Learning transition models of biological regulatory and signaling networks from noisy data

Abstract

In this paper, we present an extended 2-step probabilistic LGTS (PLGTS) transition system which aims to identify the network structure and stochastic nature of biological processes using time series data. This work is a step towards system identification in a noisy environment using transition systems. Here, the noise implies noise in transitions between states in the observed data. Interestingly, noise in the data helps in assisting system identification. Experimental results on synthetic data show that noise actually helps in understanding the system dynamics as well as constraining the solution space; thus helping to identify the most probable network structure for a given data set.