A social network analysis of primate groups

Primate social systems are difficult to characterize, and existing classification schemes have been criticized for being overly simplifying, formulated only on a verbal level or partly inconsistent. Social network analysis comprises a collection of analytical tools rooted in the framework of graph theory that were developed to study human social interaction patterns. More recently these techniques have been successfully applied to examine animal societies. Primate social systems differ from those of humans in both size and density, requiring an approach that puts more emphasis on the quality of relationships. Here, we discuss a set of network measures that are useful to describe primate social organization and we present the results of a network analysis of 70 groups from 30 different species. For this purpose we concentrated on structural measures on the group level, describing the distribution of interaction patterns, centrality, and group structuring. We found considerable variability in those measures, reflecting the high degree of diversity of primate social organizations. By characterizing primate groups in terms of their network metrics we can draw a much finer picture of their internal structure that might be useful for species comparisons as well as the interpretation of social behavior.     

Modeling fluctuations in default-mode brain network using a spiking neural network

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.     

From network structure to network reorganization: implications for adult neurogenesis

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.     

The vertebrate social behavior network: evolutionary themes and variations

Based on a wide variety of data, it is now clear that birds and teleost (bony) fish possess a core "social behavior network" within the basal forebrain and midbrain that is homologous to the social behavior network of mammals. The nodes of this network are reciprocally connected, contain receptors for sex steroid hormones, and are involved in multiple forms of social behavior. Other hodological features and neuropeptide distributions are likewise very similar across taxa. This evolutionary conservation represents a boon for experiments on phenotypic behavioral variation, as the extraordinary social diversity of teleost fish and songbirds can now be used to generate broadly relevant insights into issues of brain function that are not particularly tractable in other vertebrate groups. Two such lines of research are presented here, each of which addresses functional variation within the network as it relates to divergent patterns of social behavior. In the first set of experiments, we have used a sexually polymorphic fish to demonstrate that natural selection can operate independently on hypothalamic neuroendocrine functions that are relevant for (1) gonadal regulation and (2) sex-typical behavioral modulation. In the second set of experiments, we have exploited the diversity of avian social organizations and ecologies to isolate species-typical group size as a quasi-independent variable. These experiments have shown that specific areas and peptidergic components of the social behavior network possess functional properties that evolve in parallel with divergence and convergence in sociality.     

Characterization of functional and structural integrity in experimental focal epilepsy: reduced network efficiency coincides with white matter changes

Background

Although focal epilepsies are increasingly recognized to affect multiple and remote neural systems, the underlying spatiotemporal pattern and the relationships between recurrent spontaneous seizures, global functional connectivity, and structural integrity remain largely unknown.

Methodology/Principal Findings

Here we utilized serial resting-state functional MRI, graph-theoretical analysis of complex brain networks and diffusion tensor imaging to characterize the evolution of global network topology, functional connectivity and structural changes in the interictal brain in relation to focal epilepsy in a rat model. Epileptic networks exhibited a more regular functional topology than controls, indicated by a significant increase in shortest path length and clustering coefficient. Interhemispheric functional connectivity in epileptic brains decreased, while intrahemispheric functional connectivity increased. Widespread reductions of fractional anisotropy were found in white matter regions not restricted to the vicinity of the epileptic focus, including the corpus callosum.

Conclusions/Significance

Our longitudinal study on the pathogenesis of network dynamics in epileptic brains reveals that, despite the locality of the epileptogenic area, epileptic brains differ in their global network topology, connectivity and structural integrity from healthy brains.     

Protein conformational transitions explored by mixed elastic network models

We develop a mixed elastic network model (MENM) to study large-scale conformational transitions of proteins between two (or more) known structures. Elastic network potentials for the beginning and end states of a transition are combined, in effect, by adding their respective partition functions. The resulting effective MENM energy function smoothly interpolates between the original surfaces, and retains the beginning and end structures as local minima. Saddle points, transition paths, potentials of mean force, and partition functions can be found efficiently by largely analytic methods. To characterize the protein motions during a conformational transition, we follow "transition paths" on the MENM surface that connect the beginning and end structures and are invariant to parameterizations of the model and the mathematical form of the mixing scheme. As illustrations of the general formalism, we study large-scale conformation changes of the motor proteins KIF1A kinesin and myosin II. We generate possible transition paths for these two proteins that reveal details of their conformational motions. The MENM formalism is computationally efficient and generally applicable even for large protein systems that undergo highly collective structural changes.     

