Brownian model of transcriptome evolution and phylogenetic network visualization between tissues.

PubMed

Gu, Xun; Ruan, Hang; Su, Zhixi; Zou, Yangyun

2017-09-01

While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level. Copyright © 2017. Published by Elsevier Inc.

The Evolution of ICT Markets: An Agent-Based Model on Complex Networks

NASA Astrophysics Data System (ADS)

Zhao, Liangjie; Wu, Bangtao; Chen, Zhong; Li, Li

Information and communication technology (ICT) products exhibit positive network effects.The dynamic process of ICT markets evolution has two intrinsic characteristics: (1) customers are influenced by each others’ purchasing decision; (2) customers are intelligent agents with bounded rationality.Guided by complex systems theory, we construct an agent-based model and simulate on complex networks to examine how the evolution can arise from the interaction of customers, which occur when they make expectations about the future installed base of a product by the fraction of neighbors who are using the same product in his personal network.We demonstrate that network effects play an important role in the evolution of markets share, which make even an inferior product can dominate the whole market.We also find that the intensity of customers’ communication can influence whether the best initial strategy for firms is to improve product quality or expand their installed base.

In silico evolution of biochemical networks

NASA Astrophysics Data System (ADS)

Francois, Paul

2010-03-01

We use computational evolution to select models of genetic networks that can be built from a predefined set of parts to achieve a certain behavior. Selection is made with the help of a fitness defining biological functions in a quantitative way. This fitness has to be specific to a process, but general enough to find processes common to many species. Computational evolution favors models that can be built by incremental improvements in fitness rather than via multiple neutral steps or transitions through less fit intermediates. With the help of these simulations, we propose a kinetic view of evolution, where networks are rapidly selected along a fitness gradient. This mathematics recapitulates Darwin's original insight that small changes in fitness can rapidly lead to the evolution of complex structures such as the eye, and explain the phenomenon of convergent/parallel evolution of similar structures in independent lineages. We will illustrate these ideas with networks implicated in embryonic development and patterning of vertebrates and primitive insects.

The many faces of graph dynamics

NASA Astrophysics Data System (ADS)

Pignolet, Yvonne Anne; Roy, Matthieu; Schmid, Stefan; Tredan, Gilles

2017-06-01

The topological structure of complex networks has fascinated researchers for several decades, resulting in the discovery of many universal properties and reoccurring characteristics of different kinds of networks. However, much less is known today about the network dynamics: indeed, complex networks in reality are not static, but rather dynamically evolve over time. Our paper is motivated by the empirical observation that network evolution patterns seem far from random, but exhibit structure. Moreover, the specific patterns appear to depend on the network type, contradicting the existence of a ‘one fits it all’ model. However, we still lack observables to quantify these intuitions, as well as metrics to compare graph evolutions. Such observables and metrics are needed for extrapolating or predicting evolutions, as well as for interpolating graph evolutions. To explore the many faces of graph dynamics and to quantify temporal changes, this paper suggests to build upon the concept of centrality, a measure of node importance in a network. In particular, we introduce the notion of centrality distance, a natural similarity measure for two graphs which depends on a given centrality, characterizing the graph type. Intuitively, centrality distances reflect the extent to which (non-anonymous) node roles are different or, in case of dynamic graphs, have changed over time, between two graphs. We evaluate the centrality distance approach for five evolutionary models and seven real-world social and physical networks. Our results empirically show the usefulness of centrality distances for characterizing graph dynamics compared to a null-model of random evolution, and highlight the differences between the considered scenarios. Interestingly, our approach allows us to compare the dynamics of very different networks, in terms of scale and evolution speed.

The transmission process: A combinatorial stochastic process for the evolution of transmission trees over networks.

PubMed

Sainudiin, Raazesh; Welch, David

2016-12-07

We derive a combinatorial stochastic process for the evolution of the transmission tree over the infected vertices of a host contact network in a susceptible-infected (SI) model of an epidemic. Models of transmission trees are crucial to understanding the evolution of pathogen populations. We provide an explicit description of the transmission process on the product state space of (rooted planar ranked labelled) binary transmission trees and labelled host contact networks with SI-tags as a discrete-state continuous-time Markov chain. We give the exact probability of any transmission tree when the host contact network is a complete, star or path network - three illustrative examples. We then develop a biparametric Beta-splitting model that directly generates transmission trees with exact probabilities as a function of the model parameters, but without explicitly modelling the underlying contact network, and show that for specific values of the parameters we can recover the exact probabilities for our three example networks through the Markov chain construction that explicitly models the underlying contact network. We use the maximum likelihood estimator (MLE) to consistently infer the two parameters driving the transmission process based on observations of the transmission trees and use the exact MLE to characterize equivalence classes over the space of contact networks with a single initial infection. An exploratory simulation study of the MLEs from transmission trees sampled from three other deterministic and four random families of classical contact networks is conducted to shed light on the relation between the MLEs of these families with some implications for statistical inference along with pointers to further extensions of our models. The insights developed here are also applicable to the simplest models of "meme" evolution in online social media networks through transmission events that can be distilled from observable actions such as "likes", "mentions", "retweets" and "+1s" along with any concomitant comments. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Analyzing the evolutionary mechanisms of the Air Transportation System-of-Systems using network theory and machine learning algorithms

NASA Astrophysics Data System (ADS)

Kotegawa, Tatsuya

Complexity in the Air Transportation System (ATS) arises from the intermingling of many independent physical resources, operational paradigms, and stakeholder interests, as well as the dynamic variation of these interactions over time. Currently, trade-offs and cost benefit analyses of new ATS concepts are carried out on system-wide evaluation simulations driven by air traffic forecasts that assume fixed airline routes. However, this does not well reflect reality as airlines regularly add and remove routes. A airline service route network evolution model that projects route addition and removal was created and combined with state-of-the-art air traffic forecast methods to better reflect the dynamic properties of the ATS in system-wide simulations. Guided by a system-of-systems framework, network theory metrics and machine learning algorithms were applied to develop the route network evolution models based on patterns extracted from historical data. Constructing the route addition section of the model posed the greatest challenge due to the large pool of new link candidates compared to the actual number of routes historically added to the network. Of the models explored, algorithms based on logistic regression, random forests, and support vector machines showed best route addition and removal forecast accuracies at approximately 20% and 40%, respectively, when validated with historical data. The combination of network evolution models and a system-wide evaluation tool quantified the impact of airline route network evolution on air traffic delay. The expected delay minutes when considering network evolution increased approximately 5% for a forecasted schedule on 3/19/2020. Performance trade-off studies between several airline route network topologies from the perspectives of passenger travel efficiency, fuel burn, and robustness were also conducted to provide bounds that could serve as targets for ATS transformation efforts. The series of analysis revealed that high robustness is achievable only in exchange of lower passenger travel and fuel burn efficiency. However, increase in the network density can mitigate this trade-off.

Evolving dynamics of trading behavior based on coordination game in complex networks

NASA Astrophysics Data System (ADS)

Bian, Yue-tang; Xu, Lu; Li, Jin-sheng

2016-05-01

This work concerns the modeling of evolvement of trading behavior in stock markets. Based on the assumption of the investors' limited rationality, the evolution mechanism of trading behavior is modeled according to the investment strategy of coordination game in network, that investors are prone to imitate their neighbors' activity through comprehensive analysis on the risk dominance degree of certain investment behavior, the network topology of their relationship and its heterogeneity. We investigate by mean-field analysis and extensive simulations the evolution of investors' trading behavior in various typical networks under different risk dominance degree of investment behavior. Our results indicate that the evolution of investors' behavior is affected by the network structure of stock market and the effect of risk dominance degree of investment behavior; the stability of equilibrium states of investors' behavior dynamics is directly related with the risk dominance degree of some behavior; connectivity and heterogeneity of the network plays an important role in the evolution of the investment behavior in stock market.

A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks

NASA Astrophysics Data System (ADS)

Yasami, Yasser; Safaei, Farshad

2018-02-01

The traditional complex network theory is particularly focused on network models in which all network constituents are dealt with equivalently, while fail to consider the supplementary information related to the dynamic properties of the network interactions. This is a main constraint leading to incorrect descriptions of some real-world phenomena or incomplete capturing the details of certain real-life problems. To cope with the problem, this paper addresses the multilayer aspects of dynamic complex networks by analyzing the properties of intrinsically multilayered co-authorship networks, DBLP and Astro Physics, and presenting a novel multilayer model of dynamic complex networks. The model examines the layers evolution (layers birth/death process and lifetime) throughout the network evolution. Particularly, this paper models the evolution of each node's membership in different layers by an Infinite Factorial Hidden Markov Model considering feature cascade, and thereby formulates the link generation process for intra-layer and inter-layer links. Although adjacency matrixes are useful to describe the traditional single-layer networks, such a representation is not sufficient to describe and analyze the multilayer dynamic networks. This paper also extends a generalized mathematical infrastructure to address the problems issued by multilayer complex networks. The model inference is performed using some Markov Chain Monte Carlo sampling strategies, given synthetic and real complex networks data. Experimental results indicate a tremendous improvement in the performance of the proposed multilayer model in terms of sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, F1-score, Matthews correlation coefficient, and accuracy for two important applications of missing link prediction and future link forecasting. The experimental results also indicate the strong predictivepower of the proposed model for the application of cascade prediction in terms of accuracy.

AST: Activity-Security-Trust driven modeling of time varying networks

PubMed Central

Wang, Jian; Xu, Jiake; Liu, Yanheng; Deng, Weiwen

2016-01-01

Network modeling is a flexible mathematical structure that enables to identify statistical regularities and structural principles hidden in complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents’ interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors (e.g. activity) but also the implicit considerations (e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust (AST) driven model through synthetically considering the explicit and implicit driving forces (e.g. activity, security, and trust) underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes. PMID:26888717

Comparative empirical analysis of flow-weighted transit route networks in R-space and evolution modeling

NASA Astrophysics Data System (ADS)

Huang, Ailing; Zang, Guangzhi; He, Zhengbing; Guan, Wei

2017-05-01

Urban public transit system is a typical mixed complex network with dynamic flow, and its evolution should be a process coupling topological structure with flow dynamics, which has received little attention. This paper presents the R-space to make a comparative empirical analysis on Beijing’s flow-weighted transit route network (TRN) and we found that both the Beijing’s TRNs in the year of 2011 and 2015 exhibit the scale-free properties. As such, we propose an evolution model driven by flow to simulate the development of TRNs with consideration of the passengers’ dynamical behaviors triggered by topological change. The model simulates that the evolution of TRN is an iterative process. At each time step, a certain number of new routes are generated driven by travel demands, which leads to dynamical evolution of new routes’ flow and triggers perturbation in nearby routes that will further impact the next round of opening new routes. We present the theoretical analysis based on the mean-field theory, as well as the numerical simulation for this model. The results obtained agree well with our empirical analysis results, which indicate that our model can simulate the TRN evolution with scale-free properties for distributions of node’s strength and degree. The purpose of this paper is to illustrate the global evolutional mechanism of transit network that will be used to exploit planning and design strategies for real TRNs.

Modeling Evolution on Nearly Neutral Network Fitness Landscapes

NASA Astrophysics Data System (ADS)

Yakushkina, Tatiana; Saakian, David B.

2017-08-01

To describe virus evolution, it is necessary to define a fitness landscape. In this article, we consider the microscopic models with the advanced version of neutral network fitness landscapes. In this problem setting, we suppose a fitness difference between one-point mutation neighbors to be small. We construct a modification of the Wright-Fisher model, which is related to ordinary infinite population models with nearly neutral network fitness landscape at the large population limit. From the microscopic models in the realistic sequence space, we derive two versions of nearly neutral network models: with sinks and without sinks. We claim that the suggested model describes the evolutionary dynamics of RNA viruses better than the traditional Wright-Fisher model with few sequences.

Self-Organized Critical Behavior:. the Evolution of Frozen Spin Networks Model in Quantum Gravity

NASA Astrophysics Data System (ADS)

Chen, Jian-Zhen; Zhu, Jian-Yang

In quantum gravity, we study the evolution of a two-dimensional planar open frozen spin network, in which the color (i.e. the twice spin of an edge) labeling edge changes but the underlying graph remains fixed. The mainly considered evolution rule, the random edge model, is depending on choosing an edge randomly and changing the color of it by an even integer. Since the change of color generally violate the gauge invariance conditions imposed on the system, detailed propagation rule is needed and it can be defined in many ways. Here, we provided one new propagation rule, in which the involved even integer is not a constant one as in previous works, but changeable with certain probability. In random edge model, we do find the evolution of the system under the propagation rule exhibits power-law behavior, which is suggestive of the self-organized criticality (SOC), and it is the first time to verify the SOC behavior in such evolution model for the frozen spin network. Furthermore, the increase of the average color of the spin network in time can show the nature of inflation for the universe.

Rank-dependent deactivation in network evolution.

PubMed

Xu, Xin-Jian; Zhou, Ming-Chen

2009-12-01

A rank-dependent deactivation mechanism is introduced to network evolution. The growth dynamics of the network is based on a finite memory of individuals, which is implemented by deactivating one site at each time step. The model shows striking features of a wide range of real-world networks: power-law degree distribution, high clustering coefficient, and disassortative degree correlation.

Constraints and entropy in a model of network evolution

NASA Astrophysics Data System (ADS)

Tee, Philip; Wakeman, Ian; Parisis, George; Dawes, Jonathan; Kiss, István Z.

2017-11-01

Barabási-Albert's "Scale Free" model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the "Scale Free" model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the "Scale Free" and "constraints" model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.

Complexities’ day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory

NASA Astrophysics Data System (ADS)

Zhang, Lin; Lu, Jian; Zhou, Jialin; Zhu, Jinqing; Li, Yunxuan; Wan, Qian

2018-03-01

Didi Dache is the most popular taxi order mobile app in China, which provides online taxi-hailing service. The obtained big database from this app could be used to analyze the complexities’ day-to-day dynamic evolution of Didi taxi trip network (DTTN) from the level of complex network dynamics. First, this paper proposes the data cleaning and modeling methods for expressing Nanjing’s DTTN as a complex network. Second, the three consecutive weeks’ data are cleaned to establish 21 DTTNs based on the proposed big data processing technology. Then, multiple topology measures that characterize the complexities’ day-to-day dynamic evolution of these networks are provided. Third, these measures of 21 DTTNs are calculated and subsequently explained with actual implications. They are used as a training set for modeling the BP neural network which is designed for predicting DTTN complexities evolution. Finally, the reliability of the designed BP neural network is verified by comparing with the actual data and the results obtained from ARIMA method simultaneously. Because network complexities are the basis for modeling cascading failures and conducting link prediction in complex system, this proposed research framework not only provides a novel perspective for analyzing DTTN from the level of system aggregated behavior, but can also be used to improve the DTTN management level.

SCM: A method to improve network service layout efficiency with network evolution.

PubMed

Zhao, Qi; Zhang, Chuanhao; Zhao, Zheng

2017-01-01

Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of "software defined network + network function virtualization" (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.

Complex networks generated by the Penna bit-string model: Emergence of small-world and assortative mixing

NASA Astrophysics Data System (ADS)

Li, Chunguang; Maini, Philip K.

2005-10-01

The Penna bit-string model successfully encompasses many phenomena of population evolution, including inheritance, mutation, evolution, and aging. If we consider social interactions among individuals in the Penna model, the population will form a complex network. In this paper, we first modify the Verhulst factor to control only the birth rate, and introduce activity-based preferential reproduction of offspring in the Penna model. The social interactions among individuals are generated by both inheritance and activity-based preferential increase. Then we study the properties of the complex network generated by the modified Penna model. We find that the resulting complex network has a small-world effect and the assortative mixing property.

Exploring the patterns and evolution of self-organized urban street networks through modeling

NASA Astrophysics Data System (ADS)

Rui, Yikang; Ban, Yifang; Wang, Jiechen; Haas, Jan

2013-03-01

As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities.

Opinion evolution in different social acquaintance networks.

PubMed

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion p h and variation proportion p v are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve p v +2p h =2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Opinion evolution in different social acquaintance networks

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion ph and variation proportion pv are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve pv+2 ph=2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Neutral evolution of proteins: The superfunnel in sequence space and its relation to mutational robustness

NASA Astrophysics Data System (ADS)

Noirel, Josselin; Simonson, Thomas

2008-11-01

Following Kimura's neutral theory of molecular evolution [M. Kimura, The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983) (reprinted in 1986)], it has become common to assume that the vast majority of viable mutations of a gene confer little or no functional advantage. Yet, in silico models of protein evolution have shown that mutational robustness of sequences could be selected for, even in the context of neutral evolution. The evolution of a biological population can be seen as a diffusion on the network of viable sequences. This network is called a "neutral network." Depending on the mutation rate μ and the population size N, the biological population can evolve purely randomly (μN ≪1) or it can evolve in such a way as to select for sequences of higher mutational robustness (μN ≫1). The stringency of the selection depends not only on the product μN but also on the exact topology of the neutral network, the special arrangement of which was named "superfunnel." Even though the relation between mutation rate, population size, and selection was thoroughly investigated, a study of the salient topological features of the superfunnel that could affect the strength of the selection was wanting. This question is addressed in this study. We use two different models of proteins: on lattice and off lattice. We compare neutral networks computed using these models to random networks. From this, we identify two important factors of the topology that determine the stringency of the selection for mutationally robust sequences. First, the presence of highly connected nodes ("hubs") in the network increases the selection for mutationally robust sequences. Second, the stringency of the selection increases when the correlation between a sequence's mutational robustness and its neighbors' increases. The latter finding relates a global characteristic of the neutral network to a local one, which is attainable through experiments or molecular modeling.

Neutral evolution of proteins: The superfunnel in sequence space and its relation to mutational robustness.

PubMed

Noirel, Josselin; Simonson, Thomas

2008-11-14

Following Kimura's neutral theory of molecular evolution [M. Kimura, The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983) (reprinted in 1986)], it has become common to assume that the vast majority of viable mutations of a gene confer little or no functional advantage. Yet, in silico models of protein evolution have shown that mutational robustness of sequences could be selected for, even in the context of neutral evolution. The evolution of a biological population can be seen as a diffusion on the network of viable sequences. This network is called a "neutral network." Depending on the mutation rate mu and the population size N, the biological population can evolve purely randomly (muN<1) or it can evolve in such a way as to select for sequences of higher mutational robustness (muN>1). The stringency of the selection depends not only on the product muN but also on the exact topology of the neutral network, the special arrangement of which was named "superfunnel." Even though the relation between mutation rate, population size, and selection was thoroughly investigated, a study of the salient topological features of the superfunnel that could affect the strength of the selection was wanting. This question is addressed in this study. We use two different models of proteins: on lattice and off lattice. We compare neutral networks computed using these models to random networks. From this, we identify two important factors of the topology that determine the stringency of the selection for mutationally robust sequences. First, the presence of highly connected nodes ("hubs") in the network increases the selection for mutationally robust sequences. Second, the stringency of the selection increases when the correlation between a sequence's mutational robustness and its neighbors' increases. The latter finding relates a global characteristic of the neutral network to a local one, which is attainable through experiments or molecular modeling.

Modelling the public opinion transmission on social networks under opinion leaders

NASA Astrophysics Data System (ADS)

Li, Zuozhi; Li, Meng; Ji, Wanwan

2017-06-01

In this paper, based on Social Network Analysis (SNA), the social network model of opinion leaders influencing the public opinion transmission is explored. The hot event, A Female Driver Was Beaten Due To Lane Change, has characteristics of individual short-term and non-government intervention, which is used to data extraction, and formed of the network structure on opinion leaders influencing the public opinion transmission. And the evolution mechanism are analyzed in the three evolutionary situations. Opinion leaders influence micro-blogging public opinion on social network evolution model shows that this type of network public opinion transmission is largely constrained by opinion leaders, so the opinion leaders behavior supervising on the spread of this public opinion is pivotal, and which has a guiding significance.

Networks of Learning

NASA Astrophysics Data System (ADS)

Bettencourt, Luis; Kaiser, David

2004-03-01

Based on an a historically documented example of scientific discovery - Feynman diagrams as the main calculational tool of theoretical high energy Physics - we map the time evolution of the social network of early adopters through in the US, UK, Japan and the USSR. The spread of the technique for total number of users in each region is then modelled in terms of epidemic models, highlighting parallel and divergent aspects of this analogy. We also show that transient social arrangements develop as the idea is introduced and learned, which later disappear as the technique becomes common knowledge. Such early transient is characterized by abnormally low connectivity distribution powers and by high clustering. This interesting early non-equilibrium stage of network evolution is captured by a new dynamical model for network evolution, which coincides in its long time limit with familiar preferential aggregation dynamics.

Dual Neural Network Model for the Evolution of Speech and Language.

PubMed

Hage, Steffen R; Nieder, Andreas

2016-12-01

Explaining the evolution of speech and language poses one of the biggest challenges in biology. We propose a dual network model that posits a volitional articulatory motor network (VAMN) originating in the prefrontal cortex (PFC; including Broca's area) that cognitively controls vocal output of a phylogenetically conserved primary vocal motor network (PVMN) situated in subcortical structures. By comparing the connections between these two systems in human and nonhuman primate brains, we identify crucial biological preadaptations in monkeys for the emergence of a language system in humans. This model of language evolution explains the exclusiveness of non-verbal communication sounds (e.g., cries) in infants with an immature PFC, as well as the observed emergence of non-linguistic vocalizations in adults after frontal lobe pathologies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Regulation, Competition and Network Evolution in Aviation

NASA Technical Reports Server (NTRS)

Gillen, David; Morrison, William

2003-01-01

Our focus is the evolution of business strategies and network structure decisions in the commercial passenger aviation industry. The paper reviews the growth of hub-and-spoke networks as the dominant business model following deregulation in the latter part of the 20 century, followed by the emergence of value-based airlines as a global phenomenon at the end of the century. The paper highlights the link between airline business strategies and network structures, and examines the resulting competition between divergent network structure business models. In this context we discuss issues of market structure stability and the role played by competition policy.

Opinion formation in time-varying social networks: The case of the naming game

NASA Astrophysics Data System (ADS)

Maity, Suman Kalyan; Manoj, T. Venkat; Mukherjee, Animesh

2012-09-01

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

Evolution of complex dynamics

NASA Astrophysics Data System (ADS)

Wilds, Roy; Kauffman, Stuart A.; Glass, Leon

2008-09-01

We study the evolution of complex dynamics in a model of a genetic regulatory network. The fitness is associated with the topological entropy in a class of piecewise linear equations, and the mutations are associated with changes in the logical structure of the network. We compare hill climbing evolution, in which only mutations that increase the fitness are allowed, with neutral evolution, in which mutations that leave the fitness unchanged are allowed. The simple structure of the fitness landscape enables us to estimate analytically the rates of hill climbing and neutral evolution. In this model, allowing neutral mutations accelerates the rate of evolutionary advancement for low mutation frequencies. These results are applicable to evolution in natural and technological systems.

Evolution of regulatory networks towards adaptability and stability in a changing environment

NASA Astrophysics Data System (ADS)

Lee, Deok-Sun

2014-11-01

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

Continuum Model for River Networks

NASA Astrophysics Data System (ADS)

Giacometti, Achille; Maritan, Amos; Banavar, Jayanth R.

1995-07-01

The effects of erosion, avalanching, and random precipitation are captured in a simple stochastic partial differential equation for modeling the evolution of river networks. Our model leads to a self-organized structured landscape and to abstraction and piracy of the smaller tributaries as the evolution proceeds. An algebraic distribution of the average basin areas and a power law relationship between the drainage basin area and the river length are found.

SCM: A method to improve network service layout efficiency with network evolution

PubMed Central

Zhao, Qi; Zhang, Chuanhao

2017-01-01

Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently. PMID:29267299

A general stochastic model for studying time evolution of transition networks

NASA Astrophysics Data System (ADS)

Zhan, Choujun; Tse, Chi K.; Small, Michael

2016-12-01

We consider a class of complex networks whose nodes assume one of several possible states at any time and may change their states from time to time. Such networks represent practical networks of rumor spreading, disease spreading, language evolution, and so on. Here, we derive a model describing the dynamics of this kind of network and a simulation algorithm for studying the network evolutionary behavior. This model, derived at a microscopic level, can reveal the transition dynamics of every node. A numerical simulation is taken as an ;experiment; or ;realization; of the model. We use this model to study the disease propagation dynamics in four different prototypical networks, namely, the regular nearest-neighbor (RN) network, the classical Erdös-Renyí (ER) random graph, the Watts-Strogátz small-world (SW) network, and the Barabási-Albert (BA) scalefree network. We find that the disease propagation dynamics in these four networks generally have different properties but they do share some common features. Furthermore, we utilize the transition network model to predict user growth in the Facebook network. Simulation shows that our model agrees with the historical data. The study can provide a useful tool for a more thorough understanding of the dynamics networks.

Evolution of weighted complex bus transit networks with flow

NASA Astrophysics Data System (ADS)

Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei

2016-02-01

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

Using hybridization networks to retrace the evolution of Indo-European languages.

PubMed

Willems, Matthieu; Lord, Etienne; Laforest, Louise; Labelle, Gilbert; Lapointe, François-Joseph; Di Sciullo, Anna Maria; Makarenkov, Vladimir

2016-09-06

Curious parallels between the processes of species and language evolution have been observed by many researchers. Retracing the evolution of Indo-European (IE) languages remains one of the most intriguing intellectual challenges in historical linguistics. Most of the IE language studies use the traditional phylogenetic tree model to represent the evolution of natural languages, thus not taking into account reticulate evolutionary events, such as language hybridization and word borrowing which can be associated with species hybridization and horizontal gene transfer, respectively. More recently, implicit evolutionary networks, such as split graphs and minimal lateral networks, have been used to account for reticulate evolution in linguistics. Striking parallels existing between the evolution of species and natural languages allowed us to apply three computational biology methods for reconstruction of phylogenetic networks to model the evolution of IE languages. We show how the transfer of methods between the two disciplines can be achieved, making necessary methodological adaptations. Considering basic vocabulary data from the well-known Dyen's lexical database, which contains word forms in 84 IE languages for the meanings of a 200-meaning Swadesh list, we adapt a recently developed computational biology algorithm for building explicit hybridization networks to study the evolution of IE languages and compare our findings to the results provided by the split graph and galled network methods. We conclude that explicit phylogenetic networks can be successfully used to identify donors and recipients of lexical material as well as the degree of influence of each donor language on the corresponding recipient languages. We show that our algorithm is well suited to detect reticulate relationships among languages, and present some historical and linguistic justification for the results obtained. Our findings could be further refined if relevant syntactic, phonological and morphological data could be analyzed along with the available lexical data.

Modelling the influence of parental effects on gene-network evolution.

PubMed

Odorico, Andreas; Rünneburger, Estelle; Le Rouzic, Arnaud

2018-05-01

Understanding the importance of nongenetic heredity in the evolutionary process is a major topic in modern evolutionary biology. We modified a classical gene-network model by allowing parental transmission of gene expression and studied its evolutionary properties through individual-based simulations. We identified ontogenetic time (i.e. the time gene networks have to stabilize before being submitted to natural selection) as a crucial factor in determining the evolutionary impact of this phenotypic inheritance. Indeed, fast-developing organisms display enhanced adaptation and greater robustness to mutations when evolving in presence of nongenetic inheritance (NGI). In contrast, in our model, long development reduces the influence of the inherited state of the gene network. NGI thus had a negligible effect on the evolution of gene networks when the speed at which transcription levels reach equilibrium is not constrained. Nevertheless, simulations show that intergenerational transmission of the gene-network state negatively affects the evolution of robustness to environmental disturbances for either fast- or slow-developing organisms. Therefore, these results suggest that the evolutionary consequences of NGI might not be sought only in the way species respond to selection, but also on the evolution of emergent properties (such as environmental and genetic canalization) in complex genetic architectures. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Dynamics of functional failures and recovery in complex road networks

NASA Astrophysics Data System (ADS)

Zhan, Xianyuan; Ukkusuri, Satish V.; Rao, P. Suresh C.

2017-11-01

We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.

Generic patterns in the evolution of urban water networks: Evidence from a large Asian city

NASA Astrophysics Data System (ADS)

Krueger, Elisabeth; Klinkhamer, Christopher; Urich, Christian; Zhan, Xianyuan; Rao, P. Suresh C.

2017-03-01

We examine high-resolution urban infrastructure data using every pipe for the water distribution network (WDN) and sanitary sewer network (SSN) in a large Asian city (≈4 million residents) to explore the structure as well as the spatial and temporal evolution of these infrastructure networks. Network data were spatially disaggregated into multiple subnets to examine intracity topological differences for functional zones of the WDN and SSN, and time-stamped SSN data were examined to understand network evolution over several decades as the city expanded. Graphs were generated using a dual-mapping technique (Hierarchical Intersection Continuity Negotiation), which emphasizes the functional attributes of these networks. Network graphs for WDNs and SSNs are characterized by several network topological metrics, and a double Pareto (power-law) model approximates the node-degree distributions of both water infrastructure networks (WDN and SSN), across spatial and hierarchical scales relevant to urban settings, and throughout their temporal evolution over several decades. These results indicate that generic mechanisms govern the networks' evolution, similar to those of scale-free networks found in nature. Deviations from the general topological patterns are indicative of (1) incomplete establishment of network hierarchies and functional network evolution, (2) capacity for growth (expansion) or densification (e.g., in-fill), and (3) likely network vulnerabilities. We discuss the implications of our findings for the (re-)design of urban infrastructure networks to enhance their resilience to external and internal threats.

A dynamic network model for interbank market

NASA Astrophysics Data System (ADS)

Xu, Tao; He, Jianmin; Li, Shouwei

2016-12-01

In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.

Hybrid discrete-time neural networks.

PubMed

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

General Dynamics of Topology and Traffic on Weighted Technological Networks

NASA Astrophysics Data System (ADS)

Wang, Wen-Xu; Wang, Bing-Hong; Hu, Bo; Yan, Gang; Ou, Qing

2005-05-01

For most technical networks, the interplay of dynamics, traffic, and topology is assumed crucial to their evolution. In this Letter, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight, and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation are all consistent with empirical evidence.

A mixing evolution model for bidirectional microblog user networks

NASA Astrophysics Data System (ADS)

Yuan, Wei-Guo; Liu, Yun

2015-08-01

Microblogs have been widely used as a new form of online social networking. Based on the user profile data collected from Sina Weibo, we find that the number of microblog user bidirectional friends approximately corresponds with the lognormal distribution. We then build two microblog user networks with real bidirectional relationships, both of which have not only small-world and scale-free but also some special properties, such as double power-law degree distribution, disassortative network, hierarchical and rich-club structure. Moreover, by detecting the community structures of the two real networks, we find both of their community scales follow an exponential distribution. Based on the empirical analysis, we present a novel evolution network model with mixed connection rules, including lognormal fitness preferential and random attachment, nearest neighbor interconnected in the same community, and global random associations in different communities. The simulation results show that our model is consistent with real network in many topology features.

The Local Structure of Globalization. The Network Dynamics of Foreign Direct Investments in the International Electricity Industry

NASA Astrophysics Data System (ADS)

Koskinen, Johan; Lomi, Alessandro

2013-05-01

We study the evolution of the network of foreign direct investment (FDI) in the international electricity industry during the period 1994-2003. We assume that the ties in the network of investment relations between countries are created and deleted in continuous time, according to a conditional Gibbs distribution. This assumption allows us to take simultaneously into account the aggregate predictions of the well-established gravity model of international trade as well as local dependencies between network ties connecting the countries in our sample. According to the modified version of the gravity model that we specify, the probability of observing an investment tie between two countries depends on the mass of the economies involved, their physical distance, and the tendency of the network to self-organize into local configurations of network ties. While the limiting distribution of the data generating process is an exponential random graph model, we do not assume the system to be in equilibrium. We find evidence of the effects of the standard gravity model of international trade on evolution of the global FDI network. However, we also provide evidence of significant dyadic and extra-dyadic dependencies between investment ties that are typically ignored in available research. We show that local dependencies between national electricity industries are sufficient for explaining global properties of the network of foreign direct investments. We also show, however, that network dependencies vary significantly over time giving rise to a time-heterogeneous localized process of network evolution.

Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

PubMed Central

Huang, Lei; Liao, Li; Wu, Cathy H.

2016-01-01

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

Exact solutions for network rewiring models

NASA Astrophysics Data System (ADS)

Evans, T. S.

2007-03-01

Evolving networks with a constant number of edges may be modelled using a rewiring process. These models are used to describe many real-world processes including the evolution of cultural artifacts such as family names, the evolution of gene variations, and the popularity of strategies in simple econophysics models such as the minority game. The model is closely related to Urn models used for glasses, quantum gravity and wealth distributions. The full mean field equation for the degree distribution is found and its exact solution and generating solution are given.

Towards a Framework for Evolvable Network Design

NASA Astrophysics Data System (ADS)

Hassan, Hoda; Eltarras, Ramy; Eltoweissy, Mohamed

The layered Internet architecture that had long guided network design and protocol engineering was an “interconnection architecture” defining a framework for interconnecting networks rather than a model for generic network structuring and engineering. We claim that the approach of abstracting the network in terms of an internetwork hinders the thorough understanding of the network salient characteristics and emergent behavior resulting in impeding design evolution required to address extreme scale, heterogeneity, and complexity. This paper reports on our work in progress that aims to: 1) Investigate the problem space in terms of the factors and decisions that influenced the design and development of computer networks; 2) Sketch the core principles for designing complex computer networks; and 3) Propose a model and related framework for building evolvable, adaptable and self organizing networks We will adopt a bottom up strategy primarily focusing on the building unit of the network model, which we call the “network cell”. The model is inspired by natural complex systems. A network cell is intrinsically capable of specialization, adaptation and evolution. Subsequently, we propose CellNet; a framework for evolvable network design. We outline scenarios for using the CellNet framework to enhance legacy Internet protocol stack.

Analyzing complex networks evolution through Information Theory quantifiers

NASA Astrophysics Data System (ADS)

Carpi, Laura C.; Rosso, Osvaldo A.; Saco, Patricia M.; Ravetti, Martín Gómez

2011-01-01

A methodology to analyze dynamical changes in complex networks based on Information Theory quantifiers is proposed. The square root of the Jensen-Shannon divergence, a measure of dissimilarity between two probability distributions, and the MPR Statistical Complexity are used to quantify states in the network evolution process. Three cases are analyzed, the Watts-Strogatz model, a gene network during the progression of Alzheimer's disease and a climate network for the Tropical Pacific region to study the El Niño/Southern Oscillation (ENSO) dynamic. We find that the proposed quantifiers are able not only to capture changes in the dynamics of the processes but also to quantify and compare states in their evolution.

Modelling opinion formation driven communities in social networks

NASA Astrophysics Data System (ADS)

Iñiguez, Gerardo; Barrio, Rafael A.; Kertész, János; Kaski, Kimmo K.

2011-09-01

In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter α, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realised by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter α and its influence on the conformation of opinion and the size of the resulting communities. We present numerical studies and extract interesting features of the model that can be interpreted in terms of social behaviour.

Effect of users' opinion evolution on information diffusion in online social networks

NASA Astrophysics Data System (ADS)

Zhu, Hengmin; Kong, Yuehan; Wei, Jing; Ma, Jing

2018-02-01

The process of topic propagation always interweaves information diffusion and opinion evolution, but most previous works studied the models of information diffusion and opinion evolution separately, and seldom focused on their interaction of each other. To shed light on the effect of users' opinion evolution on information diffusion in online social networks, we proposed a model which incorporates opinion evolution into the process of topic propagation. Several real topics propagating on Sina Microblog were collected to analyze individuals' propagation intentions, and different propagation intentions were considered in the model. The topic propagation was simulated to explore the impact of different opinion distributions and intervention with opposite opinion on information diffusion. Results show that the topic with one-sided opinions can spread faster and more widely, and intervention with opposite opinion is an effective measure to guide the topic propagation. The earlier to intervene, the more effectively the topic propagation would be guided.

Advancing Nucleosynthesis in Core-Collapse Supernovae Models Using 2D CHIMERA Simulations

NASA Astrophysics Data System (ADS)

Harris, J. A.; Hix, W. R.; Chertkow, M. A.; Bruenn, S. W.; Lentz, E. J.; Messer, O. B.; Mezzacappa, A.; Blondin, J. M.; Marronetti, P.; Yakunin, K.

2014-01-01

The deaths of massive stars as core-collapse supernovae (CCSN) serve as a crucial link in understanding galactic chemical evolution since the birth of the universe via the Big Bang. We investigate CCSN in polar axisymmetric simulations using the multidimensional radiation hydrodynamics code CHIMERA. Computational costs have traditionally constrained the evolution of the nuclear composition in CCSN models to, at best, a 14-species α-network. However, the limited capacity of the α-network to accurately evolve detailed composition, the neutronization and the nuclear energy generation rate has fettered the ability of prior CCSN simulations to accurately reproduce the chemical abundances and energy distributions as known from observations. These deficits can be partially ameliorated by "post-processing" with a more realistic network. Lagrangian tracer particles placed throughout the star record the temporal evolution of the initial simulation and enable the extension of the nuclear network evolution by incorporating larger systems in post-processing nucleosynthesis calculations. We present post-processing results of the four ab initio axisymmetric CCSN 2D models of Bruenn et al. (2013) evolved with the smaller α-network, and initiated from stellar metallicity, non-rotating progenitors of mass 12, 15, 20, and 25 M⊙ from Woosley & Heger (2007). As a test of the limitations of post-processing, we provide preliminary results from an ongoing simulation of the 15 M⊙ model evolved with a realistic 150 species nuclear reaction network in situ. With more accurate energy generation rates and an improved determination of the thermodynamic trajectories of the tracer particles, we can better unravel the complicated multidimensional "mass-cut" in CCSN simulations and probe for less energetically significant nuclear processes like the νp-process and the r-process, which require still larger networks.

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 3: Advanced networks and economics

NASA Technical Reports Server (NTRS)

1986-01-01

This study projects until 2000 the evolution of long distance fiber optic networks in the U.S. Volume 1 is the executive Summary. Volume 2 focuses on fiber optic components and systems that are directly related to the operation of long-haul networks. Optimistic, pessimistic and most likely scenarios of technology development are presented. The activities of national and regional companies implementing fiber long haul networks are also highlighted, along with an analysis of the market and regulatory forces affecting network evolution. Volume 3 presents advanced fiber optic network concept definitions. Inter-LATA traffic is quantified and forms the basis for the construction of 11-, 15-, 17-, and 23-node networks. Using the technology projections from Volume 2, a financial model identifies cost drivers and determines circuit mile costs between any two LATAs. A comparison of fiber optics with alternative transmission concludes the report.

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 2: Fiber optic technology and long distance networks

NASA Astrophysics Data System (ADS)

1986-10-01

The study projects until 2000 the evolution of long distance fiber optic networks in the U.S. Volume 1 is the Executive Summary. Volume 2 focuses on fiber optic components and systems that are directly related to the operation of long-haul networks. Optimistic, pessimistic and most likely scenarios of technology development are presented. The activities of national and regional companies implementing fiber long haul networks are also highlighted, along with an analysis of the market and regulatory forces affecting network evolution. Volume 3 presents advanced fiber optic network concept definitions. Inter-LATA traffic is quantified and forms the basis for the construction of 11-, 15-, 17-, and 23-node networks. Using the technology projections from Volume 2, a financial model identifies cost drivers and determines circuit mile costs between any two LATAs. A comparison of fiber optics with alternative transmission concludes the report.

