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Towards an Exact Reconstruction of a Time-Invariant Model from Time Series Data | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

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Research Article

Towards an Exact Reconstruction of a Time-Invariant Model from Time Series Data

Michael A. Idowu1* and James Bown2

1Scottish Informatics Mathematics Biology and Statistics (SIMBIOS) Centre, School of Contemporary Sciences, University of Abertay, Dundee, Scotland, United Kingdom

2Institute of Arts, Media and Computer Games, University of Abertay Dundee, Scotland, United Kingdom

*Corresponding Author:
Dr. Michael A. Idowu
Scottish Informatics Mathematics Biology and Statistics (SIMBIOS) Centre
School of Contemporary Sciences University of Abertay
Dundee Scotland
United Kingdom DD1 1HG
E-mail: [email protected]

Received date: September 08, 2011; Accepted date: November 10, 2011; Published date: November 23, 2011

Citation: Idowu MA, Bown J (2011) Towards an Exact Reconstruction of a Time- Invariant Model from Time Series Data. J Comput Sci Syst Biol 4:055-070. doi:10.4172/jcsb.1000077

Copyright: © 2011 Idowu MA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Dynamic processes in biological systems may be profiled by measuring system properties over time. One way of representing such time series data is through weighted interaction networks, where the nodes in the network represent the measurables and the weighted edges represent interactions between any pair of nodes. Construction of these network models from time series data may involve seeking a robust data-consistent and time-invariant model to approximate and describe system dynamics. Many problems in mathematics, systems biology and physics can be recast into this form and may require finding the most consistent solution to a set of first order differential equations. This is especially challenging in cases where the number of data points is less than or equal to the number of measurables. We present a novel computational method for network reconstruction with limited time series data. To test our method, we use artificial time series data generated from known network models. We then attempt to reconstruct the original network from the time series data alone. We find good agreement between the original and predicted networks.

Keywords

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