Fundamental Limits of Inferring Dynamical Gene Regulatory Models from Single-Cell Data

A long-standing goal in systems biology is to construct computational models that capture gene regulatory dynamics. On the one hand, graph-based regulatory network models rely on prior knowledge but are constrained by unknown kinetic parameters. On the other hand, recent advancements in single-cell sequencing technologies, particularly RNA velocity and pseudotime analysis, offer data-driven approaches to infer kinetic parameters. However, accurate modeling of biological dynamics requires the estimation of the Jacobian matrix, which captures signal propagation within a regulatory system. Several computational methods have recently emerged to infer Jacobians from single-cell data, including SpliceJAC, scMomentum, and Dynamo. Here, we evaluate these methods by analyzing Jacobian eigenvalues, assessing their ability to capture stability and oscillatory properties. Using simulated ground truth data, we find that all methods are susceptible to data quality, rendering them unreliable under real-world conditions. In conclusion, beyond improvements in data quality, there is a critical need to develop more robust computational methods for estimating Jacobians from single-cell data. Addressing these challenges will enable the derivation of more accurate dynamical models of gene regulation at scale.