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 3: Advanced networks and economics

NASA Astrophysics Data System (ADS)

1986-10-01

This study projects until 2000 the evolution of long distance fiber optic networks in the U.S. Volume 1 is the executive Summary. Volume 2 focuses on fiber optic components and systems that are directly related to the operation of long-haul networks. Optimistic, pessimistic and most likely scenarios of technology development are presented. The activities of national and regional companies implementing fiber long haul networks are also highlighted, along with an analysis of the market and regulatory forces affecting network evolution. Volume 3 presents advanced fiber optic network concept definitions. Inter-LATA traffic is quantified and forms the basis for the construction of 11-, 15-, 17-, and 23-node networks. Using the technology projections from Volume 2, a financial model identifies cost drivers and determines circuit mile costs between any two LATAs. A comparison of fiber optics with alternative transmission concludes the report.

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 2: Fiber optic technology and long distance networks

NASA Technical Reports Server (NTRS)

1986-01-01

The study projects until 2000 the evolution of long distance fiber optic networks in the U.S. Volume 1 is the Executive Summary. Volume 2 focuses on fiber optic components and systems that are directly related to the operation of long-haul networks. Optimistic, pessimistic and most likely scenarios of technology development are presented. The activities of national and regional companies implementing fiber long haul networks are also highlighted, along with an analysis of the market and regulatory forces affecting network evolution. Volume 3 presents advanced fiber optic network concept definitions. Inter-LATA traffic is quantified and forms the basis for the construction of 11-, 15-, 17-, and 23-node networks. Using the technology projections from Volume 2, a financial model identifies cost drivers and determines circuit mile costs between any two LATAs. A comparison of fiber optics with alternative transmission concludes the report.

The formation and evolution of domain walls

NASA Technical Reports Server (NTRS)

Press, William H.; Ryden, Barbara S.; Spergel, David N.

1991-01-01

Domain walls are sheet-like defects produced when the low energy vacuum has isolated degenerate minima. The researchers' computer code follows the evolution of a scalar field, whose dynamics are determined by its Lagrangian density. The topology of the scalar field determines the evolution of the domain walls. This approach treats both wall dynamics and reconnection. The researchers investigated not only potentials that produce single domain walls, but also potentials that produce a network of walls and strings. These networks arise in axion models where the U(1) Peccei-Quinn symmetry is broken into Z sub N discrete symmetries. If N equals 1, the walls are bounded by strings and the network quickly disappears. For N greater than 1, the network of walls and strings behaved qualitatively just as the wall network shown in the figures given here. This both confirms the researchers' pessimistic view that domain walls cannot play an important role in the formation of large scale structure and implies that axion models with multiple minimum can be cosmologically disastrous.

Evolution of individual versus social learning on social networks.

PubMed

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Social dilemmas in an online social network: The structure and evolution of cooperation

NASA Astrophysics Data System (ADS)

Fu, Feng; Chen, Xiaojie; Liu, Lianghuan; Wang, Long

2007-11-01

We investigate two paradigms for studying the evolution of cooperation—Prisoner's Dilemma and Snowdrift game in an online friendship network, obtained from a social networking site. By structural analysis, it is revealed that the empirical social network has small-world and scale-free properties. Besides, it exhibits assortative mixing pattern. Then, we study the evolutionary version of the two types of games on it. It is found that cooperation is substantially promoted with small values of game matrix parameters in both games. Whereas the competent cooperators induced by the underlying network of contacts will be dramatically inhibited with increasing values of the game parameters. Further, we explore the role of assortativity in evolution of cooperation by random edge rewiring. We find that increasing amount of assortativity will to a certain extent diminish the cooperation level. We also show that connected large hubs are capable of maintaining cooperation. The evolution of cooperation on empirical networks is influenced by various network effects in a combined manner, compared with that on model networks. Our results can help understand the cooperative behaviors in human groups and society.

Modeling evolution of crosstalk in noisy signal transduction networks

NASA Astrophysics Data System (ADS)

Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-02-01

Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.

Research on Information Sharing Mechanism of Network Organization Based on Evolutionary Game

NASA Astrophysics Data System (ADS)

Wang, Lin; Liu, Gaozhi

2018-02-01

This article first elaborates the concept and effect of network organization, and the ability to share information is analyzed, secondly introduces the evolutionary game theory, network organization for information sharing all kinds of limitations, establishes the evolutionary game model, analyzes the dynamic evolution of network organization of information sharing, through reasoning and evolution. The network information sharing by the initial state and two sides of the game payoff matrix of excess profits and information is the information sharing of cost and risk sharing are the influence of network organization node information sharing decision.

Following the Footsteps of Chlamydial Gene Regulation

PubMed Central

Domman, D.; Horn, M.

2015-01-01

Regulation of gene expression ensures an organism responds to stimuli and undergoes proper development. Although the regulatory networks in bacteria have been investigated in model microorganisms, nearly nothing is known about the evolution and plasticity of these networks in obligate, intracellular bacteria. The phylum Chlamydiae contains a vast array of host-associated microbes, including several human pathogens. The Chlamydiae are unique among obligate, intracellular bacteria as they undergo a complex biphasic developmental cycle in which large swaths of genes are temporally regulated. Coupled with the low number of transcription factors, these organisms offer a model to study the evolution of regulatory networks in intracellular organisms. We provide the first comprehensive analysis exploring the diversity and evolution of regulatory networks across the phylum. We utilized a comparative genomics approach to construct predicted coregulatory networks, which unveiled genus- and family-specific regulatory motifs and architectures, most notably those of virulence-associated genes. Surprisingly, our analysis suggests that few regulatory components are conserved across the phylum, and those that are conserved are involved in the exploitation of the intracellular niche. Our study thus lends insight into a component of chlamydial evolution that has otherwise remained largely unexplored. PMID:26424812

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

NASA Astrophysics Data System (ADS)

Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

2017-05-01

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

The simultaneous evolution of author and paper networks

PubMed Central

Börner, Katy; Maru, Jeegar T.; Goldstone, Robert L.

2004-01-01

There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants) and computers and algorithms capable of handling this enormous stream of data. This article reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows coauthor and paper citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in PNAS. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for topics, aging, and recursive linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network. PMID:14976254

Functional evolution of new and expanded attention networks in humans

PubMed Central

Patel, Gaurav H.; Yang, Danica; Jamerson, Emery C.; Snyder, Lawrence H.; Corbetta, Maurizio; Ferrera, Vincent P.

2015-01-01

Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks. PMID:26170314

Functional evolution of new and expanded attention networks in humans.

PubMed

Patel, Gaurav H; Yang, Danica; Jamerson, Emery C; Snyder, Lawrence H; Corbetta, Maurizio; Ferrera, Vincent P

2015-07-28

Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks.

Evolution of egoism on semi-directed and undirected Barabási-Albert networks

NASA Astrophysics Data System (ADS)

Lima, F. W. S.

2015-05-01

Through Monte Carlo simulations, we study the evolution of the four strategies: Ethnocentric, altruistic, egoistic and cosmopolitan in one community of individuals. Interactions and reproduction among computational agents are simulated on undirected and semi-directed Barabási-Albert (BA) networks. We study the Hammond-Axelrod (HA) model on undirected and semi-directed BA networks for the asexual reproduction case. With a small modification in the traditional HA model, our simulations showed that egoism wins, differently from other results found in the literature where ethnocentric strategy is common. Here, mechanisms such as reciprocity are absent.

Perception Evolution Network Based on Cognition Deepening Model--Adapting to the Emergence of New Sensory Receptor.

PubMed

Xing, Youlu; Shen, Furao; Zhao, Jinxi

2016-03-01

The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way, and it does not require the predefined prototype number or the predefined similarity threshold. Meanwhile, being more advanced than the existing unsupervised neural network model, PEN permits the emergence of a new dimension of perception in the perception field of the network. When a new dimension of perception is introduced, PEN is able to integrate the new dimensional sensory inputs with the learned prototypes, i.e., the prototypes are mapped to a high-dimensional space, which consists of both the original dimension and the new dimension of the sensory inputs. In the experiment, artificial data and real-world data are used to test the proposed PEN, and the results show that PEN can work effectively.

Modeling Dynamic Evolution of Online Friendship Network

NASA Astrophysics Data System (ADS)

Wu, Lian-Ren; Yan, Qiang

2012-10-01

In this paper, we study the dynamic evolution of friendship network in SNS (Social Networking Site). Our analysis suggests that an individual joining a community depends not only on the number of friends he or she has within the community, but also on the friendship network generated by those friends. In addition, we propose a model which is based on two processes: first, connecting nearest neighbors; second, strength driven attachment mechanism. The model reflects two facts: first, in the social network it is a universal phenomenon that two nodes are connected when they have at least one common neighbor; second, new nodes connect more likely to nodes which have larger weights and interactions, a phenomenon called strength driven attachment (also called weight driven attachment). From the simulation results, we find that degree distribution P(k), strength distribution P(s), and degree-strength correlation are all consistent with empirical data.

Signal Correlations in Ecological Niches Can Shape the Organization and Evolution of Bacterial Gene Regulatory Networks

PubMed Central

Dufour, Yann S.; Donohue, Timothy J.

2015-01-01

Transcriptional regulation plays a significant role in the biological response of bacteria to changing environmental conditions. Therefore, mapping transcriptional regulatory networks is an important step not only in understanding how bacteria sense and interpret their environment but also to identify the functions involved in biological responses to specific conditions. Recent experimental and computational developments have facilitated the characterization of regulatory networks on a genome-wide scale in model organisms. In addition, the multiplication of complete genome sequences has encouraged comparative analyses to detect conserved regulatory elements and infer regulatory networks in other less well-studied organisms. However, transcription regulation appears to evolve rapidly, thus, creating challenges for the transfer of knowledge to nonmodel organisms. Nevertheless, the mechanisms and constraints driving the evolution of regulatory networks have been the subjects of numerous analyses, and several models have been proposed. Overall, the contributions of mutations, recombination, and horizontal gene transfer are complex. Finally, the rapid evolution of regulatory networks plays a significant role in the remarkable capacity of bacteria to adapt to new or changing environments. Conversely, the characteristics of environmental niches determine the selective pressures and can shape the structure of regulatory network accordingly. PMID:23046950

φ-evo: A program to evolve phenotypic models of biological networks.

PubMed

Henry, Adrien; Hemery, Mathieu; François, Paul

2018-06-01

Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on C, Python 3, and a Jupyter interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself.

Implications for Post-processing Nucleosynthesis of Core-collapse Supernova Models with Lagrangian Particles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, J. Austin; Hix, W. Raphael; Chertkow, Merek A.

In this paper, we investigate core-collapse supernova (CCSN) nucleosynthesis with self-consistent, axisymmetric (2D) simulations performed using the neutrino hydrodynamics code Chimera. Computational costs have traditionally constrained the evolution of the nuclear composition within multidimensional CCSN models to, at best, a 14-species α-network capable of tracking onlymore » $$(\\alpha ,\\gamma )$$ reactions from 4He to 60Zn. Such a simplified network limits the ability to accurately evolve detailed composition and neutronization or calculate the nuclear energy generation rate. Lagrangian tracer particles are commonly used to extend the nuclear network evolution by incorporating more realistic networks into post-processing nucleosynthesis calculations. However, limitations such as poor spatial resolution of the tracer particles; inconsistent thermodynamic evolution, including misestimation of expansion timescales; and uncertain determination of the multidimensional mass cut at the end of the simulation impose uncertainties inherent to this approach. Finally, we present a detailed analysis of the impact of such uncertainties for four self-consistent axisymmetric CCSN models initiated from solar-metallicity, nonrotating progenitors of 12, 15, 20, and 25 $${M}_{\\odot }$$ and evolved with the smaller α-network to more than 1 s after the launch of an explosion.« less

Implications for Post-processing Nucleosynthesis of Core-collapse Supernova Models with Lagrangian Particles

NASA Astrophysics Data System (ADS)

Harris, J. Austin; Hix, W. Raphael; Chertkow, Merek A.; Lee, C. T.; Lentz, Eric J.; Messer, O. E. Bronson

2017-07-01

We investigate core-collapse supernova (CCSN) nucleosynthesis with self-consistent, axisymmetric (2D) simulations performed using the neutrino hydrodynamics code Chimera. Computational costs have traditionally constrained the evolution of the nuclear composition within multidimensional CCSN models to, at best, a 14-species α-network capable of tracking only (α ,γ ) reactions from 4He to 60Zn. Such a simplified network limits the ability to accurately evolve detailed composition and neutronization or calculate the nuclear energy generation rate. Lagrangian tracer particles are commonly used to extend the nuclear network evolution by incorporating more realistic networks into post-processing nucleosynthesis calculations. However, limitations such as poor spatial resolution of the tracer particles inconsistent thermodynamic evolution, including misestimation of expansion timescales and uncertain determination of the multidimensional mass cut at the end of the simulation impose uncertainties inherent to this approach. We present a detailed analysis of the impact of such uncertainties for four self-consistent axisymmetric CCSN models initiated from solar-metallicity, nonrotating progenitors of 12, 15, 20, and 25 {M}⊙ and evolved with the smaller α-network to more than 1 s after the launch of an explosion.

Implications for Post-processing Nucleosynthesis of Core-collapse Supernova Models with Lagrangian Particles

DOE PAGES

Harris, J. Austin; Hix, W. Raphael; Chertkow, Merek A.; ...

2017-06-26

In this paper, we investigate core-collapse supernova (CCSN) nucleosynthesis with self-consistent, axisymmetric (2D) simulations performed using the neutrino hydrodynamics code Chimera. Computational costs have traditionally constrained the evolution of the nuclear composition within multidimensional CCSN models to, at best, a 14-species α-network capable of tracking onlymore » $$(\\alpha ,\\gamma )$$ reactions from 4He to 60Zn. Such a simplified network limits the ability to accurately evolve detailed composition and neutronization or calculate the nuclear energy generation rate. Lagrangian tracer particles are commonly used to extend the nuclear network evolution by incorporating more realistic networks into post-processing nucleosynthesis calculations. However, limitations such as poor spatial resolution of the tracer particles; inconsistent thermodynamic evolution, including misestimation of expansion timescales; and uncertain determination of the multidimensional mass cut at the end of the simulation impose uncertainties inherent to this approach. Finally, we present a detailed analysis of the impact of such uncertainties for four self-consistent axisymmetric CCSN models initiated from solar-metallicity, nonrotating progenitors of 12, 15, 20, and 25 $${M}_{\\odot }$$ and evolved with the smaller α-network to more than 1 s after the launch of an explosion.« less

Evolution of Cooperation in Adaptive Social Networks

NASA Astrophysics Data System (ADS)

Segbroeck, Sven Van; Santos, Francisco C.; Traulsen, Arne; Lenaerts, Tom; Pacheco, Jorge M.

Humans are organized in societies, a phenomenon that would never have been possible without the evolution of cooperative behavior. Several mechanisms that foster this evolution have been unraveled over the years, with population structure as a prominent promoter of cooperation. Modern networks of exchange and cooperation are, however, becoming increasingly volatile, and less and less based on long-term stable structure. Here, we address how this change of paradigm aspects the evolution of cooperation. We discuss analytical and numerical models in which individuals can break social ties and create new ones. Interactions are modeled as two-player dilemmas of cooperation. Once a link between two individuals has formed, the productivity of this link is evaluated. Links can be broken off at different rates. This individual capacity of forming new links or severing inconvenient ones can effectively change the nature of the game. We address random formation of new links and local linking rules as well as different individual capacities to maintain social interactions. We conclude by discussing how adaptive social networks can become an important step towards more realistic models of cultural dynamics.

Evolution of Linux operating system network

NASA Astrophysics Data System (ADS)

Xiao, Guanping; Zheng, Zheng; Wang, Haoqin

2017-01-01

Linux operating system (LOS) is a sophisticated man-made system and one of the most ubiquitous operating systems. However, there is little research on the structure and functionality evolution of LOS from the prospective of networks. In this paper, we investigate the evolution of the LOS network. 62 major releases of LOS ranging from versions 1.0 to 4.1 are modeled as directed networks in which functions are denoted by nodes and function calls are denoted by edges. It is found that the size of the LOS network grows almost linearly, while clustering coefficient monotonically decays. The degree distributions are almost the same: the out-degree follows an exponential distribution while both in-degree and undirected degree follow power-law distributions. We further explore the functionality evolution of the LOS network. It is observed that the evolution of functional modules is shown as a sequence of seven events (changes) succeeding each other, including continuing, growth, contraction, birth, splitting, death and merging events. By means of a statistical analysis of these events in the top 4 largest components (i.e., arch, drivers, fs and net), it is shown that continuing, growth and contraction events occupy more than 95% events. Our work exemplifies a better understanding and describing of the dynamics of LOS evolution.

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

PubMed

Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

2015-08-01

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses

PubMed Central

Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

2015-01-01

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure. PMID:26291697

Life span in online communities

NASA Astrophysics Data System (ADS)

Grabowski, A.; Kosiński, R. A.

2010-12-01

Recently online communities have attracted great interest and have become an important medium of information exchange between users. The aim of this work is to introduce a simple model of the evolution of online communities. This model describes (a) the time evolution of users’ activity in a web service, e.g., the time evolution of the number of online friends or written posts, (b) the time evolution of the degree distribution of a social network, and (c) the time evolution of the number of active users of a web service. In the second part of the paper we investigate the influence of the users’ lifespan (i.e., the total time in which they are active in an online community) on the process of rumor propagation in evolving social networks. Viral marketing is an important application of such method of information propagation.

Life span in online communities.

PubMed

Grabowski, A; Kosiński, R A

2010-12-01

Recently online communities have attracted great interest and have become an important medium of information exchange between users. The aim of this work is to introduce a simple model of the evolution of online communities. This model describes (a) the time evolution of users' activity in a web service, e.g., the time evolution of the number of online friends or written posts, (b) the time evolution of the degree distribution of a social network, and (c) the time evolution of the number of active users of a web service. In the second part of the paper we investigate the influence of the users' lifespan (i.e., the total time in which they are active in an online community) on the process of rumor propagation in evolving social networks. Viral marketing is an important application of such method of information propagation.

A model for the emergence of cooperation, interdependence, and structure in evolving networks.

PubMed

Jain, S; Krishna, S

2001-01-16

Evolution produces complex and structured networks of interacting components in chemical, biological, and social systems. We describe a simple mathematical model for the evolution of an idealized chemical system to study how a network of cooperative molecular species arises and evolves to become more complex and structured. The network is modeled by a directed weighted graph whose positive and negative links represent "catalytic" and "inhibitory" interactions among the molecular species, and which evolves as the least populated species (typically those that go extinct) are replaced by new ones. A small autocatalytic set, appearing by chance, provides the seed for the spontaneous growth of connectivity and cooperation in the graph. A highly structured chemical organization arises inevitably as the autocatalytic set enlarges and percolates through the network in a short analytically determined timescale. This self organization does not require the presence of self-replicating species. The network also exhibits catastrophes over long timescales triggered by the chance elimination of "keystone" species, followed by recoveries.

A model for the emergence of cooperation, interdependence, and structure in evolving networks

NASA Astrophysics Data System (ADS)

Jain, Sanjay; Krishna, Sandeep

2001-01-01

Evolution produces complex and structured networks of interacting components in chemical, biological, and social systems. We describe a simple mathematical model for the evolution of an idealized chemical system to study how a network of cooperative molecular species arises and evolves to become more complex and structured. The network is modeled by a directed weighted graph whose positive and negative links represent "catalytic" and "inhibitory" interactions among the molecular species, and which evolves as the least populated species (typically those that go extinct) are replaced by new ones. A small autocatalytic set, appearing by chance, provides the seed for the spontaneous growth of connectivity and cooperation in the graph. A highly structured chemical organization arises inevitably as the autocatalytic set enlarges and percolates through the network in a short analytically determined timescale. This self organization does not require the presence of self-replicating species. The network also exhibits catastrophes over long timescales triggered by the chance elimination of "keystone" species, followed by recoveries.

On the time to steady state: insights from numerical modeling

NASA Astrophysics Data System (ADS)

Goren, L.; Willett, S.; McCoy, S. W.; Perron, J.

2013-12-01

How fast do fluvial landscapes approach steady state after an application of tectonic or climatic perturbation? While theory and some numerical models predict that the celerity of the advective wave (knickpoint) controls the response time for perturbations, experiments and other landscape evolution models demonstrate that the time to steady state is much longer than the theoretically predicted response time. We posit that the longevity of transient features and the time to steady state are controlled by the stability of the topology and geometry of channel networks. Evolution of a channel network occurs by a combination of discrete capture events and continuous migration of water divides, processes, which are difficult to represent accurately in landscape evolution models. We therefore address the question of the time to steady state using the DAC landscape evolution model that solves accurately for the location of water divides, using a combination of analytical solution for hillslopes and low-order channels together with a numerical solution for higher order channels. DAC also includes an explicit capture criterion. We have tested fundamental predictions from DAC and show that modeled networks reproduce natural network characteristics such as the Hack's exponent and coefficient and the fractal dimension. We define two steady-state criteria: a topographic steady state, defined by global, pointwise steady elevation, and a topological steady state defined as the state in which no further reorganization of the drainage network takes place. Analyzing block uplift simulations, we find that the time to achieve either topographic or topological steady state exceeds by an order of magnitude the theoretical response time of the fluvial network. The longevity of the transient state is the result of the area feedback, by which, migration of a divide changes the local contributing area. This change propagates downstream as a slope adjustment, forcing further divide migrations and area change in adjacent tributaries and basins. In order to characterize the evolution of the drainage network on its way to steady state, we define a proxy to steady state elevation, χ, which is also the characteristic parameter of the transient stream power PDE. Through simulations of tectonic tilting we find that reorganization tends to minimize moments of the χ distribution of the landscape and of Δχ across divides.

Evolution of canalizing Boolean networks

NASA Astrophysics Data System (ADS)

Szejka, A.; Drossel, B.

2007-04-01

Boolean networks with canalizing functions are used to model gene regulatory networks. In order to learn how such networks may behave under evolutionary forces, we simulate the evolution of a single Boolean network by means of an adaptive walk, which allows us to explore the fitness landscape. Mutations change the connections and the functions of the nodes. Our fitness criterion is the robustness of the dynamical attractors against small perturbations. We find that with this fitness criterion the global maximum is always reached and that there is a huge neutral space of 100% fitness. Furthermore, in spite of having such a high degree of robustness, the evolved networks still share many features with “chaotic” networks.

Impact of Social Reward on the Evolution of the Cooperation Behavior in Complex Networks

NASA Astrophysics Data System (ADS)

Wu, Yu'E.; Chang, Shuhua; Zhang, Zhipeng; Deng, Zhenghong

2017-01-01

Social reward, as a significant mechanism explaining the evolution of cooperation, has attracted great attention both theoretically and experimentally. In this paper, we study the evolution of cooperation by proposing a reward model in network population, where a third strategy, reward, as an independent yet particular type of cooperation is introduced in 2-person evolutionary games. Specifically, a new kind of role corresponding to reward strategy, reward agents, is defined, which is aimed at increasing the income of cooperators by applying to them a social reward. Results from numerical simulations show that consideration of social reward greatly promotes the evolution of cooperation, which is confirmed for different network topologies and two evolutionary games. Moreover, we explore the microscopic mechanisms for the promotion of cooperation in the three-strategy model. As expected, the reward agents play a vital role in the formation of cooperative clusters, thus resisting the aggression of defectors. Our research might provide valuable insights into further exploring the nature of cooperation in the real world.

Impact of Social Reward on the Evolution of the Cooperation Behavior in Complex Networks

PubMed Central

Wu, Yu’e; Chang, Shuhua; Zhang, Zhipeng; Deng, Zhenghong

2017-01-01

Social reward, as a significant mechanism explaining the evolution of cooperation, has attracted great attention both theoretically and experimentally. In this paper, we study the evolution of cooperation by proposing a reward model in network population, where a third strategy, reward, as an independent yet particular type of cooperation is introduced in 2-person evolutionary games. Specifically, a new kind of role corresponding to reward strategy, reward agents, is defined, which is aimed at increasing the income of cooperators by applying to them a social reward. Results from numerical simulations show that consideration of social reward greatly promotes the evolution of cooperation, which is confirmed for different network topologies and two evolutionary games. Moreover, we explore the microscopic mechanisms for the promotion of cooperation in the three-strategy model. As expected, the reward agents play a vital role in the formation of cooperative clusters, thus resisting the aggression of defectors. Our research might provide valuable insights into further exploring the nature of cooperation in the real world. PMID:28112276

An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

NASA Astrophysics Data System (ADS)

Wang, Guanghui; Wang, Yufei; Liu, Yijun; Chi, Yuxue

2018-05-01

As the transmission of public opinion on the Internet in the “We the Media” era tends to be supraterritorial, concealed and complex, the traditional “point-to-surface” transmission of information has been transformed into “point-to-point” reciprocal transmission. A foundation for studies of the evolution of public opinion and its transmission on the Internet in the “We the Media” era can be laid by converting the massive amounts of fragmented information on public opinion that exists on “We the Media” platforms into structurally complex networks of information. This paper describes studies of structurally complex network-based modeling of public opinion on the Internet in the “We the Media” era from the perspective of the development and evolution of complex networks. The progress that has been made in research projects relevant to the structural modeling of public opinion on the Internet is comprehensively summarized. The review considers aspects such as regular grid-based modeling of the rules that describe the propagation of public opinion on the Internet in the “We the Media” era, social network modeling, dynamic network modeling, and supernetwork modeling. Moreover, an outlook for future studies that address complex network-based modeling of public opinion on the Internet is put forward as a summary from the perspective of modeling conducted using the techniques mentioned above.

Extended evolution: A conceptual framework for integrating regulatory networks and niche construction

PubMed Central

Renn, Jürgen

2015-01-01

ABSTRACT This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path‐dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems. J. Exp. Zool. (Mol. Dev. Evol.) 324B: 565–577, 2015. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution published by Wiley Periodicals, Inc. PMID:26097188

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

PubMed Central

Chen, Hong; Li, Yang

2014-01-01

The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns. PMID:24982969

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, J. Austin; Hix, W. Raphael; Chertkow, Merek A.

In this paper, we investigate core-collapse supernova (CCSN) nucleosynthesis with self-consistent, axisymmetric (2D) simulations performed using the neutrino hydrodynamics code Chimera. Computational costs have traditionally constrained the evolution of the nuclear composition within multidimensional CCSN models to, at best, a 14-species α-network capable of tracking onlymore » $$(\\alpha ,\\gamma )$$ reactions from 4He to 60Zn. Such a simplified network limits the ability to accurately evolve detailed composition and neutronization or calculate the nuclear energy generation rate. Lagrangian tracer particles are commonly used to extend the nuclear network evolution by incorporating more realistic networks into post-processing nucleosynthesis calculations. However, limitations such as poor spatial resolution of the tracer particles; inconsistent thermodynamic evolution, including misestimation of expansion timescales; and uncertain determination of the multidimensional mass cut at the end of the simulation impose uncertainties inherent to this approach. Finally, we present a detailed analysis of the impact of such uncertainties for four self-consistent axisymmetric CCSN models initiated from solar-metallicity, nonrotating progenitors of 12, 15, 20, and 25 $${M}_{\\odot }$$ and evolved with the smaller α-network to more than 1 s after the launch of an explosion.« less

Disruption of River Networks in Nature and Models

NASA Astrophysics Data System (ADS)

Perron, J. T.; Black, B. A.; Stokes, M.; McCoy, S. W.; Goldberg, S. L.

2017-12-01

Many natural systems display especially informative behavior as they respond to perturbations. Landscapes are no exception. For example, longitudinal elevation profiles of rivers responding to changes in uplift rate can reveal differences among erosional mechanisms that are obscured while the profiles are in equilibrium. The responses of erosional river networks to perturbations, including disruption of their network structure by diversion, truncation, resurfacing, or river capture, may be equally revealing. In this presentation, we draw attention to features of disrupted erosional river networks that a general model of landscape evolution should be able to reproduce, including the consequences of different styles of planetary tectonics and the response to heterogeneous bedrock structure and deformation. A comparison of global drainage directions with long-wavelength topography on Earth, Mars, and Saturn's moon Titan reveals the extent to which persistent and relatively rapid crustal deformation has disrupted river networks on Earth. Motivated by this example and others, we ask whether current models of river network evolution adequately capture the disruption of river networks by tectonic, lithologic, or climatic perturbations. In some cases the answer appears to be no, and we suggest some processes that models may be missing.

Stimulus-induced transitions between spike-wave discharges and spindles with the modulation of thalamic reticular nucleus.

PubMed

Fan, Denggui; Wang, Qingyun; Su, Jianzhong; Xi, Hongguang

2017-12-01

It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation. Specially, it is shown that stimulations applied in the cortical neuronal populations can also initiate the SWD and slow-wave oscillation from the resting states under the typical inhibitory intensity from TRN to SRN. Then, we expand into a 3-compartment coupled thalamocortical model network in linear and circular structures, respectively, to explore the spatiotemporal evolutions of wave states in different compartments. The main results are: (i) for the open-ended model network, SWD induced by stimulus in the first compartment can be transformed into sleep-like slow UP-DOWN and spindle states as it propagates into the downstream compartments; (ii) for the close-ended model network, weak stimulations performed in the first compartment can result in the consistent experimentally observed spindle oscillations in all three compartments; in contrast, stronger periodic single-pulse stimulations applied in the first compartment can induce periodic transitions between SWD and spindle oscillations. Detailed investigations reveal that multi-attractor coexistence mechanism composed of SWD, spindles and background state underlies these state evolutions. What's more, in order to demonstrate the state evolution stability with respect to the topological structures of neural network, we further expand the 3-compartment coupled network into 10-compartment coupled one, with linear and circular structures, and nearest-neighbor (NN) coupled network as well as its realization of small-world (SW) topology via random rewiring, respectively. Interestingly, for the cases of linear and circular connetivities, qualitatively similar results were obtained in addition to the more irregularity of firings. However, SWD can be eventually transformed into the consistent low-amplitude oscillations for both NN and SW networks. In particular, SWD evolves into the slow spindling oscillations and background tonic oscillations within the NN and SW network, respectively. Our modeling and simulation studies highlight the effect of network topology in the evolutions of SWD and spindling oscillations, which provides new insights into the mechanisms of cortical seizures development.

Origin and evolution of the self-organizing cytoskeleton in the network of eukaryotic organelles.

PubMed

Jékely, Gáspár

2014-09-02

The eukaryotic cytoskeleton evolved from prokaryotic cytomotive filaments. Prokaryotic filament systems show bewildering structural and dynamic complexity and, in many aspects, prefigure the self-organizing properties of the eukaryotic cytoskeleton. Here, the dynamic properties of the prokaryotic and eukaryotic cytoskeleton are compared, and how these relate to function and evolution of organellar networks is discussed. The evolution of new aspects of filament dynamics in eukaryotes, including severing and branching, and the advent of molecular motors converted the eukaryotic cytoskeleton into a self-organizing "active gel," the dynamics of which can only be described with computational models. Advances in modeling and comparative genomics hold promise of a better understanding of the evolution of the self-organizing cytoskeleton in early eukaryotes, and its role in the evolution of novel eukaryotic functions, such as amoeboid motility, mitosis, and ciliary swimming. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.

Origin and Evolution of the Self-Organizing Cytoskeleton in the Network of Eukaryotic Organelles

PubMed Central

Jékely, Gáspár

2014-01-01

The eukaryotic cytoskeleton evolved from prokaryotic cytomotive filaments. Prokaryotic filament systems show bewildering structural and dynamic complexity and, in many aspects, prefigure the self-organizing properties of the eukaryotic cytoskeleton. Here, the dynamic properties of the prokaryotic and eukaryotic cytoskeleton are compared, and how these relate to function and evolution of organellar networks is discussed. The evolution of new aspects of filament dynamics in eukaryotes, including severing and branching, and the advent of molecular motors converted the eukaryotic cytoskeleton into a self-organizing “active gel,” the dynamics of which can only be described with computational models. Advances in modeling and comparative genomics hold promise of a better understanding of the evolution of the self-organizing cytoskeleton in early eukaryotes, and its role in the evolution of novel eukaryotic functions, such as amoeboid motility, mitosis, and ciliary swimming. PMID:25183829

Competition-Driven Network Dynamics: Emergence of a Scale-Free Leadership Structure and Collective Efficiency

NASA Astrophysics Data System (ADS)

Anghel, M.; Toroczkai, Zoltán; Bassler, Kevin E.; Korniss, G.

2004-02-01

Using the minority game as a model for competition dynamics, we investigate the effects of interagent communications across a network on the global evolution of the game. Agent communication across this network leads to the formation of an influence network, which is dynamically coupled to the evolution of the game, and it is responsible for the information flow driving the agents' actions. We show that the influence network spontaneously develops hubs with a broad distribution of in-degrees, defining a scale-free robust leadership structure. Furthermore, in realistic parameter ranges, facilitated by information exchange on the network, agents can generate a high degree of cooperation making the collective almost maximally efficient.

Evolving phenotypic networks in silico.

PubMed

François, Paul

2014-11-01

Evolved gene networks are constrained by natural selection. Their structures and functions are consequently far from being random, as exemplified by the multiple instances of parallel/convergent evolution. One can thus ask if features of actual gene networks can be recovered from evolutionary first principles. I review a method for in silico evolution of small models of gene networks aiming at performing predefined biological functions. I summarize the current implementation of the algorithm, insisting on the construction of a proper "fitness" function. I illustrate the approach on three examples: biochemical adaptation, ligand discrimination and vertebrate segmentation (somitogenesis). While the structure of the evolved networks is variable, dynamics of our evolved networks are usually constrained and present many similar features to actual gene networks, including properties that were not explicitly selected for. In silico evolution can thus be used to predict biological behaviours without a detailed knowledge of the mapping between genotype and phenotype. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradonjic, Milan; Hagberg, Aric; Hengartner, Nick

We analyze component evolution in general random intersection graphs (RIGs) and give conditions on existence and uniqueness of the giant component. Our techniques generalize the existing methods for analysis on component evolution in RIGs. That is, we analyze survival and extinction properties of a dependent, inhomogeneous Galton-Watson branching process on general RIGs. Our analysis relies on bounding the branching processes and inherits the fundamental concepts from the study on component evolution in Erdos-Renyi graphs. The main challenge becomes from the underlying structure of RIGs, when the number of offsprings follows a binomial distribution with a different number of nodes andmore » different rate at each step during the evolution. RIGs can be interpreted as a model for large randomly formed non-metric data sets. Besides the mathematical analysis on component evolution, which we provide in this work, we perceive RIGs as an important random structure which has already found applications in social networks, epidemic networks, blog readership, or wireless sensor networks.« less

Toward Agent-Based Models of the Development And Evolution of Business Relations and Networks

NASA Astrophysics Data System (ADS)

Wilkinson, Ian F.; Marks, Robert E.; Young, Louise

Firms achieve competitive advantage in part through the development of cooperative relations with other firms and organisations. We describe a program of research designed to map and model the development of cooperative inter-firm relations, including the processes and paths by which firms may evolve from adversarial to more cooperative relations. Narrative-event-history methods will be used to develop stylised histories of the emergence of business relations in various contexts and to identify relevant causal mechanisms to be included in the agent-based models of relationship and network evolution. The relationship histories will provide the means of assuring the agent-based models developed.

Model and simulation of Krause model in dynamic open network

NASA Astrophysics Data System (ADS)

Zhu, Meixia; Xie, Guangqiang

2017-08-01

The construction of the concept of evolution is an effective way to reveal the formation of group consensus. This study is based on the modeling paradigm of the HK model (Hegsekmann-Krause). This paper analyzes the evolution of multi - agent opinion in dynamic open networks with member mobility. The results of the simulation show that when the number of agents is constant, the interval distribution of the initial distribution will affect the number of the final view, The greater the distribution of opinions, the more the number of views formed eventually; The trust threshold has a decisive effect on the number of views, and there is a negative correlation between the trust threshold and the number of opinions clusters. The higher the connectivity of the initial activity group, the more easily the subjective opinion in the evolution of opinion to achieve rapid convergence. The more open the network is more conducive to the unity of view, increase and reduce the number of agents will not affect the consistency of the group effect, but not conducive to stability.

Evolution of Hormone Signaling Networks in Plant Defense.

PubMed

Berens, Matthias L; Berry, Hannah M; Mine, Akira; Argueso, Cristiana T; Tsuda, Kenichi

2017-08-04

Studies with model plants such as Arabidopsis thaliana have revealed that phytohormones are central regulators of plant defense. The intricate network of phytohormone signaling pathways enables plants to activate appropriate and effective defense responses against pathogens as well as to balance defense and growth. The timing of the evolution of most phytohormone signaling pathways seems to coincide with the colonization of land, a likely requirement for plant adaptations to the more variable terrestrial environments, which included the presence of pathogens. In this review, we explore the evolution of defense hormone signaling networks by combining the model plant-based knowledge about molecular components mediating phytohormone signaling and cross talk with available genome information of other plant species. We highlight conserved hubs in hormone cross talk and discuss evolutionary advantages of defense hormone cross talk. Finally, we examine possibilities of engineering hormone cross talk for improvement of plant fitness and crop production.

Improved Maximum Parsimony Models for Phylogenetic Networks.

PubMed

Van Iersel, Leo; Jones, Mark; Scornavacca, Celine

2018-05-01

Phylogenetic networks are well suited to represent evolutionary histories comprising reticulate evolution. Several methods aiming at reconstructing explicit phylogenetic networks have been developed in the last two decades. In this article, we propose a new definition of maximum parsimony for phylogenetic networks that permits to model biological scenarios that cannot be modeled by the definitions currently present in the literature (namely, the "hardwired" and "softwired" parsimony). Building on this new definition, we provide several algorithmic results that lay the foundations for new parsimony-based methods for phylogenetic network reconstruction.

Characterizing the evolution of climate networks

NASA Astrophysics Data System (ADS)

Tupikina, L.; Rehfeld, K.; Molkenthin, N.; Stolbova, V.; Marwan, N.; Kurths, J.

2014-06-01

Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, Erdős-Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970-2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.

Evolution of ethnocentrism on undirected and directed Barabási-Albert networks

NASA Astrophysics Data System (ADS)

Lima, F. W. S.; Hadzibeganovic, Tarik; Stauffer, Dietrich

2009-12-01

Using Monte Carlo simulations, we study the evolution of contingent cooperation and ethnocentrism in the one-shot game. Interactions and reproduction among computational agents are simulated on undirected and directed Barabási-Albert (BA) networks. We first replicate the Hammond-Axelrod model of in-group favoritism on a square lattice and then generalize this model on undirected and directed BA networks for both asexual and sexual reproduction cases. Our simulations demonstrate that irrespective of the mode of reproduction, the ethnocentric strategy becomes common even though cooperation is individually costly and mechanisms such as reciprocity or conformity are absent. Moreover, our results indicate that the spread of favoritism towards similar others highly depends on the network topology and the associated heterogeneity of the studied population.

Interlocking directorates in Irish companies using a latent space model for bipartite networks

PubMed Central

Friel, Nial; Rastelli, Riccardo; Wyse, Jason; Raftery, Adrian E.

