Estimating transitions of transcriptional regulation networks using a linear dynamical system

摘要

Cells change their own states stopping replication or repressing protein syntheses, in order to adapt to the external environment such as temperature change, lack of nutrition or other stressful stimuli. As the result of the changes, cells turn into the condition of stationary phase from the exponential growth phase or into the anaerobic condition etc. In conventional studies of genetic network estimation, one fixed regulatory formation is assumed through all data series. On the contrary, we assume that the regulatory formations can be changed, and estimate the transition time points from a statistical view point. Using a linear dynamical model and microarray data, we estimate cellular internal states those are unobservable by experiments and detect transitions of internal states based on temporary descent of log-likelihood values. This approach gives us a objective standard for cellular dynamical system.