Target Controllability of Linear Networks

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pang³

et al. 2018

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be controlled to drive its dynamics from an initial steady state (or attractor) to a target steady state. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking such an approach, we derive a decomposition-based solution to the minimal control problem which can be significantly faster than the existing approaches on large networks. We apply our solution to both real-life biological networks and randomly generated networks, demonstrating promising results.

Section: Related Workmentioning

confidence: 99%

“…The proof is by induction on i. The base case is when i = 2 and BN has two blocks B 1 Proof. Since TS 2 is realized by bas(A 1 ), by its construction (Definition 4.3) we have, for every state s ∈ TS 2 , s 1 ∈ bas(A 1 ).…”

Section: Proof Supposementioning

confidence: 99%

A Decomposition-based Approach towards the Control of Boolean Networks

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pang³

et al. 2018

“…Second, some modelling frameworks are not suitable for biological networks. For example, linear dynamical networks fail to capture the non-linearity of biological networks, thus rendering control strategies for such networks inapplicable [8][9][10]. Lack of biological information prohibits the modelling of biological systems with networks of ordinary differential equations (ODEs) [11].…”

Section: Introductionmentioning

confidence: 99%

Controlling Large Boolean Networks with Temporary and Permanent Perturbations

Lecture Notes in Computer Science

Pang

2019

A salient objective of studying gene regulatory networks (GRNs) is to identify potential target genes whose perturbations would lead to effective treatment of diseases. In this paper, we develop two control methods for GRNs in the context of asynchronous Boolean networks. Our methods compute a minimal subset of nodes of a given Boolean network, such that temporary or permanent perturbations of these nodes drive the network from an initial state to a target steady state. The main advantages of our methods include: (1) temporary and permanent perturbations can be feasibly conducted with techniques for genetic modifications in biological experiments; and (2) the minimality of the identified control sets can reduce the cost of experiments to a great extent. We apply our methods to several real-life biological networks in silico to show their efficiency in terms of computation time and their efficacy with respect to the number of nodes to be perturbed.

“…In recent years, several approaches have been developed for the control of complex networks [3], [4], [8], [9], [10], [11], [12], [13], [14], [15]. Among them, the methods [3], [4], [13] were proposed to tackle the control of networks with linear time-invariant dynamics. Liu et al [3] first developed a structural controllability framework for complex networks to solve full control problems, by identifying the minimal set of (driver) nodes that can steer the entire dynamics of the system.…”

Section: Related Workmentioning

confidence: 99%

“…They proposed a k-walk method and a greedy algorithm to identify a set of driver nodes for controlling a pre-selected set of target nodes. However, Czeizler et al [13] proved that it is NPhard to find the minimal set of driver nodes for structural target control problems and they improved the greedy algorithm [4] using several heuristics. The above methods have a common distinctive advantage that they are solely based on the network structures, which are exponentially smaller than the number of states in their dynamics.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Approach Towards the Source-Target Control of Boolean Networks

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Pang

et al. 2020

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimised if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach.