arXiv:2510.13018v1 Announce Type: new
Abstract: Modeling cellular responses to genetic and chemical perturbations remains a central challenge in single-cell biology. Existing data-driven framework have advanced perturbation prediction through variational autoencoders, chemically conditioned autoencoders, and large-scale transformer pretraining. However, these models are prone to local optima in the nonconvex Waddington landscape of cell fate decisions, where poor initialization can trap trajectories in spurious lineages or implausible differentiation outcomes. While executable gene regulatory networks complement these approaches, automated design frameworks incorporate biological priors through multi-agent optimization. Yet, an approach that is completely data-driven with well-designed initialization to escape local optima and converge to a proper lineage remains elusive. In this work, we introduce a multistage reinforcement learning algorithm tailored for single-cell perturbation modeling. We first compute an explicit natural gradient update using Fisher-vector products and a conjugate gradient solver, scaled by a KL trust-region constraint to provide a safe, curvature-aware the first step for the policy. Starting with these preconditioned parameters, we then apply a second phase of proximal policy optimization (PPO) with clipped surrogates, exploiting minibatch efficiency to refine the policy. We demonstrate that this initialization substantially improves generalization on Single-cell RNA sequencing (scRNA-seq) and Single-cell ATAC sequencing (scATAC-seq) pertubation analysis.