My research combines theory and experiments to explore fundamental questions about systems-level control and design. The model organisms that I have been using are the yeast S. cerevisae and (more recently) the nematode C. elegans. The following are examples of recent or current research projects:
1. In budding yeast, we found that two key parts of the cell cycle, periodic phosphorylation-degradation and transcription, are both under the control of the same CDK-APC/C oscillator. This result had been the subject of controversy previously. The number of oscillators makes a difference for establishing synchrony in wild-type cycles, for checkpoint arrest, and for artificially induced cell cycle arrest. We also found that a few genes constitute exceptions to this rule; they oscillate when the CDK-APC/C oscillator is blocked. Based on a mathematical analysis, we showed experimentally that one of these genes (SIC1) helps cell cycles with low mitotic cyclin-CDK levels, which is counter-intuitive because Sic1 is an inhibitor of mitotic cyclins. (Published.)
2. What topological features of biological pathways can be revealed by systematic, dynamic perturbations? System-level circuit motifs point to specific mechanisms and genes that are potentially involved, which can then be further investigated with genetic experiments. (In review.)
3. What are the quantitative laws of cell cycle checkpoint dynamics? (Research in progress.)
Graduate & undergraduate research
The focus of my graduate research was on quantum fluctuation forces, also known as Casimir forces. To calculate these forces, we developed an approach in which the geometric and material properties of the objects are represented by their electromagnetic scattering matrices. In addition to deriving an analytical closed-form expression for the force and investigating it in a variety of new geometries, we derived a theorem, which showed that quantum fluctuation forces cannot create stable configurations in vacuum.
In college and early graduate school, I had worked in biophysics and computational biology, in particular, on solvation energies, protein surface maps, force-induced DNA melting, and protein-DNA recognition.