Network Inference and Analysis

Complex dynamical biomolecular systems govern virtually all biological processes, on time scales ranging from development to physiology. A paramount problem is to understand the structural and dynamical properties of such systems and their role in cellular function and dysfunction. Our group is developing model inference approaches by integrating the information from multiple types of measurement data in a variety of modeling formalisms, including differential equations, nonlinear discrete dynamical systems, such as probabilistic Boolean networks, dynamic Bayesian networks, and others.

Various data sources such as DNA sequences, measurements of gene expression (RNAseq), protein expression (protein arrays, mass spectrometry), genome-wide protein-DNA interactions (ChIP-seq), functional annotations, and literature-based relationships, can be combined in statistically principled ways. The inferred models can then be used to predict various aspects of system behavior under environmental or genetic perturbations. In particular, the predictive nature of such models sets the stage for optimal intervention strategies intended to control system behavior, particularly in the context of disease.