This paper relates some neat work done at the University of Texas to understand signal processing by beta-adrenergic receptors:

In layman's terms:

The β2 adrenergic receptor (β2AR) is involved in regulating many cellular processes such as smooth muscle relaxation in the airways and the vasculature. Drugs that activate the β2AR are used in treating asthma and chronic obstructive pulmonary disorder (COPD), and prolonged use of these drugs leads to the loss of their effects. Thus, a dynamic model of how the β2AR responds to different drugs is fundamental to their rational use. In this study a consensus model of G protein coupled receptor kinase (GRK)-mediated receptor regulation was formulated based on quantitative measures of six processes involved in β2AR regulation. This model was then used to simulate the consequences of manipulating key rates associated with the GRK-mediated β2AR regulation, leading to predictions which will provide a useful framework for further tests and elaborations of the model in basic and clinical research.

More technically:

We further used the model to predict the consequences of (1) modifying rates such as GRK phosphorylation of the receptor, arrestin binding and dissociation from the receptor, and receptor dephosphorylation that should reflect effects of knockdowns and overexpressions of these components; and (2) varying concentration and frequency of agonist stimulation “seen” by the β2AR to better mimic hormonal, neurophysiological and pharmacological stimulations of the β2AR. Exploring the consequences of rapid pulsatile agonist stimulation, we found that although resensitization was rapid, the β2AR system retained the memory of the previous stimuli and desensitized faster and much more strongly in response to subsequent stimuli. The latent memory that we predict is due to slower membrane dephosphorylation, which allows for progressive accumulation of phosphorylated receptor on the surface.

What I like here is that, by modeling, they come up with a hypothesis about the biochemical mechanism underlying a system-level property. In other words, they build a model out of the biochemical components, capture a system-level phenomenon with the model, and then can explain how this system-level phenomenon arises or emerges from the underlying physical processes. And all of this gives the researchers some hypotheses they can test - it lets them plan experiments in the most fruitful direction.

This is the future of molecular cell biology.

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