A Stochastic Markov Model of Cellular Response to Radiation

Fornalski, Krzysztof W.; Dobrzyński, L.; Janiak, Marek K.

doi:10.2203/dose-response.11-003.fornalski

Cited by 9 publications

(10 citation statements)

References 35 publications

(57 reference statements)

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“…The shape of all of the curves is virtually identical, differing only by a multiplication factor. These curves are, however, quite different from the ones obtained by Fornalski et al 17 who used Monte Carlo simulations of the cancer cells’ growth. As mentioned earlier, these curves can be perfectly described by the Gompertz curve; Figure 6 shows the fit of the Gompertz curve N cancer ( t ) = 8.27844·exp[−6.51319·exp(−0.010028· t )] to the calculated points for the exemplary case of k = 4 from Figure 5.…”

Section: Neoplastic Transformation Of Mutated Cellscontrasting

confidence: 87%

“…Obviously, it is the product of the thickness of the target (in cm) and the numerical density of the interacting objects, (eg, number of cells per cm 3 ). The concept of P hit in Equation (1) was originally used by us in the Monte Carlo chain cellular model, 17 but the validity of this formula has not been verified. Now, such a validation is presented in Appendix B.…”

Section: Creation Of Lesions In a Cell After Deposition Of Radiation mentioning

confidence: 99%

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Modeling Cell Reactions to Ionizing Radiation: From a Lesion to a Cancer

et al. 2019

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This article focuses on the analytic modeling of responses of cells in the body to ionizing radiation. The related mechanisms are consecutively taken into account and discussed. A model of the dose- and time-dependent adaptive response is considered for 2 exposure categories: acute and protracted. In case of the latter exposure, we demonstrate that the response plateaus are expected under the modelling assumptions made. The expected total number of cancer cells as a function of time turns out to be perfectly described by the Gompertz function. The transition from a collection of cancer cells into a tumor is discussed at length. Special emphasis is put on the fact that characterizing the growth of a tumor (ie, the increasing mass and volume), the use of differential equations cannot properly capture the key dynamics—formation of the tumor must exhibit properties of the phase transition, including self-organization and even self-organized criticality. As an example, a manageable percolation-type phase transition approach is used to address this problem. Nevertheless, general theory of tumor emergence is difficult to work out mathematically because experimental observations are limited to the relatively large tumors. Hence, determination of the conditions around the critical point is uncertain.

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Section: Neoplastic Transformation Of Mutated Cellscontrasting

confidence: 87%

Section: Creation Of Lesions In a Cell After Deposition Of Radiation mentioning

confidence: 99%

Modeling Cell Reactions to Ionizing Radiation: From a Lesion to a Cancer

et al. 2019

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“…Cell killing effects can explain the high-dose saturation; there will be fewer “initiating cells” in the high-dose region. This explanation has been qualitatively confirmed by Monte-Carlo simulations ( Fornalski et al . 2011 ); their analysis also shows sigmoidal behavior as the most likely one when a number of linear phenomena are imposed on each other.…”

Section: Resultssupporting

confidence: 63%

Atomic Bomb Survivors Life-Span Study: Insufficient Statistical Power to Select Radiation Carcinogenesis Model

Socol¹,

Dobrzyński²

2014

Dose-Response

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The atomic bomb survivors life-span study (LSS) is often claimed to support the linear no-threshold hypothesis (LNTH) of radiation carcinogenesis. This paper shows that this claim is baseless. The LSS data are equally or better described by an s-shaped dependence on radiation exposure with a threshold of about 0.3 Sievert (Sv) and saturation level at about 1.5 Sv. A Monte-Carlo simulation of possible LSS outcomes demonstrates that, given the weak statistical power, LSS cannot provide support for LNTH. Even if the LNTH is used at low dose and dose rates, its estimation of excess cancer mortality should be communicated as 2.5% per Sv, i.e., an increase of cancer mortality from about 20% spontaneous mortality to about 22.5% per Sv, which is about half of the usually cited value. The impact of the “neutron discrepancy problem” – the apparent difference between the calculated and measured values of neutron flux in Hiroshima – was studied and found to be marginal. Major revision of the radiation risk assessment paradigm is required.

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“…Nevertheless, the comparative analysis of the VC generated data with prior studies (discussed above), support the use of the VC simulator as a useful tool in the field of radiobiology, with particular interest in the context of radiotherapy. Several other mathematical models have been proposed to quantify the impact of low absorbed doses on the dose-response curves for ionizing radiation (Brenner et al 2001;Fornalski et al 2011;Leonard 2008;Little et al 2005;Nikjoo and Khvostumov 2003).…”

Section: Discussionmentioning

confidence: 99%

Computational Modeling of Cellular Effects Post-Irradiation with Low- and High-Let Particles and Different Absorbed Doses

Tavares¹,

Tavares²

2012

Dose-Response

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The use of computational methods to improve the understanding of biological responses to various types of radiation is an approach where multiple parameters can be modelled and a variety of data is generated. This study compares cellular effects modelled for low absorbed doses against high absorbed doses. The authors hypothesized that low and high absorbed doses would contribute to cell killing via different mechanisms, potentially impacting on targeted tumour radiotherapy outcomes. Cellular kinetics following irradiation with selective low- and high-linear energy transfer (LET) particles were investigated using the Virtual Cell (VC) radiobiology algorithm. Two different cell types were assessed using the VC radiobiology algorithm: human fibroblasts and human crypt cells. The results showed that at lower doses (0.01 to 0.2 Gy), all radiation sources used were equally able to induce cell death (p>0.05, ANOVA). On the other hand, at higher doses (1.0 to 8.0 Gy), the radiation response was LET and dose dependent (p<0.05, ANOVA). The data obtained suggests that the computational methods used might provide some insight into the cellular effects following irradiation. The results also suggest that it may be necessary to re-evaluate cellular radiation-induced effects, particularly at low doses that could affect therapeutic effectiveness.

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A Stochastic Markov Model of Cellular Response to Radiation

Cited by 9 publications

References 35 publications

Modeling Cell Reactions to Ionizing Radiation: From a Lesion to a Cancer

Modeling Cell Reactions to Ionizing Radiation: From a Lesion to a Cancer

Atomic Bomb Survivors Life-Span Study: Insufficient Statistical Power to Select Radiation Carcinogenesis Model

Computational Modeling of Cellular Effects Post-Irradiation with Low- and High-Let Particles and Different Absorbed Doses

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