Mining microbial genomes for new natural products and biosynthetic pathways

Analyses of microbial genome sequences have revealed numerous examples of ‘cryptic’ or ‘orphan’ biosynthetic gene clusters, with the potential to direct the production of novel, structurally complex natural products. This article summarizes the various methods that have been developed for discovering the products of cryptic biosynthetic gene clusters in microbes and gives an account of my group's discovery of the products of two such gene clusters in the model actinomycete Streptomyces coelicolor M145. These discoveries hint at new mechanisms, roles and specificities for natural product biosynthetic enzymes. Our efforts to elucidate these are described. The identification of new secondary metabolites of S. coelicolor raises the question: what is their biological function? Progress towards answering this question is also summarized

Bayesian Signal Subspace Estimation with Compound Gaussian Sources

International audienceIn this paper, we consider the problem of low dimensional signal subspace estimation in a Bayesian con- text. We focus on compound Gaussian signals embedded in white Gaussian noise, which is a realistic modeling for various array processing applications. Following the Bayesian framework, we derive two algorithms to compute the maximum a posteriori (MAP) estimator and the so-called minimum mean square distance (MMSD) estimator, which minimizes the average natural distance between the true range space of interest and its estimate. Such approaches have shown their interests for signal subspace esti- mation in the small sample support and/or low signal to noise ratio contexts. As a byproduct, we also introduce a generalized version of the complex Bingham Langevin distribution in order to model the prior on the subspace orthonormal basis. Finally, numerical simulations illustrate the performance of the proposed algorithms

Signal subspace change detection in structured covariance matrices

International audienceTesting common properties between covariance matricesis a relevant approach in a plethora of applications. In thispaper, we derive a new statistical test in the context of structuredcovariance matrices. Specifically, we consider low rank signalcomponent plus white Gaussian noise structure. Our aim is totest the equality of the principal subspace, i.e., subspace spannedby the principal eigenvectors of a group of covariance matrices. Adecision statistic is derived using the generalized likelihood ratiotest. As the formulation of the proposed test implies a non-trivialoptimization problem, we derive an appropriate majorizationminimizationalgorithm. Finally, numerical simulations illustratethe properties of the newly proposed detector compared to thestate of the art

Analytical Creeping Wave Model and Measurements for 60 GHz Body Area Networks

International audienceThe propagation of 60 GHz electromagnetic waves around a human body is studied analytically and experimentally. The body is treated here as a circular lossy cylinder, which is an approximation of the human torso. Analytical formulations based on creeping wave theory are given and discussed for both vertical and horizontal polarizations. An exact path gain expression is derived from analytical formulations and a simpler first order approximation is given. Path gain coefficients are shown for frequencies spanning the world available 60 GHz unlicensed band and for several sizes of the torso. Finally, the results of an experimental campaign conducted in an anechoic chamber to isolate the contribution of on-body propagation are reported. The measurement of the distance dependence of the received power on a brass cylinder and on a human body for both vertical and horizontal polarizations confirmed theoretical predictions

Détection de changement de sous-espace signal de matrices de covariance structurées

International audienceTesting common properties between covariance matrices is a relevant problem in a plethora of signal processing applications. In this paper, we derive a new statistical test in the context of structured covariance matrices. Specifically, we consider low rank signal component plus white Gaussian noise structure. Our aim is to test the equality of the principal subspace, i.e., subspace spanned by the principal eigenvectors of a group of covariance matrices. A decision statistic is derived using the generalized likelihood ratio test. As the formulation of the proposed test implies a non-trivial optimization problem, we derive an appropriate majorization-minimization algorithm. Finally, numerical simulations illustrate the properties of the newly proposed detector compared to the state of the art.Le test statistique de propriété communes entre les matrices de covariance tient une place très importante en traitement du signal. Dans cet article, nous proposons un nouveau test statistique dans le contexte de matrices de covariance structurées. Plus précisément, nous considérons un signal de rang faible corrompu par un bruit blanc gaussien additif. Notre objectif est de tester l’égalité du sous-espace signal, c’est à dire les composantes principales communes à un ensemble de matrices de covariance. Dans un premier temps, une statistique de décision est dérivée en utilisant le rapport de vraisemblance généralisée. Le maximum de vraisemblance n’ayant pas d’expression analytique dans ce cas, nous proposons un algorithme d’estimation itératif de type majoration-minimisation. Enfin, nous étudions les propriétés du détecteur proposé à l’aide de simulations numériques

Creeping Wave Model of Diffraction of an Obliquely Incident Plane Wave by a Circular Cylinder at 60 GHz

info:eu-repo/semantics/publishe

The Natural Product Domain Seeker NaPDoS: A Phylogeny Based Bioinformatic Tool to Classify Secondary Metabolite Gene Diversity

New bioinformatic tools are needed to analyze the growing volume of DNA sequence data. This is especially true in the case of secondary metabolite biosynthesis, where the highly repetitive nature of the associated genes creates major challenges for accurate sequence assembly and analysis. Here we introduce the web tool Natural Product Domain Seeker (NaPDoS), which provides an automated method to assess the secondary metabolite biosynthetic gene diversity and novelty of strains or environments. NaPDoS analyses are based on the phylogenetic relationships of sequence tags derived from polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) genes, respectively. The sequence tags correspond to PKS-derived ketosynthase domains and NRPS-derived condensation domains and are compared to an internal database of experimentally characterized biosynthetic genes. NaPDoS provides a rapid mechanism to extract and classify ketosynthase and condensation domains from PCR products, genomes, and metagenomic datasets. Close database matches provide a mechanism to infer the generalized structures of secondary metabolites while new phylogenetic lineages provide targets for the discovery of new enzyme architectures or mechanisms of secondary metabolite assembly. Here we outline the main features of NaPDoS and test it on four draft genome sequences and two metagenomic datasets. The results provide a rapid method to assess secondary metabolite biosynthetic gene diversity and richness in organisms or environments and a mechanism to identify genes that may be associated with uncharacterized biochemistry