基于AI的模拟推断：构建小鼠早期胚胎发生的真实空间-随机模型
AI-powered simulation-based inference of a genuinely spatial-stochastic model of early mouse embryogenesis
AI总结：
本研究构建小鼠早期胚胎ICM分化的多尺度空间-随机模型，结合AI驱动的SNPE算法与SBI开展贝叶斯推断，揭示FGF4反馈调控细胞随机基因表达形成稳定组织模式的机制，发现初始条件高变异性可提升组织动力学精度。
摘要：
理解多细胞生物如何可靠地调控细胞命运决定是发育生物学的核心挑战。这一问题在早期哺乳动物发育中尤为引人关注：早期细胞谱系分化的起始过程看似具有细胞自主性，最终却能在组织层面稳定呈现。本研究构建了一个多尺度的小鼠胚胎发生空间-随机模拟器，聚焦于囊胚阶段的内细胞团（ICM）分化。我们的模型包含符合生物物理现实的调控相互作用，并考虑了在细胞尺度驱动细胞命运决定的生物过程的内在随机性。我们改进了事件驱动模拟技术，纳入相关的组织尺度现象，并将其与基于模拟的推断（SBI）相结合，采用了最新的基于AI的参数学习方法：序列神经后验估计（SNPE）算法。利用该框架，我们开展了大规模贝叶斯推断分析，确定了能够复现实验观测到的系统行为的参数集。我们阐明了通过信号蛋白FGF4介导的自分泌和旁分泌反馈，如何将细胞层面命运决定基因的固有随机表达，调控为组织层面可重复的ICM模式形成。该机制显著不依赖于系统规模。FGF4不仅能确保ICM中正确的细胞谱系比例，还能增强其对扰动的恢复力。有趣的是，我们发现细胞内初始条件的高变异性不会损害组织层面动力学的准确性和精确性，反而可能有所提升。本工作为驱动ICM分化的生化过程提供了真实的空间-随机描述，并明确了其稳健进行的必要条件。
英文摘要：
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.
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