Dataset Information

A stochastic model of DNA fragments rejoining.

ABSTRACT: When cells are exposed to ionizing radiation, DNA damages in the form of single strand breaks (SSBs), double strand breaks (DSBs), base damage or their combinations are frequent events. It is known that the complexity and severity of DNA damage depends on the quality of radiation, and the microscopic dose deposited in small segments of DNA, which is often related to the linear transfer energy (LET) of the radiation. Experimental studies have suggested that under the same dose, high LET radiation induces more small DNA fragments than low-LET radiation, which affects Ku efficiently binding with DNA end and might be a main reason for high-LET radiation induced RBE [1] since DNA DSB is a major cause for radiation-induced cell death. In this work, we proposed a mathematical model of DNA fragments rejoining according to non-homologous end joining (NHEJ) mechanism. By conducting Gillespie's stochastic simulation, we found several factors that impact the efficiency of DNA fragments rejoining. Our results demonstrated that aberrant DNA damage repair can result predominantly from the occurrence of a spatial distribution of DSBs leading to short DNA fragments. Because of the low efficiency that short DNA fragments recruit repair protein and release the protein residue after fragments rejoining, Ku-dependent NHEJ is significantly interfered with short fragments. Overall, our work suggests that inhibiting the Ku-dependent NHEJ may significantly contribute to the increased efficiency for cell death and mutation observed for high LET radiation.

SUBMITTER: Li Y

PROVIDER: S-EPMC3441539 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A stochastic model of DNA fragments rejoining.

Li Yongfeng Y Qian Hong H Wang Ya Y Cucinotta Francis A FA

PloS one 20120913 9

When cells are exposed to ionizing radiation, DNA damages in the form of single strand breaks (SSBs), double strand breaks (DSBs), base damage or their combinations are frequent events. It is known that the complexity and severity of DNA damage depends on the quality of radiation, and the microscopic dose deposited in small segments of DNA, which is often related to the linear transfer energy (LET) of the radiation. Experimental studies have suggested that under the same dose, high LET radiation ...[more]

PMID: 23028515

Similar Datasets

Project description:Mechanistic modeling of DNA double strand break (DSB) rejoining is important for quantifying and medically exploiting radiation-induced cytotoxicity (e.g. in cancer radiotherapy). Most radiation-induced DSBs are quickly-rejoinable and are rejoined within the first 1-2 hours after irradiation. Others are slowly-rejoinable (persist for several hours), and yet others are essentially unrejoinable (persist for >24 hours). The dependences of DSB rejoining kinetics on radiation dose and dose rate remain incompletely understood. We hypothesize that the fraction of slowly-rejoinable and/or unrejoinable DSBs increases with increasing dose/dose rate. This radiation-dependent (RD) model was implemented using differential equations for three DSB classes: quickly-rejoinable, slowly-rejoinable and unrejoinable. Radiation converts quickly-rejoinable to slowly-rejoinable, and slowly-rejoinable to unrejoinable DSBs. We used large published data sets on DSB rejoining in yeast exposed to sparsely-ionizing (electrons and ?-rays, single or split-doses, high or low dose rates) and densely-ionizing (?-particles) radiation to compare the performances of the proposed RD formalism and the established two-lesion kinetic (TLK) model. These yeast DSB rejoining data were measured within the radiation dose range relevant for clonogenic cell survival, whereas in mammalian cells DSB rejoining is usually measured only at supra-lethal doses for technical reasons. The RD model described both sparsely-ionizing and densely-ionizing radiation data much better than the TLK model: by 217 and 14 sample-size-adjusted Akaike information criterion units, respectively. This occurred because: the RD (but not the TLK) model reproduced the observed upwardly-curving dose responses for slowly-rejoinable/unrejoinable DSBs at long times after irradiation; the RD model adequately described DSB yields at both high and low dose rates using one parameter set, whereas the TLK model overestimated low dose rate data. These results support the hypothesis that DSB rejoining is progressively impeded at increasing radiation doses/dose rates.

Project description:Viewing of ambiguous stimuli can lead to bistable perception alternating between the possible percepts. During continuous presentation of ambiguous stimuli, percept changes occur as single events, whereas during intermittent presentation of ambiguous stimuli, percept changes occur at more or less regular intervals either as single events or bursts. Response patterns can be highly variable and have been reported to show systematic differences between patients with schizophrenia and healthy controls. Existing models of bistable perception often use detailed assumptions and large parameter sets which make parameter estimation challenging. Here we propose a parsimonious stochastic model that provides a link between empirical data analysis of the observed response patterns and detailed models of underlying neuronal processes. Firstly, we use a Hidden Markov Model (HMM) for the times between percept changes, which assumes one single state in continuous presentation and a stable and an unstable state in intermittent presentation. The HMM captures the observed differences between patients with schizophrenia and healthy controls, but remains descriptive. Therefore, we secondly propose a hierarchical Brownian model (HBM), which produces similar response patterns but also provides a relation to potential underlying mechanisms. The main idea is that neuronal activity is described as an activity difference between two competing neuronal populations reflected in Brownian motions with drift. This differential activity generates switching between the two conflicting percepts and between stable and unstable states with similar mechanisms on different neuronal levels. With only a small number of parameters, the HBM can be fitted closely to a high variety of response patterns and captures group differences between healthy controls and patients with schizophrenia. At the same time, it provides a link to mechanistic models of bistable perception, linking the group differences to potential underlying mechanisms.

Dataset Information

A stochastic model of DNA fragments rejoining.

Publications

A stochastic model of DNA fragments rejoining.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets