Control of random Boolean networks via average sensitivity of Boolean functions

In this paper, we discuss how to transform the disordered phase into an ordered phase in random Boolean networks. To increase the effectiveness, a control scheme is proposed, which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes. Theoretical analysis is carried out to estimate the expected critical value of the fraction, and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes. Finally, the simulation is given for illustrating the effectiveness of the proposed method.

Physical generation of random numbers using an asymmetrical Boolean network

Autonomous Boolean networks(ABNs)have been successfully applied to the generation of random number due to their complex nonlinear dynamics and convenient on-chip integration.Most of the ABNs used for random number generators show a symmetric topology,despite their oscillations dependent on the inconsistency of time delays along links.To address this issue,we suggest an asymmetrical autonomous Boolean network(aABN)and show numerically that it provides large amplitude oscillations by using equal time delays along links and the same logical gates.Experimental results show that the chaotic features of aABN are comparable to those of symmetric ABNs despite their being made of fewer nodes.Finally,we put forward a random number generator based on aABN and show that it generates the random numbers passing the NIST test suite at 100 Mbits/s.The unpredictability of the random numbers is analyzed by restarting the random number generator repeatedly.The aABN may replace symmetrical ABNs in many applications using fewer nodes and,in turn,reducing power consumption.

Attractor-Based Simultaneous Design of the Minimum Set of Control Nodes and Controllers in Boolean Networks

Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a controller is studied. A control node corresponds to a gene that expression can be controlled. Here, a Boolean network is used as a model of gene regulatory networks, and control specifications on attractors, which represent cell types or states of cells, are imposed. It is important to design a gene regulatory network that has desired attractors and has no undesired attractors. Using a matrix-based representation of BNs, this problem can be rewritten as an integer linear programming problem. Finally, the proposed method is demonstrated by a numerical example on a WNT5A network, which is related to melanoma.

Verification of Real-Time Pricing Systems Based on Probabilistic Boolean Networks

In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the electricity price at each time. A PBN is widely used as a model of complex systems, and is appropriate as a model of real-time pricing systems. Using the PBN-based model, real-time pricing systems can be quantitatively analyzed. In this paper, we propose a verification method of real-time pricing systems using the PBN-based model and the probabilistic model checker PRISM. First, the PBN-based model is derived. Next, the reachability problem, which is one of the typical verification problems, is formulated, and a solution method is derived. Finally, the effectiveness of the proposed method is presented by a numerical example.

Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications

Set stabilization is one of the essential problems in engineering systems, and self-triggered control(STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed,respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.

Fundamental Boolean network modelling for childhood acute lymphoblastic leukaemia pathways

Background:A novel data-driven Boolean model,namely,the fundamental Boolean model(FBM),has been proposed to draw genetic regulatory insights into gene activation,inhibition,and protein decay,published in 2018.This novel Boolean model facilitates the analysis of the activation and inhibition pathways.However,the novel model does not handle the situation well,where genetic regulation might require more time steps to complete.Methods:Here,we propose extending the fundamental Boolean modelling to address the issue that some gene regulations might require more time steps to complete than others.We denoted this extension model as the temporal fundamental Boolean model(TFBM)and related networks as the temporal fundamental Boolean networks(TFBNs).The leukaemia microarray datasets downloaded from the National Centre for Biotechnology Information have been adopted to demonstrate the utility of the proposed TFBM and TFBNs.Results:We developed the TFBNs that contain 285 components and 2775 Boolean rules based on TFBM on the leukaemia microarray datasets,which are in the form of short-time series.The data contain gene expression measurements for 13 GC-sensitive children under therapy for acute lymphoblastic leukaemia,and each sample has three time points:0 hour(before GC treatment),6/8 hours(after GC treatment)and 24 hours(after GC treatment).Conclusion:We conclude that the proposed TFBM unlocks their predecessor’s limitation,Le.,FBM,that could help pharmaceutical agents identify any side effects on clinic-related data.New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways.

