基于概率模型检测和遗传算法的基因调控网络的无限范围优化控制

基因调控网络是一类基本且重要的生物网络,通过对其进行控制可以实现生物系统功能的调节。在生物系统中,通过外部的干预控制构造关于基因调控网络的控制理论成为了非常热门的研究主题。目前,作为一种重要的网络模型,带有干扰且上下文相关的概率布尔网络已经被广泛地应用于基因调控网络优化控制问题的研究中。针对无限范围的优化控制问题,文中提出了一种基于概率模型检测和遗传算法的近似最优控制策略的计算方法。首先,该方法将无限范围控制中定义的期望总成本归约为离散时间马尔科夫链上的平稳状态回报;然后,构建包含固定控制策略的带有干扰且上下文相关的概率布尔网络模型,采用带回报属性的时序逻辑公式表示固定控制策略的成本,采用概率模型检测器PRISM进行自动计算。进一步,采用遗传算法,将固定控制策略编码为遗传算法解空间中的个体,基于其控制成本,定义个体的适应度值,将PRISM作为求解器,通过在解空间上迭代地执行遗传操作获取近似最优解。将所提方法应用于WNT5A网络中,实验结果证明了该方法的有效性。     

概率级联布尔网络的集镇定及其应用

随着系统生物学和医学的迅速发展,基因调控网络已经成为一个热点研究领域.布尔网络作为研究生物系统和基因调控网络的一种重要模型,近年来引起了包括生物学家和系统科学家在内的很多学者的广泛关注.本文利用代数状态空间方法,研究了概率级联布尔网络的集镇定问题.首先给出概率级联布尔网络集镇定的定义,并利用矩阵的半张量积给出了概率级联布尔网络的代数表示.其次基于该代数表示,定义了一组合适的概率能达集,并给出了概率级联布尔网络集镇定问题可解的充要条件.最后将所得的理论结果应用于概率级联布尔网络的同步分析及n人随机级联演化布尔博弈的策略一致演化行为分析.     

切换奇异布尔网络的稳定性分析

基因调控网络的稳定性分析是系统生物学的研究热点问题之一.本文利用矩阵半张量积方法研究了切换奇异布尔网络的稳定性问题.首先给出了切换奇异布尔网络的代数表示,基于该代数表示,建立了系统解存在唯一的充要条件.然后通过将切换奇异布尔网络转化为等价的切换布尔网络,分别得到了系统在任意切换下稳定以及切换可稳的充要条件.最后给出例子验证所得结果的有效性.     

基于切换频度的马尔科夫网络控制系统均方指数镇定

针对一类马尔科夫网络控制系统(Networked control system, NCS),研究了其均方指数镇定问题. 首先将网络控制系统建模为离散时间切换系统,子系统间的切换过程由一个转移概率矩阵已知的马尔科夫链描述, 并给出了子系统间切换频度的范围;进而基于随机过程理论和切换系统稳定性理论, 利用状态反馈实现了网络控制系统均方指数镇定,状态反馈控制律可通过求解一组线性矩阵不等式获得. 最后通过数值仿真例子验证了本文方法的有效性.     

具有切换概率分布的概率布尔网络的依分布稳定和镇定

布尔网络作为研究基因调控网络的一种重要模型,近年来引起了国内外很多学者的广泛关注.本文利用代数状态空间表示方法,研究具有切换概率分布的概率布尔网络的依分布稳定和镇定问题.首先,回顾针对切换布尔网络稳定性分析的现有的研究结果.其次,给出具有切换概率分布的概率布尔网络依分布稳定的定义,并利用矩阵的半张量积建立具有切换概率分布的概率布尔网络的代数表示.再次,基于该代数表示,建立具有切换概率分布的概率布尔网络的依分布稳定的充分必要条件.最后,给出具有切换概率分布的概率布尔控制网络镇定问题可解的充要条件,并给出相应的控制设计方法.     

布尔网络的分析与控制——矩阵半张量积方法

布尔网络是描述基因调控网络的一个有力工具. 由于系统生物学的发展, 布尔网络的分析与控制成为生物学与系统控制学科的交叉热点. 本文综述作者用其原创的矩阵半张量积方法在布尔网络的分析与控制中得到的一系列结果. 内容包括: 布尔网络的拓扑结构, 布尔控制网络的能控、能观性与实现, 布尔网络的稳定性和布尔控制网络的镇定, 布尔控制网络的干扰解耦, 布尔 (控制) 网络的辨识,以及布尔网络的最优控制等.     

概率布尔控制网络的弱能控性

本文研究了概率布尔控制网络的弱能控性,系统的弱能控性是概率布尔网络精确能控的一个推广.首先利用矩阵的半张量积和逻辑变量的向量表示,概率布尔控制网络被表示为离散时间动态系统.接着给出概率布尔控制网络弱能控的定义,从离散时间系统的结构矩阵出发,构造了最大概率转移矩阵,矩阵中的元素表示相应状态之间可能发生转移的最大概率,在此基础上研究了概率布尔控制网络的弱能控的条件,同时给出了两个状态弱能达时控制序列的设计算法.最后通过例子进一步解释了弱能控的概念和控制序列设计算法的有效性.     

