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Lees J.G.,University College London | Heriche J.K.,Cell Biology & Biophysics Unit | Morilla I.,University of Malaga | Ranea J.A.,University College London | And 2 more authors.
Physical Biology

Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use. © 2011 IOP Publishing Ltd. Source

Lees J.G.,University College London | Heriche J.-K.,Cell Biology & Biophysics Unit | Morilla I.,Paris-Sorbonne University | Fernandez J.M.,Spanish National Bioinformatics Institute INB | And 7 more authors.

Motivation: Most biological processes remain only partially characterized with many components still to be identified. Given that a whole genome can usually not be tested in a functional assay, identifying the genes most likely to be of interest is of critical importance to avoid wasting resources. Results: Given a set of known functionally related genes and using a state-of-the-art approach to data integration and mining, our Functional Lists (FUN-L) method provides a ranked list of candidate genes for testing. Validation of predictions from FUN-L with independent RNAi screens confirms that FUN-L-produced lists are enriched in genes with the expected phenotypes. In this article, we describe a website front end to FUN-L. © The Author 2015. Published by Oxford University Press. Source

Iskar M.,Structural and Computational Biology Unit | Zeller G.,Structural and Computational Biology Unit | Blattmann P.,Cell Biology & Biophysics Unit | Blattmann P.,University of Heidelberg | And 12 more authors.
Molecular Systems Biology

In pharmacology, it is crucial to understand the complex biological responses that drugs elicit in the human organism and how well they can be inferred from model organisms. We therefore identified a large set of drug-induced transcriptional modules from genome-wide microarray data of drug-treated human cell lines and rat liver, and first characterized their conservation. Over 70% of these modules were common for multiple cell lines and 15% were conserved between the human in vitro and the rat in vivo system. We then illustrate the utility of conserved and cell-type-specific drug-induced modules by predicting and experimentally validating (i) gene functions, e.g., 10 novel regulators of cellular cholesterol homeostasis and (ii) new mechanisms of action for existing drugs, thereby providing a starting point for drug repositioning, e.g., novel cell cycle inhibitors and new modulators of α-adrenergic receptor, peroxisome proliferator-activated receptor and estrogen receptor. Taken together, the identified modules reveal the conservation of transcriptional responses towards drugs across cell types and organisms, and improve our understanding of both the molecular basis of drug action and human biology. Copyright © 2013 EMBO and Macmillan Publishers Limited. Source

Grecco H.E.,Max Planck Institute of Molecular Physiology | Roda-Navarro P.,Max Planck Institute of Molecular Physiology | Girod A.,Max Planck Institute of Molecular Physiology | Girod A.,Cell Biology & Biophysics Unit | And 12 more authors.
Nature Methods

Extracellular stimuli are transduced inside the cell by posttranslational modifications (PTMs), such as phosphorylation, of proteins in signaling networks. Insight into the structure of these networks requires quantification of PTM levels in individual cells. Fluorescence resonance energy transfer (FRET) measured by fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to image PTM levels in situ. FLIM on cell arrays that express fluorescent protein fusions can quantify tyrosine phosphorylation patterns in large networks in individual cells. We identified tyrosine kinase substrates by imaging their phosphorylation levels after inhibition of protein tyrosine phosphatases. Analysis of the correlation between protein phosphorylation and expression levels at single cell resolution allowed us to identify positive feedback motifs. Using FLIM on cell arrays (CA-FLIM), we uncovered components that transduce signals from epidermal growth factor receptor. © 2010 Nature America, Inc. All rights reserved. Source

Heriche J.-K.,Cell Biology & Biophysics Unit | Lees J.G.,University College London | Morilla I.,University of Malaga | Morilla I.,Swiss Institute of Bioinformatics | And 15 more authors.
Molecular Biology of the Cell

The advent of genome-wide RNA interference (RNAi)-based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function - mitotic chromosome condensation - and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest. © 2014 Hériché et al. Source

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