武汉城市圈生态网络鲁棒性模拟及稳定性分析

生态网络在不同破坏程度或干扰强度下呈现出不同的鲁棒性,势必造成区域生态系统的稳定性风险。基于武汉城市圈生态网络的构建与识别,建立节点和廊道的重要性评价体系,运用鲁棒模型模拟随机干扰情景下的鲁棒性变化评价生态网络的稳定性特征。结果表明:基于复杂网络理论对武汉城市圈生态网络进行拓扑网络提取,共生成117个节点和189条连接线,重要性等级较高的节点占23.9%,主要分布在研究区中北部区域,重要性较低的节点占节点总数的51.3%,主要分布在研究区的西北和东南地区;采用重力模型判别生态廊道重要性,得到17条重要廊道,28条一般廊道,重要廊道对生态网络的连通性和稳定性具有极大影响;节点数量和重要度对网络的稳定性具有显著影响,当节点失效率低于50%时,生态网络稳定性变化较小,整体处于较高的稳定性;当节点失效率在50%—85%时,生态网络极其不稳定;当节点失效比率达到85%时,生态网络开始瘫痪;依据生态节点和廊道的重要性以及模拟结果,分别提出生态节点保护和廊道建设的差异化管理策略。     

残基相互作用网络特征与木聚糖酶耐热性的关系

残基相互作用网络是体现蛋白质中残基与残基之间协同和制约关系的重要形式。残基相互作用网络的拓扑性质以及社团结构与蛋白质的功能和性质有密切的关系。本文在构建一系列耐热木聚糖酶和常温木聚糖酶的残基相互作用网络后,通过计算网络的度、聚类系数、连接强度、特征路径长度、接近中心性、介数中心性等拓扑参数来确定网络拓扑结构与木聚糖酶耐热性的关系。识别残基相互作用网络的hub点,分析hub点的亲疏水性、带电性以及各种氨基酸在hub点中所占的比例。进一步使用GA-Net算法对网络进行社团划分,并计算社团的规模、直径和密度。网络的高平均度、高连接强度、以及更短的最短路径等表明耐热木聚糖酶残基相互作用网络的结构更加紧密;耐热木聚糖酶网络中的hub节点比常温木聚糖酶网络hub节点具有更多的疏水性残基,hub点中Phe、Ile、Val的占比更高。社团检测后发现,耐热木聚糖酶网络拥有更大的社团规模、较小的社团直径和较大的社团密度。社团规模越大表明耐热木聚糖酶的氨基酸残基更倾向于形成大的社团,而较小的社团直径和较大的社团密度则表明社团内部氨基酸残基的相互作用比常温木聚糖酶更强。     

残基相互作用网络特征与木聚糖酶耐热性的关系

残基相互作用网络是体现蛋白质中残基与残基之间协同和制约关系的重要形式。残基相互作用网络的拓扑性质以及社团结构与蛋白质的功能和性质有密切的关系。本文在构建一系列耐热木聚糖酶和常温木聚糖酶的残基相互作用网络后,通过计算网络的度、聚类系数、连接强度、特征路径长度、接近中心性、介数中心性等拓扑参数来确定网络拓扑结构与木聚糖酶耐热性的关系。识别残基相互作用网络的hub点,分析hub点的亲疏水性、带电性以及各种氨基酸在hub点中所占的比例。进一步使用GA-Net算法对网络进行社团划分,并计算社团的规模、直径和密度。网络的高平均度、高连接强度、以及更短的最短路径等表明耐热木聚糖酶残基相互作用网络的结构更加紧密;耐热木聚糖酶网络中的hub节点比常温木聚糖酶网络hub节点具有更多的疏水性残基,hub点中Phe、Ile、Val的占比更高。社团检测后发现,耐热木聚糖酶网络拥有更大的社团规模、较小的社团直径和较大的社团密度。社团规模越大表明耐热木聚糖酶的氨基酸残基更倾向于形成大的社团,而较小的社团直径和较大的社团密度则表明社团内部氨基酸残基的相互作用比常温木聚糖酶更强。     

氨基酸的分子结构与遗传密码简并及二维集合分类

根据氨基酸遗传密码子的简并程度,可将64个遗传密码子分为高简并度类（3,4,6度简并组）和低简并度类（1,2度简并组）两大类。高简并度类有9个氨基酸,其分子量比较小,等电点的分布比较集中。低简并度类有11个氨基酸,其分子结构比较复杂,参考Taylor对氨基酸特性的分类图,本文提出以分子量（M）及等电点（P）作为氨基酸的化学特性坐标,作出其二维集合MP分类图,MP分类图可以反映出氨基酸的各种属性,如分子量的大小,简并度的高低,极性与非极性、带电荷或不带电荷,疏水性与亲水性,以及氨基酸残基的种类等。根据氨基酸的分类分析,可以认为：高简并度氨基酸多数是脂烃类和羟脂烃类的氨基酸,分子量比较小,分子结构比较简单,大部分为疏子性,主要组成跨膜结构或蛋白质的结构域,可能是出现较早的氨基酸;而低简并度的氨基酸,分子结构比较复杂,分子量比较大,多数是和蛋白质功能有密切联系的基团,可能是进化出现较晚的结构。     

