Towards an Exact Reconstruction of a Time-Invariant Model from Time Series DataMichael A. Idowu1* and James Bown2
- *Corresponding Author:
- Dr. Michael A. Idowu
Scottish Informatics Mathematics Biology and Statistics (SIMBIOS) Centre
School of Contemporary Sciences University of Abertay
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.
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.