Global analysis of phase locking in gene expression during cell cycle: the potential in network modeling
1 Department of Physics, the University of Alabama at Birmingham, Birmingham, Alabama, 35294, USA
2 The Comprehensive Diabetes Center, the University of Alabama at Birmingham, Birmingham, Alabama, 35294, USA
3 Department of Genetics, the University of Alabama at Birmingham, Birmingham, Alabama, 35294, USA
4 The Max McGee National Research Center for Juvenile Diabetes, Department of Pediatrics at the Medical College of Wisconsin and the Children's Research Institute of the Children's Hospital of Wisconsin, 8701 Watertown Plank Road, Milwaukee, Wisconsin, 53226, USA
5 The Human and Molecular Genetics Center, The Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, Wisconsin, 53226, USA
BMC Systems Biology 2010, 4:167 doi:10.1186/1752-0509-4-167Published: 3 December 2010
In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation.
In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation.
Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.