Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction
By J. Sáez-Rodríguez, Leonidas G. Alexopoulos, Jonathan P. Epperlein, Regina Samaga, Douglas A. Lauffenburger, Steffen Klamt, Peter K. Sorger. Molecular Systems Biology, 2009
385 citations recorded by the source corpus.
Large‐scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature‐derived network of 82 proteins covering the immediate‐early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature‐derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell‐type‐specific models of mammalian signalling from generic protein signalling networks.
Subfields: Computational Theory and Mathematics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/37bf5ec0f44213dea49527009fbf1e2374897fa1
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In Computational Theory and Mathematics
In Molecular Biology
- Douglas A. Lauffenburger
- Peter K. Sorger
- Melody K. Morris
- J. Sáez-Rodríguez
- Li Xie
- Philip E. Bourne
- Lei Xie
Citations in the corpus
- Logic-Based Models for the Analysis of Cell Signaling Networks (2010, methodology): “Boolean discrete time steps binary (13, 15-17, 19, 24, 25, 33); (20, 42, 43, 51, 52)”
- Structure-based Systems Biology for Analyzing Off-target Binding (2011, background): “Continued progress in generating, mining and integrating omics-data [120– 125], the active development of algorithms for network biology [126–128], and the emergence of executable biology [129,130] will provide rich resources and computational frameworks to address the network reconstruction issue.”