2016-01-01

We analyze the temporal bipartite network of the leading Irish companies and their directors from 2003 to 2013, encompassing the end of the Celtic Tiger boom and the ensuing financial crisis in 2008. We focus on the evolution of company interlocks, whereby a company director simultaneously sits on two or more boards. We develop a statistical model for this dataset by embedding the positions of companies and directors in a latent space. The temporal evolution of the network is modeled through three levels of Markovian dependence: one on the model parameters, one on the companies’ latent positions, and one on the edges themselves. The model is estimated using Bayesian inference. Our analysis reveals that the level of interlocking, as measured by a contraction of the latent space, increased before and during the crisis, reaching a peak in 2009, and has generally stabilized since then. PMID:27247395

Interlocking directorates in Irish companies using a latent space model for bipartite networks.

PubMed

Friel, Nial; Rastelli, Riccardo; Wyse, Jason; Raftery, Adrian E

2016-06-14

We analyze the temporal bipartite network of the leading Irish companies and their directors from 2003 to 2013, encompassing the end of the Celtic Tiger boom and the ensuing financial crisis in 2008. We focus on the evolution of company interlocks, whereby a company director simultaneously sits on two or more boards. We develop a statistical model for this dataset by embedding the positions of companies and directors in a latent space. The temporal evolution of the network is modeled through three levels of Markovian dependence: one on the model parameters, one on the companies' latent positions, and one on the edges themselves. The model is estimated using Bayesian inference. Our analysis reveals that the level of interlocking, as measured by a contraction of the latent space, increased before and during the crisis, reaching a peak in 2009, and has generally stabilized since then.

Reorganization of river networks under changing spatiotemporal precipitation patterns: An optimal channel network approach

NASA Astrophysics Data System (ADS)

Abed-Elmdoust, Armaghan; Miri, Mohammad-Ali; Singh, Arvind

2016-11-01

We investigate the impact of changing nonuniform spatial and temporal precipitation patterns on the evolution of river networks. To achieve this, we develop a two-dimensional optimal channel network (OCN) model with a controllable rainfall distribution to simulate the evolution of river networks, governed by the principle of minimum energy expenditure, inside a prescribed boundary. We show that under nonuniform precipitation conditions, river networks reorganize significantly toward new patterns with different geomorphic and hydrologic signatures. This reorganization is mainly observed in the form of migration of channels of different orders, widening or elongation of basins as well as formation and extinction of channels and basins. In particular, when the precipitation gradient is locally increased, the higher-order channels, including the mainstream river, migrate toward regions with higher precipitation intensity. Through pertinent examples, the reorganization of the drainage network is quantified via stream parameters such as Horton-Strahler and Tokunaga measures, order-based channel total length and river long profiles obtained via simulation of three-dimensional basin topography, while the hydrologic response of the evolved network is investigated using metrics such as hydrograph and power spectral density of simulated streamflows at the outlet of the network. In addition, using OCNs, we investigate the effect of orographic precipitation patterns on multicatchment landscapes composed of several interacting basins. Our results show that network-inspired methods can be utilized as insightful and versatile models for directly exploring the effects of climate change on the evolution of river drainage systems.

Distributed Emulation in Support of Large Networks

DTIC Science & Technology

2016-06-01

Provider LTE Long Term Evolution MB Megabyte MIPS Microprocessor without Interlocked Pipeline Stages MRT Multi-Threaded Routing Toolkit NPS Naval...environment, modifications to a network, protocol, or model can be executed – and the effects measured – without affecting real-world users or services...produce their results when analyzing performance of Long Term Evolution ( LTE ) gateways [3]. Many research scenarios allow problems to be represented

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

PubMed

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

Vulnerability of networks of interacting Markov chains.

PubMed

Kocarev, L; Zlatanov, N; Trajanov, D

2010-05-13

The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses of the neighbouring nodes. Vulnerability is treated analytically and numerically for several networks with different topological structures, as well as for two real networks--the network of infrastructures and the EU power grid--identifying the most vulnerable nodes of these networks.

Evolution of the Digital Society Reveals Balance between Viral and Mass Media Influence

NASA Astrophysics Data System (ADS)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2014-07-01

Online social networks (OSNs) enable researchers to study the social universe at a previously unattainable scale. The worldwide impact and the necessity to sustain the rapid growth of OSNs emphasize the importance of unraveling the laws governing their evolution. Empirical results show that, unlike many real-world growing networked systems, OSNs follow an intricate path that includes a dynamical percolation transition. In light of these results, we present a quantitative two-parameter model that reproduces the entire topological evolution of a quasi-isolated OSN with unprecedented precision from the birth of the network. This allows us to precisely gauge the fundamental macroscopic and microscopic mechanisms involved. Our findings suggest that the coupling between the real preexisting underlying social structure, a viral spreading mechanism, and mass media influence govern the evolution of OSNs. The empirical validation of our model, on a macroscopic scale, reveals that virality is 4-5 times stronger than mass media influence and, on a microscopic scale, individuals have a higher subscription probability if invited by weaker social contacts, in agreement with the "strength of weak ties" paradigm.

Modelling the evolution of a bi-partite network Peer referral in interlocking directorates*

PubMed Central

Edling, Christofer

2010-01-01

A central part of relational ties between social actors are constituted by shared affiliations and events. The action of joint participation reinforces personal ties between social actors as well as mutually shared values and norms that in turn perpetuate the patterns of social action that define groups. Therefore the study of bipartite networks is central to social science. Furthermore, the dynamics of these processes suggests that bipartite networks should not be considered static structures but rather be studied over time. In order to model the evolution of bipartite networks empirically we introduce a class of models and a Bayesian inference scheme that extends previous stochastic actor-oriented models for unimodal graphs. Contemporary research on interlocking directorates provides an area of research in which it seems reasonable to apply the model. Specifically, we address the question of how tie formation, i.e. director recruitment, contributes to the structural properties of the interlocking directorate network. For boards of directors on the Stockholm stock exchange we propose that a prolific mechanism in tie formation is that of peer referral. The results indicate that such a mechanism is present, generating multiple interlocks between boards. PMID:24944435

Phylogenetic comparative methods on phylogenetic networks with reticulations.

PubMed

Bastide, Paul; Solís-Lemus, Claudia; Kriebel, Ricardo; Sparks, K William; Ané, Cécile

2018-04-25

The goal of Phylogenetic Comparative Methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species' traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. One natural extension of the BM is to use a weighted average model for the trait of a hybrid, at a reticulation point. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel's λ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts, and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios, and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a dataset of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.

Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation.

PubMed

Ubaldi, Enrico; Perra, Nicola; Karsai, Márton; Vezzani, Alessandro; Burioni, Raffaella; Vespignani, Alessandro

2016-10-24

The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals' social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks.

Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation

NASA Astrophysics Data System (ADS)

Ubaldi, Enrico; Perra, Nicola; Karsai, Márton; Vezzani, Alessandro; Burioni, Raffaella; Vespignani, Alessandro

2016-10-01

The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals’ social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks.

Competition between Homophily and Information Entropy Maximization in Social Networks

PubMed Central

Zhao, Jichang; Liang, Xiao; Xu, Ke

2015-01-01

In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective. PMID:26334994

Geometry Genetics and Evolution

NASA Astrophysics Data System (ADS)

Siggia, Eric

2011-03-01

Darwin argued that highly perfected organs such as the vertebrate eye could evolve by a series of small changes, each of which conferred a selective advantage. In the context of gene networks, this idea can be recast into a predictive algorithm, namely find networks that can be built by incremental adaptation (gradient search) to perform some task. It embodies a ``kinetic'' view of evolution where a solution that is quick to evolve is preferred over a global optimum. Examples of biochemical kinetic networks were evolved for temporal adaptation, temperature compensated entrainable clocks, explore-exploit trade off in signal discrimination, will be presented as well as networks that model the spatially periodic somites (vertebrae) and HOX gene expression in the vertebrate embryo. These models appear complex by the criterion of 19th century applied mathematics since there is no separation of time or spatial scales, yet they are all derivable by gradient optimization of simple functions (several in the Pareto evolution) often based on the Shannon entropy of the time or spatial response. Joint work with P. Francois, Physics Dept. McGill University. With P. Francois, Physics Dept. McGill University

Branching pattern in natural drainage network

NASA Astrophysics Data System (ADS)

Hooshyar, M.; Singh, A.; Wang, D.

2017-12-01

The formation and growth of river channels and their network evolution are governed by the erosional and depositional processes operating on the landscape due to movement of water. The branching structure of drainage network is an important feature related to the network topology and contain valuable information about the forming mechanisms of the landscape. We studied the branching patterns in natural drainage networks, extracted from 1 m Digital Elevation Models (DEMs) of 120 catchments with minimal human impacts across the United States. We showed that the junction angles have two distinct modes an the observed modes are physically explained as the optimal angles that result in minimum energy dissipation and are linked to the exponent characterizing slope-area curve. Our findings suggest that the flow regimes, debris-flow dominated or fluvial, have distinct characteristic angles which are functions of the scaling exponent of the slope-area curve. These findings enable us to understand the geomorphological signature of hydrological processes on drainage networks and develop more refined landscape evolution models.

Modeling Day-to-day Flow Dynamics on Degradable Transport Network

PubMed Central

Gao, Bo; Zhang, Ronghui; Lou, Xiaoming

2016-01-01

Stochastic link capacity degradations are common phenomena in transport network which can cause travel time variations and further can affect travelers’ daily route choice behaviors. This paper formulates a deterministic dynamic model, to capture the day-to-day (DTD) flow evolution process in the presence of degraded link capacity degradations. The aggregated network flow dynamics are driven by travelers’ study of uncertain travel time and their choice of risky routes. This paper applies the exponential-smoothing filter to describe travelers’ study of travel time variations, and meanwhile formulates risk attitude parameter updating equation to reflect travelers’ endogenous risk attitude evolution schema. In addition, this paper conducts theoretical analyses to investigate several significant mathematical characteristics implied in the proposed DTD model, including fixed point existence, uniqueness, stability and irreversibility. Numerical experiments are used to demonstrate the effectiveness of the DTD model and verify some important dynamic system properties. PMID:27959903

Inferring tectonic activity using drainage network and RT model: an example from the western Himalayas, India

NASA Astrophysics Data System (ADS)

Sahoo, Ramendra; Jain, Vikrant

2017-04-01

Morphology of the landscape and derived features are regarded to be an important tool for inferring about tectonic activity in an area, since surface exposures of these subsurface processes may not be available or may get eroded away over time. This has led to an extensive research in application of the non-planar morphological attributes like river long profile and hypsometry for tectonic studies, whereas drainage network as a proxy for tectonic activity has not been explored greatly. Though, significant work has been done on drainage network pattern which started in a qualitative manner and over the years, has evolved to incorporate more quantitative aspects, like studying the evolution of a network under the influence of external and internal controls. Random Topology (RT) model is one of these concepts, which elucidates the connection between evolution of a drainage network pattern and the entropy of the drainage system and it states that in absence of any geological controls, a natural population of channel networks will be topologically random. We have used the entropy maximization principle to provide a theoretical structure for the RT model. Furthermore, analysis was carried out on the drainage network structures around Jwalamukhi thrust in the Kangra reentrant in western Himalayas, India, to investigate the tectonic activity in the region. Around one thousand networks were extracted from the foot-wall (fw) and hanging-wall (hw) region of the thrust sheet and later categorized based on their magnitudes. We have adopted the goodness of fit test for comparing the network patterns in fw and hw drainage with those derived using the RT model. The null hypothesis for the test was, the drainage networks in the fw are statistically more similar than those on the hw, to the network patterns derived using the RT model for any given magnitude. The test results are favorable to our null hypothesis for networks with smaller magnitudes (< 9), whereas for larger magnitudes, both hw and fw networks were found to be statistically not similar to the model network patterns. Calculation of pattern frequency for each magnitude and subsequent hypothesis testing were carried out using Matlab (v R2015a). Our results will help to define drainage network pattern as one of the geomorphic proxy to identify tectonically active area. This study also serve as a supplementary proof of the neo-tectonic control on the morphology of landscape and its derivatives around the Jwalamukhi thrust. Additionally, it will help to verify the theory of probabilistic evolution of drainage networks.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Noise effect in metabolic networks

NASA Astrophysics Data System (ADS)

Li, Zheng-Yan; Xie, Zheng-Wei; Chen, Tong; Ouyang, Qi

2009-12-01

Constraint-based models such as flux balance analysis (FBA) are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term 'noise', and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

Evolution of tag-based cooperation with emotion on complex networks

NASA Astrophysics Data System (ADS)

Lima, F. W. S.

2018-04-01

We study the evolution of the four strategies: Ethnocentric, altruistic, egoistic and cosmopolitan in one community of individuals through Monte Carlo simulations. Interactions and reproduction among computational agents are simulated on undirected Barabási-Albert (UBA) networks and Erdös-Rènyi random graphs (ER).We study the Hammond-Axelrod model on both UBA networks and ER random graphs for the asexual reproduction case. We use a modified version of the traditional Hammond-Axelrod model and we also allow the agents’ decisions about one of the strategies to take into account the emotion among their equals. Our simulations showed that egoism and altruism win, differently from other results found in the literature where ethnocentric strategy is common.

A simple model for the evolution of a non-Abelian cosmic string network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cella, G.; Pieroni, M., E-mail: giancarlo.cella@pi.infn.it, E-mail: mauro.pieroni@apc.univ-paris7.fr

2016-06-01

In this paper we present the results of numerical simulations intended to study the behavior of non-Abelian cosmic strings networks. In particular we are interested in discussing the variations in the asymptotic behavior of the system as we variate the number of generators for the topological defects. A simple model which allows for cosmic strings is presented and its lattice discretization is discussed. The evolution of the generated cosmic string networks is then studied for different values for the number of generators for the topological defects. Scaling solution appears to be approached in most cases and we present an argumentmore » to justify the lack of scaling for the residual cases.« less

An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

PubMed

García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César

2006-05-01

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.

Prebiological evolution and the metabolic origins of life.

PubMed

Pratt, Andrew J

2011-01-01

The chemoton model of cells posits three subsystems: metabolism, compartmentalization, and information. A specific model for the prebiological evolution of a reproducing system with rudimentary versions of these three interdependent subsystems is presented. This is based on the initial emergence and reproduction of autocatalytic networks in hydrothermal microcompartments containing iron sulfide. The driving force for life was catalysis of the dissipation of the intrinsic redox gradient of the planet. The codependence of life on iron and phosphate provides chemical constraints on the ordering of prebiological evolution. The initial protometabolism was based on positive feedback loops associated with in situ carbon fixation in which the initial protometabolites modified the catalytic capacity and mobility of metal-based catalysts, especially iron-sulfur centers. A number of selection mechanisms, including catalytic efficiency and specificity, hydrolytic stability, and selective solubilization, are proposed as key determinants for autocatalytic reproduction exploited in protometabolic evolution. This evolutionary process led from autocatalytic networks within preexisting compartments to discrete, reproducing, mobile vesicular protocells with the capacity to use soluble sugar phosphates and hence the opportunity to develop nucleic acids. Fidelity of information transfer in the reproduction of these increasingly complex autocatalytic networks is a key selection pressure in prebiological evolution that eventually leads to the selection of nucleic acids as a digital information subsystem and hence the emergence of fully functional chemotons capable of Darwinian evolution.

History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust.

PubMed

Sanatkar, M R; Scoglio, C; Natarajan, B; Isard, S A; Garrett, K A

2015-07-01

Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution.

A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

PubMed

Hui, David Shui Wing; Chen, Yi-Chao; Zhang, Gong; Wu, Weijie; Chen, Guanrong; Lui, John C S; Li, Yingtao

2017-06-16

This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the "trichotomy" observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

Random Evolution of Idiotypic Networks: Dynamics and Architecture

NASA Astrophysics Data System (ADS)

Brede, Markus; Behn, Ulrich

The paper deals with modelling a subsystem of the immune system, the so-called idiotypic network (INW). INWs, conceived by N.K. Jerne in 1974, are functional networks of interacting antibodies and B cells. In principle, Jernes' framework provides solutions to many issues in immunology, such as immunological memory, mechanisms for antigen recognition and self/non-self discrimination. Explaining the interconnection between the elementary components, local dynamics, network formation and architecture, and possible modes of global system function appears to be an ideal playground of statistical mechanics. We present a simple cellular automaton model, based on a graph representation of the system. From a simplified description of idiotypic interactions, rules for the random evolution of networks of occupied and empty sites on these graphs are derived. In certain biologically relevant parameter ranges the resultant dynamics leads to stationary states. A stationary state is found to correspond to a specific pattern of network organization. It turns out that even these very simple rules give rise to a multitude of different kinds of patterns. We characterize these networks by classifying `static' and `dynamic' network-patterns. A type of `dynamic' network is found to display many features of real INWs.

Role of Open and Distance Education in Integrating Education with Development--Emerging Model of Networked Collaborative Learning and Net-working.

ERIC Educational Resources Information Center

Takwale, Ram

1998-01-01

Discusses the evolution of the educational system in India, developments in new communication technologies, and plans by the open and distance education system to develop educational networks. Policies and programs adopted by the Distance Education Council are outlined. (AEF)

Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks.

PubMed

Spirov, Alexander; Holloway, David

2013-07-15

This paper surveys modeling approaches for studying the evolution of gene regulatory networks (GRNs). Modeling of the design or 'wiring' of GRNs has become increasingly common in developmental and medical biology, as a means of quantifying gene-gene interactions, the response to perturbations, and the overall dynamic motifs of networks. Drawing from developments in GRN 'design' modeling, a number of groups are now using simulations to study how GRNs evolve, both for comparative genomics and to uncover general principles of evolutionary processes. Such work can generally be termed evolution in silico. Complementary to these biologically-focused approaches, a now well-established field of computer science is Evolutionary Computations (ECs), in which highly efficient optimization techniques are inspired from evolutionary principles. In surveying biological simulation approaches, we discuss the considerations that must be taken with respect to: (a) the precision and completeness of the data (e.g. are the simulations for very close matches to anatomical data, or are they for more general exploration of evolutionary principles); (b) the level of detail to model (we proceed from 'coarse-grained' evolution of simple gene-gene interactions to 'fine-grained' evolution at the DNA sequence level); (c) to what degree is it important to include the genome's cellular context; and (d) the efficiency of computation. With respect to the latter, we argue that developments in computer science EC offer the means to perform more complete simulation searches, and will lead to more comprehensive biological predictions. Copyright © 2013 Elsevier Inc. All rights reserved.

Petri net modeling of high-order genetic systems using grammatical evolution.

PubMed

Moore, Jason H; Hahn, Lance W

2003-11-01

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.

Model criticism based on likelihood-free inference, with an application to protein network evolution.

PubMed

Ratmann, Oliver; Andrieu, Christophe; Wiuf, Carsten; Richardson, Sylvia

2009-06-30

Mathematical models are an important tool to explain and comprehend complex phenomena, and unparalleled computational advances enable us to easily explore them without any or little understanding of their global properties. In fact, the likelihood of the data under complex stochastic models is often analytically or numerically intractable in many areas of sciences. This makes it even more important to simultaneously investigate the adequacy of these models-in absolute terms, against the data, rather than relative to the performance of other models-but no such procedure has been formally discussed when the likelihood is intractable. We provide a statistical interpretation to current developments in likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty (ABCmicro). We augment the likelihood of the data with unknown error terms that correspond to freely chosen checking functions, and provide Monte Carlo strategies for sampling from the associated joint posterior distribution without the need of evaluating the likelihood. We discuss the benefit of incorporating model diagnostics within an ABC framework, and demonstrate how this method diagnoses model mismatch and guides model refinement by contrasting three qualitative models of protein network evolution to the protein interaction datasets of Helicobacter pylori and Treponema pallidum. Our results make a number of model deficiencies explicit, and suggest that the T. pallidum network topology is inconsistent with evolution dominated by link turnover or lateral gene transfer alone.

Multidimensional adaptive evolution of a feed-forward network and the illusion of compensation

PubMed Central

Bullaughey, Kevin

2016-01-01

When multiple substitutions affect a trait in opposing ways, they are often assumed to be compensatory, not only with respect to the trait, but also with respect to fitness. This type of compensatory evolution has been suggested to underlie the evolution of protein structures and interactions, RNA secondary structures, and gene regulatory modules and networks. The possibility for compensatory evolution results from epistasis. Yet if epistasis is widespread, then it is also possible that the opposing substitutions are individually adaptive. I term this possibility an adaptive reversal. Although possible for arbitrary phenotype-fitness mappings, it has not yet been investigated whether such epistasis is prevalent in a biologically-realistic setting. I investigate a particular regulatory circuit, the type I coherent feed-forward loop, which is ubiquitous in natural systems and is accurately described by a simple mathematical model. I show that such reversals are common during adaptive evolution, can result solely from the topology of the fitness landscape, and can occur even when adaptation follows a modest environmental change and the network was well adapted to the original environment. The possibility of adaptive reversals warrants a systems perspective when interpreting substitution patterns in gene regulatory networks. PMID:23289561

Evolution of individual versus social learning on social networks

PubMed Central

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-01-01

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. PMID:25631568

Evolution of opinions on social networks in the presence of competing committed groups.

PubMed

Xie, Jierui; Emenheiser, Jeffrey; Kirby, Matthew; Sreenivasan, Sameet; Szymanski, Boleslaw K; Korniss, Gyorgy

2012-01-01

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about 10% of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions A and B, and constituting fractions pA and pB of the total population respectively, are present in the network. We show for stylized social networks (including Erdös-Rényi random graphs and Barabási-Albert scale-free networks) that the phase diagram of this system in parameter space (pA,pB) consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point.

Evolution of Opinions on Social Networks in the Presence of Competing Committed Groups

PubMed Central

Xie, Jierui; Emenheiser, Jeffrey; Kirby, Matthew; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, Gyorgy

2012-01-01

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions and , and constituting fractions and of the total population respectively, are present in the network. We show for stylized social networks (including Erdös-Rényi random graphs and Barabási-Albert scale-free networks) that the phase diagram of this system in parameter space consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point. PMID:22448238

Transient and sustained elementary flux mode networks on a catalytic string-based chemical evolution model.

PubMed

Pereira, José A

2014-08-01

Theoretical models designed to test the metabolism-first hypothesis for prebiotic evolution have yield strong indications about the hypothesis validity but could sometimes use a more extensive identification between model objects and real objects towards a more meaningful interpretation of results. In an attempt to go in that direction, the string-based model SSE ("steady state evolution") was developed, where abstract molecules (strings) and catalytic interaction rules are based on some of the most important features of carbon compounds in biological chemistry. The system is open with a random inflow and outflow of strings but also with a permanent string food source. Although specific catalysis is a key aspect of the model, used to define reaction rules, the focus is on energetics rather than kinetics. Standard energy change tables were constructed and used with standard formation reactions to track energy flows through the interpretation of equilibrium constant values. Detection of metabolic networks on the reaction system was done with elementary flux mode (EFM) analysis. The combination of these model design and analysis options enabled obtaining metabolic and catalytic networks showing several central features of biological metabolism, some more clearly than in previous models: metabolic networks with stepwise synthesis, energy coupling, catalysts regulation, SN2 coupling, redox coupling, intermediate cycling, coupled inverse pathways (metabolic cycling), autocatalytic cycles and catalytic cascades. The results strongly suggest that the main biological metabolism features, including the genotype-phenotype interpretation, are caused by the principles of catalytic systems and are prior to modern genetic systems principles. It also gives further theoretical support to the thesis that the basic features of biologic metabolism are a consequence of the time evolution of a random catalyst search working on an open system with a permanent food source. The importance of the food source characteristics and evolutionary possibilities are discussed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Following the Social Media: Aspect Evolution of Online Discussion

NASA Astrophysics Data System (ADS)

Tang, Xuning; Yang, Christopher C.

Due to the advance of Internet and Web 2.0 technologies, it is easy to extract thousands of threads about a topic of interest from an online forum but it is nontrivial to capture the blueprint of different aspects (i.e., subtopic, or facet) associated with the topic. To better understand and analyze a forum discussion given topic, it is important to uncover the evolution relationships (temporal dependencies) between different topic aspects (i.e. how the discussion topic is evolving). Traditional Topic Detection and Tracking (TDT) techniques usually organize topics as a flat structure but it does not present the evolution relationships between topic aspects. In addition, the properties of short and sparse messages make the content-based TDT techniques difficult to perform well in identifying evolution relationships. The contributions in this paper are two-folded. We formally define a topic aspect evolution graph modeling framework and propose to utilize social network information, content similarity and temporal proximity to model evolution relationships between topic aspects. The experimental results showed that, by incorporating social network information, our technique significantly outperformed content-based technique in the task of extracting evolution relationships between topic aspects.

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

NASA Technical Reports Server (NTRS)

Mog, Robert A.

1999-01-01

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

PubMed Central

Mannakee, Brian K.; Gutenkunst, Ryan N.

2016-01-01

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

Firewall systems: the next generation

NASA Astrophysics Data System (ADS)

McGhie, Lynda L.

1996-01-01

To be competitive in today's globally connected marketplace, a company must ensure that their internal network security methodologies and supporting policies are current and reflect an overall understanding of today's technology and its resultant threats. Further, an integrated approach to information security should ensure that new ways of sharing information and doing business are accommodated; such as electronic commerce, high speed public broadband network services, and the federally sponsored National Information Infrastructure. There are many challenges, and success is determined by the establishment of a solid and firm baseline security architecture that accommodate today's external connectivity requirements, provides transitional solutions that integrate with evolving and dynamic technologies, and ultimately acknowledges both the strategic and tactical goals of an evolving network security architecture and firewall system. This paper explores the evolution of external network connectivity requirements, the associated challenges and the subsequent development and evolution of firewall security systems. It makes the assumption that a firewall is a set of integrated and interoperable components, coming together to form a `SYSTEM' and must be designed, implement and managed as such. A progressive firewall model will be utilized to illustrates the evolution of firewall systems from earlier models utilizing separate physical networks, to today's multi-component firewall systems enabling secure heterogeneous and multi-protocol interfaces.

Networks for image acquisition, processing and display

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.

1990-01-01

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

Biological signatures of dynamic river networks from a coupled landscape evolution and neutral community model

NASA Astrophysics Data System (ADS)

Stokes, M.; Perron, J. T.

2017-12-01

Freshwater systems host exceptionally species-rich communities whose spatial structure is dictated by the topology of the river networks they inhabit. Over geologic time, river networks are dynamic; drainage basins shrink and grow, and river capture establishes new connections between previously separated regions. It has been hypothesized that these changes in river network structure influence the evolution of life by exchanging and isolating species, perhaps boosting biodiversity in the process. However, no general model exists to predict the evolutionary consequences of landscape change. We couple a neutral community model of freshwater organisms to a landscape evolution model in which the river network undergoes drainage divide migration and repeated river capture. Neutral community models are macro-ecological models that include stochastic speciation and dispersal to produce realistic patterns of biodiversity. We explore the consequences of three modes of speciation - point mutation, time-protracted, and vicariant (geographic) speciation - by tracking patterns of diversity in time and comparing the final result to an equilibrium solution of the neutral model on the final landscape. Under point mutation, a simple model of stochastic and instantaneous speciation, the results are identical to the equilibrium solution and indicate the dominance of the species-area relationship in forming patterns of diversity. The number of species in a basin is proportional to its area, and regional species richness reaches its maximum when drainage area is evenly distributed among sub-basins. Time-protracted speciation is also modeled as a stochastic process, but in order to produce more realistic rates of diversification, speciation is not assumed to be instantaneous. Rather, each new species must persist for a certain amount of time before it is considered to be established. When vicariance (geographic speciation) is included, there is a transient signature of increased regional diversity after river capture. The results indicate that the mode of speciation and the rate of speciation relative to the rate of divide migration determine the evolutionary signature of river capture.

Some Physical Principles Governing Spatial and Temporal Organization in Living Systems

NASA Astrophysics Data System (ADS)

Ali, Md Zulfikar

Spatial and temporal organization in living organisms are crucial for a variety of biological functions and arise from the interplay of large number of interacting molecules. One of the central questions in systems biology is to understand how such an intricate organization emerges from the molecular biochemistry of the cell. In this dissertation we explore two projects. The first project relates to pattern formation in a cell membrane as an example of spatial organization, and the second project relates to the evolution of oscillatory networks as a simple example of temporal organization. For the first project, we introduce a model for pattern formation in a two-component lipid bilayer and study the interplay between membrane composition and membrane geometry, demonstrating the existence of a rich phase diagram. Pattern formation is governed by the interplay between phase separation driven by lipid-lipid interactions and tendency of lipid domains with high intrinsic curvature to deform the membrane away from its preferred position. Depending on membrane parameters, we find the formation of compact lipid micro-clusters or of striped domains. We calculate the stripe width analytically and find good agreement with stripe widths obtained from the simulations. For the second project, we introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm and apply it to study the following problems. Using the model, we study robustness and designabilty of a 2-component network that generate oscillations. We completely enumerate the sequence space and the phenotypic space, and discuss the relationship between designabilty, robustness and evolvability. We further apply the model to studies of neutral drift in networks that yield oscillatory dynamics, e.g. starting with a relatively simple network and allowing it to evolve by adding nodes and connections while requiring that oscillatory dynamics be preserved. Our studies demonstrate both the importance of employing a sequence-based evolutionary scheme and the relative rapidity (in evolutionary time) for the redistribution of function over new nodes via neutral drift. In addition we discovered another much slower timescale for network evolution, reflecting hidden order in sequence space that we interpret in terms of sparsely connected domains. Finally, we use the model to study the evolution of an oscillator from a non-oscillatory network under the influence of external periodic forcing as a model for evolution of circadian rhythm in living systems. We use a greedy algorithm based on optimizing biologically motivated fitness functions and find that the algorithm successfully produces oscillators. However, the distribution of free-period of evolved oscillators depends on the choice of fitness functions and the nature of forcing.

Interfacial welding of dynamic covalent network polymers

NASA Astrophysics Data System (ADS)

Yu, Kai; Shi, Qian; Li, Hao; Jabour, John; Yang, Hua; Dunn, Martin L.; Wang, Tiejun; Qi, H. Jerry

2016-09-01

Dynamic covalent network (or covalent adaptable network) polymers can rearrange their macromolecular chain network by bond exchange reactions (BERs) where an active unit replaces a unit in an existing bond to form a new bond. Such macromolecular events, when they occur in large amounts, can attribute to unusual properties that are not seen in conventional covalent network polymers, such as shape reforming and surface welding; the latter further enables the important attributes of material malleability and powder-based reprocessing. In this paper, a multiscale modeling framework is developed to study the surface welding of thermally induced dynamic covalent network polymers. At the macromolecular network level, a lattice model is developed to describe the chain density evolution across the interface and its connection to bulk stress relaxation due to BERs. The chain density evolution rule is then fed into a continuum level interfacial model that takes into account surface roughness and applied pressure to predict the effective elastic modulus and interfacial fracture energy of welded polymers. The model yields particularly accessible results where the moduli and interfacial strength of the welded samples as a function of temperature and pressure can be predicted with four parameters, three of which can be measured directly. The model identifies the dependency of surface welding efficiency on the applied thermal and mechanical fields: the pressure will affect the real contact area under the consideration of surface roughness of dynamic covalent network polymers; the chain density increment on the real contact area of interface is only dependent on the welding time and temperature. The modeling approach shows good agreement with experiments and can be extended to other types of dynamic covalent network polymers using different stimuli for BERs, such as light and moisture etc.

Effects of biases in domain wall network evolution. II. Quantitative analysis

NASA Astrophysics Data System (ADS)

Correia, J. R. C. C. C.; Leite, I. S. C. R.; Martins, C. J. A. P.

2018-04-01

Domain walls form at phase transitions which break discrete symmetries. In a cosmological context, they often overclose the Universe (contrary to observational evidence), although one may prevent this by introducing biases or forcing anisotropic evolution of the walls. In a previous work [Correia et al., Phys. Rev. D 90, 023521 (2014), 10.1103/PhysRevD.90.023521], we numerically studied the evolution of various types of biased domain wall networks in the early Universe, confirming that anisotropic networks ultimately reach scaling while those with a biased potential or biased initial conditions decay. We also found that the analytic decay law obtained by Hindmarsh was in good agreement with simulations of biased potentials, but not of biased initial conditions, and suggested that the difference was related to the Gaussian approximation underlying the analytic law. Here, we extend our previous work in several ways. For the cases of biased potential and biased initial conditions, we study in detail the field distributions in the simulations, confirming that the validity (or not) of the Gaussian approximation is the key difference between the two cases. For anisotropic walls, we carry out a more extensive set of numerical simulations and compare them to the canonical velocity-dependent one-scale model for domain walls, finding that the model accurately predicts the linear scaling regime after isotropization. Overall, our analysis provides a quantitative description of the cosmological evolution of these networks.

A toolbox model of evolution of metabolic pathways on networks of arbitrary topology.

PubMed

Pang, Tin Yau; Maslov, Sergei

2011-05-01

In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger "universal" network formed by the union of all species-specific networks. It remained to be understood, however, how the topological properties of this universal network influence the scaling law of functional content of genomes in the toolbox model. Here we answer this question by first analyzing the scaling properties of the toolbox model on arbitrary tree-like universal networks. We prove that critical branching topology, in which the average number of upstream neighbors of a node is equal to one, is both necessary and sufficient for quadratic scaling. We further generalize the rules of the model to incorporate reactions with multiple substrates/products as well as branched and cyclic metabolic pathways. To achieve its metabolic tasks, the new model employs evolutionary optimized pathways with minimal number of reactions. Numerical simulations of this realistic model on the universal network of all reactions in the KEGG database produced approximately quadratic scaling between the number of regulated pathways and the size of the metabolic network. To quantify the geometrical structure of individual pathways, we investigated the relationship between their number of reactions, byproducts, intermediate, and feedback metabolites. Our results validate and explain the ubiquitous appearance of the quadratic scaling for a broad spectrum of topologies of underlying universal metabolic networks. They also demonstrate why, in spite of "small-world" topology, real-life metabolic networks are characterized by a broad distribution of pathway lengths and sizes of metabolic regulons in regulatory networks.

Mapping the Structure and Dynamics of Genomics-Related MeSH Terms Complex Networks

PubMed Central

Siqueiros-García, Jesús M.; Hernández-Lemus, Enrique; García-Herrera, Rodrigo; Robina-Galatas, Andrea

2014-01-01

It has been proposed that the history and evolution of scientific ideas may reflect certain aspects of the underlying socio-cognitive frameworks in which science itself is developing. Systematic analyses of the development of scientific knowledge may help us to construct models of the collective dynamics of science. Aiming at scientific rigor, these models should be built upon solid empirical evidence, analyzed with formal tools leading to ever-improving results that support the related conclusions. Along these lines we studied the dynamics and structure of the development of research in genomics as represented by the entire collection of genomics-related scientific papers contained in the PubMed database. The analyzed corpus consisted in more than 49,000 articles published in the years 1987 (first appeareance of the term Genomics) to 2011, categorized by means of the Medical Subheadings (MeSH) content-descriptors. Complex networks were built where two MeSH terms were connected if they are descriptors of the same article(s). The analysis of such networks revealed a complex structure and dynamics that to certain extent resembled small-world networks. The evolution of such networks in time reflected interesting phenomena in the historical development of genomic research, including what seems to be a phase-transition in a period marked by the completion of the first draft of the Human Genome Project. We also found that different disciplinary areas have different dynamic evolution patterns in their MeSH connectivity networks. In the case of areas related to science, changes in topology were somewhat fast while retaining a certain core-stucture, whereas in the humanities, the evolution was pretty slow and the structure resulted highly redundant and in the case of technology related issues, the evolution was very fast and the structure remained tree-like with almost no overlapping terms. PMID:24699262

The Role of Graphlets in Viral Processes on Networks

NASA Astrophysics Data System (ADS)

Khorshidi, Samira; Al Hasan, Mohammad; Mohler, George; Short, Martin B.

2018-05-01

Predicting the evolution of viral processes on networks is an important problem with applications arising in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used for the prediction of viral spreading across networks of different types. However, it has been shown that degree distribution alone fails to predict the behavior of viruses on some real-world networks and recent attempts have been made to use assortativity to address this shortcoming. In this paper, we show that adding assortativity does not fully explain the variance in the spread of viruses for a number of real-world networks. We propose using the graphlet frequency distribution in combination with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution. Using a data-driven approach by coupling predictive modeling with viral process simulation on real-world networks, we show that simple regression models based on graphlet frequency distribution can explain over 95% of the variance in virality on networks with the same degree distribution but different network topologies. Our results not only highlight the importance of graphlets but also identify a small collection of graphlets which may have the highest influence over the viral processes on a network.

Cooperation prevails when individuals adjust their social ties.

PubMed

Santos, Francisco C; Pacheco, Jorge M; Lenaerts, Tom

2006-10-20

Conventional evolutionary game theory predicts that natural selection favours the selfish and strong even though cooperative interactions thrive at all levels of organization in living systems. Recent investigations demonstrated that a limiting factor for the evolution of cooperative interactions is the way in which they are organized, cooperators becoming evolutionarily competitive whenever individuals are constrained to interact with few others along the edges of networks with low average connectivity. Despite this insight, the conundrum of cooperation remains since recent empirical data shows that real networks exhibit typically high average connectivity and associated single-to-broad-scale heterogeneity. Here, a computational model is constructed in which individuals are able to self-organize both their strategy and their social ties throughout evolution, based exclusively on their self-interest. We show that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of cooperation in social networks. For a given average connectivity of the population, there is a critical value for the ratio W between the time scales associated with the evolution of strategy and of structure above which cooperators wipe out defectors. Moreover, the emerging social networks exhibit an overall heterogeneity that accounts very well for the diversity of patterns recently found in acquired data on social networks. Finally, heterogeneity is found to become maximal when W reaches its critical value. These results show that simple topological dynamics reflecting the individual capacity for self-organization of social ties can produce realistic networks of high average connectivity with associated single-to-broad-scale heterogeneity. On the other hand, they show that cooperation cannot evolve as a result of "social viscosity" alone in heterogeneous networks with high average connectivity, requiring the additional mechanism of topological co-evolution to ensure the survival of cooperative behaviour.

The evolution of communities in the international oil trade network

NASA Astrophysics Data System (ADS)

Zhong, Weiqiong; An, Haizhong; Gao, Xiangyun; Sun, Xiaoqi

2014-11-01

International oil trade is a subset of global trade and there exist oil trade communities. These communities evolve over time and provide clues of international oil trade patterns. A better understanding of the international oil trade patterns is necessary for governments in policy making. To study the evolution of trade communities in the international oil trade network, we set up unweighted and weighted oil trade network models based on complex network theory using data from 2002 to 2011. We detected the communities in the oil trade networks and analyzed their evolutionary properties and stabilities over time. We found that the unweighted and weighted international oil trade networks show many different features in terms of community number, community scale, distribution of countries, quality of partitions, and stability of communities. Two turning points occurred in the evolution of community stability in the international oil trade network. One is the year 2004-2005 which correlates with changes in demand and supply in the world oil market after the Iraq War, and the other is the year 2008-2009 which is connected to the 2008 financial crisis. Different causations of instability show different features and this should be considered by policy makers.

The spatiotemporal system dynamics of acquired resistance in an engineered microecology.

