Fungal network responses to grazing

Mycelial networks operate on scales from microscopic to many m² and naturally persist for extended periods. As fungi exhibit highly adaptive development, it is important to test behavioural responses on natural substrata with realistic nutrient levels across a range of spatial scales and extended time periods. Here we quantified network responses over 7.5 months in large (57 × 57 cm) microcosms to test whether grazing shifts the network to a more resilient architecture. Resource limitation constrained any ability to respond at all, with both grazed and ungrazed networks gradually thinning out over time. Added resources sustained further exploratory growth, but only transiently increased cross-connectivity and network resilience, when tested by simulated damage in silico. Grazed networks were initially weaker and emergence of new exploratory growth was curtailed. However, increased interstitial proliferation led to new cross-links, consolidating the existing mycelial network and increasing the resilience of the network to further attack.     

Quantifying the Reconfiguration of Intrinsic Networks during Working Memory

Rapid, flexible reconfiguration of connections across brain regions is thought to underlie successful cognitive control. Two intrinsic networks in particular, the cingulo-opercular (CO) and fronto-parietal (FP), are thought to underlie two operations critical for cognitive control: task-set maintenance/tonic alertness and adaptive, trial-by-trial updating. Using functional magnetic resonance imaging, we directly tested whether the functional connectivity of the CO and FP networks was related to cognitive demands and behavior. We focused on working memory because of evidence that during working memory tasks the entire brain becomes more integrated. When specifically probing the CO and FP cognitive control networks, we found that individual regions of both intrinsic networks were active during working memory and, as expected, integration across the two networks increased during task blocks that required cognitive control. Crucially, increased integration between each of the cognitive control networks and a task-related, non-cognitive control network (the hand somatosensory-motor network; SM) was related to increased accuracy. This implies that dynamic reconfiguration of the CO and FP networks so as to increase their inter-network communication underlies successful working memory.     

Homeostatic Plasticity of Striatal Neurons Intrinsic Excitability following Dopamine Depletion

The striatum is the major input structure of basal ganglia and is involved in adaptive control of behaviour through the selection of relevant informations. Dopaminergic neurons that innervate striatum die in Parkinson disease, leading to inefficient adaptive behaviour. Neuronal activity of striatal medium spiny neurons (MSN) is modulated by dopamine receptors. Although dopamine signalling had received substantial attention, consequences of dopamine depletion on MSN intrinsic excitability remain unclear. Here we show, by performing perforated patch clamp recordings on brain slices, that dopamine depletion leads to an increase in MSN intrinsic excitability through the decrease of an inactivating A-type potassium current, I _A. Despite the large decrease in their excitatory synaptic inputs determined by the decreased dendritic spines density and the increase in minimal current to evoke the first EPSP, this increase in intrinsic excitability resulted in an enhanced responsiveness to their remaining synapses, allowing them to fire similarly or more efficiently following input stimulation than in control condition. Therefore, this increase in intrinsic excitability through the regulation of I _A represents a form of homeostatic plasticity allowing neurons to compensate for perturbations in synaptic transmission and to promote stability in firing. The present observations show that this homeostatic ability to maintain firing rates within functional range also occurs in pathological conditions, allowing stabilizing neural computation within affected neuronal networks.     

生物分子网络弹性研究进展

弹性是生物分子网络重要且基础的属性之一,一方面弹性赋予生物分子网络抵抗内部噪声与环境干扰并维持其自身基本功能的能力,另一方面,弹性为网络状态的恢复制造了阻力。生物分子网络弹性研究试图回答如下3个问题：a. 生物分子网络弹性的产生机理是什么？b. 弹性影响下生物分子网络的状态如何发生转移？c. 如何预测生物网络状态转换临界点,以防止系统向不理想的状态演化？因此,研究生物分子网络弹性有助于理解生物系统内部运作机理,同时对诸如疾病发生临界点预测、生物系统状态逆转等临床应用具有重要的指导意义。鉴于此,本文主要针对以上生物分子网络弹性领域的3个热点研究问题,在研究方法和生物学应用上进行了系统地综述,并对未来生物分子网络弹性的研究方向进行了展望。     

On the long-run sensitivity of probabilistic Boolean networks

Boolean networks and, more generally, probabilistic Boolean networks, as one class of gene regulatory networks, model biological processes with the network dynamics determined by the logic-rule regulatory functions in conjunction with probabilistic parameters involved in network transitions. While there has been significant research on applying different control policies to alter network dynamics as future gene therapeutic intervention, we have seen less work on understanding the sensitivity of network dynamics with respect to perturbations to networks, including regulatory rules and the involved parameters, which is particularly critical for the design of intervention strategies. This paper studies this less investigated issue of network sensitivity in the long run. As the underlying model of probabilistic Boolean networks is a finite Markov chain, we define the network sensitivity based on the steady-state distributions of probabilistic Boolean networks and call it long-run sensitivity. The steady-state distribution reflects the long-run behavior of the network and it can give insight into the dynamics or momentum existing in a system. The change of steady-state distribution caused by possible perturbations is the key measure for intervention. This newly defined long-run sensitivity can provide insight on both network inference and intervention. We show the results for probabilistic Boolean networks generated from random Boolean networks and the results from two real biological networks illustrate preliminary applications of sensitivity in intervention for practical problems.     

