Integrative identification of core genetic regulatory modules via a structural model-based clustering method

Regulatory modules play fundamental roles in processing and dispatching signals in cell life cycle. Although current clustering methods may reduce data complexity to lower dimension, they tend to neglect biological meanings within high-throughput data. We propose a module-detection algorithm through defining network activity measures and associating them through a weighted clustering approach. We verify our method on diverse models and it provides a unique perspective for analysing model dynamics and expression data, especially with consideration of inherent biological meanings. As it can detect core regulatory modules effectively, it facilitates pathway/network modelling in systems biology.