Brief paper: Robust dynamical network structure reconstruction

Authors:
Ye Yuan;Guy-Bart Stan;Sean Warnick;Jorge Goncalves
Affiliations:
Control Group, Department of Engineering, University of Cambridge, United Kingdom;Centre for Synthetic Biology and Innovation, Department of Bioengineering, Imperial College London, United Kingdom;Information and Decision Algorithms Laboratories, Computer Science Department, Brigham Young University, United States;Control Group, Department of Engineering, University of Cambridge, United Kingdom
Venue:
Automatica (Journal of IFAC)
Year:
2011

Citing 3
Cited 0

System identification: theory for the user

System identification: theory for the user
Robust and optimal control

Robust and optimal control
Advances to Bayesian network inference for generating causal networks from observational biological data

Bioinformatics

Quantified Score

Hi-index	22.14

Visualization

Abstract

This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements (no noise) and perfect system identification. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular network structures. We conclude with biologically inspired network reconstruction examples which include noise and nonlinearities.