Spatial birth-and-death processes in random environment

We consider birth-and-death processes of objects (animals) defined in ${\bf Z}^d$ having unit death rates and random birth rates. For animals with uniformly bounded diameter we establish conditions on the rate distribution under which the following holds for almost all realizations of the birth rates: (i) the process is ergodic with at worst power-law time mixing; (ii) the unique invariant measure has exponential decay of (spatial) correlations; (iii) there exists a perfect-simulation algorithm for the invariant measure. The results are obtained by first dominating the process by a backwards oriented percolation model, and then using a multiscale analysis due to Klein to establish conditions for the absence of percolation.Comment: 48 page

Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications, Springer. The article contains 23 pages, 4 figures, 8 tables and 51 reference

Separability in Stochastic Binary Systems

A Stochastic Binary System (SBS) is a mathematical model of multi-component on-off systems subject to random failures. SBS models extend classical network reliability models (where the components subject to failure are nodes or links of a graph) and are able to represent more complex interactions between the states of the individual components and the operation of the system under study. The reliability evaluation of stochastic binary systems belongs to the class of NP-Hard computational problems. Furthermore, the number of states is exponential with respect to the size of the system (measured in the number of components). As a consequence, the representation of an SBS becomes a key element in order to develop exact and/or approximation methods for reliability evaluation. The contributions of this paper are three-fold. First, we present the concept of separable stochastic binary systems, showing key properties, such as an efficient representation and complexity in the reliability evaluation. Second, we fully characterize separable systems in two ways, using a geometrical interpretation and minimum-cost operational subsystems. Finally, we show the application of separable systems in network reliability models, specifically in the all-terminal reliability model, which has a wide spectrum of applications. Index Terms—Stochastic Binary System, Network Reliability, Computational Complexity, Chernoff Inequality

An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

Background: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes. Results: The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: Http://synapticgenes.bnd.edu.uy Conclusions: We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function. Availability: Http://synapticgenes.bnd.edu.uyFil: Pazos Obregón, Flavio. Instituto de Investigaciones Biológicas "Clemente Estable"; UruguayFil: Palazzo, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Soto, Pablo. Instituto de Investigaciones Biológicas "Clemente Estable"; UruguayFil: Guerberoff, Gustavo. Universidad de la República; UruguayFil: Yankilevich, Patricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Cantera, Rafael. Instituto de Investigaciones Biológicas "Clemente Estable"; Urugua

A study of the anti-atherosclerotic and anti-inflammatory effects of Sirolimus

Inflammation accelerates the progression of atherosclerosis. Sirolimus, a potent immunosuppressive agent, has been shown with pleiotropic antiatherosclerotic effects. This study was to explore potential anti-atherosclerotic mechanisms of Sirolimus using cell culture studies and apolipoprotein E knockout (apoE KO) mice under inflammatory stress. Results showed that Sirolimus decreased cholesterol accumulation caused by inflammatory stress in human vascular smooth muscle cells (VSMCs), macrophages, and human hepatoblastoma cell line (HepG2). Sirolimus decreased formation of atherosclerotic plaques in the aortas of inflamed apoE KO mice. Sirolimus inhibited the mRNA expression of sterol regulatory element-binding protein (SREBP) cleavage activating protein (SCAP) and SREBP-2, and decreased translocation of SCAP/SREBP-2 complex from endoplasmic reticulum (ER) to Golgi in VSMCs and HepG2 cells in the presence of IL-1 3, thereby overriding IL-lp induced transcription of LDL receptor (LDLr) and 3-hydroxy-3-methyglutaryl coenzyme A reductase (HMGR). Insulin induced gene-1 (Insig-1) is a retention factor of SCAP in the ER and modulates HMGR degradation at posttranscriptional level. Interestingly, Sirolimus accelerated HMGR degradation by up-regulating Insig-1 expression in VSMCs. Sirolimus also reversed the reduction of cholesterol efflux induced by inflammatory stress through ATP-binding cassette transporter Al (ABCA1) mediated pathway. This was mediated by increasing the gene and protein expression of ABCA1, peroxisome proliferator-activated receptor-a (PPARa), and liver X receptor-a (LXRa) both in vitro and in vivo studies. Sirolimus also directly inhibited the production of inflammatory cytokines shown in our experiments. Taken together, both in vivo and in vitro findings demonstrated that Sirolimus ameliorated cholesterol homeostasis disrupted by inflammatory stress, which was through multiple pathways. Sirolimus down-regulated LDLr-mediated cholesterol influx, down-regulated HMGR-mediated cholesterol biosynthesis, and up-regulated ABCA1 -mediated cholesterol efflux. Furthermore, Sirolimus inhibited the production of inflammatory cytokines. Our studies for the first time indicate that Sirolimus has very pronounced anti-inflammatory properties and highly beneficial anti-atherosclerosis effects expressed through rebalancing disrupted intracellular cholesterol homeostasis involving various molecular mechanisms