[Statlist] Next talk: Friday, May 16, 2014 with Mohammad Sadeh (Max Planck Institute for Molecular Genetics)

[Rey-Lutz Cecilia]

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ETH and University of Zurich

Organisers:
Profs. P. Bühlmann - R. Furrer - L. Held - T. Hothorn - H.R. Kuensch - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf

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We are glad to announce the following talk

Friday, May 16, 2014 at 15.15h  ETH Zurich HG G 19.1
with Mohammad Sadeh (Max Planck Institute for Molecular Genetics)

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Title:
Considering Unknown Unknowns - Reconstruction of Non-confoundable Causal Relations in Biological Networks

Abstract:
 Our current understanding of virtually all cellular signaling pathways is almost certainly incomplete. We miss important but sofar unknown players in the pathways. Moreover, we only have a partial account of the molecular interactions and modi cations of the known players. When analyzing the cell, we look through narrow windows leaving potentially important events in blind spots. Much network reconstruction
methods are based on investigating unknown relations of known players assuming there are not any unknown
players. This might severely bias both the computational and manual reconstruction of underlying biological networks.
Here we ask the question, which features of a network can be confounded by incomplete observations and which cannot. In the context of nested e ect model based network reconstruction, we show that in the presence of missing observations or hidden factors with their unknown e ects (unknown-unknowns), a reliable
reconstruction of the full network is not feasible. Nevertheless, we can show that certain characteristics of signaling networks like the existence of cross talk between certain branches of the network can be inferred in a not-confoundable way. We derive a simple polynomial test for inferring such not-confoundable characteristics
of signaling networks. We also define a set of edges to partially reconstruct the signaling networks when the unknown players exist. Finally, we evaluate the performance of the proposed method on simulated data and two biological studies, a first application to embryonic stem cell diff erentiation in mice and a recent study on the Wnt signaling pathway in colorectal cancer cells. We demonstrate that taking unknown hidden mechanisms into account changes our account of real biological networks.
References
[1] Sadeh, M. J., Mo a, G. and Spang, R. (2013). Considering Unknown Unknowns - Reconstruction of Non-confoundable Causal Relations in Biological Networks. In RE- COMB 234248.
[2] Anchang B, Sadeh M, Jacob J, Tresch A, Vlad M, et al. (2009) Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested e ects models. Proceedings of the National Academy of Sciences 106: 6447.
[3] Tresch A, Markowetz F (2008) Structure learning in nested e ects models. Stat Appl Genet Mol Biol 7.
[4] Markowetz, F and Bloch, J and Spang, R (2005) Non-transcriptional pathway features reconstructed from
secondary e ects of RNA interference Bioinformatics 21:4026-32.
[5] Markowetz, F and Kostka, D and Troyanskaya, O G and Spang, R (2007) Nested e ects models for high-dimensional phenotyping screens Bioinformatics 13:i305-12.
1Max Planck Institute for Molecular Genetics. Ihnestrae 63-73, D-14195 Berlin, (Germany).

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This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar
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