Models of signalling networks - what cell biologists can gain from them and give to them

Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
Journal of Cell Science (Impact Factor: 5.43). 05/2013; 126(Pt 9):1913-1921. DOI: 10.1242/jcs.112045
Source: PubMed


Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

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Available from: Kevin Janes, Aug 15, 2014
    • "Thus, if we take the long view, studies of signal transduction are still in exponential phase with many important discoveries to come. Moreover, we anticipate continued development of evermore sophisticated experimental tools—from improvements in automated deep sequencing to characterize the global transcriptome (Malone and Oliver 2011), to new mass spectrometry instrumentation to catalog the cellular metabolome (Rubakhin et al. 2013), to further refinement of mathematical, statistical, and computational theories and methods to assist with display, interpretation , and modeling of the complex networks of relationships involved in intra-and intercellular signaling (Janes and Lauffenburger 2013; see also Azeloglu and Iyengar 2014). Continued advances of this sort will allow us to address questions at an ever greater level of detail and resolution , providing answers at the molecular level to longstanding mechanistic questions about the myriad processes that comprise cell signaling. "
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