Search results for "ESA"
showing 10 items of 11914 documents
Regulation of yeast fatty acid desaturase in response to iron deficiency
2017
Unsaturated fatty acids (UFA) are essential components of phospholipids that greatly contribute to the biophysical properties of cellular membranes. Biosynthesis of UFAs relies on a conserved family of iron-dependent fatty acid desaturases, whose representative in the model yeast Saccharomyces cerevisiae is Ole1. OLE1 expression is tightly regulated to adapt UFA biosynthesis and lipid bilayer properties to changes in temperature, and in UFA or oxygen availability. Despite iron deficiency being the most extended nutritional disorder worldwide, very little is known about the mechanisms and the biological relevance of fatty acid desaturases regulation in response to iron starvation. In this re…
Affinity proteomics identifies novel functional modules related to adhesion GPCRs.
2019
Adhesion G protein-coupled receptors (ADGRs) have recently become a target of intense research. Their unique protein structure, which consists of a G protein-coupled receptor combined with long adhesive extracellular domains, suggests a dual role in cell signaling and adhesion. Despite considerable progress in the understanding of ADGR signaling over the past years, the knowledge about ADGR protein networks is still limited. For most receptors, only a few interaction partners are known thus far. We aimed to identify novel ADGR-interacting partners to shed light on cellular protein networks that rely on ADGR function. For this, we applied affinity proteomics, utilizing tandem affinity purifi…
Evaluation of the Possibility to Detect Circulating Tumor DNA From Pituitary Adenoma
2019
Objective: Circulating free DNA (cfDNA) in general and circulating tumor DNA (ctDNA) in particular is becoming an increasingly used form of liquid biopsy biomarkers. In this study, we are investigating the ability to detect ctDNA from the plasma of pituitary adenoma (PA) patients. Design: Tumor tissue samples were obtained from planed PA resections, before which blood plasma samples were taken. Somatic variants found in PA tissue samples were evaluated in related cfDNA, isolated from plasma samples. Methods: Sanger sequencing, as well as previously obtained whole-exome sequencing data, were used to evaluate somatic variants composition in tumor tissue samples. cfDNA was isolated from the sa…
Stagewise pseudo-value regression for time-varying effects on the cumulative incidence
2015
In a competing risks setting, the cumulative incidence of an event of interest describes the absolute risk for this event as a function of time. For regression analysis, one can either choose to model all competing events by separate cause-specific hazard models or directly model the association between covariates and the cumulative incidence of one of the events. With a suitable link function, direct regression models allow for a straightforward interpretation of covariate effects on the cumulative incidence. In practice, where data can be right-censored, these regression models are implemented using a pseudo-value approach. For a grid of time points, the possibly unobserved binary event s…
Partitioned learning of deep Boltzmann machines for SNP data.
2016
Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…
L1-Penalized Censored Gaussian Graphical Model
2018
Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…
Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.
2016
Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l 1-penalized maximum likelihood, imposing a further constraint on the absolute value…
Sustained oscillations in the MAP kinase cascade.
2016
Abstract The MAP kinase cascade is a network of enzymatic reactions arranged in layers. In each layer occurs a multiple futile cycle of phosphorylations. The fully phosphorylated substrate then serves as an enzyme for the layer below. This paper focuses on the existence of parameters for which Hopf bifurcations occur and generate periodic orbits. Furthermore it is explained how geometric singular perturbation theory allows to generalize results from simple models to more complex ones.
Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications
2015
The bootstrap method has become a widely used tool applied in diverse areas where results based on asymptotic theory are scarce. It can be applied, for example, for assessing the variance of a statistic, a quantile of interest or for significance testing by resampling from the null hypothesis. Recently, some approaches have been proposed in the biometrical field where hypothesis testing or model selection is performed on a bootstrap sample as if it were the original sample. P-values computed from bootstrap samples have been used, for example, in the statistics and bioinformatics literature for ranking genes with respect to their differential expression, for estimating the variability of p-v…
Biomolecular computers with multiple restriction enzymes
2017
Abstract The development of conventional, silicon-based computers has several limitations, including some related to the Heisenberg uncertainty principle and the von Neumann “bottleneck”. Biomolecular computers based on DNA and proteins are largely free of these disadvantages and, along with quantum computers, are reasonable alternatives to their conventional counterparts in some applications. The idea of a DNA computer proposed by Ehud Shapiro’s group at the Weizmann Institute of Science was developed using one restriction enzyme as hardware and DNA fragments (the transition molecules) as software and input/output signals. This computer represented a two-state two-symbol finite automaton t…