This work demonstrates the potential of dynamical systems theory to interpret gene expression in single cells, highlighting its relevance for understanding disease.
The emergence of single-cell transcriptomics has given us novel views of gene expression heterogeneity and cellular trajectories in development and disease at unprecedented resolution. However, an overarching theoretical framework to interpret single-cell gene expression data is lacking. Here we argue that dynamical systems theory can provide an interpretable, causal, and quantitative perspective to understand and analyze these enormously rich data sets, in addition to yielding potential benefits for health care.
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Islam et al. (2025) studied this question.
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