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One of the grand challenges of cell biology is inferring the gene regulatorynetwork (GRN) which describes interactions between genes and their productsthat control gene expression and cellular function. We can treat this as acausal discovery problem but with two non-standard challenges: (1) regulatorynetworks are inherently cyclic so we should not model a GRN as a directedacyclic graph (DAG), and (2) observations have significant measurement noise,so for typical sample sizes there will always be a large equivalence class ofgraphs that are likely given the data, and we want methods that capture thisuncertainty. Existing methods either focus on challenge (1), identifying cyclicstructure from dynamics, or on challenge (2) learning complex Bayesianposteriors over DAGs, but not both. In this paper we leverage the fact that itis possible to estimate the "velocity" of gene expression with RNA velocitytechniques to develop an approach that addresses both challenges. Because wehave access to velocity information, we can treat the Bayesian structurelearning problem as a problem of sparse identification of a dynamical system,capturing cyclic feedback loops through time. Since our objective is to modeluncertainty over discrete structures, we leverage Generative Flow Networks(GFlowNets) to estimate the posterior distribution over the combinatorial spaceof possible sparse dependencies. Our results indicate that our method learnsposteriors that better encapsulate the distributions of cyclic structurescompared to counterpart state-of-the-art Bayesian structure learningapproaches.

DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets