Rêve | EPFL Graph Search

Le rêve est une « disposition de l'esprit généralement nocturne, survenant au cours du sommeil, et qui procure à l'individu éveillé des souvenirs nommés eux aussi rêves ». Au cours de l'histoire et des civilisations, le rêve a été un moyen de s'affranchir du temps et de l'espace ordinaires, pour accéder au surnaturel, aux ancêtres, au divin, ou encore comme un moyen de guérison, de connaissance et de révélation. L'approche rationnelle et scientifique en fait un processus ancré dans le corps et lié à l'activité cérébrale au cours du sommeil. Le rêve pose toujours la question de son sens et de sa signification, ou de son rôle et de sa fonction (approches philosophique, psychodynamique, neurobiologique…). Dans les arts et la littérature, le rêve représente la « vie rêvée » au sens de projet chimérique ou de représentation d'un autre possible. Ce peut être aussi bien de l'agir (espoir, recherche…) que du pâtir (errance, déception…). Étymologie du mot « rêve » Le mot « rê

Evolutionary reverse engineering of gene networks

Daniel Marbach

The expression of genes is controlled by regulatory networks, which perform fundamental information processing and control mechanisms in a cell. Unraveling and modelling these networks will be indispensable to gain a systems-level understanding of biological organisms and genetically related diseases. In this thesis, we present an evolutionary reverse engineering method, which allows to simultaneously infer both the wirings and nonlinear dynamical models of gene regulatory networks from gene expression data. The proposed method reconstructs gene networks by mimicking the natural evolutionary process that constructed them. This is achieved by modelling both the way in which gene networks are encoded in the biological genome, and the different types of mutations and recombinations that drive their evolution, using an artificial genome called Analog Genetic Encoding (AGE). Since AGE mimics the evolutionary forces and constraints that shape biological gene networks, the reconstruction is naturally guided towards biologically plausible solutions. Consequently, the search space is explored more efficiently, and the networks are recovered more reliably, than with alternative methods. We have confirmed the state-of-the-art performance of AGE both in vivo (on real gene networks) and in silico (on simulated networks). In particular, AGE achieved winning performance in the in vivo gene network inference inference challenge of the 2nd DREAM (Dialogue on Reverse Engineering Assessment and Methods) conference, which consisted in predicting the structure of a synthetic-biology gene network in Saccharomyces cerevisiae from time-series data. In vivo performance assessment of network-inference methods is problematic because it is in general not possible to systematically validate predictions, except for few well-characterized gene networks. Consequently, in silico benchmarks are essential to understand the performance of network-inference methods. We have developed tools to generate biologically plausible in silico gene networks, which allow realistic performance assessment of network-inference methods. In contrast to previous in silico benchmarks, we generate network structures by extracting modules from known gene networks of model organisms, instead of using random graphs. Furthermore, we simulate network dynamics using more realistic kinetic models, which include both mRNA and proteins. We have implemented this framework in an open-source Java tool called GeneNetWeaver (GNW). Using GNW we have generated benchmarks for community-wide challenges of the 3rd and 4th DREAM conference (the DREAM in silico network challenges). Here, we assess the performance of 29 network-inference methods, which have been applied independently by participating teams of the DREAM3 challenge. Performance profiling on individual network motifs reveals that current inference methods are affected, to various degrees, by three types of systematic prediction errors. We find that these errors are induced by inaccurate prior assumptions of prevalent gene-network models. The evolutionary reverse engineering approach, which would have ranked 3rd in this challenge, can be used with a wide range of nonlinear models. It could thus provide the necessary framework for the development of models that better approximate different types of gene regulation, thereby enabling ever more accurate reconstruction of gene networks.

EPFL2009

Energy vs. Reliability Trade-offs Exploration in Biomedical Ultra-Low Power Devices

David Atienza Alonso, Andreas Peter Burg, Loris Gérard Duch, Shrikanth Ganapathy, Pablo Garcia del Valle

State-of-the-art wearable devices such as embedded biomedical monitoring systems apply voltage scaling to lower as much as possible their energy consumption and achieve longer battery lifetimes. While embedded memories often rely on Error Correction Codes (ECC) for error protection, in this paper we explore how the characteristics of biomedical applications can be exploited to develop new techniques with lower power overhead. We then introduce the Dynamic eRror compEnsation And Masking (DREAM) technique, that provides partial memory protection with less area and power overheads than ECC. Different tradeoffs between the error correction ability of the techniques and their energy consumption are examined to conclude that, when properly applied, DREAM consumes 21% less energy than a traditional ECC with Single Error Correction and Double Error Detection (SEC/DED) capabilities.

Ieee2016

Combining Multiple Results of a Reverse Engineering Algorithm: Application to the DREAM Five Gene Network Challenge

Dario Floreano, Daniel Marbach, Claudio Mattiussi

The output of reverse engineering methods for biological networks is often not a single network prediction, but an ensemble of networks that are consistent with the experimentally measured data. In this paper, we consider the problem of combining the information contained within such an ensemble in order to (1) make more accurate network predictions and (2) estimate the reliability of these predictions. We review existing methods, discuss their limitations, and point out possible research directions towards more advanced methods for this purpose. The potential of considering ensembles of networks, rather than individual inferred networks, is demonstrated by showing how an ensemble voting method achieved winning performance on the Five Gene Network Challenge of the second DREAM conference (Dialogue on Reverse Engineering Assessment and Methods 2007, New York, NY).

Blackwell Publishing, 9600 Garsington Rd, Oxford Ox4 2Dq, Oxen, England2009

Evolutionary reverse engineering of gene networks

Daniel Marbach

EPFL2009