A central challenge in biology is to predict system behavior in time, given incomplete knowledge, strong interactions, and noise. We have pursued multiple approaches to building predictive dynamical descriptions of biological systems, making progress toward general principles. We have focused on three specific problems:
- The number of global oscillators controlling the 'cell cycle', the process by which cells replicate, had been unresolved. This left a number of fundamental questions unanswered: How do different processes sync up during the cell cycle? How can the cell cycle be arrested? We found that one central oscillator controls two major cell cycle processes, periodic phosphorylation/degradation and transcription, contradicting previous views. However, we also found exceptions to this rule; pursuing one such gene, we discovered a new, counter-intuitive genetic interaction between an inhibitor and a target, which violates the usual rules of genetics.
- Can dynamic perturbations be used to identify molecular circuit topologies? We discovered dynamic 'response signatures' for specific circuit topologies and used them to solve previously hard-to-resolve questions: We identified the circuit responsible for timing robustness in yeast cell cycle control as well as a circuit leading to adaptation in the C. elegans olfactory sensory neuron AWA.
- Do cell cycle checkpoints 'fail' in predictable patterns? A mathematically optimal checkpoint strategy, which we derived, predicts how cell cycle checkpoints fail as a function of the number of errors. Our preliminary experimental results agree with our predictions but challenge current views in the field; checkpoint failure may be a significantly more common phenomenon than previously thought.
Coffee: 3:30 pm, 245 Compton