The feasibility of genome-scale biological network inference using Graphics Processing Units.
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.
Pratt & Miller Engineering, WK Smith Drive, New Hudson, MI USA.|Department of Molecular and Integrative Physiology, University of Michigan, North Campus Research Complex, Ann Arbor, MI USA.
Raghuram
R
Thiagarajan
Department of Molecular and Integrative Physiology, University of Michigan, North Campus Research Complex, Ann Arbor, MI USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA USA.
Amir
A
Alavi
Department of Molecular and Integrative Physiology, University of Michigan, North Campus Research Complex, Ann Arbor, MI USA.
Jagdeep T
JT
Podichetty
Department of Molecular and Integrative Physiology, University of Michigan, North Campus Research Complex, Ann Arbor, MI USA.|Department of Physiology, Michigan State University, 567 Wilson Road, East Lansing, MI USA.
Jason N
JN
Bazil
Department of Molecular and Integrative Physiology, University of Michigan, North Campus Research Complex, Ann Arbor, MI USA.
Daniel A
DA
Beard
GPU; Genetic regulatory networks; Network inference; Reverse engineering