A single cell contains trillions of molecules: proteins that act as scaffolds, enzymes that catalyze chemical reactions, nucleic acids that store and transmit information, lipids that compartmentalize, metabolites that serve as fuel or signals. Although these biomolecules are present in cells spanning the tree of life, modeling cellular function using the laws of physics has proven difficult due to the sheer number of atoms involved and their nonlinear dynamics. To better understand how cells function and assemble, our lab develops new technologies to measure these biomolecules from single cells, and builds AI models from the resulting data.
Single-cell sequencing has transformed biology in two ways: it provides a unified language for comparing cells across tissues, organisms, and species, and it enables high-throughput genetic screens with genomic readouts by using each cell as a container. We are developing single-cell co-assays that measure multiple genomic modalities within the same cell, linking observations directly and enabling more robust causal inference.
The Srivatsan Lab is fascinated by cellular self-assembly. From trillions of molecules, cells spontaneously organize into compartments, structures, and machines—without a blueprint or external direction. For a glimpse of this complexity, we recommend exploring David Goodsell and Martina Maritan's model of a mycoplasma cell, which illustrates the dense molecular landscape inside one of the simplest living cells. A few questions we're interested in include:
1. What macromolecular complexes assemble from individual proteins?
2. What aspects of cellular regulation can be gleaned from its genome sequence?
3. What are the minimal set of genes needed to perform a cellular function?
4. Are there cellular life forms that remain undiscovered?
5. Can we design and build novel cellular structures from first principles?