# Target Controllability of Linear Networks

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Eugen Czeizler, Christian Gratie, Wu Kai Chiu, Krishna Kanhaiya, Ion Petre

Editors: Bartocci, Ezio and Lio, Pietro and Paoletti, Nicola

Publication year: 2016

Publisher: Springer

Book title: Computational Methods in Systems Biology: 14th International Conference, CMSB 2016, Cambridge, UK, September 21-23, 2016, Proceedings

Title of series: Lecture Notes in Bioinformatics

Volume number: 9859

Start page: 67

End page: 81

ISBN: 978-3-319-45176-3

eISBN: 978-3-319-45177-0

ISSN: 0302-9743

Abstract

Computational analysis of the structure of intra-cellular molecular interaction networks can suggest novel therapeutic approaches for systemic diseases like cancer. Recent research in the area of network science has shown that network control theory can be a powerful tool in the understanding and manipulation of such bio-medical networks. In 2011, Liu et al. developed a polynomial time optimization algorithm for computing the size of the minimal set of nodes controlling a given linear network. In 2014, Gao et al. generalized the problem for target structural control, where the objective is to optimize the size of the minimal set of nodes controlling a given target within a linear network. The working hypothesis in this case is that partial control might be “cheaper” (in the size of the controlling set) than the full control of a network. The authors developed a Greedy algorithm searching for the minimal solution of the structural target control problem, however, no suggestions were given over the actual complexity of the optimization problem. In here we prove that the structural target controllability problem is NP-hard when looking to minimize the number of driven nodes within the network, i.e., the first set of nodes which need to be directly controlled in order to structurally control the target. We also show that the Greedy algorithm provided by Gao et al. in 2014 might in some special cases fail to pro- vide a valid solution, and a subsequent validation step is required. Also, we improve their search algorithm using several heuristics, obtaining in the end up to a 10-fold decrease in running time and also a significant decrease of the size of the minimal solution found by the algorithms.