by pulling each homolog in opposite direction (contrary to mitotic division where the forces are exerted on each chromatid). The anchorage of the cytoskeleton to the chromosomes takes place at the centromere thanks to a protein complex called kinetochore. This tension results in the alignment of the bivalent at the center of the cell, the chiasmata and the distal cohesion of the sister chromatids being the anchor point sustaining the force exerted on the whole structure. Impressively, human female primary oocytes remains in this tension state for decades (from the establishment of the oocyte in metaphase I during embryonic development, to the ovulation event in adulthood that resume the meiotic division), highlighting the robustness of the chiasma and the cohesion that hold the bivalents together. The cell transcription regulates of developmental genes We develop an approach for capturing genes undergoing transcriptional switching by detecting 'bimodal' gene expression patterns from scRNA-seq data. We integrate the identification of bimodal genes in ES cell differentiation with analysis of chromatin state and for kind of then identify clear cell-state dependent patterns of bimodal, bivalent genes. We show that binarization of bimodal genes can be used to identify differentially expressed genes from fractional ON/OFF proportions. In time series data from differentiating cells, we build a pseudo time approximation and use a hidden Markov model to infer gene activity switching pseudo times, which we use to infer a regulatory network. We identify pathways of switching during differentiation, novel details of those pathway, and transcription factor coordination with downstream targets. Conclusions: Genes with expression levels too low to be informative in conventional scRNA analysis can be used to infer transcriptional switching networks that connect transcriptional activity to chromatin state. in with analysis of chromatin state and for kind of then identify clear cell-state dependent patterns of bimodal, bivalent genes. We show that binarization of bimodal genes can be used to identify differentially expressed genes from fractional ON/OFF proportions. In time series data from differentiating cells, we build a pseudo time approximation and use a hidden Markov model to infer gene activity switching pseudo times, which we use to infer a regulatory network. We identify pathways of switching during differentiation, novel details of those pathway, and transcription factor coordination with downstream targets. This offers a novel and productive means of inferring regulatory networks from scRNA-seq data. Keywords: Bimodality; Bivalency; Chromatin state; Embryonic stem cells; Genome regulatory network; Hidden