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

TitleModels of signalling networks - what cell biologists can gain from them and give to them.
Publication TypeJournal Article
Year of Publication2013
AuthorsJanes, KA, Lauffenburger, DA
JournalJ Cell Sci
IssuePt 9
Date Published2013 May 1
KeywordsAnimals, Computer Simulation, Humans, Models, Biological, Signal Transduction

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.

Alternate JournalJ. Cell. Sci.
PubMed ID23720376
PubMed Central IDPMC3666249
Grant List1-DP2-OD006464 / OD / NIH HHS / United States
R01-EB010246 / EB / NIBIB NIH HHS / United States
R24-DK090963 / DK / NIDDK NIH HHS / United States
U54-CA112967 / CA / NCI NIH HHS / United States