Technical report - The application of probability-generating functions to linear-quadratic radiation survival curves

Authors
Citation
Ws. Kendal, Technical report - The application of probability-generating functions to linear-quadratic radiation survival curves, INT J RAD B, 76(4), 2000, pp. 581-587
Citations number
25
Categorie Soggetti
Experimental Biology
Journal title
INTERNATIONAL JOURNAL OF RADIATION BIOLOGY
ISSN journal
09553002 → ACNP
Volume
76
Issue
4
Year of publication
2000
Pages
581 - 587
Database
ISI
SICI code
0955-3002(200004)76:4<581:TR-TAO>2.0.ZU;2-O
Abstract
Purpose: To illustrate how probability-generating functions (PGFs) can be e mployed to derive a simple probabilistic model for clonogenic survival afte r exposure to ionizing irradiation. Methods: Both repairable and irreparable radiation damage to DNA were assum ed Co occur by independent (Poisson) processes, at intensities proportional to the irradiation dose. Also, repairable damage was assumed to be either repaired or further (lethally) injured according to a third (Bernoulli) pro cess, with the probability of lethal conversion being directly proportional to dose. Using the algebra of PGFs, these three processes were combined to yield a composite PGF that described the distribution of lethal DNA lesion s in irradiated cells. Results: The composite PGF characterized a Poisson distribution with mean, alpha D + beta D-2, where D was dose and alpha and beta were radiobiologica l constants. This distribution yielded the conventional linear-quadratic su rvival equation. To test the composite model, the derived distribution was used to predict the frequencies of multiple chromosomal aberrations in irra diated human lymphocytes. The predictions agreed well with observation. Thi s probabilistic model was consistent with single-hit mechanisms, but it was not consistent with binary misrepair mechanisms. Conclusions: A stochastic model for radiation survival has been constructed from elementary PGFs that exactly yields the linear-quadratic relationship . This approach can be used to investigate other simple probabilistic survi val models.