A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT

James L. Bown*, Mark Shovman, Paul Robertson, Andrei Boiko, Alexey Goltsov, Peter Mullen, David J. Harrison*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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 visualisation 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.
Original languageEnglish
Pages (from-to)29657-29667
Number of pages11
JournalOncotarget
Volume8
Early online date18 May 2016
DOIs
Publication statusPublished - 2 May 2017

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