PubMed

Datla, Udaya Sree; Mather, William H; Chen, Sheng; Shoultz, Isaac W; Täuber, Uwe C; Jones, Caroline N; Butzin, Nicholas C

2017-11-22

Great strides have been made in the understanding of complex networks; however, our understanding of natural microecologies is limited. Modelling of complex natural ecological systems has allowed for new findings, but these models typically ignore the constant evolution of species. Due to the complexity of natural systems, unanticipated interactions may lead to erroneous conclusions concerning the role of specific molecular components. To address this, we use a synthetic system to understand the spatiotemporal dynamics of growth and to study acquired resistance in vivo. Our system differs from earlier synthetic systems in that it focuses on the evolution of a microecology from a killer-prey relationship to coexistence using two different non-motile Escherichia coli strains. Using empirical data, we developed the first ecological model emphasising the concept of the constant evolution of species, where the survival of the prey species is dependent on location (distance from the killer) or the evolution of resistance. Our simple model, when expanded to complex microecological association studies under varied spatial and nutrient backgrounds may help to understand the complex relationships between multiple species in intricate natural ecological networks. This type of microecological study has become increasingly important, especially with the emergence of antibiotic-resistant pathogens.

Applications of statistical physics to technology price evolution

NASA Astrophysics Data System (ADS)

McNerney, James

Understanding how changing technology affects the prices of goods is a problem with both rich phenomenology and important policy consequences. Using methods from statistical physics, I model technology-driven price evolution. First, I examine a model for the price evolution of individual technologies. The price of a good often follows a power law equation when plotted against its cumulative production. This observation turns out to have significant consequences for technology policy aimed at mitigating climate change, where technologies are needed that achieve low carbon emissions at low cost. However, no theory adequately explains why technology prices follow power laws. To understand this behavior, I simplify an existing model that treats technologies as machines composed of interacting components. I find that the power law exponent of the price trajectory is inversely related to the number of interactions per component. I extend the model to allow for more realistic component interactions and make a testable prediction. Next, I conduct a case-study on the cost evolution of coal-fired electricity. I derive the cost in terms of various physical and economic components. The results suggest that commodities and technologies fall into distinct classes of price models, with commodities following martingales, and technologies following exponentials in time or power laws in cumulative production. I then examine the network of money flows between industries. This work is a precursor to studying the simultaneous evolution of multiple technologies. Economies resemble large machines, with different industries acting as interacting components with specialized functions. To begin studying the structure of these machines, I examine 20 economies with an emphasis on finding common features to serve as targets for statistical physics models. I find they share the same money flow and industry size distributions. I apply methods from statistical physics to show that industries cluster the same way according to industry type. Finally, I use these industry money flows to model the price evolution of many goods simultaneously, where network effects become important. I derive a prediction for which goods tend to improve most rapidly. The fastest-improving goods are those with the highest mean path lengths in the money flow network.

A study of knowledge supernetworks and network robustness in different business incubators

NASA Astrophysics Data System (ADS)

Zhang, Haihong; Wu, Wenqing; Zhao, Liming

2016-04-01

As the most important intangible resource of the new generation of business incubators, knowledge has been studied extensively, particularly with respect to how it spreads among incubating firms through knowledge networks. However, these homogeneous networks do not adequately describe the heterogeneity of incubating firms in different types of business incubators. To solve the problem of heterogeneity, the notion of a knowledge supernetwork has been used both to construct a knowledge interaction model among incubating firms and to distinguish social network relationships from knowledge network relationships. The process of knowledge interaction and network evolution can then be simulated with a few rules for incubating firms regarding knowledge innovation/absorption, social network connection, and entry and exit, among other aspects. Knowledge and networks have been used as performance indicators to evaluate the evolution of knowledge supernetworks. Moreover, we study the robustness of incubating firms' social networks by employing four types of attack strategies. Based on our simulation results, we conclude that there have been significant knowledge interaction and network evolution among incubating firms on a periodic basis and that both specialized and diversified business incubators have every advantage necessary in terms of both knowledge and networks to cultivate start-up companies. As far as network robustness is concerned, there is no obvious difference between the two types of business incubators with respect to the stability of their network structures, but specialized business incubators have stronger network communication abilities than diversified business incubators.

A network model of knowledge accumulation through diffusion and upgrade

NASA Astrophysics Data System (ADS)

Zhuang, Enyu; Chen, Guanrong; Feng, Gang

2011-07-01

In this paper, we introduce a model to describe knowledge accumulation through knowledge diffusion and knowledge upgrade in a multi-agent network. Here, knowledge diffusion refers to the distribution of existing knowledge in the network, while knowledge upgrade means the discovery of new knowledge. It is found that the population of the network and the number of each agent’s neighbors affect the speed of knowledge accumulation. Four different policies for updating the neighboring agents are thus proposed, and their influence on the speed of knowledge accumulation and the topology evolution of the network are also studied.

Intelligent Resource Management for Local Area Networks: Approach and Evolution

NASA Technical Reports Server (NTRS)

Meike, Roger

1988-01-01

The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described.

From sparse to dense and from assortative to disassortative in online social networks

PubMed Central

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-01-01

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. PMID:24798703

From sparse to dense and from assortative to disassortative in online social networks.

PubMed

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-05-06

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Comparison of the Light-Harvesting Networks of Plant and Cyanobacterial Photosystem I

PubMed Central

Şener, Melih K.; Jolley, Craig; Ben-Shem, Adam; Fromme, Petra; Nelson, Nathan; Croce, Roberta; Schulten, Klaus

2005-01-01

With the availability of structural models for photosystem I (PSI) in cyanobacteria and plants it is possible to compare the excitation transfer networks in this ubiquitous photosystem from two domains of life separated by over one billion years of divergent evolution, thus providing an insight into the physical constraints that shape the networks' evolution. Structure-based modeling methods are used to examine the excitation transfer kinetics of the plant PSI-LHCI supercomplex. For this purpose an effective Hamiltonian is constructed that combines an existing cyanobacterial model for structurally conserved chlorophylls with spectral information for chlorophylls in the Lhca subunits. The plant PSI excitation migration network thus characterized is compared to its cyanobacterial counterpart investigated earlier. In agreement with observations, an average excitation transfer lifetime of ∼49 ps is computed for the plant PSI-LHCI supercomplex with a corresponding quantum yield of 95%. The sensitivity of the results to chlorophyll site energy assignments is discussed. Lhca subunits are efficiently coupled to the PSI core via gap chlorophylls. In contrast to the chlorophylls in the vicinity of the reaction center, previously shown to optimize the quantum yield of the excitation transfer process, the orientational ordering of peripheral chlorophylls does not show such optimality. The finding suggests that after close packing of chlorophylls was achieved, constraints other than efficiency of the overall excitation transfer process precluded further evolution of pigment ordering. PMID:15994896

Large-scale transportation network congestion evolution prediction using deep learning theory.

PubMed

Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

2015-01-01

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

PubMed Central

Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

2015-01-01

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation. PMID:25780910

Modeling Citation Networks Based on Vigorousness and Dormancy

NASA Astrophysics Data System (ADS)

Wang, Xue-Wen; Zhang, Li-Jie; Yang, Guo-Hong; Xu, Xin-Jian

2013-08-01

In citation networks, the activity of papers usually decreases with age and dormant papers may be discovered and become fashionable again. To model this phenomenon, a competition mechanism is suggested which incorporates two factors: vigorousness and dormancy. Based on this idea, a citation network model is proposed, in which a node has two discrete stage: vigorous and dormant. Vigorous nodes can be deactivated and dormant nodes may be activated and become vigorous. The evolution of the network couples addition of new nodes and state transitions of old ones. Both analytical calculation and numerical simulation show that the degree distribution of nodes in generated networks displays a good right-skewed behavior. Particularly, scale-free networks are obtained as the deactivated vertex is target selected and exponential networks are realized for the random-selected case. Moreover, the measurement of four real-world citation networks achieves a good agreement with the stochastic model.

Time Series Analysis of Networks: Evaluating the Effectiveness of Sanctions on Iran

DTIC Science & Technology

2013-03-27

quantitatively model the evolution and “learning” of this Iranian Network? This was the question posed to three summer apprentices at the Network Science...designated them Peripheral Players. For example, in this model the Nuclear Fuel and Procurement Company is a Broker and Bank Melli is a Peripheral Player...resource/us-sanction> [3] Al Arabiya, “Sanctions against Iran stymied efforts to produce ballistic missles : report,” Al Arabiya News, 30 July 2012. 30

Coevolution of cooperation and network structure under natural selection

NASA Astrophysics Data System (ADS)

Yang, D.-P.; Lin, H.; Shuai, J. W.

2011-02-01

A coevolution model by coupling mortality and fertility selection is introduced to investigate the evolution of cooperation and network structure in the prisoner's dilemma game. The cooperation level goes through a continuous phase transition vs. defection temptation b for low mortality selection intensity β and through a discontinuous one for infinite β. The cooperation level is enhanced most at β≈1 for any b. The local and global properties of the network structure, such as cluster and cooperating k-core, are investigated for the understanding of cooperation evolution. Cooperation is promoted by forming a tight cooperating k-core at moderate β, but too large β will destroy the cooperating k-core rapidly resulting in a rapid drop of the cooperation level. Importantly, the infinite β changes the normalized sucker's payoff S from 0 to 1-b and its dynamics of the cooperation level undergoes a very slow power-law decay, which leads the evolution into the regime of neutral evolution.

The evolution of cooperation on geographical networks

NASA Astrophysics Data System (ADS)

Li, Yixiao; Wang, Yi; Sheng, Jichuan

2017-11-01

We study evolutionary public goods game on geographical networks, i.e., complex networks which are located on a geographical plane. The geographical feature effects in two ways: In one way, the geographically-induced network structure influences the overall evolutionary dynamics, and, in the other way, the geographical length of an edge influences the cost when the two players at the two ends interact. For the latter effect, we design a new cost function of cooperators, which simply assumes that the longer the distance between two players, the higher cost the cooperator(s) of them have to pay. In this study, network substrates are generated by a previous spatial network model with a cost-benefit parameter controlling the network topology. Our simulations show that the greatest promotion of cooperation is achieved in the intermediate regime of the parameter, in which empirical estimates of various railway networks fall. Further, we investigate how the distribution of edges' geographical costs influences the evolutionary dynamics and consider three patterns of the distribution: an approximately-equal distribution, a diverse distribution, and a polarized distribution. For normal geographical networks which are generated using intermediate values of the cost-benefit parameter, a diverse distribution hinders the evolution of cooperation, whereas a polarized distribution lowers the threshold value of the amplification factor for cooperation in public goods game. These results are helpful for understanding the evolution of cooperation on real-world geographical networks.

The evolutions of medical building network structure for emerging infectious disease protection and control.

PubMed

Liu, Nan; Zhang, Hongzhe; Zhang, Shanshan

2014-12-01

Emerging infectious disease is one of the most minatory threats in modern society. A perfect medical building network system need to be established to protect and control emerging infectious disease. Although in China a preliminary medical building network is already set up with disease control center, the infectious disease hospital, infectious diseases department in general hospital and basic medical institutions, there are still many defects in this system, such as simple structural model, weak interoperability among subsystems, and poor capability of the medical building to adapt to outbreaks of infectious disease. Based on the characteristics of infectious diseases, the whole process of its prevention and control and the comprehensive influence factors, three-dimensional medical architecture network system is proposed as an inevitable trend. In this conception of medical architecture network structure, the evolutions are mentioned, such as from simple network system to multilayer space network system, from static network to dynamic network, and from mechanical network to sustainable network. Ultimately, a more adaptable and corresponsive medical building network system will be established and argued in this paper.

BioTapestry now provides a web application and improved drawing and layout tools

PubMed Central

Paquette, Suzanne M.; Leinonen, Kalle; Longabaugh, William J.R.

2016-01-01

Gene regulatory networks (GRNs) control embryonic development, and to understand this process in depth, researchers need to have a detailed understanding of both the network architecture and its dynamic evolution over time and space. Interactive visualization tools better enable researchers to conceptualize, understand, and share GRN models. BioTapestry is an established application designed to fill this role, and recent enhancements released in Versions 6 and 7 have targeted two major facets of the program. First, we introduced significant improvements for network drawing and automatic layout that have now made it much easier for the user to create larger, more organized network drawings. Second, we revised the program architecture so it could continue to support the current Java desktop Editor program, while introducing a new BioTapestry GRN Viewer that runs as a JavaScript web application in a browser. We have deployed a number of GRN models using this new web application. These improvements will ensure that BioTapestry remains viable as a research tool in the face of the continuing evolution of web technologies, and as our understanding of GRN models grows. PMID:27134726

BioTapestry now provides a web application and improved drawing and layout tools.

PubMed

Paquette, Suzanne M; Leinonen, Kalle; Longabaugh, William J R

2016-01-01

Gene regulatory networks (GRNs) control embryonic development, and to understand this process in depth, researchers need to have a detailed understanding of both the network architecture and its dynamic evolution over time and space. Interactive visualization tools better enable researchers to conceptualize, understand, and share GRN models. BioTapestry is an established application designed to fill this role, and recent enhancements released in Versions 6 and 7 have targeted two major facets of the program. First, we introduced significant improvements for network drawing and automatic layout that have now made it much easier for the user to create larger, more organized network drawings. Second, we revised the program architecture so it could continue to support the current Java desktop Editor program, while introducing a new BioTapestry GRN Viewer that runs as a JavaScript web application in a browser. We have deployed a number of GRN models using this new web application. These improvements will ensure that BioTapestry remains viable as a research tool in the face of the continuing evolution of web technologies, and as our understanding of GRN models grows.

A coupled geomorphic and ecological model of tidal marsh evolution.

PubMed

Kirwan, Matthew L; Murray, A Brad

2007-04-10

The evolution of tidal marsh platforms and interwoven channel networks cannot be addressed without treating the two-way interactions that link biological and physical processes. We have developed a 3D model of tidal marsh accretion and channel network development that couples physical sediment transport processes with vegetation biomass productivity. Tidal flow tends to cause erosion, whereas vegetation biomass, a function of bed surface depth below high tide, influences the rate of sediment deposition and slope-driven transport processes such as creek bank slumping. With a steady, moderate rise in sea level, the model builds a marsh platform and channel network with accretion rates everywhere equal to the rate of sea-level rise, meaning water depths and biological productivity remain temporally constant. An increase in the rate of sea-level rise, or a reduction in sediment supply, causes marsh-surface depths, biomass productivity, and deposition rates to increase while simultaneously causing the channel network to expand. Vegetation on the marsh platform can promote a metastable equilibrium where the platform maintains elevation relative to a rapidly rising sea level, although disturbance to vegetation could cause irreversible loss of marsh habitat.

Modelling landscape evolution at the flume scale

NASA Astrophysics Data System (ADS)

Cheraghi, Mohsen; Rinaldo, Andrea; Sander, Graham C.; Barry, D. Andrew

2017-04-01

The ability of a large-scale Landscape Evolution Model (LEM) to simulate the soil surface morphological evolution as observed in a laboratory flume (1-m × 2-m surface area) was investigated. The soil surface was initially smooth, and was subjected to heterogeneous rainfall in an experiment designed to avoid rill formation. Low-cohesive fine sand was placed in the flume while the slope and relief height were 5 % and 20 cm, respectively. Non-uniform rainfall with an average intensity of 85 mm h-1 and a standard deviation of 26 % was applied to the sediment surface for 16 h. We hypothesized that the complex overland water flow can be represented by a drainage discharge network, which was calculated via the micro-morphology and the rainfall distribution. Measurements included high resolution Digital Elevation Models that were captured at intervals during the experiment. The calibrated LEM captured the migration of the main flow path from the low precipitation area into the high precipitation area. Furthermore, both model and experiment showed a steep transition zone in soil elevation that moved upstream during the experiment. We conclude that the LEM is applicable under non-uniform rainfall and in the absence of surface incisions, thereby extending its applicability beyond that shown in previous applications. Keywords: Numerical simulation, Flume experiment, Particle Swarm Optimization, Sediment transport, River network evolution model.

Dynamic Trust Models between Users over Social Networks

DTIC Science & Technology

2016-03-30

SUPPLEMENTARY NOTES 14. ABSTRACT In this project, by focusing on a number of word -of- mouth communication websites, we attempted to...analyzed evolution of trust networks in social media sites from a perspective of mediators. To this end, we proposed two stochastic models that...focusing on a number of word -of- mouth communication websites, we first attempt to construct dynamic trust models between users that enable to explain trust

System crash as dynamics of complex networks.

PubMed

Yu, Yi; Xiao, Gaoxi; Zhou, Jie; Wang, Yubo; Wang, Zhen; Kurths, Jürgen; Schellnhuber, Hans Joachim

2016-10-18

Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the "peculiar" dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed.

Atmospheric reaction systems as null-models to identify structural traces of evolution in metabolism.

PubMed

Holme, Petter; Huss, Mikael; Lee, Sang Hoon

2011-05-06

The metabolism is the motor behind the biological complexity of an organism. One problem of characterizing its large-scale structure is that it is hard to know what to compare it to. All chemical reaction systems are shaped by the same physics that gives molecules their stability and affinity to react. These fundamental factors cannot be captured by standard null-models based on randomization. The unique property of organismal metabolism is that it is controlled, to some extent, by an enzymatic machinery that is subject to evolution. In this paper, we explore the possibility that reaction systems of planetary atmospheres can serve as a null-model against which we can define metabolic structure and trace the influence of evolution. We find that the two types of data can be distinguished by their respective degree distributions. This is especially clear when looking at the degree distribution of the reaction network (of reaction connected to each other if they involve the same molecular species). For the Earth's atmospheric network and the human metabolic network, we look into more detail for an underlying explanation of this deviation. However, we cannot pinpoint a single cause of the difference, rather there are several concurrent factors. By examining quantities relating to the modular-functional organization of the metabolism, we confirm that metabolic networks have a more complex modular organization than the atmospheric networks, but not much more. We interpret the more variegated modular arrangement of metabolism as a trace of evolved functionality. On the other hand, it is quite remarkable how similar the structures of these two types of networks are, which emphasizes that the constraints from the chemical properties of the molecules has a larger influence in shaping the reaction system than does natural selection.

Irregular Collective Behavior of Heterogeneous Neural Networks

NASA Astrophysics Data System (ADS)

Luccioli, Stefano; Politi, Antonio

2010-10-01

We investigate a network of integrate-and-fire neurons characterized by a distribution of spiking frequencies. Upon increasing the coupling strength, the model exhibits a transition from an asynchronous regime to a nontrivial collective behavior. Numerical simulations of large systems indicate that, at variance with the Kuramoto model, (i) the macroscopic dynamics stays irregular and (ii) the microscopic (single-neuron) evolution is linearly stable.

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

PubMed Central

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons. PMID:26880875

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

PubMed

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

NASA Astrophysics Data System (ADS)

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-08-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure.

PubMed

Schleussner, Carl-Friedrich; Donges, Jonathan F; Engemann, Denis A; Levermann, Anders

2016-08-11

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Statistical Analysis of Bus Networks in India

PubMed Central

2016-01-01

In this paper, we model the bus networks of six major Indian cities as graphs in L-space, and evaluate their various statistical properties. While airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus transport plays an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer basic questions on its evolution, growth, robustness and resiliency. Although the common feature of small-world property is observed, our analysis reveals a wide spectrum of network topologies arising due to significant variation in the degree-distribution patterns in the networks. We also observe that these networks although, robust and resilient to random attacks are particularly degree-sensitive. Unlike real-world networks, such as Internet, WWW and airline, that are virtual, bus networks are physically constrained. Our findings therefore, throw light on the evolution of such geographically and constrained networks that will help us in designing more efficient bus networks in the future. PMID:27992590

Effects of topology on network evolution

NASA Astrophysics Data System (ADS)

Oikonomou, Panos; Cluzel, Philippe

2006-08-01

The ubiquity of scale-free topology in nature raises the question of whether this particular network design confers an evolutionary advantage. A series of studies has identified key principles controlling the growth and the dynamics of scale-free networks. Here, we use neuron-based networks of boolean components as a framework for modelling a large class of dynamical behaviours in both natural and artificial systems. Applying a training algorithm, we characterize how networks with distinct topologies evolve towards a pre-established target function through a process of random mutations and selection. We find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. Whereas homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps, scale-free networks evolve rapidly and continuously. Remarkably, this latter property is robust to variations of the degree exponent. In contrast, homogeneous random networks require a specific tuning of their connectivity to optimize their ability to evolve. These results highlight an organizing principle that governs the evolution of complex networks and that can improve the design of engineered systems.

Introducing ab initio based neural networks for transition-rate prediction in kinetic Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Messina, Luca; Castin, Nicolas; Domain, Christophe; Olsson, Pär

2017-02-01

The quality of kinetic Monte Carlo (KMC) simulations of microstructure evolution in alloys relies on the parametrization of point-defect migration rates, which are complex functions of the local chemical composition and can be calculated accurately with ab initio methods. However, constructing reliable models that ensure the best possible transfer of physical information from ab initio to KMC is a challenging task. This work presents an innovative approach, where the transition rates are predicted by artificial neural networks trained on a database of 2000 migration barriers, obtained with density functional theory (DFT) in place of interatomic potentials. The method is tested on copper precipitation in thermally aged iron alloys, by means of a hybrid atomistic-object KMC model. For the object part of the model, the stability and mobility properties of copper-vacancy clusters are analyzed by means of independent atomistic KMC simulations, driven by the same neural networks. The cluster diffusion coefficients and mean free paths are found to increase with size, confirming the dominant role of coarsening of medium- and large-sized clusters in the precipitation kinetics. The evolution under thermal aging is in better agreement with experiments with respect to a previous interatomic-potential model, especially concerning the experiment time scales. However, the model underestimates the solubility of copper in iron due to the excessively high solution energy predicted by the chosen DFT method. Nevertheless, this work proves the capability of neural networks to transfer complex ab initio physical properties to higher-scale models, and facilitates the extension to systems with increasing chemical complexity, setting the ground for reliable microstructure evolution simulations in a wide range of alloys and applications.

A case study of evolutionary computation of biochemical adaptation

NASA Astrophysics Data System (ADS)

François, Paul; Siggia, Eric D.

2008-06-01

Simulations of evolution have a long history, but their relation to biology is questioned because of the perceived contingency of evolution. Here we provide an example of a biological process, adaptation, where simulations are argued to approach closer to biology. Adaptation is a common feature of sensory systems, and a plausible component of other biochemical networks because it rescales upstream signals to facilitate downstream processing. We create random gene networks numerically, by linking genes with interactions that model transcription, phosphorylation and protein-protein association. We define a fitness function for adaptation in terms of two functional metrics, and show that any reasonable combination of them will yield the same adaptive networks after repeated rounds of mutation and selection. Convergence to these networks is driven by positive selection and thus fast. There is always a path in parameter space of continuously improving fitness that leads to perfect adaptation, implying that the actual mutation rates we use in the simulation do not bias the results. Our results imply a kinetic view of evolution, i.e., it favors gene networks that can be learned quickly from the random examples supplied by mutation. This formulation allows for deductive predictions of the networks realized in nature.

Hybrid evolutionary computing model for mobile agents of wireless Internet multimedia

NASA Astrophysics Data System (ADS)

Hortos, William S.

2001-03-01

The ecosystem is used as an evolutionary paradigm of natural laws for the distributed information retrieval via mobile agents to allow the computational load to be added to server nodes of wireless networks, while reducing the traffic on communication links. Based on the Food Web model, a set of computational rules of natural balance form the outer stage to control the evolution of mobile agents providing multimedia services with a wireless Internet protocol WIP. The evolutionary model shows how mobile agents should behave with the WIP, in particular, how mobile agents can cooperate, compete and learn from each other, based on an underlying competition for radio network resources to establish the wireless connections to support the quality of service QoS of user requests. Mobile agents are also allowed to clone themselves, propagate and communicate with other agents. A two-layer model is proposed for agent evolution: the outer layer is based on the law of natural balancing, the inner layer is based on a discrete version of a Kohonen self-organizing feature map SOFM to distribute network resources to meet QoS requirements. The former is embedded in the higher OSI layers of the WIP, while the latter is used in the resource management procedures of Layer 2 and 3 of the protocol. Algorithms for the distributed computation of mobile agent evolutionary behavior are developed by adding a learning state to the agent evolution state diagram. When an agent is in an indeterminate state, it can communicate to other agents. Computing models can be replicated from other agents. Then the agents transitions to the mutating state to wait for a new information-retrieval goal. When a wireless terminal or station lacks a network resource, an agent in the suspending state can change its policy to submit to the environment before it transitions to the searching state. The agents learn the facts of agent state information entered into an external database. In the cloning process, two agents on a host station sharing a common goal can be merged or married to compose a new agent. Application of the two-layer set of algorithms for mobile agent evolution, performed in a distributed processing environment, is made to the QoS management functions of the IP multimedia IM sub-network of the third generation 3G Wideband Code-division Multiple Access W-CDMA wireless network.

Revisiting node-based SIR models in complex networks with degree correlations

NASA Astrophysics Data System (ADS)

Wang, Yi; Cao, Jinde; Alofi, Abdulaziz; AL-Mazrooei, Abdullah; Elaiw, Ahmed

2015-11-01

In this paper, we consider two growing networks which will lead to the degree-degree correlations between two nearest neighbors in the network. When the network grows to some certain size, we introduce an SIR-like disease such as pandemic influenza H1N1/09 to the population. Due to its rapid spread, the population size changes slowly, and thus the disease spreads on correlated networks with approximately fixed size. To predict the disease evolution on correlated networks, we first review two node-based SIR models incorporating degree correlations and an edge-based SIR model without considering degree correlation, and then compare the predictions of these models with stochastic SIR simulations, respectively. We find that the edge-based model, even without considering degree correlations, agrees much better than the node-based models incorporating degree correlations with stochastic SIR simulations in many respects. Moreover, simulation results show that for networks with positive correlation, the edge-based model provides a better upper bound of the cumulative incidence than the node-based SIR models, whereas for networks with negative correlation, it provides a lower bound of the cumulative incidence.

The structure and resilience of financial market networks

NASA Astrophysics Data System (ADS)

Kauê Dal'Maso Peron, Thomas; da Fontoura Costa, Luciano; Rodrigues, Francisco A.

2012-03-01

Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

Nonlinear dynamic evolution and control in CCFN with mixed attachment mechanisms

NASA Astrophysics Data System (ADS)

Wang, Jianrong; Wang, Jianping; Han, Dun

2017-01-01

In recent years, wireless communication plays an important role in our lives. Cooperative communication, is used by a mobile station with single antenna to share with each other forming a virtual MIMO antenna system, will become a development with a diversity gain for wireless communication in tendency future. In this paper, a fitness model of evolution network based on complex networks with mixed attachment mechanisms is devised in order to study an actual network-CCFN (cooperative communication fitness network). Firstly, the evolution of CCFN is given by four cases with different probabilities, and the rate equations of nodes degree are presented to analyze the evolution of CCFN. Secondly, the degree distribution is analyzed by calculating the rate equation and numerical simulation with the examples of four fitness distributions such as power law, uniform fitness distribution, exponential fitness distribution and Rayleigh fitness distribution. Finally, the robustness of CCFN is studied by numerical simulation with four fitness distributions under random attack and intentional attack to analyze the effects of degree distribution, average path length and average degree. The results of this paper offers insights for building CCFN systems in order to program communication resources.

Cognitive Approaches to Posttraumatic Stress Disorder: The Evolution of Multirepresentational Theorizing

ERIC Educational Resources Information Center

Dalgleish, Tim

2004-01-01

The evolution of multirepresentational cognitive theorizing in psychopathology is illustrated by detailed discussion and analysis of a number of prototypical models of posttraumatic stress disorder (PTSD). Network and schema theories, which focus on a single, explicit aspect/format of mental representation, are compared with theories that focus on…

Unification of small and large time scales for biological evolution: deviations from power law.

PubMed

Chowdhury, Debashish; Stauffer, Dietrich; Kunwar, Ambarish

2003-02-14

We develop a unified model that describes both "micro" and "macro" evolutions within a single theoretical framework. The ecosystem is described as a dynamic network; the population dynamics at each node of this network describes the "microevolution" over ecological time scales (i.e., birth, ageing, and natural death of individual organisms), while the appearance of new nodes, the slow changes of the links, and the disappearance of existing nodes accounts for the "macroevolution" over geological time scales (i.e., the origination, evolution, and extinction of species). In contrast to several earlier claims in the literature, we observe strong deviations from power law in the regime of long lifetimes.

Environmental Noise, Genetic Diversity and the Evolution of Evolvability and Robustness in Model Gene Networks

PubMed Central

Steiner, Christopher F.

2012-01-01

The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or “evolvability”) can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise) compared to populations in stable or randomly varying (white noise) environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions. PMID:23284934

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1986-01-01

Over the past two decades, fiber optics has emerged as a highly practical and cost-efficient communications technology. Its competitiveness vis-a-vis other transmission media, especially satellite, has become a critical question. This report studies the likely evolution and application of fiber optic networks in the United States to the end of the century. The outlook for the technology of fiber systems is assessed and forecast, scenarios of the evolution of fiber optic network development are constructed, and costs to provide service are determined and examined parametrically as a function of network size and traffic carried. Volume 1 consists of the Executive Summary. Volume 2 focuses on fiber optic technology and long distance fiber optic networks. Volume 3 develops a traffic and financial model of a nationwide long distance transmission network. Among the study's most important conclusions are: revenue requirements per circuit for LATA-to-LATA fiber optic links are less than one cent per call minute; multiplex equipment, which is likely to be required in any competing system, is the largest contributor to circuit costs; the potential capacity of fiber optic cable is very large and as yet undefined; and fiber optic transmission combined with other network optimization schemes can lead to even lower costs than those identified in this study.

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network.

PubMed

Di Paola, Vieri; Marijuán, Pedro C; Lahoz-Beltra, Rafael

2004-01-01

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.

Biological evolution and statistical physics

NASA Astrophysics Data System (ADS)

Drossel, Barbara

2001-03-01

This review is an introduction to theoretical models and mathematical calculations for biological evolution, aimed at physicists. The methods in the field are naturally very similar to those used in statistical physics, although the majority of publications have appeared in biology journals. The review has three parts, which can be read independently. The first part deals with evolution in fitness landscapes and includes Fisher's theorem, adaptive walks, quasispecies models, effects of finite population sizes, and neutral evolution. The second part studies models of coevolution, including evolutionary game theory, kin selection, group selection, sexual selection, speciation, and coevolution of hosts and parasites. The third part discusses models for networks of interacting species and their extinction avalanches. Throughout the review, attention is paid to giving the necessary biological information, and to pointing out the assumptions underlying the models, and their limits of validity.

Good Samaritans in Networks: An Experiment on How Networks Influence Egalitarian Sharing and the Evolution of Inequality

PubMed Central

Chiang, Yen-Sheng

2015-01-01

The fact that the more resourceful people are sharing with the poor to mitigate inequality—egalitarian sharing—is well documented in the behavioral science research. How inequality evolves as a result of egalitarian sharing is determined by the structure of “who gives whom”. While most prior experimental research investigates allocation of resources in dyads and groups, the paper extends the research of egalitarian sharing to networks for a more generalized structure of social interaction. An agent-based model is proposed to predict how actors, linked in networks, share their incomes with neighbors. A laboratory experiment with human subjects further shows that income distributions evolve to different states in different network topologies. Inequality is significantly reduced in networks where the very rich and the very poor are connected so that income discrepancy is salient enough to motivate the rich to share their incomes with the poor. The study suggests that social networks make a difference in how egalitarian sharing influences the evolution of inequality. PMID:26061642

Structural Behavioral Study on the General Aviation Network Based on Complex Network

NASA Astrophysics Data System (ADS)

Zhang, Liang; Lu, Na

2017-12-01

The general aviation system is an open and dissipative system with complex structures and behavioral features. This paper has established the system model and network model for general aviation. We have analyzed integral attributes and individual attributes by applying the complex network theory and concluded that the general aviation network has influential enterprise factors and node relations. We have checked whether the network has small world effect, scale-free property and network centrality property which a complex network should have by applying degree distribution of functions and proved that the general aviation network system is a complex network. Therefore, we propose to achieve the evolution process of the general aviation industrial chain to collaborative innovation cluster of advanced-form industries by strengthening network multiplication effect, stimulating innovation performance and spanning the structural hole path.

Model of community emergence in weighted social networks

NASA Astrophysics Data System (ADS)

Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

2009-04-01

Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

In silico modeling of the yeast protein and protein family interaction network

NASA Astrophysics Data System (ADS)

Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting

PubMed Central

van Maanen, B.; Coco, G.; Bryan, K. R.

2015-01-01

An ecomorphodynamic model was developed to study how Avicennia marina mangroves influence channel network evolution in sandy tidal embayments. The model accounts for the effects of mangrove trees on tidal flow patterns and sediment dynamics. Mangrove growth is in turn controlled by hydrodynamic conditions. The presence of mangroves was found to enhance the initiation and branching of tidal channels, partly because the extra flow resistance in mangrove forests favours flow concentration, and thus sediment erosion in between vegetated areas. The enhanced branching of channels is also the result of a vegetation-induced increase in erosion threshold. On the other hand, this reduction in bed erodibility, together with the soil expansion driven by organic matter production, reduces the landward expansion of channels. The ongoing accretion in mangrove forests ultimately drives a reduction in tidal prism and an overall retreat of the channel network. During sea-level rise, mangroves can potentially enhance the ability of the soil surface to maintain an elevation within the upper portion of the intertidal zone, while hindering both the branching and headward erosion of the landward expanding channels. The modelling results presented here indicate the critical control exerted by ecogeomorphological interactions in driving landscape evolution. PMID:26339195

Punctuated equilibrium in the large-scale evolution of programming languages†

PubMed Central

Valverde, Sergi; Solé, Ricard V.

2015-01-01

The analogies and differences between biological and cultural evolution have been explored by evolutionary biologists, historians, engineers and linguists alike. Two well-known domains of cultural change are language and technology. Both share some traits relating the evolution of species, but technological change is very difficult to study. A major challenge in our way towards a scientific theory of technological evolution is how to properly define evolutionary trees or clades and how to weight the role played by horizontal transfer of information. Here, we study the large-scale historical development of programming languages, which have deeply marked social and technological advances in the last half century. We analyse their historical connections using network theory and reconstructed phylogenetic networks. Using both data analysis and network modelling, it is shown that their evolution is highly uneven, marked by innovation events where new languages are created out of improved combinations of different structural components belonging to previous languages. These radiation events occur in a bursty pattern and are tied to novel technological and social niches. The method can be extrapolated to other systems and consistently captures the major classes of languages and the widespread horizontal design exchanges, revealing a punctuated evolutionary path. PMID:25994298

Leader's opinion priority bounded confidence model for network opinion evolution

NASA Astrophysics Data System (ADS)

Zhu, Meixia; Xie, Guangqiang

2017-08-01

Aiming at the weight of trust someone given to participate in the interaction in Hegselmann-Krause's type consensus model is the same and virtual social networks among individuals with different level of education, personal influence, etc. For differences between agents, a novelty bounded confidence model was proposed with leader's opinion considered priority. Interaction neighbors can be divided into two kinds. The first kind is made up of "opinion leaders" group, another kind is made up of ordinary people. For different groups to give different weights of trust. We also analyzed the related characteristics of the new model under the symmetrical bounded confidence parameters and combined with the classical HK model were analyzed. Simulation experiment results show that no matter the network size and initial view is subject to uniform distribution or discrete distribution. We can control the "opinion-leader" good change the number of views and values, and even improve the convergence speed. Experiment also found that the choice of "opinion leaders" is not the more the better, the model well explain how the "opinion leader" in the process of the evolution of the public opinion play the role of the leader.

Neutral evolution of mutational robustness

PubMed Central

van Nimwegen, Erik; Crutchfield, James P.; Huynen, Martijn

1999-01-01

We introduce and analyze a general model of a population evolving over a network of selectively neutral genotypes. We show that the population’s limit distribution on the neutral network is solely determined by the network topology and given by the principal eigenvector of the network’s adjacency matrix. Moreover, the average number of neutral mutant neighbors per individual is given by the matrix spectral radius. These results quantify the extent to which populations evolve mutational robustness—the insensitivity of the phenotype to mutations—and thus reduce genetic load. Because the average neutrality is independent of evolutionary parameters—such as mutation rate, population size, and selective advantage—one can infer global statistics of neutral network topology by using simple population data available from in vitro or in vivo evolution. Populations evolving on neutral networks of RNA secondary structures show excellent agreement with our theoretical predictions. PMID:10449760

Discovering urban mobility patterns with PageRank based traffic modeling and prediction

NASA Astrophysics Data System (ADS)

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-11-01

Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

Topographic evolution of orogens: The long term perspective

NASA Astrophysics Data System (ADS)

Robl, Jörg; Hergarten, Stefan; Prasicek, Günther

2017-04-01

The landscape of mountain ranges reflects the competition of tectonics and climate, that build up and destroy topography, respectively. While there is a broad consensus on the acting processes, there is a vital debate whether the topography of individual orogens reflects stages of growth, steady-state or decay. This debate is fuelled by the million-year time scales hampering direct observations on landscape evolution in mountain ranges, the superposition of various process patterns and the complex interactions among different processes. In this presentation we focus on orogen-scale landscape evolution based on time-dependent numerical models and explore model time series to constrain the development of mountain range topography during an orogenic cycle. The erosional long term response of rivers and hillslopes to uplift can be mathematically formalised by the stream power and mass diffusion equations, respectively, which enables us to describe the time-dependent evolution of topography in orogens. Based on a simple one-dimensional model consisting of two rivers separated by a watershed we explain the influence of uplift rate and rock erodibility on steady-state channel profiles and show the time-dependent development of the channel - drainage divide system. The effect of dynamic drainage network reorganization adds additional complexity and its effect on topography is explored on the basis of two-dimensional models. Further complexity is introduced by coupling a mechanical model (thin viscous sheet approach) describing continental collision, crustal thickening and topography formation with a stream power-based landscape evolution model. Model time series show the impact of crustal deformation on drainage networks and consequently on the evolution of mountain range topography (Robl et al., in review). All model outcomes, from simple one-dimensional to coupled two dimensional models are presented as movies featuring a high spatial and temporal resolution. Robl, J., S. Hergarten, and G. Prasicek (in review), The topographic state of mountain ranges, Earth Science Reviews.

Two-population dynamics in a growing network model

NASA Astrophysics Data System (ADS)

Ivanova, Kristinka; Iordanov, Ivan

2012-02-01

We introduce a growing network evolution model with nodal attributes. The model describes the interactions between potentially violent V and non-violent N agents who have different affinities in establishing connections within their own population versus between the populations. The model is able to generate all stable triads observed in real social systems. In the framework of rate equations theory, we employ the mean-field approximation to derive analytical expressions of the degree distribution and the local clustering coefficient for each type of nodes. Analytical derivations agree well with numerical simulation results. The assortativity of the potentially violent network qualitatively resembles the connectivity pattern in terrorist networks that was recently reported. The assortativity of the network driven by aggression shows clearly different behavior than the assortativity of the networks with connections of non-aggressive nature in agreement with recent empirical results of an online social system.

Epidemic spreading in time-varying community networks.

PubMed

Ren, Guangming; Wang, Xingyuan

2014-06-01

The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold qc. The epidemic will survive when q > qc and die when q < qc. These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure.

Social power and opinion formation in complex networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2013-02-01

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts-Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts-Strogatz networks could not significantly change the consensus profile.