SOLVABILITY AND CONTROL DESIGN FOR SYNCHRONIZATION OF BOOLEAN NETWORKS

This paper addresses the synchronization problem of Boolean networks.Based on the matrix expression of logic,solvability conditions and design procedures of the synchronization of Boolean networks with outputs are given for both open-loop and feedback control.Necessary and sufficient conditions on open-loop control are proposed first with a constructive design procedure.Then sufficient condition for the feedback control case is obtained,and corresponding design procedure is proposed with the help of algorithms to solve logic matrix equations.Numerical examples are also provided to illustrate the proposed control design.

ON THE OBSERVABILITY OF FREE BOOLEAN NETWORKS VIA THE SEMI-TENSOR PRODUCT METHOD

This paper investigates the observabihty of free Boolean networks by using the semi-tensor product method,and presents some new results.First,the concept of observability for free Boolean networks is proposed,based on which and the algebraic form of Boolean networks,a kind of observabihty matrix is constructed.Second,by the observability matrix,a new necessary and sufficient condition is given for the observability of Boolean networks.Third,the concept of observabihty index for observable Boolean networks is defined,and an algorithm is established to calculate the observability index.Finally,a practical example of D.Melanogaster segmentation polarity gene networks is studied to support our new results.The study of the illustrative example shows that the new results obtained in this paper are very effective in investigating the observability of free Boolean networks.

Synchronization of switched Boolean networks with impulsive effects

This paper studies the state/output synchronization of switched Boolean networks （SBNs） with impulsive effects via the algebraic state space representation （ASSR） approach. First, an algebraic form is established for SBNs with impulsive effects via ASSR. Second, based on the algebraic form, some necessary and sufficient conditions are presented for the state/output synchronization of SBNs with impulsive effects under arbitrary switching signals. Third, two special kinds of switching signals, that is, free switching signal and feedback switching signal, are considered for the state synchroniza-tion of SBNs with impulsive effects. Finally, two illustrative examples are worked out to show the effectiveness of the obtained results.

Stability of Boolean networks with state-dependent random impulses

We investigate the stability of Boolean networks(BNs)with impulses triggered by both states and random factors.A hybrid index model is used to describe impulsive BNs.First,several necessary and sufficient conditions for forward completeness are obtained.Second,based on the stability criterion of probabilistic BNs and the forward completeness criterion,the necessary and sufficient conditions for the finite-time stability with probability one and the asymptotical stability in distribution are presented.The relationship between these two kinds of stability is discussed.Last,examples and time-domain simulations are provided to illustrate the obtained results.

Set Stability of Probabilistic Time-Delay Boolean Networks with Impulsive Effect

This paper investigates the set stability of probabilistic time-delay Boolean networks(PTDBN)with impulsive effect.Firstly,using the algebraic state space representation,an equivalent stochastic system is established for PTDBN with impulsive effect.Then,based on the probabilistic state transition matrix,a necessary and sufficient condition is presented for the set stability of PTDBN with impulsive effect.Finally,the obtained new result is applied to the networked evolutionary game with memories.

Robust network structures for conserving total activity in Boolean networks

One of the typical properties of biological systems is the law o f conservation o f mass,that is,the property that the mass must remain constant over time in a closed chemical reaction system.However,it is known that Boolean networks,which are a promising model of biological networks,do not always represent the conservation law.This paper thus addresses a kind of conservation law as a generic property of Boolean networks.In particular,we consider the problem of finding network structures on which,for any Boolean operation on nodes,the number of active nodes,i.ev nodes whose state is one,is constant over time.As a solution to the problem,we focus on the strongly-connected network structures and present a necessary and sufficient condition.

Complete synchronization of coupled Boolean networks with arbitrary finite delays

In this study,the complete synchronization problem of coupled delayed Boolean networks(CDBNs)is investigated.The state delays and output delays may not be equal,and the state delay in each Boolean network may be different in the proposed CDBN model.Based on the semi-tensor product of matrices,a necessary and sufficient condition for the complete synchronization of CDBNs is obtained.Then,an efficient algorithm for solving the synchronization of CDBNs is provided.Finally,numerical examples are presented to demonstrate the effectiveness of our algorithm.

