Presenter: Christopher JacksonTopic: Regulatory network inference using dynamic models
Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. These modeled regulatory relationships should reflect causal, biochemical relationships between molecules within the organism. To date, these learned networks have tended to lack predictive power. We propose network inference using time-dependent kinetic models of gene expression will construct networks based on real biophysical parameters and therefore be more predictive for biochemical relationships. We have collected 170,000 individual cell transcriptome measurements of cells responding to a chemical perturbation to test the feasibility of this modeling approach.