MeCoGen: Mechanistically Conditioned Generative Forecasting of Single-Cell Dynamics

Forecasting single-cell populations beyond the observed time horizon is challenging because cellular states and regulatory programs can transition into unseen regimes, particularly during iPSC differentiation, while observations are limited to heterogeneous, unlabeled population snapshots. We identify a fundamental conditioning bottleneck: although modern generative models can represent complex transcriptional distributions, they require reliable future biological conditions for accurate long-horizon forecasting. To address this challenge, we introduce MeCoGen (Mechanistically Conditioned Generation), a mechanistic-data-driven hybrid framework for single-cell forecasting. MeCoGen first uses a stochastic mechanistic model, informed by a subset of core regulatory genes and interactions, to predict a biologically structured future state capturing regulatory dynamics and developmental progression. This predicted state then conditions a generative model that synthesizes future high-dimensional transcriptomic profiles. To capture the sparse and stochastic nature of single-cell expression data, the generator separately learns gene occurrence and expression magnitude using discrete diffusion and conditional flow matching, respectively. We evaluate MeCoGen on three temporal single-cell systems spanning differentiation and cellular reprogramming. Across all datasets, MeCoGen consistently outperforms recent temporal forecasting baselines, while controlled conditioning experiments demonstrate that mechanistically predicted future states substantially improve long-horizon predictions over purely data-driven extrapolation. Ablation studies further validate the benefits of separately modeling gene occurrence and expression magnitude.