Stochastic Simulation of DNA Double-Strand Break Repair by Non-homologous End Joining Based on Track Structure Calculations

Friedland, W.; Jacob, Peter; Kundrát, Pavel

doi:10.1667/rr1965.1

Cited by 72 publications

(49 citation statements)

References 50 publications

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“…Another important determinant of repair kinetics is the epigenetic state of the affected chromatin region. This insight could now be used to refine existing stochastic models of DSB repair [39,40] and couple them to p53 models. Similar approaches with simpler stochastic DSB models have already succeeded in reproducing the observed heterogeneity to a certain extent [27].…”

Section: Excitability and Variation In Damage Strength Can Explain Hementioning

confidence: 99%

Recent progress and open challenges in modeling p53 dynamics in single cells

Batchelor

Loewer

2017

Current Opinion in Systems Biology

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In mammalian cells, the tumor suppressor p53 is activated upon a variety of cellular stresses and ensures an appropriate response ranging from arrest and repair to the induction of senescence and apoptosis. Quantitative measurements in individual living cells showed stimulus-dependent dynamics of p53 accumulation upon stress induction. Due to the complexity of the underlying biochemical interactions, mathematical models were indispensable for understanding the topology of the network regulating p53 dynamics. Recent work provides furhter insights into the causes of heterogeneous responses in individual cells, the rewiring of the network in response to different inputs and the role of the downstream processes in determining the cellular fate upon stress.

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Section: Excitability and Variation In Damage Strength Can Explain Hementioning

confidence: 99%

Recent progress and open challenges in modeling p53 dynamics in single cells

Batchelor

Loewer

2017

Current Opinion in Systems Biology

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“…The initial damage simulations in PARTRAC reproduced essential findings in corresponding laboratory experiments using pulsed field gel electrophoresis (PFGE) to assay DNA fragment distributions (e.g., [61,62]). Thus, it provides a reliable basis for the estimate of initial frequency and distribution of DSBs for the repair models developed subsequently [49,63,64]. Friedland et al [64] have provided a comprehensive review of the PARTRAC code and its applications (see also [65]).…”

Section: Induction Of Different Types Of Lesions In Cellular Dnamentioning

confidence: 99%

“…The solutions of the equations provide individual protein activity kinetics and overall repair kinetics, and these can be compared with experimental measurements (usually rate of repair of DSBs, post-irradiation, using PFGE in the dose range of 40-100 Gy). While most of the models dealt with the NHEJ pathway (e.g., [41,42,44,49,63,64,[111][112][113][114], some have also considered single-strand annealing (SSA) [115]; microhomology-mediated end-joining (MMEJ) [48]; application to high LET radiations [48]; and base-excision-repair (BER) [46].…”

Section: Published Computational Modeling Studies Of Dsb Repairmentioning

confidence: 99%

“…In the work of Friedland et al [63] involving 137 Cs g-irradiation, DNA damage 'induced' in a target model of human fibroblast cell nucleus with the Monte Carlo track structure code PARTRAC provided the starting conditions for the spatial distribution of DSBs 10 In mammalian somatic cells, the initiating step of HRR following DSB induction is end-resection to create a 3 0 -ended single-stranded region in which CtIP/MRE11-RAD50-NBS1 [MRN] is involved. This is followed by rapid binding of the region with replication protein A and its subsequent displacement by RAD51, which mediates the core reactions of HRR, namely, homology searching, strand exchange, Holliday junction formation, repair synthesis (using the sister chromatid as a template), Holliday junction resolution and ligation of the DNA ends effecting error-free DSB repair [89,92,110].…”

Section: Published Computational Modeling Studies Of Dsb Repairmentioning

confidence: 99%

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Genome-based, mechanism-driven computational modeling of risks of ionizing radiation: The next frontier in genetic risk estimation?

Sankaranarayanan

Nikjoo

2015

Mutation Research/Reviews in Mutation Research

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“…However, as Mueller et al [14] pointed out often different models are able to describe experimental data equally well. For instance, Friedland et al [15] noted that attachment and dissociation rates of Ku70/80 and DNA-PKcs involved in non-homologous end-joining cannot be derived unequivocally from the data. In the following, we will therefore assume that the complete set of reactions that affect the recruitment data of the considered protein species is not known.…”

Section: Introductionmentioning

confidence: 99%

Deducing Underlying Mechanisms from Protein Recruitment Data

Lengert

Drossel

2013

PLoS ONE

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By using fluorescent labelling techniques, the distribution and dynamics of proteins can be measured within living cells, allowing to study in vivo the response of cells to a triggering event, such as DNA damage. In order to evaluate the reaction rate constants and to identify the proteins and reactions that are essential for the investigated process, mechanistic models are used, which often contain many proteins and associated parameters and are therefore underdetermined by the data. In order to establish criteria for assessing the significance of a model, we present here a systematic investigation of the information that can be reliably deduced from protein recruitment data, assuming that the complete set of reactions that affect the data of the considered protein species is not known. To this purpose, we study in detail models where one or two proteins that influence each other are recruited to a substrate. We show that in many cases the kind of interaction between the proteins can be deduced by analyzing the shape of the recruitment curves of one protein. Furthermore, we discuss in general in which cases it is possible to discriminate between different models and in which cases it is impossible based on the data. Finally, we argue that if different models fit experimental data equally well, conducting experiments with different protein concentrations would allow discrimination between the alternative models in many cases.

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Stochastic Simulation of DNA Double-Strand Break Repair by Non-homologous End Joining Based on Track Structure Calculations

Cited by 72 publications

References 50 publications

Recent progress and open challenges in modeling p53 dynamics in single cells

Recent progress and open challenges in modeling p53 dynamics in single cells

Genome-based, mechanism-driven computational modeling of risks of ionizing radiation: The next frontier in genetic risk estimation?

Deducing Underlying Mechanisms from Protein Recruitment Data

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