In Silico Reconstitution of Actin-Based Symmetry Breaking and Motility

Eukaryotic cells assemble viscoelastic networks of crosslinked actin filaments to control their shape, mechanical properties, and motility. One important class of actin network is nucleated by the Arp2/3 complex and drives both membrane protrusion at the leading edge of motile cells and intracellular motility of pathogens such as Listeria monocytogenes. These networks can be reconstituted in vitro from purified components to drive the motility of spherical micron-sized beads. An Elastic Gel model has been successful in explaining how these networks break symmetry, but how they produce directed motile force has been less clear. We have combined numerical simulations with in vitro experiments to reconstitute the behavior of these motile actin networks in silico using an Accumulative Particle-Spring (APS) model that builds on the Elastic Gel model, and demonstrates simple intuitive mechanisms for both symmetry breaking and sustained motility. The APS model explains observed transitions between smooth and pulsatile motion as well as subtle variations in network architecture caused by differences in geometry and conditions. Our findings also explain sideways symmetry breaking and motility of elongated beads, and show that elastic recoil, though important for symmetry breaking and pulsatile motion, is not necessary for smooth directional motility. The APS model demonstrates how a small number of viscoelastic network parameters and construction rules suffice to recapture the complex behavior of motile actin networks. The fact that the model not only mirrors our in vitro observations, but also makes novel predictions that we confirm by experiment, suggests that the model captures much of the essence of actin-based motility in this system.     

Shaping embodied neural networks for adaptive goal-directed behavior

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.     

Self-sustained asynchronous irregular states and Up–Down states in thalamic,cortical and thalamocortical networks of nonlinear integrate-and-fire neurons

Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. However, it is not clear whether such activity states are specific to simple IF models, or if they also exist in networks where neurons are endowed with complex intrinsic properties similar to electrophysiological measurements. Here, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models. AI states can be found in each case, sometimes with surprisingly small network size of the order of a few tens of neurons. We show that the presence of LTS neurons in cortex or in thalamus, explains the robust emergence of AI states for relatively small network sizes. Finally, we investigate the role of spike-frequency adaptation (SFA). In cortical networks with strong SFA in RS cells, the AI state is transient, but when SFA is reduced, AI states can be self-sustained for long times. In thalamocortical networks, AI states are found when the cortex is itself in an AI state, but with strong SFA, the thalamocortical network displays Up and Down state transitions, similar to intracellular recordings during slow-wave sleep or anesthesia. Self-sustained Up and Down states could also be generated by two-layer cortical networks with LTS cells. These models suggest that intrinsic properties such as adaptation and low-threshold bursting activity are crucial for the genesis and control of AI states in thalamocortical networks.     

Micro- and macrorheological properties of isotropically cross-linked actin networks

Cells make use of semiflexible biopolymers such as actin or intermediate filaments to control their local viscoelastic response by dynamically adjusting the concentration and type of cross-linking molecules. The microstructure of the resulting networks mainly determines their mechanical properties. It remains an important challenge to relate structural transitions to both the molecular properties of the cross-linking molecules and the mechanical response of the network. This can be achieved best by well defined in vitro model systems in combination with microscopic techniques. Here, we show that with increasing concentrations of the cross-linker heavy meromyosin, a transition in the mechanical network response occurs. At low cross-linker densities the network elasticity is dominated by the entanglement length l_e of the polymer, whereas at high heavy meromyosin densities the cross-linker distance l_c determines the elastic behavior. Using microrheology the formation of heterogeneous networks is observed at low cross-linker concentrations. Micro- and macrorheology both report the same transition to a homogeneous cross-linked phase. This transition is set by a constant average cross-linker distance l_c ≈ 15 μm. Thus, the micro- and macromechanical properties of isotropically cross-linked in vitro actin networks are determined by only one intrinsic network parameter.     

A general mathematical performance model for wormhole-switched irregular networks

Irregular topologies are desirable network structures for building scalable cluster systems and very recently they have also been employed in SoC (system-on-chip) design. Many analytical models have been proposed in the literature to evaluate the performance of networks with different topologies such as hypercube, torus, mesh, hypermesh, Cartesian product networks, star graph, and k-ary n-cube; however, to the best of our knowledge, no mathematical model has been presented for irregular networks. Therefore, as an effort to fill this gap, this paper presents a comprehensive mathematical model for fully adaptive routing in wormhole-switched irregular networks. Moreover, since our approach holds no assumption for the network topology, the proposed analytical model covers all the aforementioned models (i.e. it covers both regular and irregular topologies). Furthermore, the model makes no preliminary assumption about the deadlock-free routing algorithm applied to the network. Finally, besides the generality of the model regarding the topology and routing algorithm, our analysis shows that the analytical model exhibits high accuracy which enables it to be used for almost all topologies with all traffic loads.     

