title: Models of signalling networks - what cell biologists can gain from them and give to them.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
authors.forename: Kevin A
authors.initials: KA
authors.lastname: Janes
authors.forename: Douglas A
authors.initials: DA
authors.lastname: Lauffenburger
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015398:Signal Transduction / Q000502:physiology*
keywords: Cell signalling; Computational biology; Systems biology