Deriving Network Structure from Discrete Data

Short Abstract — The underlying assumption of most gene regulation networks is that the driving processes are best represented by a system of coupled ordinary differential equations (ODEs) or given by stochastic simulation algorithm (SSA). The discrete nature of most experimental data presents a challenge in determining this system. We present a comparison of methods to reconstruct the network of species interactions from discretized data, based on a generalization of the REVEAL algorithm for reconstructing Boolean networks by Liang et al and the method given by Laubenbacher et al