Current methods for reconstructing dynamic regulatory networks are focused on modeling a single response network using model organisms or cell lines. Unlike these models or cell lines, humans differ in their background expression profiles due to age, genetics and life factors. In addition, there are often differences in start and end times for time series human data and in the rate of progress based on the specific individual. Thus, new methods are required to integrate time series data from multiple individuals when modeling and constructing disease response networks.