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Annual International Conference on Research in Computational Molecular Biology

RECOMB 2008: Research in Computational Molecular Biology pp 257–271Cite as

High-Resolution Modeling of Cellular Signaling Networks

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4955))

Abstract

A central challenge in systems biology is the reconstruction of biological networks from high-throughput data sets. A particularly difficult case of this is the inference of dynamic cellular signaling networks. Within signaling networks, a common motif is that of many activators and inhibitors acting upon a small set of substrates. Here we present a novel technique for high-resolution inference of signaling networks from perturbation data based on parameterized modeling of biochemical rates. We also introduce a powerful new signal-processing method for reduction of batch effects in microarray data. We demonstrate the efficacy of these techniques on data from experiments we performed on the Drosophila Rho-signaling network, correctly identifying many known features of the network. In comparison to existing techniques, we are able to provide significantly improved prediction of signaling networks on simulated data, and higher robustness to the noise inherent in all high-throughput experiments. While previous methods have been effective at inferring biological networks in broad statistical strokes, this work takes the further step of modeling both specific interactions and correlations in the background to increase the resolution. The generality of our techniques should allow them to be applied to a wide variety of networks.

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Author information

Authors and Affiliations

  1. Department of Mathematics, MIT, Cambridge, MA 02139,  

    Michael Baym & Bonnie Berger

  2. Computer Science and Artificial Intelligence Laboratory, MIT, 02139,  

    Michael Baym & Bonnie Berger

  3. Department of Genetics, Harvard Medical School, Boston, MA 02115,  

    Chris Bakal & Norbert Perrimon

  4. Howard Hughes Medical Institute, Boston, MA 02215,  

    Chris Bakal & Norbert Perrimon

Authors
  1. Michael Baym
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  2. Chris Bakal
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  3. Norbert Perrimon
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  4. Bonnie Berger
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Martin Vingron Limsoon Wong

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Baym, M., Bakal, C., Perrimon, N., Berger, B. (2008). High-Resolution Modeling of Cellular Signaling Networks. In: Vingron, M., Wong, L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science(), vol 4955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78839-3_22

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  • DOI: https://doi.org/10.1007/978-3-540-78839-3_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78838-6

  • Online ISBN: 978-3-540-78839-3

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