Inference of Genetic Networks Using Lpms: Assessment of Confidence Values of Regulations

Abstract

When we apply inference methods based on a set of differential equations into actual genetic network inference problems, we often end up with a large number of false-positive regulations. However, as we must check the inferred regulations through biochemical experiments, fewer false-positive regulations are preferable. In order to reduce the number of regulations checked, this study proposes a new method that assigns confidence values to all of the regulations contained in the target network. For this purpose, we combine a residual bootstrap method with the existing method, i.e. the inference method using linear programming machines (LPMs). Through numerical experiments on an artificial genetic network inference problem, we confirmed that most of the regulations with high confidence values are actually present in the target networks. We then used the proposed method to analyze the bacterial SOS DNA repair system, and succeeded in assigning reasonable confidence values to its regulations. Although this study combined the bootstrap method with the inference method using the LPMs, the proposed bootstrap approach could be combined with any method that has an ability to infer a genetic network from time-series of gene expression levels.