基于信号网络的功能细胞设计

细胞信号网络是细胞应对环境变化、调控细胞功能以及决定细胞命运的中央处理器。运用合成生物学方法,人工设计细胞信号网络对于"细胞机器"的构建具有重要作用。信号网络通过编码定量的动力学信号,能够在多个维度对细胞工程中的多个子功能单元进行调控。本文介绍了天然信号网络的动力学功能的研究进展,阐述了基于信号网络的功能蛋白质设计的合成生物学相关的方法和思路,并展望了信号网络在下一代合成生物学中的战略意义。     

变时滞区间细胞神经网络的全局鲁棒稳定性

利用拓扑度理论和Liapunov泛函方法讨论了变时滞区间细胞神经网络的全局鲁棒稳定性．给出了实用有效的判定条件,推广了有关文献中的结果．     

油菜基因流网络的结构特性与小团体分析

为从系统角度掌握转基因油菜（Brassica campestris）基因流规律,基于常规油菜与十字花科植物杂交的有关文献,构建了油菜基因流网络拓扑图实例并分析其网络结构特性。研究结果表明,该网络节点度服从幂律分布,具有无标度特性。从随机攻击和恶性攻击两个方面对标准结构熵和网络效率2个指标的网络稳健性进行分析,结果显示在随机移除不到10%的顶点时网络表现较好的鲁棒性,但在受到选择性移除10%的顶点时,网络具有极弱的抗攻击性。基于软件UCINET对网络进行了小团体和结构同型性分析,可将网络中298个节点22类十字花科植物划分为2个小团体和5类结构角色,其中甘蓝型油菜在基因流网络小团体中具有关键性作用。这些研究结果可为揭示转基因油菜基因流规律提供新思路,同时也可对转基因油菜商业化种植采取合理的农业生产栽培管理措施提供参考。     

基于网络特征的道路生态干扰——以澜沧江流域为例

道路网络对区域景观的生态格局和过程产生较大的影响,作为干扰因子,区域道路网络的特征和所产生的生态干扰水平存在一定的相关性.利用GIS和网络分析法,以澜沧江流域县域为基本单元,分析了研究区各县的道路网络特征指数,在揭示了区域道路网络不同特征之间的规律性的基础上,进一步对区域网络特点和生态干扰之间相关性进行了内在关系的探讨.结果表明,澜沧江流域道路网络结构的空间差异很大,流域内各县的道路网络节点数、连接线数目、α环度、β线点率、γ连接度指数等网络特征因子也存在较大差异;道路密度和道路总长度与区域海拔高度呈现较明显的负相关关系;道路密度和区域道路的廊道密度呈现正相关关系,和区域道路的α指数、β指数和γ指数关系可以用倒数模型来拟合.缓冲区分析表明,耕地比例和道路密度线性相关关系显著,而其他类型相关性较差,但区域综合的人工干扰指数(Hd)(1980,2000)和道路网络的特征指数相关显著,而且不同时期的区域景观的斑块密度、平均斑块面积和区域道路网络特征之间也存在较强的相关性,即网络扩展使得区域生态系统受干扰强度增加,破碎化严重,但较短时期内的生态系统变化和道路网络特征之间相关性不显著.     

多维视角下天津市生态网络结构演变特征综合评价

以天津市为研究区,在构建2000、2010和2020年天津市生态网络的基础上,运用复杂网络的评价指标和景观格局指数并综合稳定性、均匀性和连通性指数,从源地-廊道-节点-整体多维度对其结构演变进行综合评价。结果表明: 2000—2020年间,天津市生态源地的萎缩退化现象严重且空间分布不均匀,生态廊道也越来越稀疏;景观破碎度和形状复杂度呈先上升后下降的态势;2000和2010年的廊道平均路径长度较短,生物流动效率相对较高;2000、2010、2020年重要度较高的节点数量分别占统计节点数量的35.7%、29.4%和21.4%;2020年的网络连通鲁棒性和脆弱鲁棒性均起伏较大且差异明显,网络稳定性最差;2010年的生态网络具有较高的连接性和较强的复杂程度,2000和2020年则较为一般;2000年的网络均匀程度最好,2010年次之,2020年最差。     

