Abstract
High throughput biological experiments are critical for their role in systems biology – the ability to survey the state of cellular mechanisms on the broad scale opens possibilities for the scientific researcher to understand how multiple components come together, and what goes wrong in disease states. However, the data returned from these experiments is massive and heterogeneous, and requires intuitive and clever computational algorithms for analysis. The correlation network model has been proposed as a tool for modeling and analysis of this high throughput data; structures within the model identified by graph theory have been found to represent key players in major cellular pathways. Previous work has found that network filtering using graph theoretic structural concepts can reduce noise and strengthen biological signals in these networks. However, the process of filtering biological network using such filters is computationally intensive and the filtered networks remain large. In this research, we develop a parallel template for these network filters to improve runtime, and use this high performance environment to show that parallelization does not affect network structure or biological function of that structure.
Highlights
High-throughput assays are able to take surveys of the entire cellular landscape at once – be it gene expression, protein function, or any other experimentally quantifiable measure
The technological capacity for examining the minutiae on the grand scale is growing, and with it grows the need for analyses that are both computationally robust and informative
The increase in technological capacity is accompanied, by an increase in data heterogeneity, volume, and noise – leading to biological “big data” [10]. To accommodate these specific problem areas, the network model has been employed as an effective tool for data visualization and analysis
Summary
High-throughput assays are able to take surveys of the entire cellular landscape at once – be it gene expression, protein function, or any other experimentally quantifiable measure. The technological capacity for examining the minutiae on the grand scale is growing, and with it grows the need for analyses that are both computationally robust and informative. The increase in technological capacity is accompanied, by an increase in data heterogeneity, volume, and noise – leading to biological “big data” [10]. To accommodate these specific problem areas, the network model has been employed as an effective tool for data visualization and analysis. Graph theory has been around at least since the 1700’s, ever since Leonhard Euler proposed his solution to the Seven Bridges of Königsberg Problem, and ever since, methods to iterate through and understand the graph model have been identified, solved, and analytically improved
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