Dynamic Modeling of Cellular Senescence Gene Regulatory Network

International audienceCellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains a challenge. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN controlling senescence integrating time-series transcriptome and transcription factor depletion datasets. We infer a set of differential equations modeling the CS transcriptome using the “Sparse Identification of Nonlinear Dynamics” (SINDy) algorithm, discriminate genes with potential hidden regulators, validating the inferred GRN for time points not included in the training data. Our work is a proof of concept of a data-based method for GRN reconstruction, consolidating an iterative, powerful mathematical platform for more comprehensive senescence models that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact