Abstract
A change from one stimulus level to a higher constant level often evokes only a transient response in biological systems. The process by which the constant features of the environment are effectively ignored is usually called adaptation, and most organisms display some degree of it in their sensory systems. Examples given later include adaptation in bacterial chemotaxis, desensitization of adrenergic responses, habituation in neural systems, and signal-relay adaptation in Dictyostelium discoideum. The simplest dynamical description of a sensory system that can adapt is one in which the response is a function of a stimulus variable and an inhibitor variable. The static (i.e., time-independent) response for fixed levels of these variables can be displayed by plotting it as a function of the stimulus and the inhibitor, and the dynamic response to a particular sequence of stimuli is represented by a curve on this surface. This representation provides rapid insight into how changes in the underlying model affect the dynamic response, and thus it is useful as a low-level model identification tool for determining what components can be used to achieve a desired response. This will be demonstrated in detail in the second paper of this series. A model for signal-relay adaptation in Dictyostelium discoideum will be presented in this paper and analyzed in the second paper. This model is based on the hypothesis that adaptation is produced by the intracellular calcium-cyclic-AMP network. In view of the fact that many seemingly diverse adaptation processes also center on calcium-second-messenger networks, our results may be more generally applicable.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 35-78 |
| Number of pages | 44 |
| Journal | Mathematical Biosciences |
| Volume | 77 |
| Issue number | 1-2 |
| DOIs | |
| State | Published - Dec 1985 |
| Externally published | Yes |
Bibliographical note
Funding Information:in part by NIH Grant NS19716. in part by a grant from the University in part by NIH Grant GM2913.
Funding Information:
We should like to acknowledges upporto f NIH Grant GM29123 (H.G.O.), NIH Grant NS19716 (P.E.R.), and a grant from the Universityo f Delaware ResearchF und (P.B.M.). We woulda lso like to thank thosew ho madep reprints and reprints available to UY and thosew ho offeredo pinionso f earlier drufts of the model. They include: M. J. Berridge, M. Brenner, P. N. Devreotes,G . Gerisch,J . Mato, P. C. Newell, and L. A. Segel.