Active influence in dynamical models of structural balance in social networks

NASA Astrophysics Data System (ADS)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Multilayer Brain Networks

NASA Astrophysics Data System (ADS)

Vaiana, Michael; Muldoon, Sarah Feldt

2018-01-01

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

Collection Directions: The Evolution of Library Collections and Collecting

ERIC Educational Resources Information Center

Dempsey, Lorcan; Malpas, Constance; Lavoie, Brian

2014-01-01

This article takes a broad view of the evolution of collecting behaviors in a network environment and suggests some future directions based on various simple models. The authors look at the changing dynamics of print collections, at the greater engagement with research and learning behaviors, and at trends in scholarly communication. The goal is…

Interaction of chimera states in a multilayered network of nonlocally coupled oscillators

NASA Astrophysics Data System (ADS)

Goremyko, M. V.; Maksimenko, V. A.; Makarov, V. V.; Ghosh, D.; Bera, B.; Dana, S. K.; Hramov, A. E.

2017-08-01

The processes of formation and evolution of chimera states in the model of a multilayered network of nonlinear elements with complex coupling topology are studied. A two-layered network of nonlocally intralayer-coupled Kuramoto-Sakaguchi phase oscillators is taken as the object of investigation. Different modes implemented in this system upon variation of the degree of interlayer interaction are demonstrated.

SSIC model: A multi-layer model for intervention of online rumors spreading

NASA Astrophysics Data System (ADS)

Tian, Ru-Ya; Zhang, Xue-Fu; Liu, Yi-Jun

2015-06-01

SIR model is a classical model to simulate rumor spreading, while the supernetwork is an effective tool for modeling complex systems. Based on the Opinion SuperNetwork involving Social Sub-network, Environmental Sub-network, Psychological Sub-network, and Viewpoint Sub-network, drawing from the modeling idea of SIR model, this paper designs super SIC model (SSIC model) and its evolution rules, and also analyzes intervention effects on public opinion of four elements of supernetwork, which are opinion agent, opinion environment, agent's psychology and viewpoint. Studies show that, the SSIC model based on supernetwork has effective intervention effects on rumor spreading. It is worth noting that (i) identifying rumor spreaders in Social Sub-network and isolating them can achieve desired intervention results, (ii) improving environmental information transparency so that the public knows as much information as possible to reduce the rumors is a feasible way to intervene, (iii) persuading wavering neutrals has better intervention effects than clarifying rumors already spread everywhere, so rumors should be intervened in properly in time by psychology counseling.

A chemical model for the interstellar medium in galaxies

NASA Astrophysics Data System (ADS)

Bovino, S.; Grassi, T.; Capelo, Pedro R.; Schleicher, D. R. G.; Banerjee, R.

2016-05-01

Aims: We present and test chemical models for three-dimensional hydrodynamical simulations of galaxies. We explore the effect of changing key parameters such as metallicity, radiation, and non-equilibrium versus equilibrium metal cooling approximations on the transition between the gas phases in the interstellar medium. Methods: The microphysics was modelled by employing the public chemistry package KROME, and the chemical networks were tested to work in a wide range of densities and temperatures. We describe a simple H/He network following the formation of H2 and a more sophisticated network that includes metals. Photochemistry, thermal processes, and different prescriptions for the H2 catalysis on dust are presented and tested within a one-zone framework. The resulting network is made publicly available on the KROME webpage. Results: We find that employing an accurate treatment of the dust-related processes induces a faster HI-H2 transition. In addition, we show when the equilibrium assumption for metal cooling holds and how a non-equilibrium approach affects the thermal evolution of the gas and the HII-HI transition. Conclusions: These models can be employed in any hydrodynamical code via an interface to KROME and can be applied to different problems including isolated galaxies, cosmological simulations of galaxy formation and evolution, supernova explosions in molecular clouds, and the modelling of star-forming regions. The metal network can be used for a comparison with observational data of CII 158 μm emission both for high-redshift and for local galaxies.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

Innovation diffusion on time-varying activity driven networks

NASA Astrophysics Data System (ADS)

Rizzo, Alessandro; Porfiri, Maurizio

2016-01-01

Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass' model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

Neural network approach to prediction of temperatures around groundwater heat pump systems

NASA Astrophysics Data System (ADS)

Lo Russo, Stefano; Taddia, Glenda; Gnavi, Loretta; Verda, Vittorio

2014-01-01

A fundamental aspect in groundwater heat pump (GWHP) plant design is the correct evaluation of the thermally affected zone that develops around the injection well. This is particularly important to avoid interference with previously existing groundwater uses (wells) and underground structures. Temperature anomalies are detected through numerical methods. Computational fluid dynamic (CFD) models are widely used in this field because they offer the opportunity to calculate the time evolution of the thermal plume produced by a heat pump. The use of neural networks is proposed to determine the time evolution of the groundwater temperature downstream of an installation as a function of the possible utilization profiles of the heat pump. The main advantage of neural network modeling is the possibility of evaluating a large number of scenarios in a very short time, which is very useful for the preliminary analysis of future multiple installations. The neural network is trained using the results from a CFD model (FEFLOW) applied to the installation at Politecnico di Torino (Italy) under several operating conditions. The final results appeared to be reliable and the temperature anomalies around the injection well appeared to be well predicted.

Weighted Scaling in Non-growth Random Networks

NASA Astrophysics Data System (ADS)

Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li

2012-09-01

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

PubMed Central

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-01-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking. PMID:27510641

Assortative Mating: Encounter-Network Topology and the Evolution of Attractiveness

PubMed Central

Dipple, S.; Jia, T.; Caraco, T.; Korniss, G.; Szymanski, B. K.

2017-01-01

We model a social-encounter network where linked nodes match for reproduction in a manner depending probabilistically on each node’s attractiveness. The developed model reveals that increasing either the network’s mean degree or the “choosiness” exercised during pair formation increases the strength of positive assortative mating. That is, we note that attractiveness is correlated among mated nodes. Their total number also increases with mean degree and selectivity during pair formation. By iterating over the model’s mapping of parents onto offspring across generations, we study the evolution of attractiveness. Selection mediated by exclusion from reproduction increases mean attractiveness, but is rapidly balanced by skew in the offspring distribution of highly attractive mated pairs. PMID:28345625

Discrete dynamic modeling of cellular signaling networks.

PubMed

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Network evolution by nonlinear preferential rewiring of edges

NASA Astrophysics Data System (ADS)

Xu, Xin-Jian; Hu, Xiao-Ming; Zhang, Li-Jie

2011-06-01

The mathematical framework for small-world networks proposed in a seminal paper by Watts and Strogatz sparked a widespread interest in modeling complex networks in the past decade. However, most of research contributing to static models is in contrast to real-world dynamic networks, such as social and biological networks, which are characterized by rearrangements of connections among agents. In this paper, we study dynamic networks evolved by nonlinear preferential rewiring of edges. The total numbers of vertices and edges of the network are conserved, but edges are continuously rewired according to the nonlinear preference. Assuming power-law kernels with exponents α and β, the network structures in stationary states display a distinct behavior, depending only on β. For β>1, the network is highly heterogeneous with the emergence of starlike structures. For β<1, the network is widely homogeneous with a typical connectivity. At β=1, the network is scale free with an exponential cutoff.

Networks in Cell Biology

NASA Astrophysics Data System (ADS)

Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

2010-05-01

Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

Emergent explosive synchronization in adaptive complex networks

NASA Astrophysics Data System (ADS)

Avalos-Gaytán, Vanesa; Almendral, Juan A.; Leyva, I.; Battiston, F.; Nicosia, V.; Latora, V.; Boccaletti, S.

2018-04-01

Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. In this work, we propose an adaptive network model where the dynamical evolution of the node states toward synchronization is coupled with an evolution of the link weights based on an anti-Hebbian adaptive rule, which accounts for the presence of inhibitory effects in the system. We found that the emergent networks spontaneously develop the structural conditions to sustain explosive synchronization. Our results can enlighten the shaping mechanisms at the heart of the structural and dynamical organization of some relevant biological systems, namely, brain networks, for which the emergence of explosive synchronization has been observed.

Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses?

NASA Astrophysics Data System (ADS)

Rings, Thorsten; Lehnertz, Klaus

2016-09-01

We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain networks using intracranial electroencephalographic recordings from an epilepsy patient. For such networks, both direct and indirect directional couplings can be expected, given the brain's connection structure and effects that may arise from limitations inherent to the recording technique. Our findings indicate that particularly in larger networks (number of nodes ≫10 ), the partialized approach does not provide information about directional couplings extending the information gained with the evolution map approach.

Emergent explosive synchronization in adaptive complex networks.

PubMed

Avalos-Gaytán, Vanesa; Almendral, Juan A; Leyva, I; Battiston, F; Nicosia, V; Latora, V; Boccaletti, S

2018-04-01

Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. In this work, we propose an adaptive network model where the dynamical evolution of the node states toward synchronization is coupled with an evolution of the link weights based on an anti-Hebbian adaptive rule, which accounts for the presence of inhibitory effects in the system. We found that the emergent networks spontaneously develop the structural conditions to sustain explosive synchronization. Our results can enlighten the shaping mechanisms at the heart of the structural and dynamical organization of some relevant biological systems, namely, brain networks, for which the emergence of explosive synchronization has been observed.

Recommendation in evolving online networks

NASA Astrophysics Data System (ADS)

Hu, Xiao; Zeng, An; Shang, Ming-Sheng

2016-02-01

Recommender system is an effective tool to find the most relevant information for online users. By analyzing the historical selection records of users, recommender system predicts the most likely future links in the user-item network and accordingly constructs a personalized recommendation list for each user. So far, the recommendation process is mostly investigated in static user-item networks. In this paper, we propose a model which allows us to examine the performance of the state-of-the-art recommendation algorithms in evolving networks. We find that the recommendation accuracy in general decreases with time if the evolution of the online network fully depends on the recommendation. Interestingly, some randomness in users' choice can significantly improve the long-term accuracy of the recommendation algorithm. When a hybrid recommendation algorithm is applied, we find that the optimal parameter gradually shifts towards the diversity-favoring recommendation algorithm, indicating that recommendation diversity is essential to keep a high long-term recommendation accuracy. Finally, we confirm our conclusions by studying the recommendation on networks with the real evolution data.

Conformity hinders the evolution of cooperation on scale-free networks

NASA Astrophysics Data System (ADS)

Peña, Jorge; Volken, Henri; Pestelacci, Enea; Tomassini, Marco

2009-07-01

We study the effects of conformity, the tendency of humans to imitate locally common behaviors, in the evolution of cooperation when individuals occupy the vertices of a graph and engage in the one-shot prisoner’s dilemma or the snowdrift game with their neighbors. Two different graphs are studied: rings (one-dimensional lattices with cyclic boundary conditions) and scale-free networks of the Barabási-Albert type. The proposed evolutionary-graph model is studied both by means of Monte Carlo simulations and an extended pair-approximation technique. We find improved levels of cooperation when evolution is carried on rings and individuals imitate according to both the traditional payoff bias and a conformist bias. More importantly, we show that scale-free networks are no longer powerful amplifiers of cooperation when fair amounts of conformity are introduced in the imitation rules of the players. Such weakening of the cooperation-promoting abilities of scale-free networks is the result of a less biased flow of information in scale-free topologies, making hubs more susceptible of being influenced by less-connected neighbors.

Evolution of Cooperation in Social Dilemmas on Complex Networks

PubMed Central

Iyer, Swami; Killingback, Timothy

2016-01-01

Cooperation in social dilemmas is essential for the functioning of systems at multiple levels of complexity, from the simplest biological organisms to the most sophisticated human societies. Cooperation, although widespread, is fundamentally challenging to explain evolutionarily, since natural selection typically favors selfish behavior which is not socially optimal. Here we study the evolution of cooperation in three exemplars of key social dilemmas, representing the prisoner’s dilemma, hawk-dove and coordination classes of games, in structured populations defined by complex networks. Using individual-based simulations of the games on model and empirical networks, we give a detailed comparative study of the effects of the structural properties of a network, such as its average degree, variance in degree distribution, clustering coefficient, and assortativity coefficient, on the promotion of cooperative behavior in all three classes of games. PMID:26928428

Complex quantum network geometries: Evolution and phase transitions

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Complex quantum network geometries: Evolution and phase transitions.

PubMed

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Effects of adaptive dynamical linking in networked games

NASA Astrophysics Data System (ADS)

Yang, Zhihu; Li, Zhi; Wu, Te; Wang, Long

2013-10-01

The role of dynamical topologies in the evolution of cooperation has received considerable attention, as some studies have demonstrated that dynamical networks are much better than static networks in terms of boosting cooperation. Here we study a dynamical model of evolution of cooperation on stochastic dynamical networks in which there are no permanent partners to each agent. Whenever a new link is created, its duration is randomly assigned without any bias or preference. We allow the agent to adaptively adjust the duration of each link during the evolution in accordance with the feedback from game interactions. By Monte Carlo simulations, we find that cooperation can be remarkably promoted by this adaptive dynamical linking mechanism both for the game of pairwise interactions, such as the Prisoner's Dilemma game (PDG), and for the game of group interactions, illustrated by the public goods game (PGG). And the faster the adjusting rate, the more successful the evolution of cooperation. We also show that in this context weak selection favors cooperation much more than strong selection does. What is particularly meaningful is that the prosperity of cooperation in this study indicates that the rationality and selfishness of a single agent in adjusting social ties can lead to the progress of altruism of the whole population.

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations

PubMed Central

Beleva Guthrie, Violeta; Masica, David L; Fraser, Andrew; Federico, Joseph; Fan, Yunfan; Camps, Manel; Karchin, Rachel

2018-01-01

Abstract The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous β-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure. PMID:29522102

Random graph models for dynamic networks

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Moore, Cristopher; Newman, Mark E. J.

2017-10-01

Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.

Evolution of cooperation under social pressure in multiplex networks

NASA Astrophysics Data System (ADS)

Pereda, María

2016-09-01

In this work, we aim to contribute to the understanding of human prosocial behavior by studying the influence that a particular form of social pressure, "being watched," has on the evolution of cooperative behavior. We study how cooperation emerges in multiplex complex topologies by analyzing a particular bidirectionally coupled dynamics on top of a two-layer multiplex network (duplex). The coupled dynamics appears between the prisoner's dilemma game in a network and a threshold cascade model in the other. The threshold model is intended to abstract the behavior of a network of vigilant nodes that impose the pressure of being observed altering hence the temptation to defect of the dilemma. Cooperation or defection in the game also affects the state of a node of being vigilant. We analyze these processes on different duplex networks structures and assess the influence of the topology, average degree and correlated multiplexity, on the outcome of cooperation. Interestingly, we find that the social pressure of vigilance may impact cooperation positively or negatively, depending on the duplex structure, specifically the degree correlations between layers is determinant. Our results give further quantitative insights in the promotion of cooperation under social pressure.

Evolution of cooperation under social pressure in multiplex networks.

PubMed

Pereda, María

2016-09-01

In this work, we aim to contribute to the understanding of human prosocial behavior by studying the influence that a particular form of social pressure, "being watched," has on the evolution of cooperative behavior. We study how cooperation emerges in multiplex complex topologies by analyzing a particular bidirectionally coupled dynamics on top of a two-layer multiplex network (duplex). The coupled dynamics appears between the prisoner's dilemma game in a network and a threshold cascade model in the other. The threshold model is intended to abstract the behavior of a network of vigilant nodes that impose the pressure of being observed altering hence the temptation to defect of the dilemma. Cooperation or defection in the game also affects the state of a node of being vigilant. We analyze these processes on different duplex networks structures and assess the influence of the topology, average degree and correlated multiplexity, on the outcome of cooperation. Interestingly, we find that the social pressure of vigilance may impact cooperation positively or negatively, depending on the duplex structure, specifically the degree correlations between layers is determinant. Our results give further quantitative insights in the promotion of cooperation under social pressure.

Influence of reciprocal edges on degree distribution and degree correlations

NASA Astrophysics Data System (ADS)

Zlatić, Vinko; Štefančić, Hrvoje

2009-07-01

Reciprocal edges represent the lowest-order cycle possible to find in directed graphs without self-loops. Representing also a measure of feedback between vertices, it is interesting to understand how reciprocal edges influence other properties of complex networks. In this paper, we focus on the influence of reciprocal edges on vertex degree distribution and degree correlations. We show that there is a fundamental difference between properties observed on the static network compared to the properties of networks, which are obtained by simple evolution mechanism driven by reciprocity. We also present a way to statistically infer the portion of reciprocal edges, which can be explained as a consequence of feedback process on the static network. In the rest of the paper, the influence of reciprocal edges on a model of growing network is also presented. It is shown that our model of growing network nicely interpolates between Barabási-Albert (BA) model for undirected and the BA model for directed networks.

Topology of molecular interaction networks.

PubMed

Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

2013-09-16

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Topology of molecular interaction networks

PubMed Central

2013-01-01

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

Autonomous Agent-Based Systems and Their Applications in Fluid Dynamics, Particle Separation, and Co-evolving Networks

NASA Astrophysics Data System (ADS)

Graeser, Oliver

This thesis comprises three parts, reporting research results in Fluid Dynamics (Part I), Particle Separation (Part II) and Co-evolving Networks (Part III). Part I deals with the simulation of fluid dynamics using the lattice-Boltzmann method. Microfluidic devices often feature two-dimensional, repetitive arrays. Flows through such devices are pressure-driven and confined by solid walls. We have defined new adaptive generalised periodic boundary conditions to represent the effects of outer solid walls, and are thus able to exploit the periodicity of the array by simulating the flow through one unit cell in lieu of the entire device. The so-calculated fully developed flow describes the flow through the entire array accurately, but with computational requirements that are reduced according to the dimensions of the array. Part II discusses the problem of separating macromolecules like proteins or DNA coils. The reliable separation of such molecules is a crucial task in molecular biology. The use of Brownian ratchets as mechanisms for the separation of such particles has been proposed and discussed during the last decade. Pressure-driven flows have so far been dismissed as possible driving forces for Brownian ratchets, as they do not generate ratchet asymmetry. We propose a microfluidic design that uses pressure-driven flows to create asymmetry and hence allows particle separation. The dependence of the asymmetry on various factors of the microfluidic geometry is discussed. We further exemplify the feasibility of our approach using Brownian dynamics simulations of particles of different sizes in such a device. The results show that ratchet-based particle separation using flows as the driving force is possible. Simulation results and ratchet theory predictions are in excellent agreement. Part III deals with the co-evolution of networks and dynamic models. A group of agents occupies the nodes of a network, which defines the relationship between these agents. The evolution of the agents is defined by the rules of the dynamic model and depends on the relationship between agents, i.e., the state of the network. In return, the evolution of the network depends on the state of the dynamic model. The concept is introduced through the adaptive SIS model. We show that the previously used criterion determining the critical infected fraction, i.e., the number of infected agents required to sustain the epidemic, is inappropriate for this model. We introduce a different criterion and show that the critical infected fraction so determined is in good agreement with results obtained by numerical simulations. We further discuss the concept of co-evolving dynamics using the Snowdrift Game as a model paradigm. Co-evolution occurs through agents cutting dissatisfied links and rewiring to other agents at random. The effect of co-evolution on the emergence of cooperation is discussed using a mean-field theory and numerical simulations. A transition between a connected and a disconnected, highly cooperative state of the system is observed, and explained using the mean-field model. Quantitative deviations regarding the level of cooperation in the disconnected regime can be fully resolved through an improved mean-field theory that includes the effect of random fluctuations into its model.

A Channel Network Evolution Model with Subsurface Saturation Mechanism and Analysis of the Chaotic Behavior of the Model

DTIC Science & Technology

1990-09-01

between basin shapes and hydrologic responses is fundamental for the purpose of hydrologic predictions , especially in ungaged basins. Another goal is...47] studied this model and showed analitically how very small differences in the c field generated completely different leaf vein network structures... predictability impossible. Complexity is by no means a requirement in order for a system to exhibit SIC. A system as simple as the logistic equation x,,,,=ax,,(l

Efficient embedding of complex networks to hyperbolic space via their Laplacian

PubMed Central

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-01-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction. PMID:27445157

Efficient embedding of complex networks to hyperbolic space via their Laplacian

NASA Astrophysics Data System (ADS)

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-07-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction.

A New View of Dynamic River Networks

NASA Astrophysics Data System (ADS)

Perron, J. T.; Willett, S.; McCoy, S. W.

2014-12-01

River networks are the main conduits that transport water, sediment, and nutrients from continental interiors to the oceans. They also shape topography as they erode through bedrock. These hierarchical networks are dynamic: there are numerous examples of apparent changes in the topology of river networks through geologic time. But these examples are geographically scattered, the evidence can be ambiguous, and the mechanisms that drive changes in river networks are poorly understood. This makes it difficult to assess how pervasive river network reorganization is, how it operates, and how the interlocking river basins that compose a given landscape are changing through time. Recent progress has improved the situation. We describe three developments that have dramatically advanced our understanding of dynamic river networks. First, new topographic, geophysical and geochronological measurement techniques are revealing the rate and extent of river network adjustment. Second, laboratory experiments and computational models are clarifying how river networks respond to tectonic and climatic perturbations at scales ranging from local to continental. Third, spatial analysis of genetic data is exposing links between landscape evolution, biological evolution, and the development of biodiversity. We highlight key problems that remain unsolved, and suggest ways to build on recent advances that will bring dynamic river networks into even sharper focus.

Evolution of the Max and Mlx networks in animals.

PubMed

McFerrin, Lisa G; Atchley, William R

2011-01-01

Transcription factors (TFs) are essential for the regulation of gene expression and often form emergent complexes to perform vital roles in cellular processes. In this paper, we focus on the parallel Max and Mlx networks of TFs because of their critical involvement in cell cycle regulation, proliferation, growth, metabolism, and apoptosis. A basic-helix-loop-helix-zipper (bHLHZ) domain mediates the competitive protein dimerization and DNA binding among Max and Mlx network members to form a complex system of cell regulation. To understand the importance of these network interactions, we identified the bHLHZ domain of Max and Mlx network proteins across the animal kingdom and carried out several multivariate statistical analyses. The presence and conservation of Max and Mlx network proteins in animal lineages stemming from the divergence of Metazoa indicate that these networks have ancient and essential functions. Phylogenetic analysis of the bHLHZ domain identified clear relationships among protein families with distinct points of radiation and divergence. Multivariate discriminant analysis further isolated specific amino acid changes within the bHLHZ domain that classify proteins, families, and network configurations. These analyses on Max and Mlx network members provide a model for characterizing the evolution of TFs involved in essential networks.

Modification Propagation in Complex Networks

NASA Astrophysics Data System (ADS)

Mouronte, Mary Luz; Vargas, María Luisa; Moyano, Luis Gregorio; Algarra, Francisco Javier García; Del Pozo, Luis Salvador

To keep up with rapidly changing conditions, business systems and their associated networks are growing increasingly intricate as never before. By doing this, network management and operation costs not only rise, but are difficult even to measure. This fact must be regarded as a major constraint to system optimization initiatives, as well as a setback to derived economic benefits. In this work we introduce a simple model in order to estimate the relative cost associated to modification propagation in complex architectures. Our model can be used to anticipate costs caused by network evolution, as well as for planning and evaluating future architecture development while providing benefit optimization.

Retroviruses facilitate the rapid evolution of the mammalian placenta

PubMed Central

Chuong, Edward B.

2015-01-01

The mammalian placenta exhibits elevated expression of endogenous retroviruses (ERVs), but the evolutionary significance of this feature remains unclear. I propose that ERV-mediated regulatory evolution was, and continues to be, an important mechanism underlying the evolution of placenta development. Many recent studies have focused on the co-option of ERV-derived genes for specific functional adaptations in the placenta. However, the co-option of ERV-derived regulatory elements has the potential to co-opt entire gene regulatory networks, which, I argue, would facilitate relatively rapid developmental evolution of the placenta. I suggest a model in which an ancient retroviral infection led to the establishment of the ancestral placental developmental gene network through the co-option of ERV-derived regulatory elements. Consequently, placenta development would require elevated tolerance to ERV activity, which in turn would expose a continuous stream of novel ERV mutations that may have catalyzed the developmental diversification of the mammalian placenta. PMID:23873343

A Mechanistic Model of Human Recall of Social Network Structure and Relationship Affect.

PubMed

Omodei, Elisa; Brashears, Matthew E; Arenas, Alex

2017-12-07

The social brain hypothesis argues that the need to deal with social challenges was key to our evolution of high intelligence. Research with non-human primates as well as experimental and fMRI studies in humans produce results consistent with this claim, leading to an estimate that human primary groups should consist of roughly 150 individuals. Gaps between this prediction and empirical observations can be partially accounted for using "compression heuristics", or schemata that simplify the encoding and recall of social information. However, little is known about the specific algorithmic processes used by humans to store and recall social information. We describe a mechanistic model of human network recall and demonstrate its sufficiency for capturing human recall behavior observed in experimental contexts. We find that human recall is predicated on accurate recall of a small number of high degree network nodes and the application of heuristics for both structural and affective information. This provides new insight into human memory, social network evolution, and demonstrates a novel approach to uncovering human cognitive operations.

Empirical study on dyad act-degree distribution in some collaboration networks

NASA Astrophysics Data System (ADS)

Chang, Hui; Zhang, Pei-Pei; He, Yue; He, Da-Ren

2006-03-01

We (and cooperators) suggest studying the evolution of the extended collaboration networks by a dyad-act organizing model. The analytic and numeric studies of the model lead to a conclusion that most of the collaboration networks should show a dyad act-degree distribution (how many acts a dyad belongs to) between a power law and an exponential function, which can be described by a shifted power law. We have done an empirical study on dyad act-degree distribution in some collaboration networks. They are: the train networks in China, the bus network of Beijing, and traditional Chinese medical prescription formulation network. The results show good agreement with this conclusion. We also discuss what dyad act-degree implies in these networks and what are the possible applications of the study. The details will be published elsewhere.

Time evolution of coherent structures in networks of Hindmarch Rose neurons

NASA Astrophysics Data System (ADS)

Mainieri, M. S.; Erichsen, R.; Brunnet, L. G.

2005-08-01

In the regime of partial synchronization, networks of diffusively coupled Hindmarch-Rose neurons show coherent structures developing in a region of the phase space which is wider than in the correspondent single neuron. Such structures are kept, without important changes, during several bursting periods. In this work, we study the time evolution of these structures and their dynamical stability under damage. This system may model the behavior of ensembles of neurons coupled through a bidirectional gap junction or, in a broader sense, it could also account for the molecular cascades present in the formation of flash and short time memory.

Evolution of network architecture in a granular material under compression

NASA Astrophysics Data System (ADS)

Bassett, Danielle

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. However, capturing and characterizing the dynamic nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. Here, we utilize multilayer networks as a framework for directly quantifying the evolution of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and inter-particle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the reconfiguration and evolution of this structure throughout the compression process. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be done by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than consideration of the local inter-particle forces alone. The results discussed throughout this study suggest that these novel network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup. National Science Foundation (BCS-1441502, PHY-1554488, and BCS-1631550).

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

Epidemic spreading in time-varying community networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Guangming, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024; Wang, Xingyuan, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com

2014-06-15

The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q{sub c}. The epidemic will survive when q > q{sub c} and die when q < q{sub c}. These results can help understanding the impacts of human travel onmore » the epidemic spreading in complex networks with community structure.« less

Modeling the Chinese language as an evolving network

NASA Astrophysics Data System (ADS)

Liang, Wei; Shi, Yuming; Huang, Qiuling

2014-01-01

The evolution of Chinese language has three main features: the total number of characters is gradually increasing, new words are generated in the existing characters, and some old words are no longer used in daily-life language. Based on the features, we propose an evolving language network model. Finally, we use this model to simulate the character co-occurrence networks (nodes are characters, and two characters are connected by an edge if they are adjacent to each other) constructed from essays in 11 different periods of China, and find that characters that appear with high frequency in old words are likely to be reused when new words are formed.

Birth and death of protein domains: A simple model of evolution explains power law behavior

PubMed Central

Karev, Georgy P; Wolf, Yuri I; Rzhetsky, Andrey Y; Berezovskaya, Faina S; Koonin, Eugene V

2002-01-01

Background Power distributions appear in numerous biological, physical and other contexts, which appear to be fundamentally different. In biology, power laws have been claimed to describe the distributions of the connections of enzymes and metabolites in metabolic networks, the number of interactions partners of a given protein, the number of members in paralogous families, and other quantities. In network analysis, power laws imply evolution of the network with preferential attachment, i.e. a greater likelihood of nodes being added to pre-existing hubs. Exploration of different types of evolutionary models in an attempt to determine which of them lead to power law distributions has the potential of revealing non-trivial aspects of genome evolution. Results A simple model of evolution of the domain composition of proteomes was developed, with the following elementary processes: i) domain birth (duplication with divergence), ii) death (inactivation and/or deletion), and iii) innovation (emergence from non-coding or non-globular sequences or acquisition via horizontal gene transfer). This formalism can be described as a birth, death and innovation model (BDIM). The formulas for equilibrium frequencies of domain families of different size and the total number of families at equilibrium are derived for a general BDIM. All asymptotics of equilibrium frequencies of domain families possible for the given type of models are found and their appearance depending on model parameters is investigated. It is proved that the power law asymptotics appears if, and only if, the model is balanced, i.e. domain duplication and deletion rates are asymptotically equal up to the second order. It is further proved that any power asymptotic with the degree not equal to -1 can appear only if the hypothesis of independence of the duplication/deletion rates on the size of a domain family is rejected. Specific cases of BDIMs, namely simple, linear, polynomial and rational models, are considered in details and the distributions of the equilibrium frequencies of domain families of different size are determined for each case. We apply the BDIM formalism to the analysis of the domain family size distributions in prokaryotic and eukaryotic proteomes and show an excellent fit between these empirical data and a particular form of the model, the second-order balanced linear BDIM. Calculation of the parameters of these models suggests surprisingly high innovation rates, comparable to the total domain birth (duplication) and elimination rates, particularly for prokaryotic genomes. Conclusions We show that a straightforward model of genome evolution, which does not explicitly include selection, is sufficient to explain the observed distributions of domain family sizes, in which power laws appear as asymptotic. However, for the model to be compatible with the data, there has to be a precise balance between domain birth, death and innovation rates, and this is likely to be maintained by selection. The developed approach is oriented at a mathematical description of evolution of domain composition of proteomes, but a simple reformulation could be applied to models of other evolving networks with preferential attachment. PMID:12379152

Birth and death of protein domains: a simple model of evolution explains power law behavior.

PubMed

Karev, Georgy P; Wolf, Yuri I; Rzhetsky, Andrey Y; Berezovskaya, Faina S; Koonin, Eugene V

2002-10-14

Power distributions appear in numerous biological, physical and other contexts, which appear to be fundamentally different. In biology, power laws have been claimed to describe the distributions of the connections of enzymes and metabolites in metabolic networks, the number of interactions partners of a given protein, the number of members in paralogous families, and other quantities. In network analysis, power laws imply evolution of the network with preferential attachment, i.e. a greater likelihood of nodes being added to pre-existing hubs. Exploration of different types of evolutionary models in an attempt to determine which of them lead to power law distributions has the potential of revealing non-trivial aspects of genome evolution. A simple model of evolution of the domain composition of proteomes was developed, with the following elementary processes: i) domain birth (duplication with divergence), ii) death (inactivation and/or deletion), and iii) innovation (emergence from non-coding or non-globular sequences or acquisition via horizontal gene transfer). This formalism can be described as a birth, death and innovation model (BDIM). The formulas for equilibrium frequencies of domain families of different size and the total number of families at equilibrium are derived for a general BDIM. All asymptotics of equilibrium frequencies of domain families possible for the given type of models are found and their appearance depending on model parameters is investigated. It is proved that the power law asymptotics appears if, and only if, the model is balanced, i.e. domain duplication and deletion rates are asymptotically equal up to the second order. It is further proved that any power asymptotic with the degree not equal to -1 can appear only if the hypothesis of independence of the duplication/deletion rates on the size of a domain family is rejected. Specific cases of BDIMs, namely simple, linear, polynomial and rational models, are considered in details and the distributions of the equilibrium frequencies of domain families of different size are determined for each case. We apply the BDIM formalism to the analysis of the domain family size distributions in prokaryotic and eukaryotic proteomes and show an excellent fit between these empirical data and a particular form of the model, the second-order balanced linear BDIM. Calculation of the parameters of these models suggests surprisingly high innovation rates, comparable to the total domain birth (duplication) and elimination rates, particularly for prokaryotic genomes. We show that a straightforward model of genome evolution, which does not explicitly include selection, is sufficient to explain the observed distributions of domain family sizes, in which power laws appear as asymptotic. However, for the model to be compatible with the data, there has to be a precise balance between domain birth, death and innovation rates, and this is likely to be maintained by selection. The developed approach is oriented at a mathematical description of evolution of domain composition of proteomes, but a simple reformulation could be applied to models of other evolving networks with preferential attachment.

Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

PubMed Central

Pinho, Ricardo; Garcia, Victor; Irimia, Manuel; Feldman, Marcus W.

2014-01-01

Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals. PMID:25375153

Controls on stream network branching angles, tested using landscape evolution models

NASA Astrophysics Data System (ADS)

Theodoratos, Nikolaos; Seybold, Hansjörg; Kirchner, James W.

2016-04-01

Stream networks are striking landscape features. The topology of stream networks has been extensively studied, but their geometry has received limited attention. Analyses of nearly 1 million stream junctions across the contiguous United States [1] have revealed that stream branching angles vary systematically with climate and topographic gradients at continental scale. Stream networks in areas with wet climates and gentle slopes tend to have wider branching angles than in areas with dry climates or steep slopes, but the mechanistic linkages underlying these empirical correlations remain unclear. Under different climatic and topographic conditions different runoff generation mechanisms and, consequently, transport processes are dominant. Models [2] and experiments [3] have shown that the relative strength of channel incision versus diffusive hillslope transport controls the spacing between valleys, an important geometric property of stream networks. We used landscape evolution models (LEMs) to test whether similar factors control network branching angles as well. We simulated stream networks using a wide range of hillslope diffusion and channel incision parameters. The resulting branching angles vary systematically with the parameters, but by much less than the regional variability in real-world stream networks. Our results suggest that the competition between hillslope and channeling processes influences branching angles, but that other mechanisms may also be needed to account for the variability in branching angles observed in the field. References: [1] H. Seybold, D. H. Rothman, and J. W. Kirchner, 2015, Climate's watermark in the geometry of river networks, Submitted manuscript. [2] J. T. Perron, W. E. Dietrich, and J. W. Kirchner, 2008, Controls on the spacing of first-order valleys, Journal of Geophysical Research, 113, F04016. [3] K. E. Sweeney, J. J. Roering, and C. Ellis, 2015, Experimental evidence for hillslope control of landscape scale, Science, 349(6243), 51-53.

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks

PubMed Central

Meyer-Bäse, Anke; Roberts, Rodney G.; Illan, Ignacio A.; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts. PMID:29051730

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks.

PubMed

Meyer-Bäse, Anke; Roberts, Rodney G; Illan, Ignacio A; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts.

Does the Type of Event Influence How User Interactions Evolve on Twitter?

PubMed Central

del Val, Elena; Rebollo, Miguel; Botti, Vicente

2015-01-01

The number of people using on-line social networks as a new way of communication is continually increasing. The messages that a user writes in these networks and his/her interactions with other users leave a digital trace that is recorded. Thanks to this fact and the use of network theory, the analysis of messages, user interactions, and the complex structures that emerge is greatly facilitated. In addition, information generated in on-line social networks is labeled temporarily, which makes it possible to go a step further analyzing the dynamics of the interaction patterns. In this article, we present an analysis of the evolution of user interactions that take place in television, socio-political, conference, and keynote events on Twitter. Interactions have been modeled as networks that are annotated with the time markers. We study changes in the structural properties at both the network level and the node level. As a result of this analysis, we have detected patterns of network evolution and common structural features as well as differences among the events. PMID:25961305

Structural power and the evolution of collective fairness in social networks.

PubMed

Santos, Fernando P; Pacheco, Jorge M; Paiva, Ana; Santos, Francisco C

2017-01-01

From work contracts and group buying platforms to political coalitions and international climate and economical summits, often individuals assemble in groups that must collectively reach decisions that may favor each part unequally. Here we quantify to which extent our network ties promote the evolution of collective fairness in group interactions, modeled by means of Multiplayer Ultimatum Games (MUG). We show that a single topological feature of social networks-which we call structural power-has a profound impact on the tendency of individuals to take decisions that favor each part equally. Increased fair outcomes are attained whenever structural power is high, such that the networks that tie individuals allow them to meet the same partners in different groups, thus providing the opportunity to strongly influence each other. On the other hand, the absence of such close peer-influence relationships dismisses any positive effect created by the network. Interestingly, we show that increasing the structural power of a network leads to the appearance of well-defined modules-as found in human social networks that often exhibit community structure-providing an interaction environment that maximizes collective fairness.

Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

NASA Astrophysics Data System (ADS)

Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z.; Read, Jordan S.; Ibelings, Bas W.; Valesini, Fiona J.; Brookes, Justin D.

2015-09-01

Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, within a learning framework, as a means to improve the accuracy and scope of model predictions. The framework is comprised of a data assimilation component that utilizes diverse data streams from sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry, and, in doing so, also advance the role of prediction in aquatic ecosystem management.

The data distribution satellite system

NASA Technical Reports Server (NTRS)

Bruno, Ronald C.; Weinberg, Aaron

1991-01-01

The Data Distributed Satellite (DDS) will be capable of providing the space research community with inexpensive and easy access to space payloads and space data. Furthermore, the DDS is shown to be a natural outgrowth of advances and evolution in both NASA's Space Network and commercial satellite communications. The roadmap and timescale for this evolution is described along with key demonstrations, proof-of-concept models, and required technology development that will support the projected system evolution toward the DDS.

Dynamic Creative Interaction Networks and Team Creativity Evolution: A Longitudinal Study

ERIC Educational Resources Information Center

Jiang, Hui; Zhang, Qing-Pu; Zhou, Yang

2018-01-01

To assess the dynamical effects of creative interaction networks on team creativity evolution, this paper elaborates a theoretical framework that links the key elements of creative interaction networks, including node, edge and network structure, to creativity in teams. The process of team creativity evolution is divided into four phases,…

Information-theoretic metamodel of organizational evolution

NASA Astrophysics Data System (ADS)

Sepulveda, Alfredo

2011-12-01

Social organizations are abstractly modeled by holarchies---self-similar connected networks---and intelligent complex adaptive multiagent systems---large networks of autonomous reasoning agents interacting via scaled processes. However, little is known of how information shapes evolution in such organizations, a gap that can lead to misleading analytics. The research problem addressed in this study was the ineffective manner in which classical model-predict-control methods used in business analytics attempt to define organization evolution. The purpose of the study was to construct an effective metamodel for organization evolution based on a proposed complex adaptive structure---the info-holarchy. Theoretical foundations of this study were holarchies, complex adaptive systems, evolutionary theory, and quantum mechanics, among other recently developed physical and information theories. Research questions addressed how information evolution patterns gleamed from the study's inductive metamodel more aptly explained volatility in organization. In this study, a hybrid grounded theory based on abstract inductive extensions of information theories was utilized as the research methodology. An overarching heuristic metamodel was framed from the theoretical analysis of the properties of these extension theories and applied to business, neural, and computational entities. This metamodel resulted in the synthesis of a metaphor for, and generalization of organization evolution, serving as the recommended and appropriate analytical tool to view business dynamics for future applications. This study may manifest positive social change through a fundamental understanding of complexity in business from general information theories, resulting in more effective management.

Punctuated equilibrium in the large-scale evolution of programming languages.

