Abstract
Viruses evade detection by the host immune system through the suppression of antiviral pathways. These pathways are thus obscured when measuring the host response to viral infection and cannot be inferred by current network reconstruction methodology. Here we aim to close this gap by providing a novel computational framework for the inference of such inhibited pathways as well as the proteins targeted by the virus to achieve this inhibition. We demonstrate the power of our method by testing it on the response to influenza infection in humans, with and without the viral inhibitory protein NS1, revealing its direct targets and their inhibitory effects.
Original language | English |
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Pages (from-to) | 394-404 |
Number of pages | 11 |
Journal | Journal of Computational Biology |
Volume | 21 |
Issue number | 5 |
DOIs | |
State | Published - 1 May 2014 |
Keywords
- Algorithms
- Computational molecular biology
- Gene networks
- Graphs and networks
- Linear programming