From Cell Death to Metabolism:Holin-Antiholin Homologues with New Functions

Programmed cell death in bacteria is generally triggered by membrane proteins with functions analogous to those of bacteriophage holins: they disrupt the membrane potential, whereas antiholins antagonize this process. The holin-like class of proteins is present in all three domains of life, but their functions can be different, depending on the species. Using a series of biochemical and genetic approaches, in a recent article in mBio, Charbonnier et al. (mBio 8:e00976-17, 2017, https://doi.org/10.1128/mBio.00976-17) demonstrate that the antiholin homologue in Bacillus subtilis transports pyruvate and is regulated in an unconventional way by its substrate molecule. Here, we discuss the connection between cell death and metabolism in various bacteria carrying genes encoding these holin-antiholin analogues and place the recent study by Charbonnier et al. in an evolutionary context

Diameters in preferential attachment models

In this paper, we investigate the diameter in preferential attachment (PA-) models, thus quantifying the statement that these models are small worlds. The models studied here are such that edges are attached to older vertices proportional to the degree plus a constant, i.e., we consider affine PA-models. There is a substantial amount of literature proving that, quite generally, PA-graphs possess power-law degree sequences with a power-law exponent \tau>2. We prove that the diameter of the PA-model is bounded above by a constant times \log{t}, where t is the size of the graph. When the power-law exponent \tau exceeds 3, then we prove that \log{t} is the right order, by proving a lower bound of this order, both for the diameter as well as for the typical distance. This shows that, for \tau>3, distances are of the order \log{t}. For \tau\in (2,3), we improve the upper bound to a constant times \log\log{t}, and prove a lower bound of the same order for the diameter. Unfortunately, this proof does not extend to typical distances. These results do show that the diameter is of order \log\log{t}. These bounds partially prove predictions by physicists that the typical distance in PA-graphs are similar to the ones in other scale-free random graphs, such as the configuration model and various inhomogeneous random graph models, where typical distances have been shown to be of order \log\log{t} when \tau\in (2,3), and of order \log{t} when \tau>3

Upper bounds for number of removed edges in the Erased Configuration Model

Models for generating simple graphs are important in the study of real-world complex networks. A well established example of such a model is the erased configuration model, where each node receives a number of half-edges that are connected to half-edges of other nodes at random, and then self-loops are removed and multiple edges are concatenated to make the graph simple. Although asymptotic results for many properties of this model, such as the limiting degree distribution, are known, the exact speed of convergence in terms of the graph sizes remains an open question. We provide a first answer by analyzing the size dependence of the average number of removed edges in the erased configuration model. By combining known upper bounds with a Tauberian Theorem we obtain upper bounds for the number of removed edges, in terms of the size of the graph. Remarkably, when the degree distribution follows a power-law, we observe three scaling regimes, depending on the power law exponent. Our results provide a strong theoretical basis for evaluating finite-size effects in networks

The structure of typical clusters in large sparse random configurations

The initial purpose of this work is to provide a probabilistic explanation of a recent result on a version of Smoluchowski's coagulation equations in which the number of aggregations is limited. The latter models the deterministic evolution of concentrations of particles in a medium where particles coalesce pairwise as time passes and each particle can only perform a given number of aggregations. Under appropriate assumptions, the concentrations of particles converge as time tends to infinity to some measure which bears a striking resemblance with the distribution of the total population of a Galton-Watson process started from two ancestors. Roughly speaking, the configuration model is a stochastic construction which aims at producing a typical graph on a set of vertices with pre-described degrees. Specifically, one attaches to each vertex a certain number of stubs, and then join pairwise the stubs uniformly at random to create edges between vertices. In this work, we use the configuration model as the stochastic counterpart of Smoluchowski's coagulation equations with limited aggregations. We establish a hydrodynamical type limit theorem for the empirical measure of the shapes of clusters in the configuration model when the number of vertices tends to $\infty$. The limit is given in terms of the distribution of a Galton-Watson process started with two ancestors

«Золота» пора кооперації в умовах непу

Рецензія на книгу: Голець В.В. Кооперація і неп (20-і рр. XX ст.). - Чернігів: Просвіта, 2006. - 244 с

Random graphs: From static to dynamic

Many empirical studies on real-life networks show that many networks are small worlds, meaning that typical distances in these networks are small, and many of them have power-law degree sequences, meaning that the number of nodes with degree k falls off as kˆ (-τ) for some exponent τ>1. These networks are modeled by means of scale-free random graphs. One way to construct such a random graph is to start with a fixed number of nodes and randomly add edges between pairs of nodes. Using a growth model is a second way to construct a random graph. In such a model one starts with a given graph, and at each discrete time step a new node is added to the graph and the node is connected to some of the old nodes, where nodes with a high number of edges are preferred (preferential attachment). In this thesis two types of random graphs are considered: static random graphs and dynamic random graphs. A static random graph aims to describe a network and its topology at a given time instant, and a dynamical random graph aims to explain how the network came to be as it is. In this thesis two static random graphs are studied which produce power-law degree sequences: 'the configuration model' and 'the inhomogeneous random graph'. Two dynamic random graphs are introduced: 'the preferential attachment model with random initial degrees' and 'the geometric preferential attachment model with fitness'. In this thesis the degree sequence, the typical distance and the diameter for each of the models is considered, which are influenced by the power-law exponent τ. If τ>3, then each node in the graph has the same kind of neighborhood and the typical distance is proportional to log(n) if the graph consists of n nodes. If τ∈(2,3), then nodes with a high degree will appear and the typical distance is proportional to loglog(n). If τ∈(1,2), then the graph has a star-shaped structure and the distance is bounded by some constant.Electrical Engineering, Mathematics and Computer Scienc

Application of transcriptomics to enhance early diagnostics of mycobacterial infections, with an emphasis on Mycobacterium avium ssp. paratuberculosis

Mycobacteria cause a wide variety of disease in human and animals. Species that infect ruminants include M. bovis and M. avium ssp. paratuberculosis (MAP). MAP is the causative agent of Johne's disease in ruminants, which is a chronic granulomatous enteric infection that leads to severe economic losses worldwide. Characteristic of MAP infection is the long, latent phase in which intermittent shedding can takeplace,whilediagnostic tests areunable to reliablydetect aninfection in this stage. This leads to unnoticed dissemination within herds and the presence of many undetected, silent carriers, which makes the eradication of Johne's disease difficult. To improve the control of MAP infection, research is aimed at improving early diagnosis. Transcriptomic approaches can be applied to characterize host-pathogen interactions during infection, and to develop novel biomarkers using transcriptional profiles. Studies have focused on the identification of specific RNAs that are expressed in different infection stages, which will assist in the development and clinical implementation of early diagnostic tests