A Dynamics-based Approach for the Target Control of Boolean Networks
We study the target control problem of asynchronous Boolean networks, to identify a set of nodes, the perturbation of which can drive the dynamics of the network from any initial state to the desired steady state (or attractor). We are particularly interested in temporary perturbations, which are applied for sufficient time and then released to retrieve the original dynamics. Temporary perturbations have the apparent advantage of averting unforeseen consequences, which might be induced by permanent perturbations. Despite the infamous state-space explosion problem, in this work, we develop an efficient method to compute the temporary target control for a given target attractor of a Boolean network. We apply our method to a number of real-life biological networks and compare its performance with the stable motif-based control method to demonstrate its efficacy and efficiency.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Molecular Network Control Through Boolean Canalization
Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studie…
Translating and evaluating single-cell Boolean network interventions in the multiscale setting
Intracellular networks process cellular-level information and control cell fate. They can be computationally modeled using Boolean networks, which are implicit-time causal models of discrete binary events. These networks…
Deep Reinforcement Learning for Control of Probabilistic Boolean Networks
Probabilistic Boolean Networks (PBNs) were introduced as a computational model for the study of complex dynamical systems, such as Gene Regulatory Networks (GRNs). Controllability in this context is the process of making…
Deep Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+1Effect of memory in non-Markovian Boolean networks
One successful model of interacting biological systems is the Boolean network. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However,…
Node and edge control strategy identification via trap spaces in Boolean networks
The study of control mechanisms of biological systems allows for interesting applications in bioengineering and medicine, for instance in cell reprogramming or drug target identification. A control strategy often consist…