### Abstract

Recent research has revealed new applications of network control science within bio-medicine, pharmacology, and medical therapeutics. These new insights and new applications generated in turn a rediscovery of some old, unresolved algorithmic questions, this time with a much stronger motivation for their tackling. One of these questions regards the so-called Structural Target Control optimization problem, known in previous literature also as Structural Output Controllability problem. Given a directed network (graph) and a target subset of nodes, the task is to select a small (or the smallest) set of nodes from which the target can be independently controlled, i.e., it can be driven from any given initial configuration to any desired final one, through a finite sequence of input values. In recent work, this problem has been shown to be NP-hard, and several heuristic algorithms were introduced and analyzed, both on randomly generated networks, and on bio-medical ones. In this paper, we show that the Structural Target Controllability problem is fixed parameter tractable when parameterized by the number of target nodes. We also prove that the problem is hard to approximate at a factor better than O(log n).

Original language | English |
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Title of host publication | Algorithms for Computational Biology - 5th International Conference, AlCoB 2018, Proceedings |

Editors | Carlos Martin-Vide, Jesper Jansson, Miguel A. Vega-Rodriguez |

Publisher | Springer Verlag |

Pages | 103-114 |

Number of pages | 12 |

ISBN (Print) | 9783319919379 |

DOIs | |

Publication status | Published - Jan 1 2018 |

Event | 5th International Conference on Algorithms for Computational Biology, AlCoB 2018 - Hong Kong, China Duration: Jun 25 2018 → Jun 26 2018 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10849 LNBI |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 5th International Conference on Algorithms for Computational Biology, AlCoB 2018 |
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Country | China |

City | Hong Kong |

Period | 6/25/18 → 6/26/18 |

### Fingerprint

### Keywords

- Approximation algorithms
- Fixed parameter algorithms
- Protein interaction networks
- Structural network control
- Systems biology

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Algorithms for Computational Biology - 5th International Conference, AlCoB 2018, Proceedings*(pp. 103-114). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10849 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-319-91938-6_9

**Fixed parameter algorithms and hardness of approximation results for the structural target controllability problem.** / Czeizler, Eugen; Popa, Alexandru; Popescu, Victor.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Algorithms for Computational Biology - 5th International Conference, AlCoB 2018, Proceedings.*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10849 LNBI, Springer Verlag, pp. 103-114, 5th International Conference on Algorithms for Computational Biology, AlCoB 2018, Hong Kong, China, 6/25/18. https://doi.org/10.1007/978-3-319-91938-6_9

}

TY - GEN

T1 - Fixed parameter algorithms and hardness of approximation results for the structural target controllability problem

AU - Czeizler, Eugen

AU - Popa, Alexandru

AU - Popescu, Victor

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Recent research has revealed new applications of network control science within bio-medicine, pharmacology, and medical therapeutics. These new insights and new applications generated in turn a rediscovery of some old, unresolved algorithmic questions, this time with a much stronger motivation for their tackling. One of these questions regards the so-called Structural Target Control optimization problem, known in previous literature also as Structural Output Controllability problem. Given a directed network (graph) and a target subset of nodes, the task is to select a small (or the smallest) set of nodes from which the target can be independently controlled, i.e., it can be driven from any given initial configuration to any desired final one, through a finite sequence of input values. In recent work, this problem has been shown to be NP-hard, and several heuristic algorithms were introduced and analyzed, both on randomly generated networks, and on bio-medical ones. In this paper, we show that the Structural Target Controllability problem is fixed parameter tractable when parameterized by the number of target nodes. We also prove that the problem is hard to approximate at a factor better than O(log n).

AB - Recent research has revealed new applications of network control science within bio-medicine, pharmacology, and medical therapeutics. These new insights and new applications generated in turn a rediscovery of some old, unresolved algorithmic questions, this time with a much stronger motivation for their tackling. One of these questions regards the so-called Structural Target Control optimization problem, known in previous literature also as Structural Output Controllability problem. Given a directed network (graph) and a target subset of nodes, the task is to select a small (or the smallest) set of nodes from which the target can be independently controlled, i.e., it can be driven from any given initial configuration to any desired final one, through a finite sequence of input values. In recent work, this problem has been shown to be NP-hard, and several heuristic algorithms were introduced and analyzed, both on randomly generated networks, and on bio-medical ones. In this paper, we show that the Structural Target Controllability problem is fixed parameter tractable when parameterized by the number of target nodes. We also prove that the problem is hard to approximate at a factor better than O(log n).

KW - Approximation algorithms

KW - Fixed parameter algorithms

KW - Protein interaction networks

KW - Structural network control

KW - Systems biology

UR - http://www.scopus.com/inward/record.url?scp=85049205513&partnerID=8YFLogxK

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U2 - 10.1007/978-3-319-91938-6_9

DO - 10.1007/978-3-319-91938-6_9

M3 - Conference contribution

SN - 9783319919379

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 103

EP - 114

BT - Algorithms for Computational Biology - 5th International Conference, AlCoB 2018, Proceedings

A2 - Martin-Vide, Carlos

A2 - Jansson, Jesper

A2 - Vega-Rodriguez, Miguel A.

PB - Springer Verlag

ER -