  Understanding how multicellular organisms reliably orchestrate cell-fate
decisions is a central challenge in developmental biology. This is particularly
intriguing in early mammalian development, where early cell-lineage
differentiation arises from processes that initially appear cell-autonomous but
later materialize reliably at the tissue level. In this study, we develop a
multi-scale, spatial-stochastic simulator of mouse embryogenesis, focusing on
inner-cell mass (ICM) differentiation in the blastocyst stage. Our model
features biophysically realistic regulatory interactions and accounts for the
innate stochasticity of the biological processes driving cell-fate decisions at
the cellular scale. We advance event-driven simulation techniques to
incorporate relevant tissue-scale phenomena and integrate them with
Simulation-Based Inference (SBI), building on a recent AI-based parameter
learning method: the Sequential Neural Posterior Estimation (SNPE) algorithm.
Using this framework, we carry out a large-scale Bayesian inferential analysis
and determine parameter sets that reproduce the experimentally observed system
behavior. We elucidate how autocrine and paracrine feedbacks via the signaling
protein FGF4 orchestrate the inherently stochastic expression of
fate-specifying genes at the cellular level into reproducible ICM patterning at
the tissue scale. This mechanism is remarkably independent of the system size.
FGF4 not only ensures correct cell lineage ratios in the ICM, but also enhances
its resilience to perturbations. Intriguingly, we find that high variability in
intracellular initial conditions does not compromise, but rather can enhance
the accuracy and precision of tissue-level dynamics. Our work provides a
genuinely spatial-stochastic description of the biochemical processes driving
ICM differentiation and the necessary conditions under which it can proceed
robustly.
