<p>Cell fate decisions typically occur at critical tipping points during development. Identifying these points offers valuable insights into the underlying mechanisms that govern cell fate determination. In this study, we introduce a novel approach called Network Relative Entropy (NRE), which is designed to detect crucial time points during development by analyzing variations in network structures between consecutive time points. After validating the NRE method using simulation data, we apply it to experimental datasets to discern the critical points of early embryonic development. Our findings indicate that the predictions made by the NRE method closely match experimental observations. Furthermore, by ranking the NREs, we identify distinct gene subsets, which we refer to as signaling genes. Statistical analysis reveals a notable divergence in the expression patterns of these signaling genes at the critical points compared to their preceding states. Additionally, we map the correlation coefficients of these signaling genes onto the known protein-protein interaction (PPI) networks. Notably, the correlations among signaling genes exhibit a significant increase at the critical points. These observations provide additional evidence for the reliability of the NRE method from an alternative perspective.</p>