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

Abstract 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 methods such as Dynamo and scMomentum generate models that capture the behavioral properties of the cells, however, they do not reliably capture the structural properties. Our results highlight the limitations of dynamic GRN inference, namely, the inability of kinetics to alleviate the need for further structural constrains for the models to adequately reflect the structural properties of the GRN. Developing more robust approaches for Jacobian estimation from single-cell data will be essential for building accurate, large-scale dynamical models of gene regulation. Graphical Abstract Overview of Benchmarking Jacobian-based inference in single-cell RNA Data.