Inferring signalling networks from images

The mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost‐effective means to infer connectivity between genes on a systems‐level is by quantifying the similarity between high‐dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens.

[1]  P. Sorger,et al.  Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis , 2009, Nature.

[2]  Peer Bork,et al.  Towards Cellular Systems in 4D , 2005, Cell.

[3]  B. Berger,et al.  Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen. , 2010, Genome research.

[4]  M. Boutros,et al.  Clustering phenotype populations by genome-wide RNAi and multiparametric imaging , 2010, Molecular systems biology.

[5]  J. Ferrell,et al.  Modeling the Cell Cycle: Why Do Certain Circuits Oscillate? , 2011, Cell.

[6]  C. Bakal,et al.  Quantitative Morphological Signatures Define Local Signaling Networks Regulating Cell Morphology , 2007, Science.

[7]  M. Kirschner,et al.  Dynamic instability of microtubule growth , 1984, Nature.

[8]  Gary D Bader,et al.  The Genetic Landscape of a Cell , 2010, Science.

[9]  Varpu Marjomäki,et al.  Molecular Systems Biology Peer Review Process File Single-cell Analysis of the Population Context Advances Rnai Screening at Multiple Levels Transaction Report , 2022 .

[10]  Xiaobo Zhou,et al.  Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens , 2008, BMC Bioinformatics.

[11]  N. Perrimon,et al.  Genome-Wide RNAi Analysis of Growth and Viability in Drosophila Cells , 2004, Science.

[12]  Y. Kalaidzidis,et al.  Systems survey of endocytosis by multiparametric image analysis , 2010, Nature.

[13]  Julian Mintseris,et al.  A Protein Complex Network of Drosophila melanogaster , 2011, Cell.

[14]  P. Liberali,et al.  Population context determines cell-to-cell variability in endocytosis and virus infection , 2009, Nature.

[15]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[16]  Lani F. Wu,et al.  Cellular Heterogeneity: Do Differences Make a Difference? , 2010, Cell.

[17]  Marc Vidal,et al.  Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis , 2005, Nature.

[18]  Wolfgang Huber,et al.  EBImage—an R package for image processing with applications to cellular phenotypes , 2010, Bioinform..

[19]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[20]  Anne E Carpenter,et al.  Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software , 2011, Bioinform..

[21]  D. Gerlich,et al.  Molecular control of animal cell cytokinesis , 2012, Nature Cell Biology.

[22]  C. Bakal,et al.  Differential RNAi screening provides insights into the rewiring of signalling networks during oxidative stress. , 2012, Molecular bioSystems.

[23]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[24]  C. Bakal,et al.  Genomic screening with RNAi: results and challenges. , 2010, Annual review of biochemistry.

[25]  A. Oudenaarden,et al.  Cellular Decision Making and Biological Noise: From Microbes to Mammals , 2011, Cell.

[26]  S. L. Wong,et al.  Towards a proteome-scale map of the human protein–protein interaction network , 2005, Nature.