Predictive Phenomics
Integrating multi-omics with mechanistic models to predict how cells respond to genetic, environmental, and drug perturbations.
A phenotype emerges from many molecular layers acting together. We integrate quantitative multi-omics measurements, especially proteomics and phosphoproteomics, with mechanistic models of signaling and metabolism to predict how cells respond to genetic, environmental, and drug perturbations, and to identify which measurements matter most for those predictions.
Selected work:
- A phosphoproteomics data resource for systems-level modeling of kinase signaling networks (bioRxiv, 2023).
- Targeted quantification of protein phosphorylation and its contributions to mathematical modeling of signaling pathways (Molecules, 2023).
- Prediction of optimal drug schedules for controlling autophagy (Scientific Reports, 2019).
- A cell population–resolved multi-omics atlas of the developing lung (Am. J. Respir. Cell Mol. Biol., 2025).
Applications: drug scheduling in cancer, and lung development and disease.
TODO (Song): add current projects, collaborators, and funding.
People
- Song Feng — Assistant Professor & Principal Investigator