Structural bistability of the GAL regulatory network and characterization of its domains of attraction

Bistability is a system-level property, exploited by many biomolecular interaction networks as a key mechanism to accomplish different cellular functions (e.g., differentiation, cell cycle, switch-like response to external stimuli). Bistability has also been experimentally found to occur in the regulatory network of the galactose metabolic pathway in the model organism Saccharomyces cerevisiae. In this yeast, bistability generates a persistent memory of the type of carbon source available in the extracellular medium: under the same experimental conditions, cells previously grown with different nutrients generate different responses and get stably locked into two distinct steady states. The molecular interactions of the GAL regulatory network have been thoroughly dissected through wet-lab experiments; thus, this system provides a formidable benchmark to our ability to characterize and reproduce in silico the behavior of bistable biological systems. To this aim, a number of models have been proposed in the literature; however, we found that they are not able to replicate the persistent memory behavior observed in (Acar et al., 2005 ). The present study proposes a novel model of the GAL regulatory network, which, in addition to reproducing in silico the experimental findings, can be formally analyzed for structural multistability of the network, using chemical reaction network theory (CRNT), and allows the characterization of the domains of attraction (DA). This work provides further insights into the GAL system and proposes an easily generalizable approach to the study of bistability-associated behaviors in biological systems.     

An improved model considering traditional network properties to assess spatial vulnerability of a network system

We have recently reported a new spatial vulnerability model, which proposed two important curves (i.e., impact curve and neutral curve) and two quantified indices (i.e., absolute spatial vulnerability index and relative spatial vulnerability index) to assess the global impact of spatially local hazards on network systems (Li et al. 2015 Guo XM, Hu XB, Li H, et al. 2015. A study on spatial-temporal rainstorm risk at civil airports in China. J Risk Anal Crisis Response 5:188–198 [Google Scholar]). This paper aims to further investigate and improve the practicability of the new spatial vulnerability model. As some traditional network properties, such as the shortest path, betweenness and connectivity, are often used to assess the vulnerability of network systems, this paper develops a methodology of applying traditional network properties to analyze the spatial vulnerability of network systems. To this end, we firstly describe the new spatial vulnerability model, then analyze its relationship with traditional network properties, and at last conduct a case study on the Beijing subway network to verify this relationship. The results show that, when the global impact of spatially local hazards on network systems is concerned in vulnerability assessment, the combination of some traditional network properties and the new spatial vulnerability model can deliver an effective approach.     

Inferring general relations between network characteristics from specific network ensembles

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.     

Sets2Networks: network inference from repeated observations of sets

Background

The skeleton of complex systems can be represented as networks where vertices represent entities, and edges represent the relations between these entities. Often it is impossible, or expensive, to determine the network structure by experimental validation of the binary interactions between every vertex pair. It is usually more practical to infer the network from surrogate observations. Network inference is the process by which an underlying network of relations between entities is determined from indirect evidence. While many algorithms have been developed to infer networks from quantitative data, less attention has been paid to methods which infer networks from repeated co-occurrence of entities in related sets. This type of data is ubiquitous in the field of systems biology and in other areas of complex systems research. Hence, such methods would be of great utility and value.

Results

Here we present a general method for network inference from repeated observations of sets of related entities. Given experimental observations of such sets, we infer the underlying network connecting these entities by generating an ensemble of networks consistent with the data. The frequency of occurrence of a given link throughout this ensemble is interpreted as the probability that the link is present in the underlying real network conditioned on the data. Exponential random graphs are used to generate and sample the ensemble of consistent networks, and we take an algorithmic approach to numerically execute the inference method. The effectiveness of the method is demonstrated on synthetic data before employing this inference approach to problems in systems biology and systems pharmacology, as well as to construct a co-authorship collaboration network. We predict direct protein-protein interactions from high-throughput mass-spectrometry proteomics, integrate data from Chip-seq and loss-of-function/gain-of-function followed by expression data to infer a network of associations between pluripotency regulators, extract a network that connects 53 cancer drugs to each other and to 34 severe adverse events by mining the FDA’s Adverse Events Reporting Systems (AERS), and construct a co-authorship network that connects Mount Sinai School of Medicine investigators. The predicted networks and online software to create networks from entity-set libraries are provided online at http://www.maayanlab.net/S2N.

