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Inferring Time-Varying Network Topologies from Gene Expression Data


Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.



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Correspondence to Arvind Rao.

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Rao, A., Hero, A.O., States, D.J. et al. Inferring Time-Varying Network Topologies from Gene Expression Data. J Bioinform Sys Biology 2007, 51947 (2007).

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  • Expression Data
  • Cluster Method
  • Gene Expression Data
  • Network Topology
  • System Biology