In this talk, we will first discuss the application of optimal-transport-based algorithms to the identification of gene-regulatory networks using temporal single-cell gene expression counts. After demonstrating its effectiveness on simulated data, we apply this method to single-cell gene expression from the human somatic cell population undergoing conversion to induced pluripotent stem cells and developmental timepoints in Drosophila. Our results recover the temporal sequencing of gene expression data and make predictions for the underlying GRNs. Next, we propose a generative model scNODE that can predict realistic in silico single cell gene expression at any time point to enable temporal downstream analyses. scNODE integrates a variational autoencoder (VAE) with neural ordinary differential equations (ODEs) to predict gene expression in a continuous and non-linear latent space. Importantly, scNODE adds a regularization term to integrate the overall dynamics of cell developments to the latent space, such that the learned latent representation is informative and interpretable.