title: An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein.
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.
authors: Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway.
authors: Junbai
authors: J
authors: Wang
authors: School of Mathematical Sciences, Monash University, Melbourne 3800, Victoria, Australia; School of Mathematics, Hefei University of Technology, Hefei, Anhui 230009, China.
authors: Qianqian
authors: Q
authors: Wu
authors: College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USA.
authors: Xiaohua Tony
authors: XT
authors: Hu
authors: School of Mathematical Sciences, Monash University, Melbourne 3800, Victoria, Australia. Electronic address: tianhai.tian@monash.edu.
authors: Tianhai
authors: T
authors: Tian
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004260:DNA Repair / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D015233:Models, Statistical; D015398:Signal Transduction / Q000235:genetics; D016159:Tumor Suppressor Protein p53 / Q000235:genetics*
chemical_list: C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53
keywords: Gaussian graphical model; Genetic regulation; Mathematical modelling; Reverse-engineering; Robustness