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Gain and kinetics of activation in the G-protein cascade of phototransduction
Pages 10-14

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From page 10...
... Now that a quantitative description of the activation steps in transduction has been obtained, perhaps the greatest challenge for the future is to provide a comprehensive description of the shutoff reactions, so that a complete account of the photoreceptor's response to light can be achieved. The transduction of light into a neural signal in rod and cone photoreceptors provides an excellent example of signaling by a guanine nucleotide binding protein (G protein)
From page 11...
... Although qualitative information is available on rhodopsin phosphorylation, on the Gprotein's GTPase activity, on the decline in cytoplasmic Ca2+ concentration, and on calcium's role in activating guanylyl cyclase, there is not as yet a quantitative understanding of the way in which all these factors fit together. For this reason, the following analysis is restricted to the activation steps in phototransduction, and it therefore provides a description only of the onset phase of the response to light.
From page 12...
... As the quantity of activated PDE increases, the soluble cytoplasmic messenger cGMP will be hydrolyzed more rapidly, and the free cGMP concentration will decline (Fig.
From page 13...
... This parameter, A, specifies the amplification constant of transduction and is given by r (kcat/2Km~ L vcy~O NAVBp ~ where kept and Km are the turnover number and Michaelis constant for the fully activated PDE, Vcy~o is the cytoplasmic volume of the outer segment, NAV is Avogadro's number, and BP is the cytoplasm's buffering power for cGMP (74. Comparison with Experiment Amphibian Rods.
From page 14...
... Now that activation of the electrical response can be described quantitatively, a major challenge for the future will be to provide a comprehensive description of the shutoff reactions. Once the inactivation steps can be described quantitatively at a molecular level, it should be straightforward to incorporate them into the stochastic model WALK.


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