PubMed

Valverde, Sergi; Solé, Ricard V

2015-06-06

The analogies and differences between biological and cultural evolution have been explored by evolutionary biologists, historians, engineers and linguists alike. Two well-known domains of cultural change are language and technology. Both share some traits relating the evolution of species, but technological change is very difficult to study. A major challenge in our way towards a scientific theory of technological evolution is how to properly define evolutionary trees or clades and how to weight the role played by horizontal transfer of information. Here, we study the large-scale historical development of programming languages, which have deeply marked social and technological advances in the last half century. We analyse their historical connections using network theory and reconstructed phylogenetic networks. Using both data analysis and network modelling, it is shown that their evolution is highly uneven, marked by innovation events where new languages are created out of improved combinations of different structural components belonging to previous languages. These radiation events occur in a bursty pattern and are tied to novel technological and social niches. The method can be extrapolated to other systems and consistently captures the major classes of languages and the widespread horizontal design exchanges, revealing a punctuated evolutionary path. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The complex network of musical tastes

NASA Astrophysics Data System (ADS)

Buldú, Javier M.; Cano, P.; Koppenberger, M.; Almendral, Juan A.; Boccaletti, S.

2007-06-01

We present an empirical study of the evolution of a social network constructed under the influence of musical tastes. The network is obtained thanks to the selfless effort of a broad community of users who share playlists of their favourite songs with other users. When two songs co-occur in a playlist a link is created between them, leading to a complex network where songs are the fundamental nodes. In this representation, songs in the same playlist could belong to different musical genres, but they are prone to be linked by a certain musical taste (e.g. if songs A and B co-occur in several playlists, an user who likes A will probably like also B). Indeed, playlist collections such as the one under study are the basic material that feeds some commercial music recommendation engines. Since playlists have an input date, we are able to evaluate the topology of this particular complex network from scratch, observing how its characteristic parameters evolve in time. We compare our results with those obtained from an artificial network defined by means of a null model. This comparison yields some insight on the evolution and structure of such a network, which could be used as ground data for the development of proper models. Finally, we gather information that can be useful for the development of music recommendation engines and give some hints about how top-hits appear.

Emergence, evolution and scaling of online social networks.

PubMed

Wang, Le-Zhi; Huang, Zi-Gang; Rong, Zhi-Hai; Wang, Xiao-Fan; Lai, Ying-Cheng

2014-01-01

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Modeling the coevolution of topology and traffic on weighted technological networks

NASA Astrophysics Data System (ADS)

Xie, Yan-Bo; Wang, Wen-Xu; Wang, Bing-Hong

2007-02-01

For many technological networks, the network structures and the traffic taking place on them mutually interact. The demands of traffic increment spur the evolution and growth of the networks to maintain their normal and efficient functioning. In parallel, a change of the network structure leads to redistribution of the traffic. In this paper, we perform an extensive numerical and analytical study, extending results of Wang [Phys. Rev. Lett. 94, 188702 (2005)]. By introducing a general strength-coupling interaction driven by the traffic increment between any pair of vertices, our model generates networks of scale-free distributions of strength, weight, and degree. In particular, the obtained nonlinear correlation between vertex strength and degree, and the disassortative property demonstrate that the model is capable of characterizing weighted technological networks. Moreover, the generated graphs possess both dense clustering structures and an anticorrelation between vertex clustering and degree, which are widely observed in real-world networks. The corresponding theoretical predictions are well consistent with simulation results.

Temporal node centrality in complex networks

NASA Astrophysics Data System (ADS)

Kim, Hyoungshick; Anderson, Ross

2012-02-01

Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

A group evolving-based framework with perturbations for link prediction

NASA Astrophysics Data System (ADS)

Si, Cuiqi; Jiao, Licheng; Wu, Jianshe; Zhao, Jin

2017-06-01

Link prediction is a ubiquitous application in many fields which uses partially observed information to predict absence or presence of links between node pairs. The group evolving study provides reasonable explanations on the behaviors of nodes, relations between nodes and community formation in a network. Possible events in group evolution include continuing, growing, splitting, forming and so on. The changes discovered in networks are to some extent the result of these events. In this work, we present a group evolving-based characterization of node's behavioral patterns, and via which we can estimate the probability they tend to interact. In general, the primary aim of this paper is to offer a minimal toy model to detect missing links based on evolution of groups and give a simpler explanation on the rationality of the model. We first introduce perturbations into networks to obtain stable cluster structures, and the stable clusters determine the stability of each node. Then fluctuations, another node behavior, are assumed by the participation of each node to its own belonging group. Finally, we demonstrate that such characteristics allow us to predict link existence and propose a model for link prediction which outperforms many classical methods with a decreasing computational time in large scales. Encouraging experimental results obtained on real networks show that our approach can effectively predict missing links in network, and even when nearly 40% of the edges are missing, it also retains stationary performance.

Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.

PubMed

Morrison, Erin S; Badyaev, Alexander V

2018-05-01

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

A novel framework of classical and quantum prisoner's dilemma games on coupled networks.

PubMed

Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

2016-03-15

Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner's dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner's dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner's dilemma is greatly impacted by the combined effect of entanglement and coupling.

A novel framework of classical and quantum prisoner’s dilemma games on coupled networks

PubMed Central

Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

2016-01-01

Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner’s dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner’s dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner’s dilemma is greatly impacted by the combined effect of entanglement and coupling. PMID:26975447

A probabilistic dynamic energy model for ad-hoc wireless sensors network with varying topology

NASA Astrophysics Data System (ADS)

Al-Husseini, Amal

In this dissertation we investigate the behavior of Wireless Sensor Networks (WSNs) from the degree distribution and evolution perspective. In specific, we focus on implementation of a scale-free degree distribution topology for energy efficient WSNs. WSNs is an emerging technology that finds its applications in different areas such as environment monitoring, agricultural crop monitoring, forest fire monitoring, and hazardous chemical monitoring in war zones. This technology allows us to collect data without human presence or intervention. Energy conservation/efficiency is one of the major issues in prolonging the active life WSNs. Recently, many energy aware and fault tolerant topology control algorithms have been presented, but there is dearth of research focused on energy conservation/efficiency of WSNs. Therefore, we study energy efficiency and fault-tolerance in WSNs from the degree distribution and evolution perspective. Self-organization observed in natural and biological systems has been directly linked to their degree distribution. It is widely known that scale-free distribution bestows robustness, fault-tolerance, and access efficiency to system. Fascinated by these properties, we propose two complex network theoretic self-organizing models for adaptive WSNs. In particular, we focus on adopting the Barabasi and Albert scale-free model to fit into the constraints and limitations of WSNs. We developed simulation models to conduct numerical experiments and network analysis. The main objective of studying these models is to find ways to reducing energy usage of each node and balancing the overall network energy disrupted by faulty communication among nodes. The first model constructs the wireless sensor network relative to the degree (connectivity) and remaining energy of every individual node. We observed that it results in a scale-free network structure which has good fault tolerance properties in face of random node failures. The second model considers additional constraints on the maximum degree of each node as well as the energy consumption relative to degree changes. This gives more realistic results from a dynamical network perspective. It results in balanced network-wide energy consumption. The results show that networks constructed using the proposed approach have good properties for different centrality measures. The outcomes of the presented research are beneficial to building WSN control models with greater self-organization properties which leads to optimal energy consumption.

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

Background Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. Results A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Conclusions Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. PMID:21849086

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks.

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced.

Evolution of Boolean networks under selection for a robust response to external inputs yields an extensive neutral space

NASA Astrophysics Data System (ADS)

Szejka, Agnes; Drossel, Barbara

2010-02-01

We study the evolution of Boolean networks as model systems for gene regulation. Inspired by biological networks, we select simultaneously for robust attractors and for the ability to respond to external inputs by changing the attractor. Mutations change the connections between the nodes and the update functions. In order to investigate the influence of the type of update functions, we perform our simulations with canalizing as well as with threshold functions. We compare the properties of the fitness landscapes that result for different versions of the selection criterion and the update functions. We find that for all studied cases the fitness landscape has a plateau with maximum fitness resulting in the fact that structurally very different networks are able to fulfill the same task and are connected by neutral paths in network (“genotype”) space. We find furthermore a connection between the attractor length and the mutational robustness, and an extremely long memory of the initial evolutionary stage.

Common neighbour structure and similarity intensity in complex networks

NASA Astrophysics Data System (ADS)

Hou, Lei; Liu, Kecheng

2017-10-01

Complex systems as networks always exhibit strong regularities, implying underlying mechanisms governing their evolution. In addition to the degree preference, the similarity has been argued to be another driver for networks. Assuming a network is randomly organised without similarity preference, the present paper studies the expected number of common neighbours between vertices. A symmetrical similarity index is accordingly developed by removing such expected number from the observed common neighbours. The developed index can not only describe the similarities between vertices, but also the dissimilarities. We further apply the proposed index to measure of the influence of similarity on the wring patterns of networks. Fifteen empirical networks as well as artificial networks are examined in terms of similarity intensity and degree heterogeneity. Results on real networks indicate that, social networks are strongly governed by the similarity as well as the degree preference, while the biological networks and infrastructure networks show no apparent similarity governance. Particularly, classical network models, such as the Barabási-Albert model, the Erdös-Rényi model and the Ring Lattice, cannot well describe the social networks in terms of the degree heterogeneity and similarity intensity. The findings may shed some light on the modelling and link prediction of different classes of networks.

Context-aided analysis of community evolution in networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pallotta, Giuliana; Konjevod, Goran; Cadena, Jose

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure basedmore » on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.« less

Context-aided analysis of community evolution in networks

DOE PAGES

Pallotta, Giuliana; Konjevod, Goran; Cadena, Jose; ...

2017-09-15

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure basedmore » on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.« less

Research on social communication network evolution based on topology potential distribution

NASA Astrophysics Data System (ADS)

Zhao, Dongjie; Jiang, Jian; Li, Deyi; Zhang, Haisu; Chen, Guisheng

2011-12-01

Aiming at the problem of social communication network evolution, first, topology potential is introduced to measure the local influence among nodes in networks. Second, from the perspective of topology potential distribution the method of network evolution description based on topology potential distribution is presented, which takes the artificial intelligence with uncertainty as basic theory and local influence among nodes as essentiality. Then, a social communication network is constructed by enron email dataset, the method presented is used to analyze the characteristic of the social communication network evolution and some useful conclusions are got, implying that the method is effective, which shows that topology potential distribution can effectively describe the characteristic of sociology and detect the local changes in social communication network.

Preferential attachment in multiple trade networks

NASA Astrophysics Data System (ADS)

Foschi, Rachele; Riccaboni, Massimo; Schiavo, Stefano

2014-08-01

In this paper we develop a model for the evolution of multiple networks which is able to replicate the concentrated and sparse nature of world trade data. Our model is an extension of the preferential attachment growth model to the case of multiple networks. Countries trade a variety of goods of different complexity. Every country progressively evolves from trading less sophisticated to high-tech goods. The probabilities of capturing more trade opportunities at a given level of complexity and of starting to trade more complex goods are both proportional to the number of existing trade links. We provide a set of theoretical predictions and simulative results. A calibration exercise shows that our model replicates the same concentration level of world trade as well as the sparsity pattern of the trade matrix. We also discuss a set of numerical solutions to deal with large multiple networks.

Constraints imposed by pollinator behaviour on the ecology and evolution of plant mating systems.

PubMed

Devaux, C; Lepers, C; Porcher, E

2014-07-01

Most flowering plants rely on pollinators for their reproduction. Plant-pollinator interactions, although mutualistic, involve an inherent conflict of interest between both partners and may constrain plant mating systems at multiple levels: the immediate ecological plant selfing rates, their distribution in and contribution to pollination networks, and their evolution. Here, we review experimental evidence that pollinator behaviour influences plant selfing rates in pairs of interacting species, and that plants can modify pollinator behaviour through plastic and evolutionary changes in floral traits. We also examine how theoretical studies include pollinators, implicitly or explicitly, to investigate the role of their foraging behaviour in plant mating system evolution. In doing so, we call for more evolutionary models combining ecological and genetic factors, and additional experimental data, particularly to describe pollinator foraging behaviour. Finally, we show that recent developments in ecological network theory help clarify the impact of community-level interactions on plant selfing rates and their evolution and suggest new research avenues to expand the study of mating systems of animal-pollinated plant species to the level of the plant-pollinator networks. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Evolution of continental-scale drainage in response to mantle dynamics and surface processes: An example from the Ethiopian Highlands

NASA Astrophysics Data System (ADS)

Sembroni, Andrea; Molin, Paola; Pazzaglia, Frank J.; Faccenna, Claudio; Abebe, Bekele

2016-05-01

Ethiopia offers an excellent opportunity to study the effects and linkage between mantle dynamics and surface processes on landscape evolution. The Ethiopian Highlands (NW Ethiopia), characterized by a huge basaltic plateau, is part of the African Superswell, a wide region of dynamically-supported anomalously high topography related to the rising of the Afar plume. The initiation and steadiness of dynamic support beneath Ethiopia has been explored in several studies. However the presence, role, and timing of dynamic support beneath Ethiopia and its relationship with continental flood basalts volcanism and surface processes are poorly defined. Here, we present a geomorphological analysis of the Ethiopian Highlands supplying new constraints on the evolution of river network. We investigated the general topographic features (filtered topography, swath profiles, local relief) and the river network (river longitudinal profiles) of the study area. We also apply a knickpoint celerity model in order to provide a chronological framework to the evolution of the river network. The results trace the long-term progressive capture of the Ethiopian Highlands drainage system and confirm the long-term dynamic support of the area, documenting its impact on the contrasting development of the Blue Nile and Tekeze basins.

Evolution of continental-scale drainage in response to mantle dynamics and surface processes: an example from the Ethiopian Highlands.

NASA Astrophysics Data System (ADS)

Sembroni, Andrea; Molin, Paola; Pazzaglia, Frank J.; Faccenna, Claudio; Abebe, Bekele

2016-04-01

Ethiopia offers an excellent opportunity to study the effects and linkage between mantle dynamics and surface processes on landscape evolution. The Ethiopian Highlands (NW Ethiopia), characterized by a huge basaltic plateau, is part of the African Superswell, a wide region of dynamically-supported anomalously high topography related to the rising of the Afar plume. The initiation and steadiness of dynamic support beneath Ethiopia has been explored in several studies. However the presence, role, and timing of dynamic support beneath Ethiopia and its relationship with continental flood basalts volcanism and surface processes are poorly defined. Here, we present a geomorphological analysis of the Ethiopian Highlands supplying new constrains on the evolution of river network. We investigated the general topographic features (filtered topography, swath profiles, local relief) and the river network (river longitudinal profiles) of the study area. We also apply a knickpoint celerity model in order to provide a chronological framework to the evolution of the river network. The results trace the long-term progressive capture of the Ethiopian Highlands drainage system and confirm the long-term dynamic support of the area, documenting its impact on the contrasting development of the Blue Nile and Tekeze basins.

Dynamic social networks facilitate cooperation in the N-player Prisoner’s Dilemma

NASA Astrophysics Data System (ADS)

Rezaei, Golriz; Kirley, Michael

2012-12-01

Understanding how cooperative behaviour evolves in network communities, where the individual members interact via social dilemma games, is an on-going challenge. In this paper, we introduce a social network based model to investigate the evolution of cooperation in the N-player Prisoner’s Dilemma game. As such, this work complements previous studies focused on multi-player social dilemma games and endogenous networks. Agents in our model, employ different game-playing strategies reflecting varying cognitive capacities. When an agent plays cooperatively, a social link is formed with each of the other N-1 group members. Subsequent cooperative actions reinforce this link. However, when an agent defects, the links in the social network are broken. Computational simulations across a range of parameter settings are used to examine different scenarios: varying population and group sizes; the group formation process (or partner selection); and agent decision-making strategies under varying dilemma constraints (cost-to-benefit ratios), including a “discriminator” strategy where the action is based on a function of the weighted links within an agent’s social network. The simulation results show that the proposed social network model is able to evolve and maintain cooperation. As expected, as the value of N increases the equilibrium proportion of cooperators in the population decreases. In addition, this outcome is dependent on the dilemma constraint (cost-to-benefit ratio). However, in some circumstances the dynamic social network plays an increasingly important role in promoting and sustaining cooperation, especially when the agents adopt the discriminator strategy. The adjustment of social links results in the formation of communities of “like-minded” agents. Subsequently, this local optimal behaviour promotes the evolution of cooperative behaviour at the system level.

Homophyly/Kinship Model: Naturally Evolving Networks

NASA Astrophysics Data System (ADS)

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-10-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

Homophyly/Kinship Model: Naturally Evolving Networks

PubMed Central

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-01-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network. PMID:26478264

Hierarchical coordinate systems for understanding complexity and its evolution, with applications to genetic regulatory networks.

PubMed

Egri-Nagy, Attila; Nehaniv, Chrystopher L

2008-01-01

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.

Evolutionary Topology of a Currency Network in Asia

NASA Astrophysics Data System (ADS)

Feng, Xiaobing; Wang, Xiaofan

Although recently there are extensive research on currency network using minimum spanning trees approach, the knowledge about the actual evolution of a currency web in Asia is still limited. In the paper, we study the structural evolution of an Asian network using daily exchange rate data. It was found that the correlation between Asian currencies and US Dollar, the previous regional key currency has become weaker and the intra-Asia interactions have increased. This becomes more salient after the exchange rate reform of China. Different from the previous studies, we further reveal that it is the trade volume, national wealth gap and countries growth cycle that has contributed to the evolutionary topology of the minimum spanning tree. These findings provide a valuable platform for theoretical modeling and further analysis.

The adaptive safety analysis and monitoring system

NASA Astrophysics Data System (ADS)

Tu, Haiying; Allanach, Jeffrey; Singh, Satnam; Pattipati, Krishna R.; Willett, Peter

2004-09-01

The Adaptive Safety Analysis and Monitoring (ASAM) system is a hybrid model-based software tool for assisting intelligence analysts to identify terrorist threats, to predict possible evolution of the terrorist activities, and to suggest strategies for countering terrorism. The ASAM system provides a distributed processing structure for gathering, sharing, understanding, and using information to assess and predict terrorist network states. In combination with counter-terrorist network models, it can also suggest feasible actions to inhibit potential terrorist threats. In this paper, we will introduce the architecture of the ASAM system, and discuss the hybrid modeling approach embedded in it, viz., Hidden Markov Models (HMMs) to detect and provide soft evidence on the states of terrorist network nodes based on partial and imperfect observations, and Bayesian networks (BNs) to integrate soft evidence from multiple HMMs. The functionality of the ASAM system is illustrated by way of application to the Indian Airlines Hijacking, as modeled from open sources.

Structuring evolution: biochemical networks and metabolic diversification in birds.

PubMed

Morrison, Erin S; Badyaev, Alexander V

2016-08-25

Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.

Agent-based paradigm for integration of interactive cable television operations and business support systems

NASA Astrophysics Data System (ADS)

Wattawa, Scott

1995-11-01

Offering interactive services and data in a hybrid fiber/coax cable system requires the coordination of a host of operations and business support systems. New service offerings and network growth and evolution create never-ending changes in the network infrastructure. Agent-based enterprise models provide a flexible mechanism for systems integration of service and support systems. Agent models also provide a mechanism to decouple interactive services from network architecture. By using the Java programming language, agents may be made safe, portable, and intelligent. This paper investigates the application of the Object Management Group's Common Object Request Brokering Architecture to the integration of a multiple services metropolitan area network.

Complications of stent placement in patients with esophageal cancer: A systematic review and network meta-analysis

PubMed Central

Doosti-Irani, Amin; Mansournia, Mohammad Ali; Rahimi-Foroushani, Abbas; Haddad, Peiman

2017-01-01

Background Palliative treatments and stents are necessary for relieving dysphagia in patients with esophageal cancer. The aim of this study was to simultaneously compare available treatments in terms of complications. Methods Web of Science, Medline, Scopus, Cochrane Library and Embase were searched. Statistical heterogeneity was assessed using the Chi2 test and was quantified by I2. The results of this study were summarized in terms of Risk Ratio (RR). The random effects model was used to report the results. The rank probability for each treatment was calculated using the p-score. Results Out of 17855 references, 24 RCTs reported complications including treatment related death (TRD), bleeding, stent migration, aspiration, severe pain and fistula formation. In the ranking of treatments, thermal ablative therapy (p-score = 0.82), covered Evolution® stent (p-score = 0.70), brachytherapy (p-score = 0.72) and antireflux stent (p-score = 0.74) were better treatments in the network of TRD. Thermal ablative therapy (p-score = 0.86), the conventional stent (p-score = 0.62), covered Evolution® stent (p-score = 0.96) and brachytherapy (p-score = 0.82) were better treatments in the network of bleeding complications. Covered Evolution® (p-score = 0.78), uncovered (p-score = 0.88) and irradiation stents (p-score = 0.65) were better treatments in network of stent migration complications. In the network of severe pain, Conventional self-expandable nitinol alloy covered stent (p-score = 0.73), polyflex (p-score = 0.79), latex prosthesis (p-score = 0.96) and brachytherapy (p-score = 0.65) were better treatments. Conclusion According to our results, thermal ablative therapy, covered Evolution® stents, brachytherapy, and antireflux stents are associated with a lower risk of TRD. Moreover, thermal ablative therapy, conventional, covered Evolution® and brachytherapy had lower risks of bleeding. Overall, fewer complications were associated with covered Evolution® stent and brachytherapy. PMID:28968416

Macroscopic description of complex adaptive networks coevolving with dynamic node states

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Macroscopic description of complex adaptive networks coevolving with dynamic node states.

PubMed

Wiedermann, Marc; Donges, Jonathan F; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Anatomical Network Analysis Shows Decoupling of Modular Lability and Complexity in the Evolution of the Primate Skull

PubMed Central

Esteve-Altava, Borja; Boughner, Julia C.; Diogo, Rui; Villmoare, Brian A.; Rasskin-Gutman, Diego

2015-01-01

Modularity and complexity go hand in hand in the evolution of the skull of primates. Because analyses of these two parameters often use different approaches, we do not know yet how modularity evolves within, or as a consequence of, an also-evolving complex organization. Here we use a novel network theory-based approach (Anatomical Network Analysis) to assess how the organization of skull bones constrains the co-evolution of modularity and complexity among primates. We used the pattern of bone contacts modeled as networks to identify connectivity modules and quantify morphological complexity. We analyzed whether modularity and complexity evolved coordinately in the skull of primates. Specifically, we tested Herbert Simon’s general theory of near-decomposability, which states that modularity promotes the evolution of complexity. We found that the skulls of extant primates divide into one conserved cranial module and up to three labile facial modules, whose composition varies among primates. Despite changes in modularity, statistical analyses reject a positive feedback between modularity and complexity. Our results suggest a decoupling of complexity and modularity that translates to varying levels of constraint on the morphological evolvability of the primate skull. This study has methodological and conceptual implications for grasping the constraints that underlie the developmental and functional integration of the skull of humans and other primates. PMID:25992690

Randomizing growing networks with a time-respecting null model

NASA Astrophysics Data System (ADS)

Ren, Zhuo-Ming; Mariani, Manuel Sebastian; Zhang, Yi-Cheng; Medo, Matúš

2018-05-01

Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology—a time-respecting null model—that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.

Investigation of Higher Brain Functions in Music Composition Using Models of the Cortex Based on Physical System Analogies.

NASA Astrophysics Data System (ADS)

Leng, Xiaodan

The trion model was developed using the Mountcastle organizational principle for the column as the basic neuronal network in the cortex and the physical system analogy of Fisher's ANNNI spin model. An essential feature is that it is highly structured in time and in spatial connections. Simulations of a network of trions have shown that large numbers of quasi-stable, periodic spatial-temporal firing patterns can be excited. Characteristics of these patterns include the quality of being readily enhanced by only a small change in connection strengths, and that the patterns evolve in certain natural sequences from one to another. With only somewhat different parameters than used for studying memory and pattern recognition, much more flowing and intriguing patterns emerged from the simulations. The results were striking when these probabilistic evolutions were mapped onto pitches and instruments to produce music: For example different simple mappings of the same evolution give music having the "flavor" of a minuet, a waltz, folk music, or styles of specific periods. A theme can be learned so that evolutions have this theme and its variations reoccurring more often. That the trion model is a viable model for the coding of musical structure in human composition and perception is suggested. It is further proposed that model is relevant for examining creativity in the higher cognitive functions of mathematics and chess, which are similar to music. An even higher level of cortical organization was modeled by coupling together several trion networks. Further, one of the crucial features of higher brain function, especially in music composition or appreciation, is the role of emotion and mood as controlled by the many neuromodulators or neuropeptides. The MILA model whose underlying basis is zero-level representation of Kac-Moody algebra is used to modulate periodically the firing threshold of each network. Our preliminary results show that the introduction of "neuromodulation" into the dynamics of a few coupled trion networks greatly enhanced the richness of the music. Neuromodulation plays a very important role in cognitive processes. I discuss many aspects of cognitive processes such as, leaning and memory, innervation of cortical functions and coordination between music and emotions. The implications of my work are discussed.

Properties of four real world collaboration--competition networks

NASA Astrophysics Data System (ADS)

Fu, Chun-Hua; Xu, Xiu-Lian; He, Da-Ren

2009-03-01

Our research group has empirically investigated 9 real world collaboration networks and 25 real world cooperation-competition networks. Among the 34 real world systems, all the 9 real world collaboration networks and 6 real world cooperation-competition networks show the unimodal act-size distribution and the shifted power law distribution of degree and act-degree. We have proposed a collaboration network evolution model for an explanation of the rules [1]. The other 14 real world cooperation-competition networks show that the act-size distributions are not unimodal; instead, they take qualitatively the same shifted power law forms as the degree and act-degree distributions. The properties of four systems (the main land movie film network, Beijing restaurant network, 2004 Olympic network, and Tao-Bao notebook computer sale network) are reported in detail as examples. Via a numerical simulation, we show that the new rule can still be explained by the above-mentioned model. [1] H. Chang, B. B. Su, et al. Phsica A, 2007, 383: 687-702.

Hybrid modeling and empirical analysis of automobile supply chain network

NASA Astrophysics Data System (ADS)

Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying

2017-05-01

Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

Molecular Evolution of the Neural Crest Regulatory Network in Ray-Finned Fish

PubMed Central

Kratochwil, Claudius F.; Geissler, Laura; Irisarri, Iker; Meyer, Axel

2015-01-01

Abstract Gene regulatory networks (GRN) are central to developmental processes. They are composed of transcription factors and signaling molecules orchestrating gene expression modules that tightly regulate the development of organisms. The neural crest (NC) is a multipotent cell population that is considered a key innovation of vertebrates. Its derivatives contribute to shaping the astounding morphological diversity of jaws, teeth, head skeleton, or pigmentation. Here, we study the molecular evolution of the NC GRN by analyzing patterns of molecular divergence for a total of 36 genes in 16 species of bony fishes. Analyses of nonsynonymous to synonymous substitution rate ratios (dN/dS) support patterns of variable selective pressures among genes deployed at different stages of NC development, consistent with the developmental hourglass model. Model-based clustering techniques of sequence features support the notion of extreme conservation of NC-genes across the entire network. Our data show that most genes are under strong purifying selection that is maintained throughout ray-finned fish evolution. Late NC development genes reveal a pattern of increased constraints in more recent lineages. Additionally, seven of the NC-genes showed signs of relaxation of purifying selection in the famously species-rich lineage of cichlid fishes. This suggests that NC genes might have played a role in the adaptive radiation of cichlids by granting flexibility in the development of NC-derived traits—suggesting an important role for NC network architecture during the diversification in vertebrates. PMID:26475317

Smart pitch control strategy for wind generation system using doubly fed induction generator

NASA Astrophysics Data System (ADS)

Raza, Syed Ahmed

A smart pitch control strategy for a variable speed doubly fed wind generation system is presented in this thesis. A complete dynamic model of DFIG system is developed. The model consists of the generator, wind turbine, aerodynamic and the converter system. The strategy proposed includes the use of adaptive neural network to generate optimized controller gains for pitch control. This involves the generation of controller parameters of pitch controller making use of differential evolution intelligent technique. Training of the back propagation neural network has been carried out for the development of an adaptive neural network. This tunes the weights of the network according to the system states in a variable wind speed environment. Four cases have been taken to test the pitch controller which includes step and sinusoidal changes in wind speeds. The step change is composed of both step up and step down changes in wind speeds. The last case makes use of scaled wind data collected from the wind turbine installed at King Fahd University beach front. Simulation studies show that the differential evolution based adaptive neural network is capable of generating the appropriate control to deliver the maximum possible aerodynamic power available from wind to the generator in an efficient manner by minimizing the transients.

Entropy of dynamical social networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

2012-02-01

Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

Constitutive modelling of composite biopolymer networks.

PubMed

Fallqvist, B; Kroon, M

2016-04-21

The mechanical behaviour of biopolymer networks is to a large extent determined at a microstructural level where the characteristics of individual filaments and the interactions between them determine the response at a macroscopic level. Phenomena such as viscoelasticity and strain-hardening followed by strain-softening are observed experimentally in these networks, often due to microstructural changes (such as filament sliding, rupture and cross-link debonding). Further, composite structures can also be formed with vastly different mechanical properties as compared to the individual networks. In this present paper, we present a constitutive model presented in a continuum framework aimed at capturing these effects. Special care is taken to formulate thermodynamically consistent evolution laws for dissipative effects. This model, incorporating possible anisotropic network properties, is based on a strain energy function, split into an isochoric and a volumetric part. Generalisation to three dimensions is performed by numerical integration over the unit sphere. Model predictions indicate that the constitutive model is well able to predict the elastic and viscoelastic response of biological networks, and to an extent also composite structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Critical Robotic Lunar Missions

NASA Astrophysics Data System (ADS)

Plescia, J. B.

2018-04-01

Perhaps the most critical missions to understanding lunar history are in situ dating and network missions. These would constrain the volcanic and thermal history and interior structure. These data would better constrain lunar evolution models.

Two classes of bipartite networks: nested biological and social systems.

PubMed

Burgos, Enrique; Ceva, Horacio; Hernández, Laura; Perazzo, R P J; Devoto, Mariano; Medan, Diego

2008-10-01

Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly different classes. Projected graphs, linking the agents of only one guild, are obtained from the original bipartite graph. The corresponding evolution of its statistical properties is also studied. An example of a biological mutualistic network is analyzed in detail, and it is found that the model provides a very good fitting of all the main statistical features. The model also provides a proper qualitative description of the same features observed in social webs, suggesting the possible reasons underlying the difference in the organization of these two kinds of bipartite networks.

Effects of Vertex Activity and Self-organized Criticality Behavior on a Weighted Evolving Network

NASA Astrophysics Data System (ADS)

Zhang, Gui-Qing; Yang, Qiu-Ying; Chen, Tian-Lun

2008-08-01

Effects of vertex activity have been analyzed on a weighted evolving network. The network is characterized by the probability distribution of vertex strength, each edge weight and evolution of the strength of vertices with different vertex activities. The model exhibits self-organized criticality behavior. The probability distribution of avalanche size for different network sizes is also shown. In addition, there is a power law relation between the size and the duration of an avalanche and the average of avalanche size has been studied for different vertex activities.

International migration network: Topology and modeling

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

International migration network: topology and modeling.

PubMed

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

Coupling Landform Evolution and Soil Pedogenesis - Initial Results From the SSSPAM5D Model

NASA Astrophysics Data System (ADS)

Willgoose, G. R.; Welivitiya, W. D. D. P.; Hancock, G. R.; Cohen, S.

2015-12-01

Evolution of soil on a dynamic landform is a crucial next step in landscape evolution modelling. Some attempts have been taken such as MILESD by Vanwalleghem et al. to develop a first model which is capable of simultaneously evolving both the soil profile and the landform. In previous work we have presented physically based models for soil pedogenesis, mARM and SSSPAM. In this study we present the results of coupling a landform evolution model with our SSSPAM5D soil pedogenesis model. In previous work the SSSPAM5D soil evolution model was used to identify trends of the soil profile evolution on a static landform. Two pedogenetic processes, namely (1) armouring due to erosion, and (2) physical and chemical weathering were used in those simulations to evolve the soil profile. By incorporating elevation changes (due to erosion and deposition) we have advanced the SSSPAM5D modelling framework into the realm of landscape evolution. Simulations have been run using elevation and soil grading data of the engineered landform (spoil heap) at the Ranger Uranium Mine, Northern Territory, Australia. The results obtained for the coupled landform-soil evolution simulations predict the erosion of high slope areas, development of rudimentary channel networks in the landform and deposition of sediments in lowland areas, and qualitatively consistent with landform evolution models on their own. Examination of the soil profile characteristics revealed that hill crests are weathering dominated and tend to develop a thick soil layer. The steeper hillslopes at the edge of the landform are erosion dominated with shallow soils while the foot slopes are deposition dominated with thick soil layers. The simulation results of our coupled landform and soil evolution model provide qualitatively correct and timely characterization of the soil evolution on a dynamic landscape. Finally we will compare the characteristics of erosion and deposition predicted by the coupled landform-soil SSSPAM landscape simulator, with landform evolution simulations using a static soil.

The role of weak selection and high mutation rates in nearly neutral evolution.

PubMed

Lawson, Daniel John; Jensen, Henrik Jeldtoft

2009-04-21

Neutral dynamics occur in evolution if all types are 'effectively equal' in their reproductive success, where the definition of 'effectively equal' depends on the population size and the details of mutations. Empirically observed neutral genetic evolution in extremely large clonal populations can only be explained under current models if selection is completely absent. Such models typically consider the case where population dynamics occurs on a different timescale to evolution. However, this assumption is invalid when mutations are not rare in a whole population. We show that this has important consequences for the occurrence of neutral evolution in clonal populations. In highly connected type spaces, neutral dynamics can occur for all population sizes despite significant selective differences, via the forming of effectively neutral networks connecting rare neutral types. Biological implications include an explanation for the high diversity of rare types that survive in large clonal populations, and a theoretical justification for the use of neutral null models.

Interdependency enriches the spatial reciprocity in prisoner's dilemma game on weighted networks

NASA Astrophysics Data System (ADS)

Meng, Xiaokun; Sun, Shiwen; Li, Xiaoxuan; Wang, Li; Xia, Chengyi; Sun, Junqing

2016-01-01

To model the evolution of cooperation under the realistic scenarios, we propose an interdependent network-based game model which simultaneously considers the difference of individual roles in the spatial prisoner's dilemma game. In our model, the system is composed of two lattices on which an agent designated as a cooperator or defector will be allocated, meanwhile each agent will be endowed as a specific weight taking from three typical distributions on one lattice (i.e., weighted lattice), and set to be 1.0 on the other one (i.e., un-weighted or standard lattice). In addition, the interdependency will be built through the utility coupling between point-to-point partners. Extensive simulations indicate that the cooperation will be continuously elevated for the weighted lattice as the utility coupling strength (α) increases; while the cooperation will take on a nontrivial evolution on the standard lattice as α varies, and will be still greatly promoted when compared to the case of α = 0. At the same time, the full T - K phase diagrams are also explored to illustrate the evolutionary behaviors, and it is powerfully shown that the interdependency drives the defectors to survive within the narrower range, but individual weighting of utility will further broaden the coexistence space of cooperators and defectors, which renders the nontrivial evolution of cooperation in our model. Altogether, the current consequences about the evolution of cooperation will be helpful for us to provide the insights into the prevalent cooperation phenomenon within many real-world systems.

Are genetically robust regulatory networks dynamically different from random ones?

NASA Astrophysics Data System (ADS)

Sevim, Volkan; Rikvold, Per Arne

We study a genetic regulatory network model developed to demonstrate that genetic robustness can evolve through stabilizing selection for optimal phenotypes. We report preliminary results on whether such selection could result in a reorganization of the state space of the system. For the chosen parameters, the evolution moves the system slightly toward the more ordered part of the phase diagram. We also find that strong memory effects cause the Derrida annealed approximation to give erroneous predictions about the model's phase diagram.

Networking in 1993.

ERIC Educational Resources Information Center

Clement, John; Abrahams, Janice

1994-01-01

Describes the growth and evolution of educational networking including the growth in the number of users; networking tools such as Gopher; Internet information resources; problems; evaluations of network use in education; the evolution of educational communities on the Internet; integrating networks into the process of educational change; and the…

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2013-01-01

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

PubMed Central

Qi, Yi; Wang, Rubin; Jiao, Xianfa; Du, Ying

2014-01-01

We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution. PMID:24516505

Evolvable social agents for bacterial systems modeling.

PubMed

Paton, Ray; Gregory, Richard; Vlachos, Costas; Saunders, Jon; Wu, Henry

2004-09-01

We present two approaches to the individual-based modeling (IbM) of bacterial ecologies and evolution using computational tools. The IbM approach is introduced, and its important complementary role to biosystems modeling is discussed. A fine-grained model of bacterial evolution is then presented that is based on networks of interactivity between computational objects representing genes and proteins. This is followed by a coarser grained agent-based model, which is designed to explore the evolvability of adaptive behavioral strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of the two proposed individual-based bacterial models are discussed, and some results from simulation experiments are presented, illustrating their adaptive properties.

Theoretical Modeling and Computer Simulations for the Origins and Evolution of Reproducing Molecular Systems and Complex Systems with Many Interactive Parts

NASA Technical Reports Server (NTRS)

Liang, Shoudan

2000-01-01

Our research effort has produced nine publications in peer-reviewed journals listed at the end of this report. The work reported here are in the following areas: (1) genetic network modeling; (2) autocatalytic model of pre-biotic evolution; (3) theoretical and computational studies of strongly correlated electron systems; (4) reducing thermal oscillations in atomic force microscope; (5) transcription termination mechanism in prokaryotic cells; and (6) the low glutamine usage in thennophiles obtained by studying completely sequenced genomes. We discuss the main accomplishments of these publications.

Sediment and Vegetation Controls on Delta Channel Networks

NASA Astrophysics Data System (ADS)

Lauzon, R.; Murray, A. B.; Piliouras, A.; Kim, W.

2016-12-01

Numerous factors control the patterns of distributary channels formed on a delta, including water and sediment discharge, grain size, sea level rise rates, and vegetation type. In turn, these channel networks influence the shape and evolution of a delta, including what types of plant and animal life - such as humans - it can support. Previous fluvial modeling and flume experiments, outside of the delta context, have addressed how interactions between sediment and vegetation, through their influence on lateral transport of sediment, determine what type of channel networks develops. Similar interactions likely also shape delta flow patterns. Vegetation introduces cohesion, tending to reduce channel migration rates and strengthen existing channel banks, reinforcing existing channels and resulting in localized, relatively stable flow patterns. On the other hand, sediment transport processes can result in lateral migration and frequent switching of active channels, resulting in flow resembling that of a braided stream. While previous studies of deltas have indirectly explored the effects of vegetation through the introduction of cohesive sediment, we directly incorporate key effects of vegetation on flow and sediment transport into the delta-building model DeltaRCM to explore how these effects influence delta channel network formation. Model development is informed by laboratory flume experiments at UT Austin. Here we present initial results of experiments exploring the effects of sea level rise rate, sediment grain size, vegetation type, and vegetation growth rate on delta channel network morphology. These results support the hypothesis that the ability for lateral transport of sediment to occur plays a key role in determining the evolution of delta channel networks and delta morphology.

P2S--Coupled simulation with the Precipitation-Runoff Modeling System (PRMS) and the Stream Temperature Network (SNTemp) Models

USGS Publications Warehouse

Markstrom, Steven L.

