Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userReaction Rate Theory of Radiation Exposure and Scaling Hypothesis in Mutation Frequency
72
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We develop a kinetic reaction model for cells having irradiated DNA molecules due to ionizing radiation exposure. Our theory simultaneously accounts for the time-dependent reactions of the DNA damage, the DNA mutation, the DNA repair, and the proliferation and apoptosis of cells in a tissue with a minimal set of model parameters. In contrast to existing theories for radiation exposition, we do not assume the relationships between the total dose and the induced mutation frequency. Our theory provides a universal scaling function that reasonably explains the mega-mouse experiments in Ref.[W. L. Russell and E. M. Kelly, Proc. Natl. Acad. Sci. USA. {bf 79} (1982) 542.] with different dose rates. Furthermore, we have estimated the effective dose rate, which is biologically equivalent to the ionizing effects other than those caused by artificial irradiation. This value is $ 1.11 times 10^{-3} ~rm{[Gy/hr]}$, which is significantly larger than the effect caused by natural background radiation.
rate research
Read More
Low-dose ionizing radiation may induce far-reaching consequences in human, especially regarding intrauterine development. Many studies have documented that the risks of in utero irradiation remain controversial and no effect is reported at doses below 50 mGy. Animal models are often used to clarify the non-fully understood impact of intrauterine irradiation and allow the manipulation of several experimental setups, making possible the analysis of a wide range of end points. We investigated the impact of in utero low-dose X-ray irradiation on postnatal development in rat offspring through a set of well-established behavioral parameters and weight gain. To investigate the hypothesis of postnatal behavioral and physiological alterations due to prenatal low-dose ionizing radiation we exposed pregnant Wistar to 15 mGy of X-rays on gestational days 8 and 15 and control mothers. This low-dose value into diagnostic range can be achieved in a single radiological exam. Four male animals were select from each litter. At infant age, eye-opening test and negative geotaxis tests were performed. Animals were tested at postnatal ages 30 and 70 days in open field, elevated plus-maze, and hole board tests. We evaluated the weight gain of all animals throughout the experiment. The results presented differences between irradiated and non-irradiated animals. Exposed animals presented lower weight gain in adult life, impairment in central nervous system since infant phase, behavioral alterations persisting into later life, and motor coordination impairment. Effects at doses under 100 mGy have not been reported, however, the present study demonstrate that 15 mGy intrauterine exposure was able to generate deleterious effects.
We present a novel model to estimate biological effects caused by artificial radiation exposure, Whack-a-mole (WAM) model. It is important to take account of the recovery effects during the time course of the cellular reactions. The inclusion of the dose-rate dependence is essential in the risk estimation of low dose radiation, while nearly all the existing theoretical models relies on the total dose dependence only. By analyzing the experimental data of the relation between the radiation dose and the induced mutation frequency of 5 organisms, mouse, drosophila, chrysanthemum, maize and tradescantia, we found that all the data can be reproduced by WAM model. Most remarkably, a scaling function, which is derived from WAM model, consistently accounts for the observed mutation frequencies of 5 organisms. This is the first rationale to account for the dose rate dependence as well as to give a unified understanding of a general feature of organisms.
Even in the steady-state, the number of biomolecules in living cells fluctuates dynamically; and the frequency spectrum of this chemical fluctuation carries valuable information about the mechanism and the dynamics of the intracellular reactions creating these biomolecules. Although recent advances in single-cell experimental techniques enable the direct monitoring of the time-traces of the biological noise in each cell, the development of the theoretical tools needed to extract the information encoded in the stochastic dynamics of intracellular chemical fluctuation is still in its adolescence. Here, we present a simple and general equation that relates the power-spectrum of the product number fluctuation to the product lifetime and the reaction dynamics of the product creation process. By analyzing the time traces of the protein copy number using this theory, we can extract the power spectrum of the mRNA number, which cannot be directly measured by currently available experimental techniques. From the power spectrum of the mRNA number, we can further extract quantitative information about the transcriptional regulation dynamics. Our power spectrum analysis of gene expression noise is demonstrated for the gene network model of luciferase expression under the control of the Bmal 1a promoter in mouse fibroblast cells. Additionally, we investigate how the non-Poisson reaction dynamics and the cell-to-cell heterogeneity in transcription and translation affect the power-spectra of the mRNA and protein number.
We introduce a statistical and linear response theory of selective conduction in biological ion channels with multiple binding sites and possible point mutations. We derive an effective grand-canonical ensemble and generalised Einstein relations for the selectivity filter, assuming strongly coordinated ionic motion, and allowing for ionic Coulomb blockade. The theory agrees well with data from the KcsA K$^+$ channel and a mutant. We show that the Eisenman relations for thermodynamic selectivity follow from the condition for fast conduction and find that maximum conduction requires the binding sites to be nearly identical.
Microbial communities are ubiquitous in nature and come in a multitude of forms, ranging from communities dominated by a handful of species to communities containing a wide variety of metabolically distinct organisms. This huge range in diversity is not a curiosity - microbial diversity has been linked to outcomes of substantial ecological and medical importance. However, the mechanisms underlying microbial diversity are still under debate, as simple mathematical models only permit as many species to coexist as there are resources. A plethora of mechanisms have been proposed to explain the origins of microbial diversity, but many of these analyses omit a key property of real microbial ecosystems: the propensity of the microbes themselves to change their growth properties within and across generations. In order to explore the impact of this key property on microbial diversity, we expand upon a recently developed model of microbial diversity in fluctuating environments. We implement changes in growth strategy in two distinct ways. First, we consider the regulation of a cells enzyme levels within short, ecological times, and second we consider evolutionary changes driven by mutations across generations. Interestingly, we find that these two types of microbial responses to the environment can have drastically different outcomes. Enzyme regulation may collapse diversity over long enough times while, conversely, strategy-randomizing mutations can produce a rich-get-poorer effect that promotes diversity. This work makes explicit, using a simple serial-dilutions framework, the conflicting ways that microbial adaptation and evolution can affect community diversity.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
