Title : Inference of gene regulation network topology by perturbation analysis

This paper addresses the problem of identifying the large-scale topology of gene regulation networks from features that can be derived from microarray data sets. Understanding large-scale structures of gene regulation is fundamentally important in biology. Three main classes of network models, exponential network, scale-free network, and small-world network, have been used to describe topological features of various naturally occurring systems. Recent analysis of network properties of known biological networks have shown that they display scale free features, but it is not clear yet whether the scale free features are generic to all biological networks due to the limited availability of information on pathways and connectivity. To overcome these limitations and to expand the knowledge of biological network topology, we propose a novel method for network topology inference from microarray data. The proposed method is more robust than current reverse engineering approaches because it does not require inferring individual connectivity. Preliminary results with simulated data are encouraging. The trained neural network is able to classify networks as random network or scale free network with 90% accuracy and the mean connectivity can be predicted with around 85% to 90% accuracy. Our work shows that the neural network based topology inference method can predict the class of network topology and mean connectivity of network without knowing the underlying connectivity of structures from measured time series data.