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.

Download full-text


Available from: Kevin Janes, Aug 15, 2014
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: SUMMARY We have come a long way in the 55 years since Edmond Fischer and the late Edwin Krebs discovered that the activity of glycogen phosphorylase is regulated by reversible protein phosphorylation. Many of the fundamental molecular mechanisms that operate in biological signaling have since been characterized and the vast web of interconnected pathways that make up the cellular signaling network has been mapped in considerable detail. Nonetheless, it is important to consider how fast this field is still moving and the issues at the current boundaries of our understanding. One must also appreciate what experimental strategies have allowed us to attain our present level of knowledge. We summarize here some key issues (both conceptual and methodological), raise unresolved questions, discuss potential pitfalls, and highlight areas in which our understanding is still rudimentary. We hope these wide-ranging ruminations will be useful to investigators who carry studies of signal transduction forward during the rest of the 21st century.
    Full-text · Article · Oct 2014 · Cold Spring Harbor perspectives in biology
  • [Show abstract] [Hide abstract]
    ABSTRACT: This chapter intends to review the most recent development in computational investigation of regulatory networks. It covers both top-down systems biology approaches (i.e. data mining methods for analyzing large amount of omics datasets) and bottom-up systems biology methods (i.e. mathematical modeling using differential equations or chemical reaction systems) for reconstructing cancer-related biological networks in general. Particularly, two case studies are provided to illustrate the usage of these approaches for developing genetic regulatory networks and cell signaling pathways using microarray and proteomics datasets, respectively. A future outlook of this research field is also discussed.
    No preview · Chapter · Jan 2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: The simultaneous measurement of multiple cytokines in parallel by using multiplex proteome arrays (MPA) is of great interest to understanding the inflammatory response following myocardial infarction; however, since cytokines are pleiotropic and redundant, increase of information throughput (IT) attained by measuring multiple cytokines remain to be determined. We aimed this study to assess the IT of an MPA system designed to assess 8 cytokines – commercially available at the time of the study – serum levels, before (control state) and after experimental myocardial cryoinjury (activated state) in rats.
    No preview · Article · Nov 2013 · Journal of Theoretical Biology
Show more