Simulation-free Structure Learning for Stochastic Dynamics
This work addresses the challenge of modeling and inferring causal relationships in stochastic, high-dimensional systems like cell biology, which is incremental as it combines existing tasks into a unified approach.
The authors tackled the joint problem of learning network structure and modeling stochastic population dynamics in high-dimensional physical systems, presenting StructureFlow, which achieved simultaneous structure learning and trajectory inference on synthetic, simulated, and experimental datasets.
Modeling dynamical systems and unraveling their underlying causal relationships is central to many domains in the natural sciences. Various physical systems, such as those arising in cell biology, are inherently high-dimensional and stochastic in nature, and admit only partial, noisy state measurements. This poses a significant challenge for addressing the problems of modeling the underlying dynamics and inferring the network structure of these systems. Existing methods are typically tailored either for structure learning or modeling dynamics at the population level, but are limited in their ability to address both problems together. In this work, we address both problems simultaneously: we present StructureFlow, a novel and principled simulation-free approach for jointly learning the structure and stochastic population dynamics of physical systems. We showcase the utility of StructureFlow for the tasks of structure learning from interventions and dynamical (trajectory) inference of conditional population dynamics. We empirically evaluate our approach on high-dimensional synthetic systems, a set of biologically plausible simulated systems, and an experimental single-cell dataset. We show that StructureFlow can learn the structure of underlying systems while simultaneously modeling their conditional population dynamics -- a key step toward the mechanistic understanding of systems behavior.