Molecular trans-regulatory networks (TRNs) comprised of cell signalling, transcriptional, translational, and (epi)genomic regulations are central to health and disease. Computational approaches are instrumental in characterising TRNs of cells at the systems level. Our research lies at the interface of bioinformatics and systems biology. We develop computational and statistical models to reconstruct cell signalling, epigenomic/transcriptional, and proteomic networks, and characterise their cross-talk and trans-regulations in various cellular processes and systems. By integrating heterogeneous trans-omic data with the goal of generating testable hypotheses and predictions, we aim to tackle the following research questions:
How do different layers of regulations talk to each other in controlling stem cell fate?
Can we accurately predict stem cell differentiation trajectories based on their TRNs?
The mechanisms of stem/progenitor cells in establishing identities and making cell fate decisions
