NDLI: On the performance of particle swarm optimization for parameterizing kinetic models of cellular networks

Content Provider	IEEE Xplore Digital Library
Author	Berestovsky, N. Fukui, R. Nakhleh, L.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA (Berestovsky, N.; Fukui, R.; Nakhleh, L.)
Abstract	Recent advances in high-throughput technologies and an increased knowledge of biochemical systems have enabled the reconstruction of cellular regulatory networks. While these reconstructions often consist of the connectivity patterns among the network's components, an important task in this area is to derive values for the kinetic parameters of the system so that its dynamics can be elucidated and testable hypotheses can be generated. The problem here is to reverse engineer the model (in our case, values of kinetic parameters) given the data that consists of a network connectivity and time-series data. Particle swarm optimization (PSO) has been recently used for this reverse engineering task. In this paper, we introduce three scoring metrics for assessing the optimality of the solution found by PSO. Using biological data sets, we study the performance of the PSO framework under the three scoring metrics. Our results show that PSO achieves very good parameterization of the cellular networks we consider, regardless of the complexity of the dynamics patterns generated by the underlying kinetics. Given the flexibility of the PSO framework, as well as its natural amenability to parallelization and general high-performance implementations and execution, our results indicate PSO may be a good candidate for parameterizing cellular networks.
Starting Page	184
Ending Page	191
File Size	2901248
Page Count	8
File Format	PDF
ISBN	9781467311908
e-ISBN	9781467311915
DOI	10.1109/CIBCB.2012.6217229
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Vectors Biological system modeling Parameter estimation Discrete Fourier transforms Data models Particle swarm optimization time series data cellular networks kinetic models
Content Type	Text
Resource Type	Article

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