DYVIPAC: an integrated analysis and visualisation framework to probe multi-dimensional biological networks
Lan K Nguyen1, Andrea Degasperi1, Philip Cotter1
1Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.
Abstract:
Biochemical networks are dynamic and multi-dimensional systems, consisting of tens or hundreds of molecular components. Diseases such as cancer commonly arise due to changes in the dynamics of signalling and gene regulatory networks caused by genetic alternations. Elucidating the network dynamics in health and disease is crucial to better understand the disease mechanisms and derive effective therapeutic strategies. However, current approaches to analyse and visualise systems dynamics can often provide only low-dimensional projections of the network dynamics, which often does not present the multi-dimensional picture of the system behaviour. More efficient and reliable methods for multi-dimensional systems analysis and visualisation are thus required. To address this issue, we here present an integrated analysis and visualisation framework for high-dimensional network behaviour which exploits the advantages provided by parallel coordinates graphs. We demonstrate the applicability of the framework, named "Dynamics Visualisation based on Parallel Coordinates" (DYVIPAC), to a variety of signalling networks ranging in topological wirings and dynamic properties. The framework was proved useful in acquiring an integrated understanding of systems behaviour.
Insights
This study introduces DYVIPAC, a novel framework using parallel coordinates to visualize complex, high-dimensional biochemical network dynamics. It aids understanding of disease mechanisms by offering a multi-dimensional view of system behavior.
Area of Science:
- Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Biochemical networks are complex, multi-dimensional systems crucial for cellular functions.
- Disease, like cancer, often results from altered network dynamics due to genetic changes.
- Current methods for analyzing network dynamics offer limited low-dimensional views, hindering comprehensive understanding.
Purpose of the Study:
- To develop an integrated analysis and visualization framework for high-dimensional biochemical network behavior.
- To overcome limitations of current methods in presenting multi-dimensional system dynamics.
- To facilitate a better understanding of disease mechanisms and therapeutic strategies.
Main Methods:
- Developed a novel framework named "Dynamics Visualisation based on Parallel Coordinates" (DYVIPAC).
- Utilized parallel coordinates graphs to analyze and visualize high-dimensional network dynamics.
- Applied the framework to various signaling networks with diverse topological and dynamic properties.
Main Results:
- Demonstrated the applicability and utility of the DYVIPAC framework across different network types.
- Showcased the framework's ability to provide an integrated understanding of complex systems behavior.
- Successfully visualized multi-dimensional network dynamics that are often missed by conventional methods.
Conclusions:
- DYVIPAC offers an efficient and reliable method for analyzing and visualizing high-dimensional network dynamics.
- The framework enhances the understanding of system behavior in both health and disease states.
- This approach is valuable for elucidating disease mechanisms and informing therapeutic strategy development.
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