The aims of this project are:
- Infer from single cell genomics data gene regulatory network (GRN) topologies involving cross regulatory interactions between transcription factors (TFs).
- Uncover a higher-level regulatory organization of TFs beyond regulons, which embody complex design principles and dynamical behaviors.
- Interrogate the repertoire of dynamic behaviors realizable by the inferred TF-TF network topologies based on systems biology-oriented network modeling techniques (e.g. Boolean/ODE network modeling).
- Based on the extracted knowledge suggest potential new experiments aimed toward the elucidation of candidate systems-level molecular mechanisms underlying robust cell type specification, gene expression heterogeneity within cell populations, as well as regulatory divergence between homologous cell types.
The data analyzed was downloaded as a loom file from the data base Scope. Here I focused on the data reported in: A single-cell transcriptome atlas of the ageing Drosophila brain. The data set encompass ~57k cells. Boolean network modeling is used as a first approximation to gain intuition on the possible repertoire of dynamic behaviors that the inferred GRNs can exhibit.
Data analysis, mathematical modeling and visualization tasks were carried out in Python, Mathematica and R (see programs included)