Integrated study to infer dynamic protein-gene interactions in human p53 regulatory networks

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.