2012-01-01

A software program, called P2S, has been developed which couples the daily stream temperature simulation capabilities of the U.S. Geological Survey Stream Network Temperature model with the watershed hydrology simulation capabilities of the U.S. Geological Survey Precipitation-Runoff Modeling System. The Precipitation-Runoff Modeling System is a modular, deterministic, distributed-parameter, physical-process watershed model that simulates hydrologic response to various combinations of climate and land use. Stream Network Temperature was developed to help aquatic biologists and engineers predict the effects of changes that hydrology and energy have on water temperatures. P2S will allow scientists and watershed managers to evaluate the effects of historical climate and projected climate change, landscape evolution, and resource management scenarios on watershed hydrology and in-stream water temperature.

Cost- and reliability-oriented aggregation point association in long-term evolution and passive optical network hybrid access infrastructure for smart grid neighborhood area network

NASA Astrophysics Data System (ADS)

Cheng, Xiao; Feng, Lei; Zhou, Fanqin; Wei, Lei; Yu, Peng; Li, Wenjing

2018-02-01

With the rapid development of the smart grid, the data aggregation point (AP) in the neighborhood area network (NAN) is becoming increasingly important for forwarding the information between the home area network and wide area network. Due to limited budget, it is unable to use one-single access technology to meet the ongoing requirements on AP coverage. This paper first introduces the wired and wireless hybrid access network with the integration of long-term evolution (LTE) and passive optical network (PON) system for NAN, which allows a good trade-off among cost, flexibility, and reliability. Then, based on the already existing wireless LTE network, an AP association optimization model is proposed to make the PON serve as many APs as possible, considering both the economic efficiency and network reliability. Moreover, since the features of the constraints and variables of this NP-hard problem, a hybrid intelligent optimization algorithm is proposed, which is achieved by the mixture of the genetic, ant colony and dynamic greedy algorithm. By comparing with other published methods, simulation results verify the performance of the proposed method in improving the AP coverage and the performance of the proposed algorithm in terms of convergence.

Evaluation of a landscape evolution model to simulate stream piracies: Insights from multivariable numerical tests using the example of the Meuse basin, France

NASA Astrophysics Data System (ADS)

Benaïchouche, Abed; Stab, Olivier; Tessier, Bruno; Cojan, Isabelle

2016-01-01

In landscapes dominated by fluvial erosion, the landscape morphology is closely related to the hydrographic network system. In this paper, we investigate the hydrographic network reorganization caused by a headward piracy mechanism between two drainage basins in France, the Meuse and the Moselle. Several piracies occurred in the Meuse basin during the past one million years, and the basin's current characteristics are favorable to new piracies by the Moselle river network. This study evaluates the consequences over the next several million years of a relative lowering of the Moselle River (and thus of its basin) with respect to the Meuse River. The problem is addressed with a numerical modeling approach (landscape evolution model, hereafter LEM) that requires empirical determinations of parameters and threshold values. Classically, fitting of the parameters is based on analysis of the relationship between the slope and the drainage area and is conducted under the hypothesis of equilibrium. Application of this conventional approach to the capture issue yields incomplete results that have been consolidated by a parametric sensitivity analysis. The LEM equations give a six-dimensional parameter space that was explored with over 15,000 simulations using the landscape evolution model GOLEM. The results demonstrate that stream piracies occur in only four locations in the studied reach near the city of Toul. The locations are mainly controlled by the local topography and are model-independent. Nevertheless, the chronology of the captures depends on two parameters: the river concavity (given by the fluvial advection equation) and the hillslope erosion factor. Thus, the simulations lead to three different scenarios that are explained by a phenomenon of exclusion or a string of events.

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

NASA Astrophysics Data System (ADS)

Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

2016-06-01

Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

An evolving model of online bipartite networks

NASA Astrophysics Data System (ADS)

Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang

2013-12-01

Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

Multi-language naming game

NASA Astrophysics Data System (ADS)

Zhou, Jianfeng; Lou, Yang; Chen, Guanrong; Tang, Wallace K. S.

2018-04-01

Naming game is a simulation-based experiment used to study the evolution of languages. The conventional naming game focuses on a single language. In this paper, a novel naming game model named multi-language naming game (MLNG) is proposed, where the agents are different-language speakers who cannot communicate with each other without a translator (interpreter) in between. The MLNG model is general, capable of managing k different languages with k ≥ 2. For illustration, the paper only discusses the MLNG with two different languages, and studies five representative network topologies, namely random-graph, WS small-world, NW small-world, scale-free, and random-triangle topologies. Simulation and analysis results both show that: 1) using the network features and based on the proportion of translators the probability of establishing a conversation between two or three agents can be theoretically estimated; 2) the relationship between the convergence speed and the proportion of translators has a power-law-like relation; 3) different agents require different memory sizes, thus a local memory allocation rule is recommended for saving memory resources. The new model and new findings should be useful for further studies of naming games and for better understanding of languages evolution from a dynamical network perspective.

The evolving cobweb of relations among partially rational investors

PubMed Central

DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents’ behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors. PMID:28196144

The evolving cobweb of relations among partially rational investors.

PubMed

DeLellis, Pietro; DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents' behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors.

Effect of node attributes on the temporal dynamics of network structure

NASA Astrophysics Data System (ADS)

Momeni, Naghmeh; Fotouhi, Babak

2017-03-01

Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call "quality"), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.

Link prediction measures considering different neighbors’ effects and application in social networks

NASA Astrophysics Data System (ADS)

Luo, Peng; Wu, Chong; Li, Yongli

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks

NASA Astrophysics Data System (ADS)

Tucker, Gregory E.; Lancaster, Stephen T.; Gasparini, Nicole M.; Bras, Rafael L.; Rybarczyk, Scott M.

2001-10-01

We describe a new set of data structures and algorithms for dynamic terrain modeling using a triangulated irregular network (TINs). The framework provides an efficient method for storing, accessing, and updating a Delaunay triangulation and its associated Voronoi diagram. The basic data structure consists of three interconnected data objects: triangles, nodes, and directed edges. Encapsulating each of these geometric elements within a data object makes it possible to essentially decouple the TIN representation from the modeling applications that make use of it. Both the triangulation and its corresponding Voronoi diagram can be rapidly retrieved or updated, making these methods well suited to adaptive remeshing schemes. We develop a set of algorithms for defining drainage networks and identifying closed depressions (e.g., lakes) for hydrologic and geomorphic modeling applications. We also outline simple numerical algorithms for solving network routing and 2D transport equations within the TIN framework. The methods are illustrated with two example applications, a landscape evolution model and a distributed rainfall-runoff model.

Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks

PubMed Central

Anseán, David; Otero, José; Couso, Inés

2017-01-01

A soft sensor is presented that approximates certain health parameters of automotive rechargeable batteries from on-vehicle measurements of current and voltage. The sensor is based on a model of the open circuit voltage curve. This last model is implemented through monotonic neural networks and estimate over-potentials arising from the evolution in time of the Lithium concentration in the electrodes of the battery. The proposed soft sensor is able to exploit the information contained in operational records of the vehicle better than the alternatives, this being particularly true when the charge or discharge currents are between moderate and high. The accuracy of the neural model has been compared to different alternatives, including data-driven statistical models, first principle-based models, fuzzy observers and other recurrent neural networks with different topologies. It is concluded that monotonic echo state networks can outperform well established first-principle models. The algorithms have been validated with automotive Li-FePO4 cells. PMID:29267219

Modeling the future evolution of the virtual water trade network: A combination of network and gravity models

NASA Astrophysics Data System (ADS)

Sartori, Martina; Schiavo, Stefano; Fracasso, Andrea; Riccaboni, Massimo

2017-12-01

The paper investigates how the topological features of the virtual water (VW) network and the size of the associated VW flows are likely to change over time, under different socio-economic and climate scenarios. We combine two alternative models of network formation -a stochastic and a fitness model, used to describe the structure of VW flows- with a gravity model of trade to predict the intensity of each bilateral flow. This combined approach is superior to existing methodologies in its ability to replicate the observed features of VW trade. The insights from the models are used to forecast future VW flows in 2020 and 2050, under different climatic scenarios, and compare them with future water availability. Results suggest that the current trend of VW exports is not sustainable for all countries. Moreover, our approach highlights that some VW importers might be exposed to "imported water stress" as they rely heavily on imports from countries whose water use is unsustainable.

Framework for cascade size calculations on random networks

NASA Astrophysics Data System (ADS)

Burkholz, Rebekka; Schweitzer, Frank

2018-04-01

We present a framework to calculate the cascade size evolution for a large class of cascade models on random network ensembles in the limit of infinite network size. Our method is exact and applies to network ensembles with almost arbitrary degree distribution, degree-degree correlations, and, in case of threshold models, for arbitrary threshold distribution. With our approach, we shift the perspective from the known branching process approximations to the iterative update of suitable probability distributions. Such distributions are key to capture cascade dynamics that involve possibly continuous quantities and that depend on the cascade history, e.g., if load is accumulated over time. As a proof of concept, we provide two examples: (a) Constant load models that cover many of the analytically tractable casacade models, and, as a highlight, (b) a fiber bundle model that was not tractable by branching process approximations before. Our derivations cover the whole cascade dynamics, not only their steady state. This allows us to include interventions in time or further model complexity in the analysis.

Role of nuclear reactions on stellar evolution of intermediate-mass stars

NASA Astrophysics Data System (ADS)

Möller, H.; Jones, S.; Fischer, T.; Martínez-Pinedo, G.

2018-01-01

The evolution of intermediate-mass stars (8 - 12 solar masses) represents one of the most challenging subjects in nuclear astrophysics. Their final fate is highly uncertain and strongly model dependent. They can become white dwarfs, they can undergo electron-capture or core-collapse supernovae or they might even proceed towards explosive oxygen burning and a subsequent thermonuclear explosion. We believe that an accurate description of nuclear reactions is crucial for the determination of the pre-supernova structure of these stars. We argue that due to the possible development of an oxygen-deflagration, a hydrodynamic description has to be used. We implement a nuclear reaction network with ∼200 nuclear species into the implicit hydrodynamic code AGILE. The reaction network considers all relevant nuclear electron captures and beta-decays. For selected relevant nuclear species, we include a set of updated reaction rates, for which we discuss the role for the evolution of the stellar core, at the example of selected stellar models. We find that the final fate of these intermediate-mass stars depends sensitively on the density threshold for weak processes that deleptonize the core.

Skill complementarity enhances heterophily in collaboration networks

PubMed Central

Xie, Wen-Jie; Li, Ming-Xia; Jiang, Zhi-Qiang; Tan, Qun-Zhao; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

2016-01-01

Much empirical evidence shows that individuals usually exhibit significant homophily in social networks. We demonstrate, however, skill complementarity enhances heterophily in the formation of collaboration networks, where people prefer to forge social ties with people who have professions different from their own. We construct a model to quantify the heterophily by assuming that individuals choose collaborators to maximize utility. Using a huge database of online societies, we find evidence of heterophily in collaboration networks. The results of model calibration confirm the presence of heterophily. Both empirical analysis and model calibration show that the heterophilous feature is persistent along the evolution of online societies. Furthermore, the degree of skill complementarity is positively correlated with their production output. Our work sheds new light on the scientific research utility of virtual worlds for studying human behaviors in complex socioeconomic systems. PMID:26743687

Large-scale Heterogeneous Network Data Analysis

DTIC Science & Technology

2012-07-31

Mining (KDD’09), 527-535, 2009. [20] B. Long, Z. M. Zhang, X. Wu, and P. S. Yu . Spectral Clustering for Multi-type Relational Data. In Proceedings of...and Data Mining (KDD’06), 374-383, 2006. [33] Y. Sun, Y. Yu , and J. Han. Ranking-Based Clustering of Heterogeneous Information Networks with Star...publications in 2012 so far:  Yi-Kuang Ko, Jing- Kai Lou, Cheng-Te Li, Shou-de Lin, and Shyh-Kang Jeng. “A Social Network Evolution Model Based on

A characterization of the coupled evolution of grain fabric and pore space using complex networks: Pore connectivity and optimized flows in the presence of shear bands

NASA Astrophysics Data System (ADS)

Russell, Scott; Walker, David M.; Tordesillas, Antoinette

2016-03-01

A framework for the multiscale characterization of the coupled evolution of the solid grain fabric and its associated pore space in dense granular media is developed. In this framework, a pseudo-dual graph transformation of the grain contact network produces a graph of pores which can be readily interpreted as a pore space network. Survivability, a new metric succinctly summarizing the connectivity of the solid grain and pore space networks, measures material robustness. The size distribution and the connectivity of pores can be characterized quantitatively through various network properties. Assortativity characterizes the pore space with respect to the parity of the number of particles enclosing the pore. Multiscale clusters of odd parity versus even parity contact cycles alternate spatially along the shear band: these represent, respectively, local jamming and unjamming regions that continually switch positions in time throughout the failure regime. Optimal paths, established using network shortest paths in favor of large pores, provide clues on preferential paths for interstitial matter transport. In systems with higher rolling resistance at contacts, less tortuous shortest paths thread through larger pores in shear bands. Notably the structural patterns uncovered in the pore space suggest that more robust models of interstitial pore flow through deforming granular systems require a proper consideration of the evolution of in situ shear band and fracture patterns - not just globally, but also inside these localized failure zones.

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks

PubMed Central

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P.; Gerstein, Mark

2010-01-01

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers’ continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems. PMID:20439753

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks.

PubMed

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P; Gerstein, Mark

2010-05-18

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers' continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

Underwater Wireless Sensor Networks: how do acoustic propagation models impact the performance of higher-level protocols?

PubMed

Llor, Jesús; Malumbres, Manuel P

2012-01-01

Several Medium Access Control (MAC) and routing protocols have been developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of the main difficulties to compare and validate the performance of different proposals is the lack of a common standard to model the acoustic propagation in the underwater environment. In this paper we analyze the evolution of underwater acoustic prediction models from a simple approach to more detailed and accurate models. Then, different high layer network protocols are tested with different acoustic propagation models in order to determine the influence of environmental parameters on the obtained results. After several experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical layer parameters related to the network scenario and (b) the acoustic propagation model. Conditions like ocean surface activity, scenario location, bathymetry or floor sediment composition, may change the signal propagation behavior. So, when designing network architectures for UWSNs, the role of the physical layer should be seriously taken into account in order to assert that the obtained simulation results will be close to the ones obtained in real network scenarios.

Underwater Wireless Sensor Networks: How Do Acoustic Propagation Models Impact the Performance of Higher-Level Protocols?

PubMed Central

Llor, Jesús; Malumbres, Manuel P.

2012-01-01

Several Medium Access Control (MAC) and routing protocols have been developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of the main difficulties to compare and validate the performance of different proposals is the lack of a common standard to model the acoustic propagation in the underwater environment. In this paper we analyze the evolution of underwater acoustic prediction models from a simple approach to more detailed and accurate models. Then, different high layer network protocols are tested with different acoustic propagation models in order to determine the influence of environmental parameters on the obtained results. After several experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical layer parameters related to the network scenario and (b) the acoustic propagation model. Conditions like ocean surface activity, scenario location, bathymetry or floor sediment composition, may change the signal propagation behavior. So, when designing network architectures for UWSNs, the role of the physical layer should be seriously taken into account in order to assert that the obtained simulation results will be close to the ones obtained in real network scenarios. PMID:22438712

Emerging directions in the study of the ecology and evolution of plant-animal mutualistic networks: a review.

PubMed

Gu, Hao; Goodale, Eben; Chen, Jin

2015-03-18

The study of mutualistic plant and animal networks is an emerging field of ecological research. We reviewed progress in this field over the past 30 years. While earlier studies mostly focused on network structure, stability, and biodiversity maintenance, recent studies have investigated the conservation implications of mutualistic networks, specifically the influence of invasive species and how networks respond to habitat loss. Current research has also focused on evolutionary questions including phylogenetic signal in networks, impact of networks on the coevolution of interacting partners, and network influences on the evolution of interacting species. We outline some directions for future research, particularly the evolution of specialization in mutualistic networks, and provide concrete recommendations for environmental managers.

Evolution of a Rapidly Learned Representation for Speech.

ERIC Educational Resources Information Center

Nakisa, Ramin Charles; Plunkett, Kim

1998-01-01

Describes a connectionist model accounting for newborn infants' ability to finely discriminate almost all human speech contrasts and the fact that their phonemic category boundaries are identical, even for phonemes outside their target language. The model posits an innately guided learning in which an artificial neural network is stored in a…

Movement Patterns, Social Dynamics, and the Evolution of Cooperation

PubMed Central

Smaldino, Paul E.; Schank, Jeffrey C.

2012-01-01

The structure of social interactions influences many aspects of social life, including the spread of information and behavior, and the evolution of social phenotypes. After dispersal, organisms move around throughout their lives, and the patterns of their movement influence their social encounters over the course of their lifespan. Though both space and mobility are known to influence social evolution, there is little analysis of the influence of specific movement patterns on evolutionary dynamics. We explored the effects of random movement strategies on the evolution of cooperation using an agent-based prisoner’s dilemma model with mobile agents. This is the first systematic analysis of a model in which cooperators and defectors can use different random movement strategies, which we chose to fall on a spectrum between highly exploratory and highly restricted in their search tendencies. Because limited dispersal and restrictions to local neighborhood size are known to influence the ability of cooperators to effectively assort, we also assessed the robustness of our findings with respect to dispersal and local capacity constraints. We show that differences in patterns of movement can dramatically influence the likelihood of cooperator success, and that the effects of different movement patterns are sensitive to environmental assumptions about offspring dispersal and local space constraints. Since local interactions implicitly generate dynamic social interaction networks, we also measured the average number of unique and total interactions over a lifetime and considered how these emergent network dynamics helped explain the results. This work extends what is known about mobility and the evolution of cooperation, and also has general implications for social models with randomly moving agents. PMID:22838026

An Agent-Based Dynamic Model for Analysis of Distributed Space Exploration Architectures

NASA Astrophysics Data System (ADS)

Sindiy, Oleg V.; DeLaurentis, Daniel A.; Stein, William B.

2009-07-01

A range of complex challenges, but also potentially unique rewards, underlie the development of exploration architectures that use a distributed, dynamic network of resources across the solar system. From a methodological perspective, the prime challenge is to systematically model the evolution (and quantify comparative performance) of such architectures, under uncertainty, to effectively direct further study of specialized trajectories, spacecraft technologies, concept of operations, and resource allocation. A process model for System-of-Systems Engineering is used to define time-varying performance measures for comparative architecture analysis and identification of distinguishing patterns among interoperating systems. Agent-based modeling serves as the means to create a discrete-time simulation that generates dynamics for the study of architecture evolution. A Solar System Mobility Network proof-of-concept problem is introduced representing a set of longer-term, distributed exploration architectures. Options within this set revolve around deployment of human and robotic exploration and infrastructure assets, their organization, interoperability, and evolution, i.e., a system-of-systems. Agent-based simulations quantify relative payoffs for a fully distributed architecture (which can be significant over the long term), the latency period before they are manifest, and the up-front investment (which can be substantial compared to alternatives). Verification and sensitivity results provide further insight on development paths and indicate that the framework and simulation modeling approach may be useful in architectural design of other space exploration mass, energy, and information exchange settings.

Female mating preferences determine system-level evolution in a gene network model.

PubMed

Fierst, Janna L

2013-06-01

Environmental patterns of directional, stabilizing and fluctuating selection can influence the evolution of system-level properties like evolvability and mutational robustness. Intersexual selection produces strong phenotypic selection and these dynamics may also affect the response to mutation and the potential for future adaptation. In order to to assess the influence of mating preferences on these evolutionary properties, I modeled a male trait and female preference determined by separate gene regulatory networks. I studied three sexual selection scenarios: sexual conflict, a Gaussian model of the Fisher process described in Lande (in Proc Natl Acad Sci 78(6):3721-3725, 1981) and a good genes model in which the male trait signalled his mutational condition. I measured the effects these mating preferences had on the potential for traits and preferences to evolve towards new states, and mutational robustness of both the phenotype and the individual's overall viability. All types of sexual selection increased male phenotypic robustness relative to a randomly mating population. The Fisher model also reduced male evolvability and mutational robustness for viability. Under good genes sexual selection, males evolved an increased mutational robustness for viability. Females choosing their mates is a scenario that is sufficient to create selective forces that impact genetic evolution and shape the evolutionary response to mutation and environmental selection. These dynamics will inevitably develop in any population where sexual selection is operating, and affect the potential for future adaptation.

Can We Recognize an Innovation? Perspective from an Evolving Network Model

NASA Astrophysics Data System (ADS)

Jain, Sanjay; Krishna, Sandeep

"Innovations" are central to the evolution of societies and the evolution of life. But what constitutes an innovation? We can often agree after the event, when its consequences and impact over a long term are known, whether something was an innovation, and whether it was a "big" innovation or a "minor" one. But can we recognize an innovation "on the fly" as it appears? Successful entrepreneurs often can. Is it possible to formalize that intuition? We discuss this question in the setting of a mathematical model of evolving networks. The model exhibits self-organization , growth, stasis, and collapse of a complex system with many interacting components, reminiscent of real-world phenomena. A notion of "innovation" is formulated in terms of graph-theoretic constructs and other dynamical variables of the model. A new node in the graph gives rise to an innovation, provided it links up "appropriately" with existing nodes; in this view innovation necessarily depends upon the existing context. We show that innovations, as defined by us, play a major role in the birth, growth, and destruction of organizational structures. Furthermore, innovations can be categorized in terms of their graph-theoretic structure as they appear. Different structural classes of innovation have potentially different qualitative consequences for the future evolution of the system, some minor and some major. Possible general lessons from this specific model are briefly discussed.

The post-genomic era of biological network alignment.

PubMed

Faisal, Fazle E; Meng, Lei; Crawford, Joseph; Milenković, Tijana

2015-12-01

Biological network alignment aims to find regions of topological and functional (dis)similarities between molecular networks of different species. Then, network alignment can guide the transfer of biological knowledge from well-studied model species to less well-studied species between conserved (aligned) network regions, thus complementing valuable insights that have already been provided by genomic sequence alignment. Here, we review computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches' biomedical applications. We discuss recent innovative efforts of improving the existing view of network alignment. We conclude with open research questions in comparative biological network research that could further our understanding of principles of life, evolution, disease, and therapeutics.

Structure and Evolution of the Foreign Exchange Networks

NASA Astrophysics Data System (ADS)

Kwapień, J.; Gworek, S.; Drożdż, S.

2009-01-01

We investigate topology and temporal evolution of the foreign currency exchange market viewed from a weighted network perspective. Based on exchange rates for a set of 46 currencies (including precious metals), we construct different representations of the FX network depending on a choice of the base currency. Our results show that the network structure is not stable in time, but there are main clusters of currencies, which persist for a long period of time despite the fact that their size and content are variable. We find a long-term trend in the network's evolution which affects the USD and EUR nodes. In all the network representations, the USD node gradually loses its centrality, while, on contrary, the EUR node has become slightly more central than it used to be in its early years. Despite this directional trend, the overall evolution of the network is noisy.

Conserved noncoding sequences conserve biological networks and influence genome evolution.

PubMed

Xie, Jianbo; Qian, Kecheng; Si, Jingna; Xiao, Liang; Ci, Dong; Zhang, Deqiang

2018-05-01

Comparative genomics approaches have identified numerous conserved cis-regulatory sequences near genes in plant genomes. Despite the identification of these conserved noncoding sequences (CNSs), our knowledge of their functional importance and selection remains limited. Here, we used a combination of DNA methylome analysis, microarray expression analyses, and functional annotation to study these sequences in the model tree Populus trichocarpa. Methylation in CG contexts and non-CG contexts was lower in CNSs, particularly CNSs in the 5'-upstream regions of genes, compared with other sites in the genome. We observed that CNSs are enriched in genes with transcription and binding functions, and this also associated with syntenic genes and those from whole-genome duplications, suggesting that cis-regulatory sequences play a key role in genome evolution. We detected a significant positive correlation between CNS number and protein interactions, suggesting that CNSs may have roles in the evolution and maintenance of biological networks. The divergence of CNSs indicates that duplication-degeneration-complementation drives the subfunctionalization of a proportion of duplicated genes from whole-genome duplication. Furthermore, population genomics confirmed that most CNSs are under strong purifying selection and only a small subset of CNSs shows evidence of adaptive evolution. These findings provide a foundation for future studies exploring these key genomic features in the maintenance of biological networks, local adaptation, and transcription.

Traffic Dimensioning and Performance Modeling of 4G LTE Networks

ERIC Educational Resources Information Center

Ouyang, Ye

2011-01-01

Rapid changes in mobile techniques have always been evolutionary, and the deployment of 4G Long Term Evolution (LTE) networks will be the same. It will be another transition from Third Generation (3G) to Fourth Generation (4G) over a period of several years, as is the case still with the transition from Second Generation (2G) to 3G. As a result,…

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks.

PubMed

Sendiña-Nadal, I; Danziger, M M; Wang, Z; Havlin, S; Boccaletti, S

2016-02-18

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph's hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks

NASA Astrophysics Data System (ADS)

Sendiña-Nadal, I.; Danziger, M. M.; Wang, Z.; Havlin, S.; Boccaletti, S.

2016-02-01

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph’s hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

An Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning

NASA Astrophysics Data System (ADS)

Parete-Koon, Suzanne; Hix, William Raphael; Thielemann, Friedrich-Karl

2010-02-01

The nuclei of the ``iron peak'' are formed late in the evolution of massive stars and during supernovae. Silicon burning during these events is responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. The large number of nuclei involved make accurate modeling of silicon burning computationally expensive. Examination of the physics of silicon burning reveals that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present an improvement on our hybrid equilibrium-network scheme that takes advantage of this quasi-equilibrium (QSE) to reduce the number of independent variables calculated. Because the membership and number of these groups vary as the temperature, density and electron faction change, achieving maximal efficiency requires dynamic adjustment of group number and membership. The resultant QSE-reduced network is up to 20 times faster than the full network it replaces without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multi-dimensional applications. )

General purpose graphics-processing-unit implementation of cosmological domain wall network evolution.

PubMed

Correia, J R C C C; Martins, C J A P

2017-10-01

Topological defects unavoidably form at symmetry breaking phase transitions in the early universe. To probe the parameter space of theoretical models and set tighter experimental constraints (exploiting the recent advances in astrophysical observations), one requires more and more demanding simulations, and therefore more hardware resources and computation time. Improving the speed and efficiency of existing codes is essential. Here we present a general purpose graphics-processing-unit implementation of the canonical Press-Ryden-Spergel algorithm for the evolution of cosmological domain wall networks. This is ported to the Open Computing Language standard, and as a consequence significant speedups are achieved both in two-dimensional (2D) and 3D simulations.

A bipartite fitness model for online music streaming services

NASA Astrophysics Data System (ADS)

Pongnumkul, Suchit; Motohashi, Kazuyuki

2018-01-01

This paper proposes an evolution model and an analysis of the behavior of music consumers on online music streaming services. While previous studies have observed power-law degree distributions of usage in online music streaming services, the underlying behavior of users has not been well understood. Users and songs can be described using a bipartite network where an edge exists between a user node and a song node when the user has listened that song. The growth mechanism of bipartite networks has been used to understand the evolution of online bipartite networks Zhang et al. (2013). Existing bipartite models are based on a preferential attachment mechanism László Barabási and Albert (1999) in which the probability that a user listens to a song is proportional to its current popularity. This mechanism does not allow for two types of real world phenomena. First, a newly released song with high quality sometimes quickly gains popularity. Second, the popularity of songs normally decreases as time goes by. Therefore, this paper proposes a new model that is more suitable for online music services by adding fitness and aging functions to the song nodes of the bipartite network proposed by Zhang et al. (2013). Theoretical analyses are performed for the degree distribution of songs. Empirical data from an online streaming service, Last.fm, are used to confirm the degree distribution of the object nodes. Simulation results show improvements from a previous model. Finally, to illustrate the application of the proposed model, a simplified royalty cost model for online music services is used to demonstrate how the changes in the proposed parameters can affect the costs for online music streaming providers. Managerial implications are also discussed.

Scaling properties of cosmic (super)string networks

NASA Astrophysics Data System (ADS)

Martins, C. J. A. P.

2014-10-01

I use a combination of state-of-the-art numerical simulations and analytic modelling to discuss the scaling properties of cosmic defect networks, including superstrings. Particular attention is given to the role of extra degrees of freedom in the evolution of these networks. Compared to the 'plain vanilla' case of Goto-Nambu strings, three such extensions play important but distinct roles in the network dynamics: the presence of charges/currents on the string worldsheet, the existence of junctions, and the possibility of a hierarchy of string tensions. I also comment on insights gained from studying simpler defect networks, including Goto-Nambu strings themselves, domain walls and semilocal strings.

A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice

PubMed Central

Snijders, Tom A.B.; Lomi, Alessandro; Torló, Vanina Jasmine

2012-01-01

We propose a new stochastic actor-oriented model for the co-evolution of two-mode and one-mode networks. The model posits that activities of a set of actors, represented in the two-mode network, co-evolve with exchanges and interactions between the actors, as represented in the one-mode network. The model assumes that the actors, not the activities, have agency. The empirical value of the model is demonstrated by examining how employment preferences co-evolve with friendship and advice relations in a group of seventy-five MBA students. The analysis shows that activity in the two-mode network, as expressed by number of employment preferences, is related to activity in the friendship network, as expressed by outdegrees. Further, advice ties between students lead to agreement with respect to employment preferences. In addition, considering the multiplexity of advice and friendship ties yields a better understanding of the dynamics of the advice relation: tendencies to reciprocation and homophily in advice relations are mediated to an important extent by friendship relations. The discussion pays attention to the implications of this study in the broader context of current efforts to model the co-evolutionary dynamics of social networks and individual behavior. PMID:23690653

Evolution of extortion in the social-influenced prisoner’s dilemma

NASA Astrophysics Data System (ADS)

Wang, Zhipeng; Li, Miao; Wang, Dan; Chen, Qinghe

2016-01-01

The introduction of extortion strategy has attracted much attention since it dominates any evolutionary opponent in iterated prisoner’s dilemma games. Despite several studies argue that extortion is difficult to survive under strategy imitation and birth-death updating rules in well-mixed populations, it has recently been proven that a myopic best response rule facilitate the evolution of cooperation and extortion. However, such updating rules require a strong assumption of complete knowledge of all players, which is unlikely to hold in social networks in reality. To solve this problem, we introduce the concept of social influence into the model to limit players’ knowledge within their neighborhood. It turns out that this myopia initiated by social influence prevents players from observing superior strategies and therefore enables cooperators and extortioners to be evolutionarily stable. We also suggest that heterogeneous networks contribute to the evolution of cooperation and extortion under such social influence.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bobyshev, A.; DeMar, P.; Grigaliunas, V.

The LHC is entering its fourth year of production operation. Most Tier1 facilities have been in operation for almost a decade, when development and ramp-up efforts are included. LHC's distributed computing model is based on the availability of high capacity, high performance network facilities for both the WAN and LAN data movement, particularly within the Tier1 centers. As a result, the Tier1 centers tend to be on the leading edge of data center networking technology. In this paper, we analyze past and current developments in Tier1 LAN networking, as well as extrapolating where we anticipate networking technology is heading. Ourmore » analysis will include examination into the following areas: Evolution of Tier1 centers to their current state Evolving data center networking models and how they apply to Tier1 centers Impact of emerging network technologies (e.g. 10GE-connected hosts, 40GE/100GE links, IPv6) on Tier1 centers Trends in WAN data movement and emergence of software-defined WAN network capabilities Network virtualization« less

Physical Model of the Genotype-to-Phenotype Map of Proteins

NASA Astrophysics Data System (ADS)

Tlusty, Tsvi; Libchaber, Albert; Eckmann, Jean-Pierre

2017-04-01

How DNA is mapped to functional proteins is a basic question of living matter. We introduce and study a physical model of protein evolution which suggests a mechanical basis for this map. Many proteins rely on large-scale motion to function. We therefore treat protein as learning amorphous matter that evolves towards such a mechanical function: Genes are binary sequences that encode the connectivity of the amino acid network that makes a protein. The gene is evolved until the network forms a shear band across the protein, which allows for long-range, soft modes required for protein function. The evolution reduces the high-dimensional sequence space to a low-dimensional space of mechanical modes, in accord with the observed dimensional reduction between genotype and phenotype of proteins. Spectral analysis of the space of 1 06 solutions shows a strong correspondence between localization around the shear band of both mechanical modes and the sequence structure. Specifically, our model shows how mutations are correlated among amino acids whose interactions determine the functional mode.

Two-dimensional plasmons in the random impedance network model of disordered thin film nanocomposites

NASA Astrophysics Data System (ADS)

Olekhno, N. A.; Beltukov, Y. M.

2018-05-01

Random impedance networks are widely used as a model to describe plasmon resonances in disordered metal-dielectric nanocomposites. Two-dimensional networks are applied when considering thin films despite the fact that such networks correspond to the two-dimensional electrodynamics [Clerc et al., J. Phys. A 29, 4781 (1996), 10.1088/0305-4470/29/16/006]. In the present work, we propose a model of two-dimensional systems with the three-dimensional Coulomb interaction and show that this model is equivalent to the planar network with long-range capacitive links between distant sites. In the case of a metallic film, we obtain the well-known dispersion of two-dimensional plasmons ω ∝√{k } . We study the evolution of resonances with a decrease in the metal filling factor within the framework of the proposed model. In the subcritical region with the metal filling p lower than the percolation threshold pc, we observe a gap with Lifshitz tails in the spectral density of states (DOS). In the supercritical region p >pc , the DOS demonstrates a crossover between plane-wave two-dimensional plasmons and resonances of finite clusters.

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

PubMed

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

2015-02-01

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Predicting network modules of cell cycle regulators using relative protein abundance statistics.

PubMed

Oguz, Cihan; Watson, Layne T; Baumann, William T; Tyson, John J

2017-02-28

Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of "feasible" parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network. Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in "regulation of cell size" and "regulation of G1/S transition" contribute most to predictive variability, whereas proteins involved in "positive regulation of transcription involved in exit from mitosis," "mitotic spindle assembly checkpoint" and "negative regulation of cyclin-dependent protein kinase by cyclin degradation" contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the areas under receiver operating characteristics curves (AUC). Our models generate an AUC range of 0.83-0.87 as opposed to randomized models with AUC values around 0.50. By using differential evolution and random forest modeling, we show that the model prediction statistics generate distinct network module-specific patterns within the cell cycle network.

Leveraging social networks for understanding the evolution of epidemics

PubMed Central

2011-01-01

Background To understand how infectious agents disseminate throughout a population it is essential to capture the social model in a realistic manner. This paper presents a novel approach to modeling the propagation of the influenza virus throughout a realistic interconnection network based on actual individual interactions which we extract from online social networks. The advantage is that these networks can be extracted from existing sources which faithfully record interactions between people in their natural environment. We additionally allow modeling the characteristics of each individual as well as customizing his daily interaction patterns by making them time-dependent. Our purpose is to understand how the infection spreads depending on the structure of the contact network and the individuals who introduce the infection in the population. This would help public health authorities to respond more efficiently to epidemics. Results We implement a scalable, fully distributed simulator and validate the epidemic model by comparing the simulation results against the data in the 2004-2005 New York State Department of Health Report (NYSDOH), with similar temporal distribution results for the number of infected individuals. We analyze the impact of different types of connection models on the virus propagation. Lastly, we analyze and compare the effects of adopting several different vaccination policies, some of them based on individual characteristics -such as age- while others targeting the super-connectors in the social model. Conclusions This paper presents an approach to modeling the propagation of the influenza virus via a realistic social model based on actual individual interactions extracted from online social networks. We implemented a scalable, fully distributed simulator and we analyzed both the dissemination of the infection and the effect of different vaccination policies on the progress of the epidemics. The epidemic values predicted by our simulator match real data from NYSDOH. Our results show that our simulator can be a useful tool in understanding the differences in the evolution of an epidemic within populations with different characteristics and can provide guidance with regard to which, and how many, individuals should be vaccinated to slow down the virus propagation and reduce the number of infections. PMID:22784620

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

PubMed Central

Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

2012-01-01

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms. PMID:22807664

Contagion processes on the static and activity-driven coupling networks

NASA Astrophysics Data System (ADS)

Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

2016-03-01

The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

On building a memory evolutive system for application to learning and cognition modeling.

PubMed

de Lima do Rego Monteiro, Julio; Kogler, Joao Eduardo; Ribeiro, Joao Henrique Ranhel; Netto, Marcio Lobo

2010-01-01

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich's formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.

The Rise of China in the International Trade Network: A Community Core Detection Approach

PubMed Central

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995–2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism. PMID:25136895

The rise of China in the International Trade Network: a community core detection approach.

PubMed

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism.

The QSE-Reduced Nuclear Reaction Network for Silicon Burning

NASA Astrophysics Data System (ADS)

Hix, W. Raphael; Parete-Koon, Suzanne T.; Freiburghaus, Christian; Thielemann, Friedrich-Karl

2007-09-01

Iron and neighboring nuclei are formed in massive stars shortly before core collapse and during their supernova outbursts, as well as during thermonuclear supernovae. Complete and incomplete silicon burning are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present a new hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. This allows accurate prediction of the nuclear abundance evolution, deleptonization, and energy generation at a greatly reduced computational cost when compared to a conventional nuclear reaction network. During silicon burning, the resultant QSE-reduced network is approximately an order of magnitude faster than the full network it replaces and requires the tracking of less than a third as many abundance variables, without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multidimensional applications.

The algebra of the general Markov model on phylogenetic trees and networks.

PubMed

Sumner, J G; Holland, B R; Jarvis, P D

2012-04-01

It is known that the Kimura 3ST model of sequence evolution on phylogenetic trees can be extended quite naturally to arbitrary split systems. However, this extension relies heavily on mathematical peculiarities of the associated Hadamard transformation, and providing an analogous augmentation of the general Markov model has thus far been elusive. In this paper, we rectify this shortcoming by showing how to extend the general Markov model on trees to include incompatible edges; and even further to more general network models. This is achieved by exploring the algebra of the generators of the continuous-time Markov chain together with the “splitting” operator that generates the branching process on phylogenetic trees. For simplicity, we proceed by discussing the two state case and then show that our results are easily extended to more states with little complication. Intriguingly, upon restriction of the two state general Markov model to the parameter space of the binary symmetric model, our extension is indistinguishable from the Hadamard approach only on trees; as soon as any incompatible splits are introduced the two approaches give rise to differing probability distributions with disparate structure. Through exploration of a simple example, we give an argument that our extension to more general networks has desirable properties that the previous approaches do not share. In particular, our construction allows for convergent evolution of previously divergent lineages; a property that is of significant interest for biological applications.

Microbial interaction networks in soil and in silico

NASA Astrophysics Data System (ADS)

Vetsigian, Kalin

2012-02-01

Soil harbors a huge number of microbial species interacting through secretion of antibiotics and other chemicals. What patterns of species interactions allow for this astonishing biodiversity to be sustained, and how do these interactions evolve? I used a combined experimental-theoretical approach to tackle these questions. Focusing on bacteria from the genus Steptomyces, known for their diverse secondary metabolism, I isolated 64 natural strains from several individual grains of soil and systematically measured all pairwise interactions among them. Quantitative measurements on such scale were enabled by a novel experimental platform based on robotic handling, a custom scanner array and automatic image analysis. This unique platform allowed the simultaneous capturing of ˜15,000 time-lapse movies of growing colonies of each isolate on media conditioned by each of the other isolates. The data revealed a rich network of strong negative (inhibitory) and positive (stimulating) interactions. Analysis of this network and the phylogeny of the isolates, together with mathematical modeling of microbial communities, revealed that: 1) The network of interactions has three special properties: ``balance'', ``bi- modality'' and ``reciprocity''; 2) The interaction network is fast evolving; 3) Mathematical modeling explains how rapid evolution can give rise to the three special properties through an interplay between ecology and evolution. These properties are not a result of stable co-existence, but rather of continuous evolutionary turnover of strains with different production and resistance capabilities.