生物鲁棒性的研究进展

生物鲁棒性是指在受到外部扰动或内部参数摄动等不确定因素干扰时,生物系统保持其结构和功能稳定的一种特性。目前已经发现生物鲁棒性普遍存在于生物系统整体、器官、细胞、分子等各种层次,如细菌趋化、细胞周期、细胞信号通讯、基因突变、生物发育、基因网络等等。产生生物鲁棒性的作用机制主要是生物系统的反馈、冗余、模块和结构稳定等。稳定鲁棒性和品质鲁棒性是生物鲁棒性研究的两个重要命题,数学模型是生物鲁棒性研究的重要手段。认识生物鲁棒性对癌症、MDS、糖尿病等疾病的发生、发展和治疗有重要意义。丈章从上述几个方面综述了生物鲁棒性的研究进展。     

Coherent coupling of feedback loops: a design principle of cell signaling networks

MOTIVATION: It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we have examined large-scale signaling networks and found that a number of feedback loops are present mostly in coupled structures. In particular, the coupling was made in a coherent way implying that same types of feedback loops are interlinked together. RESULTS: We have investigated the role of such coherently coupled feedback loops through extensive Boolean network simulations and found that a high proportion of coherent couplings can enhance the robustness of a network against its state perturbations. Moreover, we found that the robustness achieved by coherently coupled feedback loops can be kept evolutionarily stable. All these results imply that the coherent coupling of feedback loops might be a design principle of cell signaling networks devised to achieve the robustness.     

Geometric robustness theory and biological networks

We provide a geometric framework for investigating the robustness of information flows over biological networks. We use information measures to quantify the impact of knockout perturbations on simple networks. Robustness has two components, a measure of the causal contribution of a node or nodes, and a measure of the change or exclusion dependence, of the network following node removal. Causality is measured as statistical contribution of a node to network function, wheras exclusion dependence measures a distance between unperturbed network and reconfigured network function. We explore the role that redundancy plays in increasing robustness, and how redundacy can be exploited through error-correcting codes implemented by networks. We provide examples of the robustness measure when applied to familiar boolean functions such as the AND, OR and XOR functions. We discuss the relationship between robustness measures and related measures of complexity and how robustness always implies a minimal level of complexity.     

Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems

A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.     

Accounting for robustness in modeling signal transduction responses

Abstract

A classical question in systems biology is to find a Boolean model which is able to predict the observed responses of a signaling network. It has been previously shown that such models can be tailored based on experimental data. While fitting a minimum-size network to the experimentally observed data is a natural assumption, it can potentially result in a network which is not so robust against the noises in the training dataset. Indeed, it is widely accepted now that biological systems are generally evolved to be very robust. Therefore, in the present work, we extended the classical formulation of Boolean network construction in order to put weight on the robustness of the created network. We show that our method results generally in more relevant networks. Consequently, considering robustness as a design principle of biological networks can result in more realistic models.     

Parasites lead to evolution of robustness against gene loss in host signaling networks

Many biological networks can maintain their function against single gene loss. However, the evolutionary mechanisms responsible for such robustness remain unclear. Here, we demonstrate that antagonistic host–parasite interactions can act as a selective pressure driving the emergence of robustness against gene loss. Using a model of host signaling networks and simulating their coevolution with parasites that interfere with network function, we find that networks evolve both redundancy and specific architectures that allow them to maintain their response despite removal of proteins. We show that when the parasite pressure is removed, subsequent evolution can lead to loss of redundancy while architecture‐based robustness is retained. Contrary to intuition, increased parasite virulence hampers evolution of robustness by limiting the generation of population level diversity in the host. However, when robustness emerges under high virulence, it tends to be stronger. These findings predict an increased presence of robustness mechanisms in biological networks operating under parasite interference. Conversely, the presence of such mechanisms could indicate current or past parasite interference.     

A network model for the correlation between epistasis and genomic complexity

The study of genetic interactions (epistasis) is central to the understanding of genome organization and evolution. A general correlation between epistasis and genomic complexity has been recently shown, such that in simpler genomes epistasis is antagonistic on average (mutational effects tend to cancel each other out), whereas a transition towards synergistic epistasis occurs in more complex genomes (mutational effects strengthen each other). Here, we use a simple network model to identify basic features explaining this correlation. We show that, in small networks with multifunctional nodes, lack of redundancy, and absence of alternative pathways, epistasis is antagonistic on average. In contrast, lack of multi-functionality, high connectivity, and redundancy favor synergistic epistasis. Moreover, we confirm the previous finding that epistasis is a covariate of mutational robustness: in less robust networks it tends to be antagonistic whereas in more robust networks it tends to be synergistic. We argue that network features associated with antagonistic epistasis are typically found in simple genomes, such as those of viruses and bacteria, whereas the features associated with synergistic epistasis are more extensively exploited by higher eukaryotes.     

