Effect of the SOS Response on the Mean Fitness of Unicellular Populations: A Quasispecies Approach

The goal of this paper is to develop a mathematical model that analyzes the selective advantage of the SOS response in unicellular organisms. To this end, this paper develops a quasispecies model that incorporates the SOS response. We consider a unicellular, asexually replicating population of organisms, whose genomes consist of a single, double-stranded DNA molecule, i.e. one chromosome. We assume that repair of post-replication mismatched base-pairs occurs with probability , and that the SOS response is triggered when the total number of mismatched base-pairs is at least . We further assume that the per-mismatch SOS elimination rate is characterized by a first-order rate constant . For a single fitness peak landscape where the master genome can sustain up to mismatches and remain viable, this model is analytically solvable in the limit of infinite sequence length. The results, which are confirmed by stochastic simulations, indicate that the SOS response does indeed confer a fitness advantage to a population, provided that it is only activated when DNA damage is so extensive that a cell will die if it does not attempt to repair its DNA

Comment on ``SRAM-PUF Based Entities Authentication Scheme for Resource-constrained IoT Devices\u27\u27

The cloud-based Internet of Things (IoT) creates opportunities for more direct integration of the physical world and computer-based systems, allowing advanced applications based on sensing, analyzing and controlling the physical world. IoT deployments, however, are at a particular risk of counterfeiting, through which an adversary can corrupt the entire ecosystem. Therefore, entity authentication of edge devices is considered an essential part of the security of IoT systems. A recent paper of Farha et al. suggested an entity authentication scheme suitable for low-resource IoT edge devices, which relies on SRAM-based physically unclonable functions (PUFs). In this paper we analyze this scheme. We show that, while it claims to offer strong PUF functionality, the scheme creates only a weak PUF: an active attacker can completely read out the secret PUF response of the edge device after a very small amount of queries, converting the scheme into a weak PUF scheme which can then be counterfeited easily. After analyzing the scheme, we propose an alternative construction for an authentication method based on SRAM-PUF which better protects the secret SRAM startup state

An extended genotyping framework for Salmonella enterica serovar Typhi, the cause of human typhoid.

The population of Salmonella enterica serovar Typhi (S. Typhi), the causative agent of typhoid fever, exhibits limited DNA sequence variation, which complicates efforts to rationally discriminate individual isolates. Here we utilize data from whole-genome sequences (WGS) of nearly 2,000 isolates sourced from over 60 countries to generate a robust genotyping scheme that is phylogenetically informative and compatible with a range of assays. These data show that, with the exception of the rapidly disseminating H58 subclade (now designated genotype 4.3.1), the global S. Typhi population is highly structured and includes dozens of subclades that display geographical restriction. The genotyping approach presented here can be used to interrogate local S. Typhi populations and help identify recent introductions of S. Typhi into new or previously endemic locations, providing information on their likely geographical source. This approach can be used to classify clinical isolates and provides a universal framework for further experimental investigations

The various parameters and their definitions in our model.

<p>The various parameters and their definitions in our model.</p

Comparison of the mean fitnesses obtained from both stochastic simulations (dots) and the analytical solution (solid line) of our model.

<p>Parameter values are , , , , , . The population size was set at .</p

Comparison of the mean fitnesses obtained from both stochastic simulations (dots) and the analytical solution (solid line) of our model.

<p>Parameter values are , , , , , . The population size was set at .</p

(Color online) Illustration of the SOS repair mechanism being considered in this paper.

<p>A DNA genome with two base-pair mismatches is restored to a fully complementary genome in two repair steps, where during each step a single mismatch (i.e. lesion) is eliminated. The first lesion is repaired correctly, so that the original base-pair of the master genome strands (solid blue lines) is restored, while the second lesion is repaired incorrectly, so that a mutation (dotted red lines) becomes fixed in the genome.</p