UC San Diego

In response to stress, intracellular signaling proteins activate gene expression programs that protect the cell, address the instigating stress, or result in programmed cell death. In many cases, information about the stimulus is encoded in the dynamics of the signal. Stress-induced signaling dynamics can therefore dictate the cellular response to stress. Recently, it was shown that these dynamics are affected by the resting state of the cell prior to stimulation. If this relationship between steady state and stimulus-induced dynamics was known, then we might predict the cellular response to a particular stimulus using steady state measurements, or engineer a stimulus to elicit a desired response. These are the foundations of diagnostic biomarkers and personalized medicine. To characterize the relationship between steady state and the cellular response, I developed a suite of computational methods and applied them to the p53, NF- [kappa]B, and cell death pathways. First, I developed a method to derive analytical expressions for the steady states of mass action models. By applying this method to a model of cell death, I show how the steady state concentrations of different signaling proteins can affect the tolerance to the death-inducing ligand, TRAIL. Next I extended this method to examine perturbations in the steady state that don't affect the steady state protein concentrations. Applying this method to the p53 and NF- [kappa]B stress-response pathways, I show that a protein turnover signaling motif controls the stimulus-sensitivity of these two different pathways. Finally, using a Monte Carlo method, I show how sampling of the steady state prior to simulation can identify steady state predictors of the response to TRAIL. Interestingly, kinetic features, rather than steady state concentrations, figured prominently among the best predictors. If true, this has severe consequences for clinical biomarker discovery, which is based on measurements of protein abundance and not kinetic features