Reverse-time inference of targeted biological dynamics

Mesoscopic bio-systems typically evolve towards functionally important target-states, such as cell cycle checkpoints or decision boundaries for the release of complex behavior. To infer the underlying directional out-of-equilibrium dynamics from such data, we develop a theory of target-state-aligned ensembles that reveals whether and when the system can be represented by a single, effective stochastic equation of motion. We show how, in this equation, genuine biological forces can be separated from spurious forces, which, invariably arise from target-state-alignment. We apply our inference scheme to canonical biological examples such as cell division and morphogenesis.