Comment: The 2005 Neyman Lecture: Dynamic Indeterminism in Science

Comment on ``The 2005 Neyman Lecture: Dynamic Indeterminism in Science'' [arXiv:0808.0620]Comment: Published in at http://dx.doi.org/10.1214/07-STS246A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optimality in DNA repair

Peer reviewedPublisher PD

Mathematical methods and models for radiation carcinogenesis studies

Research on radiation carcinogenesis requires a twofold approach. Studies of primary molecular lesions and subsequent cytogenetic changes are essential, but they cannot at present provide numerical estimates of the risk of small doses of ionizing radiations. Such estimates require extrapolations from dose, time, and age dependences of tumor rates observed in animal studies and epidemiological investigations, and they necessitate the use of statistical methods that correct for competing risks. A brief survey is given of the historical roots of such methods, of the basic concepts and quantities which are required, and of the maximum likelihood estimates which can be derived for right censored and double censored data. Non-parametric and parametric models for the analysis of tumor rates and their time and dose dependences are explained

Studies of the dose-effect relation

Dose-effect relations and, specifically, cell survival curves are surveyed with emphasis on the interplay of the random factors — biological variability, stochastic reaction of the cell, and the statistics of energy deposition —that co-determine their shape. The global parameters mean inactivation dose, , and coefficient of variance, V, represent this interplay better than conventional parameters. Mechanisms such as lesion interaction, misrepair, repair overload, or repair depletion have been invoked to explain sigmoid dose dependencies, but these notions are partly synonymous and are largely undistinguishable on the basis of observed dose dependencies. All dose dependencies reflect, to varying degree, the microdosimetric fluctuations of energy deposition, and these have certain implications, e.g. the linearity of the dose dependence at small doses, that apply regardless of unresolved molecular mechanisms of cellular radiation action

Renormalization of radiobiological response functions by energy loss fluctuations and complexities in chromosome aberration induction: deactivation theory for proton therapy from cells to tumor control

We employ a multi-scale mechanistic approach to investigate radiation induced cell toxicities and deactivation mechanisms as a function of linear energy transfer in hadron therapy. Our theoretical model consists of a system of Markov chains in microscopic and macroscopic spatio-temporal landscapes, i.e., stochastic birth-death processes of cells in millimeter-scale colonies that incorporates a coarse-grained driving force to account for microscopic radiation induced damage. The coupling, hence the driving force in this process, stems from a nano-meter scale radiation induced DNA damage that incorporates the enzymatic end-joining repair and mis-repair mechanisms. We use this model for global fitting of the high-throughput and high accuracy clonogenic cell-survival data acquired under exposure of the therapeutic scanned proton beams, the experimental design that considers $\gamma$-H2AX as the biological endpoint and exhibits maximum observed achievable dose and LET, beyond which the majority of the cells undergo collective biological deactivation processes. An estimate to optimal dose and LET calculated from tumor control probability by extension to $~ 10^6$ cells per $mm$-size voxels is presented. We attribute the increase in degree of complexity in chromosome aberration to variabilities in the observed biological responses as the beam linear energy transfer (LET) increases, and verify consistency of the predicted cell death probability with the in-vitro cell survival assay of approximately 100 non-small cell lung cancer (NSCLC) cells

Stochastic Model for Tumor Control Probability: Effects of Cell Cycle and (A)symmetric Proliferation

Estimating the required dose in radiotherapy is of crucial importance since the administrated dose should be sufficient to eradicate the tumor and at the same time should inflict minimal damage on normal cells. The probability that a given dose and schedule of ionizing radiation eradicates all the tumor cells in a given tissue is called the tumor control probability (TCP), and is often used to compare various treatment strategies used in radiation therapy. In this paper, we aim to investigate the effects of including cell-cycle phase on the TCP by analyzing a stochastic model of a tumor comprised of actively dividing cells and quiescent cells with different radiation sensitivities. We derive an exact phase-diagram for the steady-state TCP of the model and show that at high, clinically-relevant doses of radiation, the distinction between active and quiescent tumor cells (i.e. accounting for cell-cycle effects) becomes of negligible importance in terms of its effect on the TCP curve. However, for very low doses of radiation, these proportions become significant determinants of the TCP. Moreover, we use a novel numerical approach based on the method of characteristics for partial differential equations, validated by the Gillespie algorithm, to compute the TCP as a function of time. We observe that our results differ from the results in the literature using similar existing models, even though similar parameters values are used, and the reasons for this are discussed.Comment: 12 pages, 5 figure