A key challenge in many large data analysis problems is to provide tractable algorithms that can capture the complex structure and dynamics inherent to high-dimensional systems. In this talk, I focus on a framework for computationally efficient identification and estimation of a rich class of systems composed of interconnected linear and nonlinear blocks. The methodology leverages message passing techniques to provide a scalable general approach with provable guarantees in consistency and convergence in a wide variety of settings. This work improves upon current estimation of large neural dynamical networks via state-of-the-art multi-neuron imaging.