We study the target control of asynchronous Boolean networks, to identify interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with these three types of perturbations. We compare our methods with the stable motif-based control method on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.

SUMMARY: Direct cell reprogramming, also called transdifferentiation, has great potential for tissue engineering and regenerative medicine. Boolean networks, a popular modelling framework for gene regulatory networks, make it possible to identify intervention targets for direct cell reprogramming with computational methods. In this work, we present our software, CABEAN, for the control of asynchronous Boolean networks. CABEAN identifies efficacious nodes, whose perturbations can drive the dynamics of a network from a source attractor (the initial cell type) to a target attractor (the desired cell type). CABEAN provides several control methods integrating practical constraints. Thus, it has the ability to provide a rich set of control sets, such that biologists can select suitable ones for validation based on specific experimental settings. AVAILABILITY AND IMPLEMENTATION: The executable binary and the user guide of the software are publicly available at https://satoss.uni.lu/software/CABEAN/.

In recent years, great efforts have been made to analyze biological systems to understand the long-run behaviors. As a well-established formalism for modelling real-life biological systems, Boolean networks (BNs) allow their representation and analysis using formal reasoning and tools. Most biological systems are robust-they can withstand the loss of links and cope with external environmental perturbations. Hence, the BNs used to model such systems are necessarily large and dense, and yet modular. However, existing analysis methods only work well on networks of moderate size. Thus, there is a great need for efficient methods that can handle large-scale BNs and for doing so it is inevitable to exploit both the structural and dynamic properties of the networks. In this paper, we propose a method towards the optimal decomposition of BNs to balance the relation between the structure and dynamics of a network. We show that our method can greatly improve the existing decomposition-based attractor detection by analyzing a number of large real-life biological networks.

Knowledge graph has become an essential tool for semantic analysis with the development of natural language processing and deep learning. A high-quality knowledge graph is handy for building a high-performance knowledge-driven application. Despite recent advances in information extraction (IE) techniques, no suitable automated methods can be applied to constructing a domain-specific, comprehensive, and high-quality knowledge graph. However, a semi-automatic strategy, which can ensure the basic quality requirements of a knowledge graph, has been successfully implemented in the elementary science domain. This paper presents a semantic annotation system developed for building a high-quality legal knowledge graph (SALKG) using the semi-automatic strategy. We introduce its system design, architecture, algorithms, functions, and implementation. To investigate the effectiveness of SALKG, we conduct a preliminary annotation experiment with 280 legal texts which were collected from the Harvard Caselaw Access Project. The user evaluation from 32 graduate students demonstrates the high usability of SALKG in semantic annotation and the potential for building a high-quality legal knowledge graph. The system can also be adapted to other fields for constructing domain-specific knowledge graphs.

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 optimized 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.

MOTIVATION: The control of Boolean networks has traditionally focussed on strategies where the perturbations are applied to the nodes of the network for an extended period of time. In this work, we study if and how a Boolean network can be controlled by perturbing a minimal set of nodes for a single-step and letting the system evolve afterwards according to its original dynamics. More precisely, given a Boolean network (BN), we compute a minimal subset Cmin of the nodes such that BN can be driven from any initial state in an attractor to another 'desired' attractor by perturbing some or all of the nodes of Cmin for a single-step. Such kind of control is attractive for biological systems because they are less time consuming than the traditional strategies for control while also being financially more viable. However, due to the phenomenon of state-space explosion, computing such a minimal subset is computationally inefficient and an approach that deals with the entire network in one-go, does not scale well for large networks. RESULTS: We develop a 'divide-and-conquer' approach by decomposing the network into smaller partitions, computing the minimal control on the projection of the attractors to these partitions and then composing the results to obtain Cmin for the whole network. We implement our method and test it on various real-life biological networks to demonstrate its applicability and efficiency. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cellular reprogramming, a technique that opens huge opportunities in modern and regenerative medicine, heavily relies on identifying key genes to perturb. Most of the existing computational methods for controlling which attractor (steady state) the cell will reach focus on finding mutations to apply to the initial state. However, it has been shown, and is proved in this article, that waiting between perturbations so that the update dynamics of the system prepares the ground, allows for new reprogramming strategies. To identify such sequential perturbations, we consider a qualitative model of regulatory networks, and rely on Binary Decision Diagrams to model their dynamics and the putative perturbations. Our method establishes a set identification of sequential perturbations, whether permanent (mutations) or only temporary, to achieve the existential or inevitable reachability of an arbitrary state of the system. We apply an implementation for temporary perturbations on models from the literature, illustrating that we are able to derive sequential perturbations to achieve trans-differentiation.

As a well-established computational framework, probabilistic Boolean networks (PBNs) are widely used for modelling, simulation, and analysis of biological systems. To analyze the steady-state dynamics of PBNs is of crucial importance to explore the characteristics of biological systems. However, the analysis of large PBNs, which often arise in systems biology, is prone to the infamous state-space explosion problem. Therefore, the employment of statistical methods often remains the only feasible solution. We present ${\mathsf{ASSA-PBN}}$ , a software toolbox for modelling, simulation, and analysis of PBNs. ${\mathsf{ASSA-PBN}}$ provides efficient statistical methods with three parallel techniques to speed up the computation of steady-state probabilities. Moreover, particle swarm optimisation (PSO) and differential evolution (DE) are implemented for the estimation of PBN parameters. Additionally, we implement in-depth analyses of PBNs, including long-run influence analysis, long-run sensitivity analysis, computation of one-parameter profile likelihoods, and the visualization of one-parameter profile likelihoods. A PBN model of apoptosis is used as a case study to illustrate the main functionalities of ${\mathsf{ASSA-PBN}}$ and to demonstrate the capabilities of ${\mathsf{ASSA-PBN}}$ to effectively analyse biological systems modelled as PBNs.

Wireless Body Area Networks (WBANs) are made up of tiny physiological sensors implanted in/on the human body which detect the health status of human body and transmit the information collected to the remote server via one or more coordinators. The rapid proliferation of WBANs has stimulated enormous research efforts that aim to maximize the lifetime of battery-powered sensor nodes and extend the overall network lifetime through designing optimal power allocation or relay selection schemes. However, previous works fail to jointly optimize the highly related resources of WBANs, thus may severely limit the performance of maximizing the network lifetime of WBANs. In this paper, a network lifetime maximization based joint resource allocation scheme is proposed for WBANs. We show that the problem of optimizing network lifetime is equivalent to maximizing the residual energy of sensor nodes which can be expressed as function of transmission modes of nodes, cooperative nodes, transmission power and time slots of both source and relay nodes, subject to resource allocation constraints. Through solving the optimization problem, an optimal joint transmission mode, relay selection, transmission power and time slot allocation strategy can be obtained. Numerical results demonstrate that the proposed algorithm is capable for extending the lifetime of network as well as guaranteeing user QoS requirements.