Joint trajectory and network inference via reference fitting
This work addresses the problem of reconstructing interactions in complex biological systems for researchers in systems biology, offering a novel approach that combines temporal and perturbational data to improve causal insights.
The paper tackles the challenge of network inference in systems biology by proposing a method that jointly learns cellular trajectories and infers directed, signed networks from dynamical and perturbational single-cell data, leveraging min-entropy estimation for stochastic dynamics.
Network inference, the task of reconstructing interactions in a complex system from experimental observables, is a central yet extremely challenging problem in systems biology. While much progress has been made in the last two decades, network inference remains an open problem. For systems observed at steady state, limited insights are available since temporal information is unavailable and thus causal information is lost. Two common avenues for gaining causal insights into system behaviour are to leverage temporal dynamics in the form of trajectories, and to apply interventions such as knock-out perturbations. We propose an approach for leveraging both dynamical and perturbational single cell data to jointly learn cellular trajectories and power network inference. Our approach is motivated by min-entropy estimation for stochastic dynamics and can infer directed and signed networks from time-stamped single cell snapshots.