The feasibility of genome-scale biological network inference using Graphics Processing Units |
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Authors: | Raghuram Thiagarajan Amir Alavi Jagdeep T Podichetty Jason N Bazil Daniel A Beard |
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Institution: | 1.Pratt & Miller Engineering,New Hudson,USA;2.Department of Molecular and Integrative Physiology,University of Michigan,Ann Arbor,USA;3.Department of Physiology,Michigan State University,East Lansing,USA;4.Computational Biology Department, School of Computer Science,Carnegie Mellon University,Pittsburgh,USA |
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Abstract: | Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called ‘big data’ applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster. |
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