A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT
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James L. Bown1,2, Mark Shovman2,4, Paul Robertson2, Andrei Boiko1,2, Alexey Goltsov1, Peter Mullen3, David J. Harrison3
1School of Science, Engineering and Technology, Abertay University, Dundee, DD1 1HG, UK
2School of Arts, Media and Computer Games, Abertay University, Dundee, DD1 1HG, UK
3School of Medicine, University of St Andrews, St Andrews, KY16 9TF, UK
4Yahoo Labs, Haifa, 31905, Israel
James L. Bown, email: firstname.lastname@example.org
David J. Harrison, email: email@example.com
Keywords: interactive visualization, systems biology, signaling networks, combination therapy, biomarker discovery
Received: January 25, 2016 Accepted: March 31, 2016 Published: May 18, 2016
Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.
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