Journal article
An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein
Methods (San Diego, Calif.), v 110, pp 3-13
01 Nov 2016
PMID: 27514497
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is a very important but challenging task. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this challenge, a new integrated method is proposed by combining a top-down approach and a bottom-up approach. First, the top-down approach uses probabilistic graphical models to predict the network structure of DNA repair pathway that is regulated by the p53 protein. Two networks are predicted, namely a network of eight genes with eight inferred interactions and an extended network of 21 genes with 17 interactions. Then, the bottom-up approach using differential equation models is developed to study the detailed genetic regulations based on either a fully connected regulatory network or a gene network obtained by the top-down approach. Model simulation error, parameter identifiability and robustness property are used as criteria to select the optimal network. Simulation results together with permutation tests of input gene network structures indicate that the prediction accuracy and robustness property of the two predicted networks using the top-down approach are better than those of the corresponding fully connected networks. In particular, the proposed approach reduces computational cost significantly for inferring model parameters. Overall, the new integrated method is a promising approach for investigating the dynamics of genetic regulation.
Metrics
Details
- Title
- An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein
- Creators
- Junbai Wang - Oslo University HospitalQianqian Wu - Hefei University of TechnologyXiaohua Tony Hu - Drexel UniversityTianhai Tian - Monash University
- Publication Details
- Methods (San Diego, Calif.), v 110, pp 3-13
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000388063800002
- Scopus ID
- 2-s2.0-84994560221
- Other Identifier
- 991019167645404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biochemistry & Molecular Biology