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A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT
James L Bown1,2, Mark Shovman2,3, Paul Robertson2
1School of Science, Engineering and Technology, Abertay University, Dundee, DD1 1HG, UK.
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 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.
Insights
This study introduces SiViT, a novel toolkit making complex cancer cell signaling models interactive for researchers. SiViT aids in understanding drug resistance biomarkers and pathway crosstalk, accelerating cancer therapy research.
Area of Science:
- * Computational biology and bioinformatics
- * Cancer cell signaling and targeted therapy
- * Systems biology and dynamical modeling
Background:
- * Targeted cancer therapies aim to disrupt aberrant cellular signaling pathways.
- * Translating candidate biomarkers to clinical practice is challenging due to pathway network complexity.
- * Systems biology offers insights into complex signaling pathway dynamics but faces model inaccessibility.
Purpose of the Study:
- * To present SiViT, a novel visualization toolkit for interactive systems biology models of cancer cell signaling.
- * To enable clinicians and biologists to explore pathway dynamics without specialized computational expertise.
- * To facilitate biomarker discovery and the design of anticancer drug strategies.
Main Methods:
- * Development of SiViT, a visualization toolkit leveraging computer games technology.
- * Conversion of systems biology models into interactive simulations.
- * Experimental validation of SiViT predictions using a Her2 signaling model.
Main Results:
- * SiViT successfully animated the effects of genetic mutations and drug inhibitors on pathway dynamics.
- * Experimental validation confirmed SiViT's predictions regarding drug resistance biomarkers and pathway crosstalk.
- * The toolkit demonstrated potential for suggesting further experiments and assessing biomarker effectiveness.
Conclusions:
- * SiViT enhances accessibility to complex systems biology models for cancer research.
- * The toolkit supports preclinical research, combinatorial strategy design, and biomarker discovery.
- * Interactive simulation and real data iteration can lead to more accurate and useful biological models.