Output feedback stabilizer design of Boolean networks based on network structure

In genetic regulatory networks,a stable configuration can represent the evolutionary behavior of cell death or unregulated growth in genes.We present analytical investigations on output feedback stabilizer design of Boolean networks(BNs)to achieve global stabilization via the semi-tensor product method.Based on network structure information describing coupling connections among nodes,an output feedback stabilizer is designed to achieve global stabilization.Compared with the traditional pinning control design,the output feedback stabilizer design is not based on the state transition matrix of BNs,which can efficiently determine pinning control nodes and reduce computational complexity.Our proposed method is efficient in that the calculation of the state transition matrix with dimension 2^n×2^n is avoided;here n is the number of nodes in a BN.Finally,a signal transduction network and a D.melanogaster segmentation polarity gene network are presented to show the efficiency of the proposed method.Results are shown to be simple and concise,compared with traditional pinning control for BNs.

Recent developments in Boolean networks control

The aim of this survey paper is to provide the state of the art of the research on control and optimal control of Boolean control networks,under the assumption that all the state variables are accessible and hence available for feedback.Necessary and sufficient conditions for stabilisability to a limit cycle or to an equilibrium point are given.Additionally,it is shown that when such conditions are satisfied,stabilisation can always be achieved by means of state feedback.Analogous results are obtained for the safe control problem that is investigated for the first time in this survey.Finite and infinite horizon optimal control are subsequently considered,and solution algorithms are provided,based on suitable adaptations of theRiccati difference and algebraic equations.Finally,an appropriate definition of the cost function allows to restate and to solve both stabilisation and safe control as infinite horizon optimal control problems.

Optimal one-bit perturbation in Boolean networks based on cascading aggregation

We investigate the problem of finding optimal one-bit perturbation that maximizes the size of the basin of attractions(BOAs)of desired attractors and minimizes the size of the BOAs of undesired attractors for large-scale Boolean networks by cascading aggregation.First,via the aggregation,a necessary and sufficient condition is given to ensure the invariance of desired attractors after one-bit perturbation.Second,an algorithm is proposed to identify whether the one-bit perturbation will cause the emergence of new attractors or not.Next,the change of the size of BOAs after one-bit perturbation is provided in an algorithm.Finally,the efficiency of the proposed method is verified by a T-cell receptor network.

Context-Aware System Modeling Based on Boolean Control Network

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

基于SAT问题实例特性的端到端SAT求解模型

当前基于神经网络的端到端SAT求解模型在各类SAT问题求解上展现了巨大潜力。然而SAT问题难以容忍误差存在,神经网络模型无法保证不产生预测误差。为利用SAT问题实例特性来减少模型预测误差,提出了错误偏好变量嵌入架构(architecture of embedding error-preference variables, AEEV)。该架构包含错误偏好变量嵌入调整算法和动态部分标签训练模式。首先,为利用参与越多未满足子句的变量越可能被错误分类这一特性,提出了错误偏好变量嵌入调整算法,在消息传递过程中根据变量参与的未满足子句个数来调整其嵌入。此外,提出了动态部分标签监督训练模式,该模式利用了SAT问题实例的变量赋值之间存在复杂依赖关系这一特性,避免为全部变量提供标签,仅为错误偏好变量提供一组来自真实解的标签,保持其他变量标签为预测值不变,以在训练过程管理一个更小的搜索空间。最后,在3-SAT、k-SAT、k-Coloring、3-Clique、SHA-1原像攻击以及收集的SAT竞赛数据集上进行了实验验证。结果表明,相较于目前较先进的基于神经网络的端到端求解模型QuerySAT,AEEV在包含600个变量的k-SAT数据集上准确率提升了45.81%。

非线性可分Boolean函数分解及神经网络实现

对于2n个顶点用"0"或"1"标记的n维超方体,利用n输入Boolean函数f的输出vi(i=0,1,…,2n-1)与偏移量σi的关系,用一组平行的超平面实现分隔n维超方体顶点。从而,实现非线性可分Boolean函数的分解及二进神经网络隐层神经元的实现。同时,给出几个例子说明分解方法以及其有效性。

异步时滞布尔网络的内同步

提出异步时滞布尔网络的内同步模型,通过利用矩阵半张量积理论,研究异步时滞影响下的布尔网络内同步问题,分析时滞对布尔网络内同步的影响,给出了异步时滞布尔网络内同步实现的充分必要条件.通过数值仿真实例进一步验证了该模型的可行性以及所得结果的正确性.