### Abstract

Original language | English |
---|---|

Number of pages | 15 |

Journal | Journal of Computer Science and Systems Biology |

Volume | 4 |

Issue number | 4 |

DOIs | |

Publication status | Published - 23 Nov 2011 |

### Fingerprint

### Cite this

}

**Towards an exact reconstruction of a time-invariant model from time series data.** / Idowu, Michael A.; Bown, James L.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Towards an exact reconstruction of a time-invariant model from time series data

AU - Idowu, Michael A.

AU - Bown, James L.

PY - 2011/11/23

Y1 - 2011/11/23

N2 - 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.

AB - 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.

U2 - 10.4172/jcsb.1000077

DO - 10.4172/jcsb.1000077

M3 - Article

VL - 4

JO - Journal of Computer Science and Systems Biology

JF - Journal of Computer Science and Systems Biology

SN - 0974-7230

IS - 4

ER -