基于马尔可夫决策过程的接纳控制模型研究

针对异构网络中系统容量有限、资源利用率低的问题,在分析对比传统呼叫接纳控制模型的基础上,提出一种基于马尔可夫决策过程理论的接纳控制模型。理论采用定义五元组的方式来描述建模过程,推导出目标评价函数,并通过求解具有QoS约束条件下的方程进行数值分析。仿真结果表明,该模型能满足网络动态实时性,解决系统容量有限情况下的最优接纳控制问题,从而能够在一定程度上降低各类呼叫业务的阻塞概率,达到提高不同用户服务质量的体验性、网络的系统收益最大化的目的。     

概率布尔控制网络的集可控

本文研究概率布尔控制网络的集可控性问题.首先,利用矩阵半张量积方法,得到概率布尔控制网络的代数表示.其次,借助一个新的算子构造不同的可控矩阵,进而通过可控矩阵考虑自由控制序列和网络输入控制下概率布尔控制网络的集可控性问题,得到了概率布尔控制网络集可控性的充要条件.最后,给出数值例子说明本文结果的有效性.     

基于事件触发和状态翻转的布尔控制网络输出跟踪

利用矩阵半张量积研究事件触发和翻转控制共同作用下布尔控制网络的输出跟踪问题.首先,基于布尔控制网络代数状态空间表示,构造增广系统将输出跟踪问题转化为状态集镇定问题;其次,得到布尔控制网络在两种控制下输出跟踪问题有解的充要条件,并在满足该条件时提出一种基于最小翻转节点集时间最优控制设计方法,进一步给出有限时间内寻找翻转节点集的计算过程;最后,给出一个算例说明结果的可行性.     

Robust H∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise

Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean‐square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three‐gene network to illustrate the applicability and usefulness of the design. Copyright © 2010 John Wiley & Sons, Ltd.     

State feedback stabilization for probabilistic Boolean networks

A probabilistic Boolean network (PBN) is a discrete-time system composed of a family of Boolean networks (BNs) between which the PBN switches in a stochastic fashion. Studying control-related problems in PBNs may provide new insights into the intrinsic control in biological systems and enable us to develop strategies for manipulating complex biological systems using exogenous inputs. This paper investigates the problem of state feedback stabilization for PBNs. Based on the algebraic representation of logic functions, a necessary and sufficient condition is derived for the existence of a globally stabilizing state feedback controller, and a control design method is proposed when the presented condition holds. It is shown that the controller designed via the proposed procedure can simultaneously stabilize a collection of PBNs that are composed of the same constituent BNs.     

Controllability of probabilistic Boolean control networks

This paper deals with the controllability of probabilistic Boolean control networks. First, a survey on the semi-tensor product approach to probabilistic Boolean networks is given. Second, the controllability of probabilistic Boolean control networks via two kinds inputs is studied. Finally, examples are given to show the efficiency of the obtained results.     

Generating probabilistic Boolean networks from a prescribed stationary distribution

Modeling gene regulation is an important problem in genomic research. Boolean networks (BN) and its generalization probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem which is ill-posed and challenging. Because there may be many networks or no network having the given properties and the size of the inverse problem is huge. In this paper, we consider the problem of constructing PBNs from a given stationary distribution and a set of given Boolean Networks (BNs). We first formulate the inverse problem as a constrained least squares problem. We then propose a heuristic method based on Conjugate Gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. We also introduce an estimation method for the parameters of the PBNs. Numerical examples are then given to demonstrate the effectiveness of the proposed methods.     

An integer programming approach to optimal control problems in context-sensitive probabilistic Boolean networks

A Boolean network is one of the models of biological networks such as gene regulatory networks, and has been extensively studied. In particular, a probabilistic Boolean network (PBN) is well known as an extension of Boolean networks, but in the existing methods to solve the optimal control problem of PBNs, it is necessary to compute the state transition diagram with 2ⁿ nodes for a given PBN with n states. To avoid this computation, an integer programming-based approach is proposed for a context-sensitive PBN (CS-PBN), which is a general form of PBNs. In the proposed method, a CS-PBN is transformed into a linear system with binary variables, and the optimal control problem is reduced to an integer linear programming problem. By a numerical example, the effectiveness of the proposed method is shown.     

Modeling and verifying probabilistic Multi-Agent Systems using knowledge and social commitments

Multi-Agent Systems (MASs) have long been modeled through knowledge and social commitments independently. In this paper, we present a new method that merges the two concepts to model and verify MASs in the presence of uncertainty. To express knowledge and social commitments simultaneously in uncertain settings, we define a new multi-modal logic called Probabilistic Computation Tree Logic of Knowledge and Commitments (PCTL^kc in short) which combines two existing probabilistic logics namely, probabilistic logic of knowledge PCTLK and probabilistic logic of commitments PCTLC. To model stochastic MASs, we present a new version of interpreted systems that captures the probabilistic behavior and accounts for the communication between interacting components. Then, we introduce a new probabilistic model checking procedure to check the compliance of target systems against some desirable properties written in PCTL^kc and report the obtained verification results. Our proposed model checking technique is reduction-based and consists in transforming the problem of model checking PCTL^kc into the problem of model checking a well established logic, namely PCTL. So doing provides us with the privilege of re-using the PRISM model checker to implement the proposed model checking approach. Finally, we demonstrate the effectiveness of our approach by presenting a real case study. This framework can be considered as a step forward towards closing the gap of capturing interactions between knowledge and social commitments in stochastic agent-based systems.