Paper Info
Title
An automated procedure for the extraction of metabolic network information from time series data.
Abstract
Novel high-throughput measurement techniques in vivo are beginning to produce dense high-quality time series which can be used to investigate the structure and regulation of biochemical networks. We propose an automated information extraction procedure which takes advantage of the unique S-system structure and supports model building from time traces, curve fitting, model selection, and structure identification based on parameter estimation. The procedure comprises of three modules: model Generation, parameter estimation or model Fitting, and model Selection (GFS algorithm). The GFS algorithm has been implemented in MATLAB and returns a list of candidate S-systems which adequately explain the data and guides the search to the most plausible model for the time series under study. By combining two strategies (namely decoupling and limiting connectivity) with methods of data smoothing, the proposed algorithm is scalable up to realistic situations of moderate size. We illustrate the proposed methodology with a didactic example.
Year
DOI
Venue
2006
10.1142/S0219720006002259
J. Bioinformatics and Computational Biology
Keywords
Field
DocType
parameter estimation,biochemical systems theory,inverse problem,s-system,pathway identification,metabolic profile,time series,model building,curve fitting,metabolic network,high throughput,system theory,time series data,information extraction,model selection
Time series,Curve fitting,Computer science,Model building,Model selection,Smoothing,Biochemical systems theory,Information extraction,Artificial intelligence,Bioinformatics,Estimation theory,Machine learning
Journal
Volume
Issue
ISSN
4
3
0219-7200
Citations 
PageRank 
References 
6
0.59
6
Authors
2
Name
Order
Citations
PageRank
Simeone Marino1314.35
Eberhard O. Voit233029.03