Conclusions

The network inference method presented here can be applied to resolve different types of networks in current systems biology and systems pharmacology as well as in other fields of research.     

Actin network growth under load

Many processes in eukaryotic cells, including the crawling motion of the whole cell, rely on the growth of branched actin networks from surfaces. In addition to their well-known role in generating propulsive forces, actin networks can also sustain substantial pulling loads thanks to their persistent attachment to the surface from which they grow. The simultaneous network elongation and surface attachment inevitably generate a force that opposes network growth. Here, we study the local dynamics of a growing actin network, accounting for simultaneous network elongation and surface attachment, and show that there exist several dynamical regimes that depend on both network elasticity and the kinetic parameters of actin polymerization. We characterize this in terms of a phase diagram and provide a connection between mesoscopic theories and the microscopic dynamics of an actin network at a surface. Our framework predicts the onset of instabilities that lead to the local detachment of the network and translate to oscillatory behavior and waves, as observed in many cellular phenomena and in vitro systems involving actin network growth, such as the saltatory dynamics of actin-propelled oil drops.     

城市能源代谢生态网络分析研究进展

运用生态网络分析方法研究城市能源代谢可以打破以往研究中的"黑箱模式",有助于考察城市内部能源的代谢过程和代谢路径。对研究城市能源代谢的一般方法进行了分析和对比,发现在探究城市能源代谢方面,生态网络分析相较于其他方法更具优越性;因此,全面整理和阐述了生态网络分析方法的主要内容,并系统综述了该方法在城市能源代谢中的应用。在此基础之上,指出在未来城市能源代谢的生态网络分析中应从研究尺度、上升性分析和环境元分析的结合应用以及增加不同模型精细度的对比研究3个方面做出改进。     

Functional network organization of the human brain

Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.     

Direct queries for discovering network resource properties in a distributed environment

The development and performance of networkaware applications depends on the availability of accurate predictions of network resource properties. Obtaining this information directly from the network is a scalable solution that provides the accurate performance predictions and topology information needed for planning and adapting application behavior across a variety of networks. The performance predictions obtained directly from the network are as accurate as applicationlevel benchmarks, but the networkbased technique provides the added advantages of scalability and topology discovery. We describe how to determine network properties directly from the network using SNMP. We provide an overview of SNMP and describe the features it provides that make it possible to extract both available bandwidth and network topology information from network devices. The available bandwidth predictions based on network queries using SNMP are compared with traditional predictions based on application history to demonstrate that they are equally useful. To demonstrate the feasibility of topology discovery, we present results for a large Ethernet LAN.     

Gene network inference via structural equation modeling in genetical genomics experiments

Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis-trans-, and trans-regulation. After using local structural models to identify regulator-target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator-target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis-trans-regulated target genes, from trans-regulator genes to target genes, and from trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.     

Motor network degeneration in amyotrophic lateral sclerosis: a structural and functional connectivity study

Background

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by motor neuron degeneration. How this disease affects the central motor network is largely unknown. Here, we combined for the first time structural and functional imaging measures on the motor network in patients with ALS and healthy controls.

Methodology/Principal Findings

Structural measures included whole brain cortical thickness and diffusion tensor imaging (DTI) of crucial motor tracts. These structural measures were combined with functional connectivity analysis of the motor network based on resting state fMRI. Focal cortical thinning was observed in the primary motor area in patients with ALS compared to controls and was found to correlate with disease progression. DTI revealed reduced FA values in the corpus callosum and in the rostral part of the corticospinal tract. Overall functional organisation of the motor network was unchanged in patients with ALS compared to healthy controls, however the level of functional connectedness was significantly correlated with disease progression rate. Patients with increased connectedness appear to have a more progressive disease course.

Conclusions/Significance

We demonstrate structural motor network deterioration in ALS with preserved functional connectivity measures. The positive correlation between functional connectedness of the motor network and disease progression rate could suggest spread of disease along functional connections of the motor network.