A 'hot-spot' mutation alters the mechanical properties of keratin filament networks

Keratins 5 and 14 polymerize to form the intermediate filament network in the progenitor basal cells of many stratified epithelia including epidermis, where it provides crucial mechanical support. Inherited mutations in K5 or K14 result in epidermolysis bullosa simplex (EBS), a skin-fragility disorder. The impact that such mutations exert on the intrinsic mechanical properties of K5/K14 filaments is unknown. Here we show, by using differential interference contrast microscopy, that a 'hot-spot' mutation in K14 greatly reduces the ability of reconstituted mutant filaments to bundle under crosslinking conditions. Rheological assays measure similar small-deformation mechanical responses for crosslinked solutions of wild-type and mutant keratins. The mutation, however, markedly reduces the resilience of crosslinked networks against large deformations. Single-particle tracking, which probes the local organization of filament networks, shows that the mutant polymer exhibits highly heterogeneous structures compared to those of wild-type filaments. Our results indicate that the fragility of epithelial cells expressing mutant keratin may result from an impaired ability of keratin polymers to be crosslinked into a functional network.     

Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity

Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.     

From Metaphor to Measurement: Resilience of What to What?

Resilience is the magnitude of disturbance that can be tolerated before a socioecological system (SES) moves to a different region of state space controlled by a different set of processes. Resilience has multiple levels of meaning: as a metaphor related to sustainability, as a property of dynamic models, and as a measurable quantity that can be assessed in field studies of SES. The operational indicators of resilience have, however, received little attention in the literature. To assess a system's resilience, one must specify which system configuration and which disturbances are of interest. This paper compares resilience properties in two contrasting SES, lake districts and rangelands, with respect to the following three general features: (a) The ability of an SES to stay in the domain of attraction is related to slowly changing variables, or slowly changing disturbance regimes, which control the boundaries of the domain of attraction or the frequency of events that could push the system across the boundaries. Examples are soil phosphorus content in lake districts woody vegetation cover in rangelands, and property rights systems that affect land use in both lake districts and rangelands. (b) The ability of an SES to self-organize is related to the extent to which reorganization is endogenous rather than forced by external drivers. Self-organization is enhanced by coevolved ecosystem components and the presence of social networks that facilitate innovative problem solving. (c) The adaptive capacity of an SES is related to the existence of mechanisms for the evolution of novelty or learning. Examples include biodiversity at multiple scales and the existence of institutions that facilitate experimentation, discovery, and innovation. Received 20 March 2001; accepted 6 June 2001.     

Network reconfiguration and neuronal plasticity in rhythm-generating networks

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.     

What is a healthy ecosystem?

Rapid deterioration of the world's major ecosystems has intensified the need for effective environmental monitoring and the development of operational indicators of ecosystem health. Ecosystem health represents a desired endpoint of environmental management, but it requires adaptive, ongoing definition and assessment. We propose that a healthy ecosystem is one that is sustainable – that is, it has the ability to maintain its structure (organization) and function (vigor) over time in the face of external stress (resilience). Various methods to quantify these three ecosystem attributes (vigor, organization, and resilience) are discussed. These attributes are then folded into a comprehensive assessment of ecosystem health. A network analysis based ecosystem health assessment is developed and tested using trophic exchange networks representing several different aquatic ecosystems. Results indicate the potential of such an ecosystem health assessment for evaluating the relative health of similar ecosystems, and quantifying the effects of natural or anthropogenic stress on the health of a particular ecosystem over time.     

Development of rhythmic pattern generators

In contrast to the wealth of knowledge about the organizational rules of adult central pattern generators, far less is known about how these networks are assembled during development. The basic architecture for adult central pattern generators appears early in development but different generators may follow completely different developmental pathways to reach maturity. Recent evidence suggests that neuromodulatory inputs, in addition to their short-term adaptive control of central pattern generator activity, play a crucial role in both the final developmental tuning and the long-term maintenance of adult network function.     

危机干扰下旅游共生网络韧性变化及机制——以武陵山片区为例

旅游共生网络韧性是提升旅游业抗风险能力的关键。结合共生理论与韧性理论,建立"干扰-响应-状态"旅游共生网络韧性研究框架,引入共生力度指标优化生长性、层级性、匹配性、连通性和传输性等网络韧性指标的测度方法,基于此分析了危机干扰下武陵山片区旅游共生网络韧性变化及机制。结果表明:(1)旅游共生网络韧性表现出复杂的时空变化特征,节点的共生力度呈差异性增强变化,生长性呈波动增强变化,层级性和匹配性呈摆动变化,连通性和传输性呈非线性非同步增强变化;(2)旅游共生网络对干扰具有不同响应特征,表现为不同时期节点共生力度和节点失效对连通性和传输性的差异影响,以及网络抗干扰能力不同程度的恢复变化;(3)危机干扰下旅游共生网络韧性表现出波动变化特征,结构与要素间呈复杂交互作用机制。要素的协调作用和有序发展,是提升旅游共生网络韧性的重要途径。研究对促进区域旅游业韧性发展具有重要意义。