![Sample our Bioscience journals, sign in here to start your access, Latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5925/TOC-Subject-Sample-2021-Bioscience.jpg"})
Abstract
Service-theoretic concepts and methods, widely used in other fields (e.g., telecommunication and operations research), are useful also in a biochemical setting because the treatment of biocatalysts (enzymes, receptors) as servers and their ligands as customers, based on the established formal methods of service or queuing theory, may lead to insights and results unobtainable by conventional, mass-action-law-based theories. In this article, we apply the service-theoretic approach to receptor-agonist systems and show how by changing the stochastic time pattern of “operationally relevant” point events (e.g., instants of agonist arrival, instants of postclimax agonist departure) a great variety of dose-response curves may be generated, even in very simple reaction schemes, which, according to mass action kinetics, invariably lead to hyperbolic r(A) curves (r and A stand for response and agonist concentration, respectively). The molecular timing inherent to a hyperbolic response system is not optimal: for instance, at the agonist concentration A50, half of the agonist molecules are rejected (“lost”) because of unfortunate timing of the arrival events. The fraction of lost arrivers can be diminished considerably if the arrivals are better timed: “sub-Poisson” arrivals improve the timing and, thus, convert hyperbolic r(A) curves into “lifted” nonhyperbolic ones. Conversely, “super-Poisson” arrivals make the nonoptimal timing in hyperbolic response systems even worse and, thus, convert hyperbolic r(A) curves into “depressed” nonhyperbolic ones. Furthermore, under special timing conditions, nonhyperbolic r(A) curves can be generated, which are partly lifted, partly depressed relative to the reference hyperbola, and which resemble in shape well-known nonhyperbolic forms of enzyme and receptor kinetics (negatively cooperative, positively cooperative, and sigmoidal kinetics). In addition unusual (undulatory and sawtooth-like) r(A) curves can be generated solely by changing the temporal pattern of arrival and service completion instants. Virtually any shape of dose-response curves may be obtained by allowing for probability distributions whose characteristic shape varies with their mean; we call such distributions “variomorphic” and apply them to the arrival process of agonist molecules.
| Abbreviations | ||
| CV | = | coefficient of variation |
| D | = | deterministic |
| Ek | = | Erlangian distribution of order k |
| G | = | general distribution |
| Gvario | = | general and variomorphic distribution |
| H2 | = | hyperexponential distribution of order 2 |
| M | = | exponential distribution |
| PH | = | phase-type distribution |
| Abbreviations | ||
| CV | = | coefficient of variation |
| D | = | deterministic |
| Ek | = | Erlangian distribution of order k |
| G | = | general distribution |
| Gvario | = | general and variomorphic distribution |
| H2 | = | hyperexponential distribution of order 2 |
| M | = | exponential distribution |
| PH | = | phase-type distribution |