Inference of the core gene regulatory network underlying seam cell development in Caenorhabditis elegans

Gene regulatory networks are fundamental in cellular decision-making, yet even in well-studied systems, their topologies are often poorly characterised. The nematode Caenorhabditis elegans contains a population of stem-like cells, known as seam cells. While seam cells are essential to generate the majority of the animal epidermis as well as specific neurons, the architecture of the underlying gene network has not been elucidated. Here, we combine experiments, mathematical modelling and statistical inference to uncover the architecture of the seam cell gene network focusing on three core transcription factors (TFs), the GATA factors ELT-1, EGL-18 and the Engrailed homolog CEH-16. We use single-molecule FISH (smFISH) to quantify TF mRNA abundance in single seam cells in both wild type and mutant backgrounds. We then predict potential TF interactions and their quantitative strengths using a combination of Modular Response Analysis, ordinary differential equations and a Bayesian model discovery approach. Taken together, our findings suggest new relationships between core TFs in seam cells and highlight an approach that can be used to infer quantitative networks from smFISH data.