Design principles of gene regulatory networks underlying low-dimensional cell-fate decision systems

Cell-fate decisions, driven by changes in gene expression, are often binary switches. This is reflected in transcriptomic data, where the first principal component (PC1) explains a significant percentage of variance. Interestingly, PC1 variance remains stably high despite errors in gene signature, a phenomenon we term "PC1 stability." While binary decisions are often attributed to "core toggle switches" of two mutually inhibiting transcription factors, actual Gene Regulatory Networks (GRNs) are far more complex. The role of this complexity in maintaining low dimensionality and PC1 stability is unclear.

We analyzed GRNs across various cell-fate contexts and simulated their behavior over diverse parameters. Our findings reveal that GRNs often feature two mutually inhibiting "teams" of genes, where intra-team members positively influence each other, and cross-team members exert negative influences. Perturbing these networks showed strong teams promote both high PC1 variance and PC1 stability. Using artificial networks, we found that multiple toggle-switch-based networks can give rise to high PC1 variance, teams are necessary for PC1 stability. We thus propose that team-based topology is key to robust, low-dimensional cell-fate canalization, and the absence of PC1 stability in transcriptomic data for a given gene signature can indicate a lack of strong, cohesive interactions in the underlying gene regulatory network.