The Biological Property of Synthetic Evolved Digital Circuits with ESD Immunity – Redundancy or Degeneracy?

In the ongoing evolutionary process, biological systems have displayed a fundamental and remarkable property of robustness, i.e., the property allows the system to maintain its functions despite external and internal perturbations. Redundancy and degeneracy are thought to be the underlying structural mechanisms of biological robustness. Inspired by this, we explored the proximate cause of the immunity of the synthetic evolved digital circuits to ESD interference and discussed the biological characteristics behind the evolutionary circuits. First, we proposed an evolutionary method for intrinsic immune circuit design. The circuits' immunity was evaluated using the functional fault models based on probability distributions. Then, several benchmark circuits, including ADDER, MAJORITY, and C17, were evolved for high intrinsic immunity. Finally, using the quantitative definitions based on information theory, we measured the topological characteristics of redundancy and degeneracy in the evolved circuits and compared their contributions to the immunity. The results show that redundant elements are necessary for the ESD immune circuit design, whereas degeneracy is the key to making use of the redundancy robustly and efficiently.     

Intrinsic properties of Boolean dynamics in complex networks

We study intrinsic properties of attractor in Boolean dynamics of complex networks with scale-free topology, comparing with those of the so-called Kauffman's random Boolean networks. We numerically study both frozen and relevant nodes in each attractor in the dynamics of relatively small networks (20?N?200). We investigate numerically robustness of an attractor to a perturbation. An attractor with cycle length of ?_c in a network of size N consists of ?_c states in the state space of 2^N states; each attractor has the arrangement of N nodes, where the cycle of attractor sweeps ?_c states. We define a perturbation as a flip of the state on a single node in the attractor state at a given time step. We show that the rate between unfrozen and relevant nodes in the dynamics of a complex network with scale-free topology is larger than that in Kauffman's random Boolean network model. Furthermore, we find that in a complex scale-free network with fluctuation of the in-degree number, attractors are more sensitive to a state flip for a highly connected node (i.e. input-hub node) than to that for a less connected node. By some numerical examples, we show that the number of relevant nodes increases, when an input-hub node is coincident with and/or connected with an output-hub node (i.e. a node with large output-degree) one another.     

Robustness in phenotypic plasticity and heterogeneity patterns enabled by EMT networks

Epithelial-mesenchymal plasticity (EMP) is a key arm of cancer metastasis and is observed across many contexts. Cells undergoing EMP can reversibly switch between three classes of phenotypes: epithelial (E), mesenchymal (M), and hybrid E/M. While a large number of multistable regulatory networks have been identified to be driving EMP in various contexts, the exact mechanisms and design principles that enable robustness in driving EMP across contexts are not yet fully understood. Here, we investigated dynamic and structural robustness in EMP networks with regard to phenotypic heterogeneity and plasticity. We use two different approaches to simulate these networks: a computationally inexpensive, parameter-independent continuous state space Boolean model, and an ODE-based parameter-agnostic framework (RACIPE), both of which yielded similar phenotypic distributions. While the latter approach is useful for measurements of plasticity, the former model enabled us to extensively investigate robustness in phenotypic heterogeneity. Using perturbations to network topology and by varying network parameters, we show that multistable EMP networks are structurally and dynamically more robust compared with their randomized counterparts, thereby highlighting their topological hallmarks. These features of robustness are governed by a balance of positive and negative feedback loops embedded in these networks. Using a combination of the number of negative and positive feedback loops weighted by their lengths, we identified a metric that can explain the structural and dynamical robustness of these networks. This metric enabled us to compare networks across multiple sizes, and the network principles thus obtained can be used to identify fragilities in large networks without simulating their dynamics. Our analysis highlights a network topology-based approach to quantify robustness in the phenotypic heterogeneity and plasticity emergent from EMP networks.     

The causes of epistasis in genetic networks

Epistasis refers to the nonadditive interactions between genes in determining phenotypes. Considerable efforts have shown that, even for a given organism, epistasis may vary both in intensity and sign. Recent comparative studies supported that the overall sign of epistasis switches from positive to negative as the complexity of an organism increases, and it has been hypothesized that this change shall be a consequence of the underlying gene network properties. Why should this be the case? What characteristics of genetic networks determine the sign of epistasis? Here we show, by evolving genetic networks that differ in their complexity and robustness against perturbations but that perform the same tasks, that robustness increased with complexity and that epistasis was positive for small nonrobust networks but negative for large robust ones. Our results indicate that robustness and negative epistasis emerge as a consequence of the existence of redundant elements in regulatory structures of genetic networks and that the correlation between complexity and epistasis is a byproduct of such redundancy, allowing for the decoupling of epistasis from the underlying network complexity.