Hierarchical parallel scheme for global parameter estimation in systems biology

Summary form only given. We present a sophisticated and efficient parallel scheme for the DIRECT global optimization algorithm of Jones et al. (1993). Although several sequential implementations for this algorithm have been successfully applied to large scale MDO problems, few parallel versions of t...

Full description

Saved in:
Bibliographic Details
Main Authors: He, J., Sosonkina, M., Shaffer, C.A., Tyson, J.J., Watson, L.T., Zwolak, J.W.
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
Clustering algorithms
Data structures
Design optimization
Large-scale systems
Load management
Master-slave
Mathematical model
Parameter estimation
Systems biology
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary form only given. We present a sophisticated and efficient parallel scheme for the DIRECT global optimization algorithm of Jones et al. (1993). Although several sequential implementations for this algorithm have been successfully applied to large scale MDO problems, few parallel versions of the DIRECT algorithm have addressed well algorithm characteristics such as a single starting point, an unpredictable workload, and a strong data dependency. These challenges engender many interesting design issues including domain decomposition, data access and management, and workload balancing. A hierarchical parallel scheme has been developed to address these challenges at three levels. Each level is supported by parallel and distributed data structures to access shared data sets, distribute workload, or exchange messages. Parameter estimation problems in systems biology provide an ideal application context for the present work. Global nonlinear parameter estimation results obtained on a 200 node Linux cluster are given for a cell cycle model for frog eggs.
DOI:10.1109/IPDPS.2004.1302958