One of the central challenges of understanding complex systems---such
as financial markets or cellular information processing networks---is
to identify which system components are causally related. This work
introduces a probabilistic framework for learning the causal structure
of sparsely coupled nonlinear dynamical systems from observed time
series data. The proposed algorithm adopts a continuous time Gaussian
Process model of the system dynamics and provides an estimated
distribution over directed network topologies representing the latent
interaction among system components. The method is shown to identify
robustly the topological structure of a diverse class of synthetic
gene regulatory networks. (Joint work with Sandy Klemm and Karsten Borgwardt.)