2015 - Washington, United States of America Duration: 9 Nov 2015 → 12 Nov 2015 https://cci.drexel.edu/ieeebibm/bibm2015/ https://ieeexplore.ieee.org/xpl/conhome/7350074/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine 2015
Abbreviated title	BIBM 2015
Country/Territory	United States of America
City	Washington
Period	9/11/15 → 12/11/15
Internet address	https://cci.drexel.edu/ieeebibm/bibm2015/ https://ieeexplore.ieee.org/xpl/conhome/7350074/proceeding (Proceedings)

Keywords

DNA repair pathway
Genetic regulation
Microarray expression data
Network inference

Access to Document

10.1109/BIBM.2015.7359692

Cite this

Wang, J., Wu, Q. Q., & Tian, T. (2015). Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks. In J. Huan, S. Miyano, A. Shehu, X. Hu, B. Ma, S. Rajasekaran, V. K. Gombar, I. Matthieu, I. Yoo, J. Zhou, B. Chen, V. Pai, & B. Pierce (Eds.), Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine (pp. 273-276). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIBM.2015.7359692

Wang, Junbai ; Wu, Qian Qian ; Tian, Tianhai. / Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks. Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine. editor / Jun (Luke) Huan ; Satoru Miyano ; Amarda Shehu ; Xiaohua Hu ; Bin Ma ; Sanguthevar Rajasekaran ; Vijay K Gombar ; Ing Matthieu ; Illhoi Yoo ; Jiayu Zhou ; Brian Chen ; Vinay Pai ; Brian Pierce. New York NY USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 273-276

@inproceedings{f5da957306f04ad2a037969c953f4a5f,

title = "Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks",

abstract = "Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is very important but challenging. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this problem, a new integrated method is proposed by combining both the top-down and bottom-up approaches. Firstly, a top-down approach, using probability graphical models, is employed to predict the network structure of DNA repair pathway that involves p53 regulation. Then, a bottom-up approach, using differential equation models, is applied to study the detailed genetic regulations based on either a fully-connected regulatory network or gene networks inferred with the top-down approach. Optimal network is selected based on model simulation error and robustness property. Overall, the proposed new integrated method is efficient for studying large dynamical genetic regulations.",

keywords = "DNA repair pathway, Genetic regulation, Microarray expression data, Network inference",

author = "Junbai Wang and Wu, {Qian Qian} and Tianhai Tian",

year = "2015",

doi = "10.1109/BIBM.2015.7359692",

language = "English",

isbn = "9781467367981",

pages = "273--276",

editor = "Huan, {Jun (Luke)} and Satoru Miyano and Amarda Shehu and Xiaohua Hu and Bin Ma and Sanguthevar Rajasekaran and Gombar, {Vijay K} and Ing Matthieu and Illhoi Yoo and Jiayu Zhou and Brian Chen and Vinay Pai and Brian Pierce",

booktitle = "Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Conference on Bioinformatics and Biomedicine 2015, BIBM 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

url = "https://cci.drexel.edu/ieeebibm/bibm2015/, https://ieeexplore.ieee.org/xpl/conhome/7350074/proceeding",

}

Wang, J, Wu, QQ & Tian, T 2015, Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks. in J Huan, S Miyano, A Shehu, X Hu, B Ma, S Rajasekaran, VK Gombar, I Matthieu, I Yoo, J Zhou, B Chen, V Pai & B Pierce (eds), Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, Institute of Electrical and Electronics Engineers, New York NY USA, pp. 273-276, IEEE International Conference on Bioinformatics and Biomedicine 2015, Washington, District of Columbia, United States of America, 9/11/15. https://doi.org/10.1109/BIBM.2015.7359692

Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks. / Wang, Junbai; Wu, Qian Qian; Tian, Tianhai.
Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine. ed. / Jun (Luke) Huan; Satoru Miyano; Amarda Shehu; Xiaohua Hu; Bin Ma; Sanguthevar Rajasekaran; Vijay K Gombar; Ing Matthieu; Illhoi Yoo; Jiayu Zhou; Brian Chen; Vinay Pai; Brian Pierce. New York NY USA: IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 273-276.

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

TY - GEN

T1 - Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks

AU - Wang, Junbai

AU - Wu, Qian Qian

AU - Tian, Tianhai

PY - 2015

Y1 - 2015

N2 - Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is very important but challenging. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this problem, a new integrated method is proposed by combining both the top-down and bottom-up approaches. Firstly, a top-down approach, using probability graphical models, is employed to predict the network structure of DNA repair pathway that involves p53 regulation. Then, a bottom-up approach, using differential equation models, is applied to study the detailed genetic regulations based on either a fully-connected regulatory network or gene networks inferred with the top-down approach. Optimal network is selected based on model simulation error and robustness property. Overall, the proposed new integrated method is efficient for studying large dynamical genetic regulations.

AB - Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is very important but challenging. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this problem, a new integrated method is proposed by combining both the top-down and bottom-up approaches. Firstly, a top-down approach, using probability graphical models, is employed to predict the network structure of DNA repair pathway that involves p53 regulation. Then, a bottom-up approach, using differential equation models, is applied to study the detailed genetic regulations based on either a fully-connected regulatory network or gene networks inferred with the top-down approach. Optimal network is selected based on model simulation error and robustness property. Overall, the proposed new integrated method is efficient for studying large dynamical genetic regulations.

KW - DNA repair pathway

KW - Genetic regulation

KW - Microarray expression data

KW - Network inference

UR - http://ieeexplore.ieee.org.ezproxy.lib.monash.edu.au/stamp/stamp.jsp?tp=&arnumber=7359692

U2 - 10.1109/BIBM.2015.7359692

DO - 10.1109/BIBM.2015.7359692

M3 - Conference Paper

SN - 9781467367981

SP - 273

EP - 276

BT - Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine

A2 - Huan, Jun (Luke)

A2 - Miyano, Satoru

A2 - Shehu, Amarda

A2 - Hu, Xiaohua

A2 - Ma, Bin

A2 - Rajasekaran, Sanguthevar

A2 - Gombar, Vijay K

A2 - Matthieu, Ing

A2 - Yoo, Illhoi

A2 - Zhou, Jiayu

A2 - Chen, Brian

A2 - Pai, Vinay

A2 - Pierce, Brian

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - New York NY USA

T2 - IEEE International Conference on Bioinformatics and Biomedicine 2015

Y2 - 9 November 2015 through 12 November 2015

ER -

Wang J, Wu QQ, Tian T. Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks. In Huan J, Miyano S, Shehu A, Hu X, Ma B, Rajasekaran S, Gombar VK, Matthieu I, Yoo I, Zhou J, Chen B, Pai V, Pierce B, editors, Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine. New York NY USA: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 273-276 doi: 10.1109/BIBM.2015.7359692

Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks

Abstract

Conference

Keywords

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Integrated study to infer dynamic protein-gene interactions in human P53 regulatory networks

Abstract

Conference

Keywords

Access to Document

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