Simulation-conditioned generative modeling for biologically realistic pattern prediction

Pattern formation underlies biological organization across scales, but predicting experimentally observed patterns remains difficult because mechanistic models and data-driven generative models fail in complementary ways. Coarse-grained mechanistic models can encode causal constraints and global morphology, yet they omit fine-scale features such as texture, color gradients, and stochastic replicate-to-replicate variation. In contrast, generative image models can produce realistic images but are not inherently grounded in the biophysical rules that shape real patterns. Here, we introduce a simulation-conditioned generative framework that uses mechanistic simulations as spatial priors for gene