The co-evolution of cultures, social network communities, and agent locations in an extension of Axelrod’s model of cultural dissemination

NASA Astrophysics Data System (ADS)

Pfau, Jens; Kirley, Michael; Kashima, Yoshihisa

2013-01-01

We introduce a variant of the Axelrod model of cultural dissemination in which agents change their physical locations, social links, and cultures. Numerical simulations are used to investigate the evolution of social network communities and the cultural diversity within and between these communities. An analysis of the simulation results shows that an initial peak in the cultural diversity within network communities is evident before agents segregate into a final configuration of culturally homogeneous communities. Larger long-range interaction probabilities facilitate the initial emergence of culturally diverse network communities, which leads to a more pronounced initial peak in cultural diversity within communities. At equilibrium, the number of communities, and hence cultures, increases when the initial cultural diversity increases. However, the number of communities decreases when the lattice size or population density increases. A phase transition between two regimes of initial cultural diversity is evident. For initial diversities below a critical value, a single network community and culture emerges that dominates the population. For initial diversities above the critical value, multiple culturally homogeneous communities emerge. The critical value of initial diversity at which this transition occurs increases with increasing lattice size and population density and generally with increasing absolute population size. We conclude that larger initial diversities promote cultural heterogenization, while larger lattice sizes, population densities, and in fact absolute population sizes promote homogenization.

Applications of flow-networks to opinion-dynamics

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Kurths, Jürgen

2015-04-01

Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.

Energy model for rumor propagation on social networks

NASA Astrophysics Data System (ADS)

Han, Shuo; Zhuang, Fuzhen; He, Qing; Shi, Zhongzhi; Ao, Xiang

2014-01-01

With the development of social networks, the impact of rumor propagation on human lives is more and more significant. Due to the change of propagation mode, traditional rumor propagation models designed for word-of-mouth process may not be suitable for describing the rumor spreading on social networks. To overcome this shortcoming, we carefully analyze the mechanisms of rumor propagation and the topological properties of large-scale social networks, then propose a novel model based on the physical theory. In this model, heat energy calculation formula and Metropolis rule are introduced to formalize this problem and the amount of heat energy is used to measure a rumor’s impact on a network. Finally, we conduct track experiments to show the evolution of rumor propagation, make comparison experiments to contrast the proposed model with the traditional models, and perform simulation experiments to study the dynamics of rumor spreading. The experiments show that (1) the rumor propagation simulated by our model goes through three stages: rapid growth, fluctuant persistence and slow decline; (2) individuals could spread a rumor repeatedly, which leads to the rumor’s resurgence; (3) rumor propagation is greatly influenced by a rumor’s attraction, the initial rumormonger and the sending probability.

Criticality Is an Emergent Property of Genetic Networks that Exhibit Evolvability

PubMed Central

Torres-Sosa, Christian; Huang, Sui; Aldana, Maximino

2012-01-01

Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype) while allowing for switching between multiple phenotypes (network states) as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i) preserve all the already acquired phenotypes (dynamical attractor states) and (ii) generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation) while conserving the existing phenotypes (conservation) suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators) similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape. PMID:22969419

Self-determined mechanisms in complex networks

NASA Astrophysics Data System (ADS)

Liu, Yang; Yuan, Jian; Shan, Xiuming; Ren, Yong; Ma, Zhengxin

2008-03-01

Self-organized networks are pervasive in communication systems such as the Internet, overlay networks, peer-to-peer networks, and cluster-based services. These networks evolve into complex topologies, under specific driving forces, i.e. user demands, technological innovations, design objectives and so on. Our study focuses on the driving forces behind individual evolutions of network components, and their stimulation and domination to the self-organized networks which are defined as self-determined mechanisms in this paper. Understanding forces underlying the evolution of networks should enable informed design decisions and help to avoid unwanted surprises, such as congestion collapse. A case study on the macroscopic evolution of the Internet topology of autonomous systems under a specific driving force is then presented. Using computer simulations, it is found that the power-law degree distribution can originate from a connection preference to larger numbers of users, and that the small-world property can be caused by rapid growth in the number of users. Our results provide a new feasible perspective to understand intrinsic fundamentals in the topological evolution of complex networks.

Application of stochastic processes in random growth and evolutionary dynamics

NASA Astrophysics Data System (ADS)

Oikonomou, Panagiotis

We study the effect of power-law distributed randomness on the dynamical behavior of processes such as stochastic growth patterns and evolution. First, we examine the geometrical properties of random shapes produced by a generalized stochastic Loewner Evolution driven by a superposition of a Brownian motion and a stable Levy process. The situation is defined by the usual stochastic Loewner Evolution parameter, kappa, as well as alpha which defines the power-law tail of the stable Levy distribution. We show that the properties of these patterns change qualitatively and singularly at critical values of kappa and alpha. It is reasonable to call such changes "phase transitions". These transitions occur as kappa passes through four and as alpha passes through one. Numerical simulations are used to explore the global scaling behavior of these patterns in each "phase". We show both analytically and numerically that the growth continues indefinitely in the vertical direction for alpha greater than 1, goes as logarithmically with time for alpha equals to 1, and saturates for alpha smaller than 1. The probability density has two different scales corresponding to directions along and perpendicular to the boundary. Scaling functions for the probability density are given for various limiting cases. Second, we study the effect of the architecture of biological networks on their evolutionary dynamics. In recent years, studies of the architecture of large networks have unveiled a common topology, called scale-free, in which a majority of the elements are poorly connected except for a small fraction of highly connected components. We ask how networks with distinct topologies can evolve towards a pre-established target phenotype through a process of random mutations and selection. We use networks of Boolean components as a framework to model a large class of phenotypes. Within this approach, we find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. While homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps, scale-free networks evolve rapidly and continuously towards the target phenotype. Moreover, we show that scale-free networks always evolve faster than homogeneous random networks; remarkably, this property does not depend on the precise value of the topological parameter. By contrast, homogeneous random networks require a specific tuning of their topological parameter in order to optimize their fitness. This model suggests that the evolutionary paths of biological networks, punctuated or continuous, may solely be determined by the network topology.

Mathematical Modeling of the Origins of Life

NASA Technical Reports Server (NTRS)

Pohorille, Andrew

2006-01-01

The emergence of early metabolism - a network of catalyzed chemical reactions that supported self-maintenance, growth, reproduction and evolution of the ancestors of contemporary cells (protocells) was a critical, but still very poorly understood step on the path from inanimate to animate matter. Here, it is proposed and tested through mathematical modeling of biochemically plausible systems that the emergence of metabolism and its initial evolution towards higher complexity preceded the emergence of a genome. Even though the formation of protocellular metabolism was driven by non-genomic, highly stochastic processes the outcome was largely deterministic, strongly constrained by laws of chemistry. It is shown that such concepts as speciation and fitness to the environment, developed in the context of genomic evolution, also held in the absence of a genome.

Exploring the evolution of node neighborhoods in Dynamic Networks

NASA Astrophysics Data System (ADS)

Orman, Günce Keziban; Labatut, Vincent; Naskali, Ahmet Teoman

2017-09-01

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.

Earth Regimes Network Evolution Study (ERNESt): Introducing the Space Mobile Network

NASA Technical Reports Server (NTRS)

Menrad, Bob

2016-01-01

Speaker and Presenter at the Lincoln Laboratory Communications Workshop on April 5, 2016 at the Massachusetts Institute of Technology Lincoln Laboratory in Lexington, MA. A visual presentation titled Earth Regimes Network Evolution Study (ERNESt).

Using circuit theory to model connectivity in ecology, evolution, and conservation.

PubMed

McRae, Brad H; Dickson, Brett G; Keitt, Timothy H; Shah, Viral B

2008-10-01

Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.

Exploring the evolution of London's street network in the information space: A dual approach

NASA Astrophysics Data System (ADS)

Masucci, A. Paolo; Stanilov, Kiril; Batty, Michael

2014-01-01

We study the growth of London's street network in its dual representation, as the city has evolved over the past 224 years. The dual representation of a planar graph is a content-based network, where each node is a set of edges of the planar graph and represents a transportation unit in the so-called information space, i.e., the space where information is handled in order to navigate through the city. First, we discuss a novel hybrid technique to extract dual graphs from planar graphs, called the hierarchical intersection continuity negotiation principle. Then we show that the growth of the network can be analytically described by logistic laws and that the topological properties of the network are governed by robust log-normal distributions characterizing the network's connectivity and small-world properties that are consistent over time. Moreover, we find that the double-Pareto-like distributions for the connectivity emerge for major roads and can be modeled via a stochastic content-based network model using simple space-filling principles.

Are there laws of genome evolution?

PubMed

Koonin, Eugene V

2011-08-01

Research in quantitative evolutionary genomics and systems biology led to the discovery of several universal regularities connecting genomic and molecular phenomic variables. These universals include the log-normal distribution of the evolutionary rates of orthologous genes; the power law-like distributions of paralogous family size and node degree in various biological networks; the negative correlation between a gene's sequence evolution rate and expression level; and differential scaling of functional classes of genes with genome size. The universals of genome evolution can be accounted for by simple mathematical models similar to those used in statistical physics, such as the birth-death-innovation model. These models do not explicitly incorporate selection; therefore, the observed universal regularities do not appear to be shaped by selection but rather are emergent properties of gene ensembles. Although a complete physical theory of evolutionary biology is inconceivable, the universals of genome evolution might qualify as "laws of evolutionary genomics" in the same sense "law" is understood in modern physics.

Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

NASA Astrophysics Data System (ADS)

Kang, Peter K.; Dentz, Marco; Le Borgne, Tanguy; Lee, Seunghak; Juanes, Ruben

2017-08-01

We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes.

Petunia, Your Next Supermodel?

PubMed Central

Vandenbussche, Michiel; Chambrier, Pierre; Rodrigues Bento, Suzanne; Morel, Patrice

2016-01-01

Plant biology in general, and plant evo–devo in particular would strongly benefit from a broader range of available model systems. In recent years, technological advances have facilitated the analysis and comparison of individual gene functions in multiple species, representing now a fairly wide taxonomic range of the plant kingdom. Because genes are embedded in gene networks, studying evolution of gene function ultimately should be put in the context of studying the evolution of entire gene networks, since changes in the function of a single gene will normally go together with further changes in its network environment. For this reason, plant comparative biology/evo–devo will require the availability of a defined set of ‘super’ models occupying key taxonomic positions, in which performing gene functional analysis and testing genetic interactions ideally is as straightforward as, e.g., in Arabidopsis. Here we review why petunia has the potential to become one of these future supermodels, as a representative of the Asterid clade. We will first detail its intrinsic qualities as a model system. Next, we highlight how the revolution in sequencing technologies will now finally allows exploitation of the petunia system to its full potential, despite that petunia has already a long history as a model in plant molecular biology and genetics. We conclude with a series of arguments in favor of a more diversified multi-model approach in plant biology, and we point out where the petunia model system may further play a role, based on its biological features and molecular toolkit. PMID:26870078

A Hybrid Cellular Automaton Model of Clonal Evolution in Cancer: The Emergence of the Glycolytic Phenotype

PubMed Central

Gerlee, P.; Anderson, A.R.A.

2009-01-01

We present a cellular automaton model of clonal evolution in cancer aimed at investigating the emergence of the glycolytic phenotype. In the model each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment. The response network is modelled using a feed-forward neural network, which is subject to mutations when the cells divide. This implies that cells might react differently to the environment and when space and nutrients are limited only the fittest cells will survive. With this model we have investigated the impact of the environment on the growth dynamics of the tumour. In particular we have analysed the influence of the tissue oxygen concentration and extra-cellular matrix density on the dynamics of the model. We found that the environment influences both the growth and evolutionary dynamics of the tumour. For low oxygen concentration we observe tumours with a fingered morphology, while increasing the matrix density gives rise to more compact tumours with wider fingers. The distribution of phenotypes in the tumour is also affected, and we observe that the glycolytic phenotype is most likely to emerge in a poorly oxygenated tissue with a high matrix density. Our results suggest that it is the combined effect of the oxygen concentration and matrix density that creates an environment where the glycolytic phenotype has a growth advantage and consequently is most likely to appear. PMID:18068192

A genotype network reveals homoplastic cycles of convergent evolution in influenza A (H3N2) haemagglutinin.

PubMed

Wagner, Andreas

2014-07-07

Networks of evolving genotypes can be constructed from the worldwide time-resolved genotyping of pathogens like influenza viruses. Such genotype networks are graphs where neighbouring vertices (viral strains) differ in a single nucleotide or amino acid. A rich trove of network analysis methods can help understand the evolutionary dynamics reflected in the structure of these networks. Here, I analyse a genotype network comprising hundreds of influenza A (H3N2) haemagglutinin genes. The network is rife with cycles that reflect non-random parallel or convergent (homoplastic) evolution. These cycles also show patterns of sequence change characteristic for strong and local evolutionary constraints, positive selection and mutation-limited evolution. Such cycles would not be visible on a phylogenetic tree, illustrating that genotype network analysis can complement phylogenetic analyses. The network also shows a distinct modular or community structure that reflects temporal more than spatial proximity of viral strains, where lowly connected bridge strains connect different modules. These and other organizational patterns illustrate that genotype networks can help us study evolution in action at an unprecedented level of resolution. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

A general model for metabolic scaling in self-similar asymmetric networks

PubMed Central

Savage, Van M.; Enquist, Brian J.

2017-01-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber’s Law can still be attained within many asymmetric networks. PMID:28319153

A general model for metabolic scaling in self-similar asymmetric networks.

PubMed

Brummer, Alexander Byers; Savage, Van M; Enquist, Brian J

2017-03-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

Epidemic dynamics on a risk-based evolving social network

NASA Astrophysics Data System (ADS)

Antwi, Shadrack; Shaw, Leah

2013-03-01

Social network models have been used to study how behavior affects the dynamics of an infection in a population. Motivated by HIV, we consider how a trade-off between benefits and risks of sexual connections determine network structure and disease prevalence. We define a stochastic network model with formation and breaking of links as changes in sexual contacts. Each node has an intrinsic benefit its neighbors derive from connecting to it. Nodes' infection status is not apparent to others, but nodes with more connections (higher degree) are assumed more likely to be infected. The probability to form and break links is determined by a payoff computed from the benefit and degree-dependent risk. The disease is represented by a SI (susceptible-infected) model. We study network and epidemic evolution via Monte Carlo simulation and analytically predict the behavior with a heterogeneous mean field approach. The dependence of network connectivity and infection threshold on parameters is determined, and steady state degree distribution and epidemic levels are obtained. We also study a situation where system-wide infection levels alter perception of risk and cause nodes to adjust their behavior. This is a case of an adaptive network, where node status feeds back to change network geometry.

Percolation in insect nest networks: Evidence for optimal wiring

NASA Astrophysics Data System (ADS)

Valverde, Sergi; Corominas-Murtra, Bernat; Perna, Andrea; Kuntz, Pascale; Theraulaz, Guy; Solé, Ricard V.

2009-06-01

Optimization has been shown to be a driving force for the evolution of some biological structures, such as neural maps in the brain or transport networks. Here we show that insect networks also display characteristic traits of optimality. By using a graph representation of the chamber organization of termite nests and a disordered lattice model, it is found that these spatial nests are close to a percolation threshold. This suggests that termites build efficient systems of galleries spanning most of the nest volume at low cost. The evolutionary consequences are outlined.

Evolutionary transitions in controls reconcile adaptation with continuity of evolution.

PubMed

Badyaev, Alexander V

2018-05-19

Evolution proceeds by accumulating functional solutions, necessarily forming an uninterrupted lineage from past solutions of ancestors to the current design of extant forms. At the population level, this process requires an organismal architecture in which the maintenance of local adaptation does not preclude the ability to innovate in the same traits and their continuous evolution. Representing complex traits as networks enables us to visualize a fundamental principle that resolves tension between adaptation and continuous evolution: phenotypic states encompassing adaptations traverse the continuous multi-layered landscape of past physical, developmental and functional associations among traits. The key concept that captures such traversing is network controllability - the ability to move a network from one state into another while maintaining its functionality (reflecting evolvability) and to efficiently propagate information or products through the network within a phenotypic state (maintaining its robustness). Here I suggest that transitions in network controllability - specifically in the topology of controls - help to explain how robustness and evolvability are balanced during evolution. I will focus on evolutionary transitions in degeneracy of metabolic networks - a ubiquitous property of phenotypic robustness where distinct pathways achieve the same end product - to suggest that associated changes in network controls is a common rule underlying phenomena as distinct as phenotypic plasticity, organismal accommodation of novelties, genetic assimilation, and macroevolutionary diversification. Capitalizing on well understood principles by which network structure translates into function of control nodes, I show that accumulating redundancy in one type of network controls inevitably leads to the emergence of another type of controls, forming evolutionary cycles of network controllability that, ultimately, reconcile local adaptation with continuity of evolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Emergence of cooperativity in a model biofilm

NASA Astrophysics Data System (ADS)

Rotrattanadumrong, Rachapun; Endres, Robert G.

2017-06-01

Evolution to multicellularity from an aggregate of cells involves altruistic cooperation between individual cells, which is in conflict with Darwinian evolution. How cooperation arises and how a cell community resolves such conflicts remains unclear. In this study, we investigated the spontaneous emergence of cell differentiation and the subsequent division of labour in evolving cellular metabolic networks. In spatially extended cell aggregates, our findings reveal that resource limitation can lead to the formation of subpopulations and cooperation of cells, and hence multicellular communities. A specific example of our model can explain the recently observed oscillatory growth in Bacillus subtilis biofilms.

The Robustness of a Signaling Complex to Domain Rearrangements Facilitates Network Evolution

PubMed Central

Sato, Paloma M.; Yoganathan, Kogulan; Jung, Jae H.; Peisajovich, Sergio G.

2014-01-01

The rearrangement of protein domains is known to have key roles in the evolution of signaling networks and, consequently, is a major tool used to synthetically rewire networks. However, natural mutational events leading to the creation of proteins with novel domain combinations, such as in frame fusions followed by domain loss, retrotranspositions, or translocations, to name a few, often simultaneously replace pre-existing genes. Thus, while proteins with new domain combinations may establish novel network connections, it is not clear how the concomitant deletions are tolerated. We investigated the mechanisms that enable signaling networks to tolerate domain rearrangement-mediated gene replacements. Using as a model system the yeast mitogen activated protein kinase (MAPK)-mediated mating pathway, we analyzed 92 domain-rearrangement events affecting 11 genes. Our results indicate that, while domain rearrangement events that result in the loss of catalytic activities within the signaling complex are not tolerated, domain rearrangements can drastically alter protein interactions without impairing function. This suggests that signaling complexes can maintain function even when some components are recruited to alternative sites within the complex. Furthermore, we also found that the ability of the complex to tolerate changes in interaction partners does not depend on long disordered linkers that often connect domains. Taken together, our results suggest that some signaling complexes are dynamic ensembles with loose spatial constraints that could be easily re-shaped by evolution and, therefore, are ideal targets for cellular engineering. PMID:25490747

Network evolution induced by the dynamical rules of two populations

NASA Astrophysics Data System (ADS)

Platini, Thierry; Zia, R. K. P.

2010-10-01

We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (Na and Nb) and preferred degree (κa and \\kappa_b\\ll \\kappa_a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees langkbbrang and langkabrang presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal Na = Nb, the ratio of the restricted degree θ0 = langkabrang/langkbbrang appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t < t1 = κb) the total number of links presents a linear evolution, where the two populations are indistinguishable and where θ0 = 1. Interestingly, in the intermediate time regime (defined for t_1\\lt t\\lt t_2\\propto \\kappa_a and for which θ0 = 5), the system reaches a transient stationary state, where the number of contacts among introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ0 = 3.

Status and Trends in Networking at LHC Tier1 Facilities

NASA Astrophysics Data System (ADS)

Bobyshev, A.; DeMar, P.; Grigaliunas, V.; Bigrow, J.; Hoeft, B.; Reymund, A.

2012-12-01

The LHC is entering its fourth year of production operation. Most Tier1 facilities have been in operation for almost a decade, when development and ramp-up efforts are included. LHC's distributed computing model is based on the availability of high capacity, high performance network facilities for both the WAN and LAN data movement, particularly within the Tier1 centers. As a result, the Tier1 centers tend to be on the leading edge of data center networking technology. In this paper, we analyze past and current developments in Tier1 LAN networking, as well as extrapolating where we anticipate networking technology is heading. Our analysis will include examination into the following areas: • Evolution of Tier1 centers to their current state • Evolving data center networking models and how they apply to Tier1 centers • Impact of emerging network technologies (e.g. 10GE-connected hosts, 40GE/100GE links, IPv6) on Tier1 centers • Trends in WAN data movement and emergence of software-defined WAN network capabilities • Network virtualization

Modeled post-glacial landscape evolution at the southern margin of the Laurentide Ice Sheet: hydrological connection of uplands controls the pace and style of fluvial network expansion

NASA Astrophysics Data System (ADS)

Lai, J.; Anders, A. M.

2017-12-01

Landscapes of the US Midwest were repeatedly affected by the southern margin of the Laurentide Ice Sheet during the Quaternary. Glacial processes removed pre-glacial relief and left constructional landforms including low-relief till plains and high-relief moraines. As the ice retreated, meltwater was collected in subglacial or proglacial lakes and outburst floods of glacial lakes episodically carved deep valleys. These valleys provided the majority of post-glacial landscape relief. However, a significant fraction of the area of low-relief till plains was occupied by closed depressions and remained unconnected to these meltwater valleys. This area is referred to as non-contributing area (NCA) because it does not typically contribute surface runoff to stream networks. Decreasing fractions of NCA on older glacial landscape surfaces suggests that NCA becomes integrated into external drainage networks over time. We propose that this integration could occur via two different paths: 1) through capture of NCA as channel heads propagate into the upland or, 2) through erosion of a channel along a flow path that, perhaps intermittently, connects NCA to the external drainage network. We refer the two cases as "disconnected" and "connected" cases since the crucial difference between them is the hydrological connectivity on the upland. We investigate the differences in the evolution of channel networks and morphology in low relief landscapes under disconnected and connected drainage regimes through numerical simulations of fluvial and hillslope processes. We observe a substantially faster evolution of the channel network in the connected case than in the disconnected case. Modeled landscapes show that channel network in the connected case has longer, more sinuous channels. We also find that the connected case removes lower amounts of total mass than the disconnected case when the same degree of channel integration is achieved. Observed landscapes in US Midwest are more comparable to the connected case than the disconnected case. This finding suggest that the hydrological connectivity in these landscapes may not be entirely controlled by topographic drainage divides.

Theory of Turing Patterns on Time Varying Networks.

PubMed

Petit, Julien; Lauwens, Ben; Fanelli, Duccio; Carletti, Timoteo

2017-10-06

The process of pattern formation for a multispecies model anchored on a time varying network is studied. A nonhomogeneous perturbation superposed to an homogeneous stable fixed point can be amplified following the Turing mechanism of instability, solely instigated by the network dynamics. By properly tuning the frequency of the imposed network evolution, one can make the examined system behave as its averaged counterpart, over a finite time window. This is the key observation to derive a closed analytical prediction for the onset of the instability in the time dependent framework. Continuously and piecewise constant periodic time varying networks are analyzed, setting the framework for the proposed approach. The extension to nonperiodic settings is also discussed.

Network of listed companies based on common shareholders and the prediction of market volatility

NASA Astrophysics Data System (ADS)

Li, Jie; Ren, Da; Feng, Xu; Zhang, Yongjie

2016-11-01

In this paper, we build a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013. We analyze the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. Additionally, we consider the economic implications of topological characteristic changes on market volatility and use them to make future predictions. Our study finds that the network diameter significantly predicts volatility. After adding control variables used in traditional financial studies (volume, turnover and previous volatility), network topology still significantly influences volatility and improves the predictive ability of the model.

Life's attractors : understanding developmental systems through reverse engineering and in silico evolution.

PubMed

Jaeger, Johannes; Crombach, Anton

2012-01-01

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study-the gap gene network in dipterans (flies, midges, and mosquitoes)-to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

Neural network modeling of emotion

NASA Astrophysics Data System (ADS)

Levine, Daniel S.

2007-03-01

This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

An Analysis on a Negotiation Model Based on Multiagent Systems with Symbiotic Learning and Evolution

NASA Astrophysics Data System (ADS)

Hossain, Md. Tofazzal

This study explores an evolutionary analysis on a negotiation model based on Masbiole (Multiagent Systems with Symbiotic Learning and Evolution) which has been proposed as a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. In Masbiole, agents evolve in consideration of not only their own benefits and losses, but also the benefits and losses of opponent agents. To aid effective application of Masbiole, we develop a competitive negotiation model where rigorous and advanced intelligent decision-making mechanisms are required for agents to achieve solutions. A Negotiation Protocol is devised aiming at developing a set of rules for agents' behavior during evolution. Simulations use a newly developed evolutionary computing technique, called Genetic Network Programming (GNP) which has the directed graph-type gene structure that can develop and design the required intelligent mechanisms for agents. In a typical scenario, competitive negotiation solutions are reached by concessions that are usually predetermined in the conventional MAS. In this model, however, not only concession is determined automatically by symbiotic evolution (making the system intelligent, automated, and efficient) but the solution also achieves Pareto optimal automatically.

Network morphospace

PubMed Central

Avena-Koenigsberger, Andrea; Goñi, Joaquín; Solé, Ricard; Sporns, Olaf

2015-01-01

The structure of complex networks has attracted much attention in recent years. It has been noted that many real-world examples of networked systems share a set of common architectural features. This raises important questions about their origin, for example whether such network attributes reflect common design principles or constraints imposed by selectional forces that have shaped the evolution of network topology. Is it possible to place the many patterns and forms of complex networks into a common space that reveals their relations, and what are the main rules and driving forces that determine which positions in such a space are occupied by systems that have actually evolved? We suggest that these questions can be addressed by combining concepts from two currently relatively unconnected fields. One is theoretical morphology, which has conceptualized the relations between morphological traits defined by mathematical models of biological form. The second is network science, which provides numerous quantitative tools to measure and classify different patterns of local and global network architecture across disparate types of systems. Here, we explore a new theoretical concept that lies at the intersection between both fields, the ‘network morphospace’. Defined by axes that represent specific network traits, each point within such a space represents a location occupied by networks that share a set of common ‘morphological’ characteristics related to aspects of their connectivity. Mapping a network morphospace reveals the extent to which the space is filled by existing networks, thus allowing a distinction between actual and impossible designs and highlighting the generative potential of rules and constraints that pervade the evolution of complex systems. PMID:25540237

An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

PubMed Central

Zhang, Xuejun; Lei, Jiaxing

2015-01-01

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840

Gene networks and the evolution of plant morphology.

PubMed

Das Gupta, Mainak; Tsiantis, Miltos

2018-06-06

Elaboration of morphology depends on the precise orchestration of gene expression by key regulatory genes. The hierarchy and relationship among the participating genes is commonly known as gene regulatory network (GRN). Therefore, the evolution of morphology ultimately occurs by the rewiring of gene network structures or by the co-option of gene networks to novel domains. The availability of high-resolution expression data combined with powerful statistical tools have opened up new avenues to formulate and test hypotheses on how diverse gene networks influence trait development and diversity. Here we summarize recent studies based on both big-data and genetics approaches to understand the evolution of plant form and physiology. We also discuss recent genome-wide investigations on how studying open-chromatin regions may help study the evolution of gene expression patterns. Copyright © 2018. Published by Elsevier Ltd.

Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks.

PubMed

Chang, Xiao; Wang, Zhuo; Hao, Pei; Li, Yuan-Yuan; Li, Yi-Xue

2010-06-01

The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level. Copyright 2010 Elsevier Inc. All rights reserved.

Research on NGN network control technology

NASA Astrophysics Data System (ADS)

Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang

2004-04-01

Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.

Antibiotic combination efficacy (ACE) networks for a Pseudomonas aeruginosa model

PubMed Central

Barbosa, Camilo; Beardmore, Robert; Jansen, Gunther

2018-01-01

The spread of antibiotic resistance is always a consequence of evolutionary processes. The consideration of evolution is thus key to the development of sustainable therapy. Two main factors were recently proposed to enhance long-term effectiveness of drug combinations: evolved collateral sensitivities between the drugs in a pair and antagonistic drug interactions. We systematically assessed these factors by performing over 1,600 evolution experiments with the opportunistic nosocomial pathogen Pseudomonas aeruginosa in single- and multidrug environments. Based on the growth dynamics during these experiments, we reconstructed antibiotic combination efficacy (ACE) networks as a new tool for characterizing the ability of the tested drug combinations to constrain bacterial survival as well as drug resistance evolution across time. Subsequent statistical analysis of the influence of the factors on ACE network characteristics revealed that (i) synergistic drug interactions increased the likelihood of bacterial population extinction—irrespective of whether combinations were compared at the same level of inhibition or not—while (ii) the potential for evolved collateral sensitivities between 2 drugs accounted for a reduction in bacterial adaptation rates. In sum, our systematic experimental analysis allowed us to pinpoint 2 complementary determinants of combination efficacy and to identify specific drug pairs with high ACE scores. Our findings can guide attempts to further improve the sustainability of antibiotic therapy by simultaneously reducing pathogen load and resistance evolution. PMID:29708964

On the evolution of dispersal via heterogeneity in spatial connectivity

PubMed Central

Henriques-Silva, Renato; Boivin, Frédéric; Calcagno, Vincent; Urban, Mark C.; Peres-Neto, Pedro R.

2015-01-01

Dispersal has long been recognized as a mechanism that shapes many observed ecological and evolutionary processes. Thus, understanding the factors that promote its evolution remains a major goal in evolutionary ecology. Landscape connectivity may mediate the trade-off between the forces in favour of dispersal propensity (e.g. kin-competition, local extinction probability) and those against it (e.g. energetic or survival costs of dispersal). It remains, however, an open question how differing degrees of landscape connectivity may select for different dispersal strategies. We implemented an individual-based model to study the evolution of dispersal on landscapes that differed in the variance of connectivity across patches ranging from networks with all patches equally connected to highly heterogeneous networks. The parthenogenetic individuals dispersed based on a flexible logistic function of local abundance. Our results suggest, all else being equal, that landscapes differing in their connectivity patterns will select for different dispersal strategies and that these strategies confer a long-term fitness advantage to individuals at the regional scale. The strength of the selection will, however, vary across network types, being stronger on heterogeneous landscapes compared with the ones where all patches have equal connectivity. Our findings highlight how landscape connectivity can determine the evolution of dispersal strategies, which in turn affects how we think about important ecological dynamics such as metapopulation persistence and range expansion. PMID:25673685

Deformation and evolution of an experimental drainage network subjected to oblique deformation: Insight from chi-maps

NASA Astrophysics Data System (ADS)

Guerit, Laure; Goren, Liran; Dominguez, Stéphane; Malavieille, Jacques; Castelltort, Sébastien

2017-04-01

The morphology of a fluvial landscape reflects a balance between its own dynamics and external forcings, and therefore holds the potential to reveal local or large-scale tectonic patterns. Commonly, particular focus has been cast on the longitudinal profiles of rivers as they constitute sensitive recorders of vertical movements, that can be recovered based on models of bedrock incision. However, several recent studies have suggested that maps of rescaled distance along channel called chi (χ), derived from the commonly observed power law relation between the slope and the drainage area , could reveal transient landscapes in state of reorganization of basin geometry and location of water divides. If river networks deforms in response to large amount of distributed strain, then they might be used to reconstruct the mode and rate of horizontal deformation away from major active structures through the use of the parameter χ. To explore how streams respond to tectonic horizontal deformation, we develop an experimental model for studying river pattern evolution over a doubly-vergent orogenic wedge growing in a context of oblique convergence. We use a series of sprinklers located about the experimental table to activate erosion, sediment transport and river development on the surface of the experimental wedge. At the end of the experiment, the drainage network is statistically rotated clockwise, confirming that rivers can record the distribution of motion along the wedge. However, the amount of rotation does not match with the imposed deformation, and thus we infer that stream networks are not purely passive markers. Based on the comparison between the observed evolution of the fluvial system and the predictions made from χ maps, we show that the plan-view morphology of the streams results from the competition between the imposed deformation and fluvial processes of drainage reorganization.

Epidemic spreading on evolving signed networks

NASA Astrophysics Data System (ADS)

Saeedian, M.; Azimi-Tafreshi, N.; Jafari, G. R.; Kertesz, J.

2017-02-01

Most studies of disease spreading consider the underlying social network as obtained without the contagion, though epidemic influences people's willingness to contact others: A "friendly" contact may be turned to "unfriendly" to avoid infection. We study the susceptible-infected disease-spreading model on signed networks, in which each edge is associated with a positive or negative sign representing the friendly or unfriendly relation between its end nodes. In a signed network, according to Heider's theory, edge signs evolve such that finally a state of structural balance is achieved, corresponding to no frustration in physics terms. However, the danger of infection affects the evolution of its edge signs. To describe the coupled problem of the sign evolution and disease spreading, we generalize the notion of structural balance by taking into account the state of the nodes. We introduce an energy function and carry out Monte Carlo simulations on complete networks to test the energy landscape, where we find local minima corresponding to the so-called jammed states. We study the effect of the ratio of initial friendly to unfriendly connections on the propagation of disease. The steady state can be balanced or a jammed state such that a coexistence occurs between susceptible and infected nodes in the system.

Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks

PubMed Central

Ollivier, Julien F.; Soyer, Orkun S.

2016-01-01

Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications. PMID:27163612

The Robustness Analysis of Wireless Sensor Networks under Uncertain Interference

PubMed Central

Deng, Changjian

2013-01-01

Based on the complex network theory, robustness analysis of condition monitoring wireless sensor network under uncertain interference is present. In the evolution of the topology of sensor networks, the density weighted algebraic connectivity is taken into account, and the phenomenon of removing and repairing the link and node in the network is discussed. Numerical simulation is conducted to explore algebraic connectivity characteristics and network robustness performance. It is found that nodes density has the effect on algebraic connectivity distribution in the random graph model; high density nodes carry more connections, use more throughputs, and may be more unreliable. Moreover, the results show that, when network should be more error tolerant or robust by repairing nodes or adding new nodes, the network should be better clustered in median and high scale wireless sensor networks and be meshing topology in small scale networks. PMID:24363613

Modeling the Flow Behavior, Recrystallization, and Crystallographic Texture in Hot-Deformed Fe-30 Wt Pct Ni Austenite

NASA Astrophysics Data System (ADS)

Abbod, M. F.; Sellars, C. M.; Cizek, P.; Linkens, D. A.; Mahfouf, M.

2007-10-01

The present work describes a hybrid modeling approach developed for predicting the flow behavior, recrystallization characteristics, and crystallographic texture evolution in a Fe-30 wt pct Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 °C and 1050 °C and strain rates between 0.1 and 10 s-1. The evolution of grain structure, crystallographic texture, and dislocation substructure was characterized in detail for a deformation temperature of 950 °C and strain rates of 0.1 and 10 s-1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modeling method utilizes a combination of empirical, physically-based, and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallization behavior is predicted from the measured microstructural state variables of internal dislocation density, subgrain size, and misorientation between subgrains using a physically-based model. The texture evolution is modeled using artificial neural networks.

Evolutionary games on multilayer networks: a colloquium

NASA Astrophysics Data System (ADS)

Wang, Zhen; Wang, Lin; Szolnoki, Attila; Perc, Matjaž

2015-05-01

Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.

Optimization of controllability and robustness of complex networks by edge directionality

NASA Astrophysics Data System (ADS)

Liang, Man; Jin, Suoqin; Wang, Dingjie; Zou, Xiufen

2016-09-01

Recently, controllability of complex networks has attracted enormous attention in various fields of science and engineering. How to optimize structural controllability has also become a significant issue. Previous studies have shown that an appropriate directional assignment can improve structural controllability; however, the evolution of the structural controllability of complex networks under attacks and cascading has always been ignored. To address this problem, this study proposes a new edge orientation method (NEOM) based on residual degree that changes the link direction while conserving topology and directionality. By comparing the results with those of previous methods in two random graph models and several realistic networks, our proposed approach is demonstrated to be an effective and competitive method for improving the structural controllability of complex networks. Moreover, numerical simulations show that our method is near-optimal in optimizing structural controllability. Strikingly, compared to the original network, our method maintains the structural controllability of the network under attacks and cascading, indicating that the NEOM can also enhance the robustness of controllability of networks. These results alter the view of the nature of controllability in complex networks, change the understanding of structural controllability and affect the design of network models to control such networks.

Centrality in earthquake multiplex networks

NASA Astrophysics Data System (ADS)

Lotfi, Nastaran; Darooneh, Amir Hossein; Rodrigues, Francisco A.

2018-06-01

Seismic time series has been mapped as a complex network, where a geographical region is divided into square cells that represent the nodes and connections are defined according to the sequence of earthquakes. In this paper, we map a seismic time series to a temporal network, described by a multiplex network, and characterize the evolution of the network structure in terms of the eigenvector centrality measure. We generalize previous works that considered the single layer representation of earthquake networks. Our results suggest that the multiplex representation captures better earthquake activity than methods based on single layer networks. We also verify that the regions with highest seismological activities in Iran and California can be identified from the network centrality analysis. The temporal modeling of seismic data provided here may open new possibilities for a better comprehension of the physics of earthquakes.

Measures of node centrality in mobile social networks

NASA Astrophysics Data System (ADS)

Gao, Zhenxiang; Shi, Yan; Chen, Shanzhi

2015-02-01

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

Pre-supernova models for massive stars produced with large nuclear reaction network by MESA

NASA Astrophysics Data System (ADS)

Park, Byeongchan; Kwak, Kyujin

2018-04-01

Core-collapsed Supernova (CCSN) is one of violent phenomena in the universe. CCSN generates heavy elements and leaves a neutron star behind. It has been known that the physical properties of CCSN depend on those of pre-supernova such as mass, metallicities including distribution of elements, and the density and temperature profile which are obtained from the stellar evolution calculation. In particular, the production of heavy elements in CCSN is sensitive to the abundance profiles in the pre-supernova models. In this study, we evolve a massive main sequence star with 15Msun and solar metallicity to the pre-supernova stage by using two different networks, small and large. The large nuclear reaction network includes more than four times isotopes than the small network. Our calculations were done by MESA (Modules for Experiments in Stellar Astrophysics) which allowed us to use the large network containing about a hundred isotopes. We compare the results obtained with two networks.

Neutral Community Dynamics and the Evolution of Species Interactions.

PubMed

Coelho, Marco Túlio P; Rangel, Thiago F

2018-04-01

A contemporary goal in ecology is to determine the ecological and evolutionary processes that generate recurring structural patterns in mutualistic networks. One of the great challenges is testing the capacity of neutral processes to replicate observed patterns in ecological networks, since the original formulation of the neutral theory lacks trophic interactions. Here, we develop a stochastic-simulation neutral model adding trophic interactions to the neutral theory of biodiversity. Without invoking ecological differences among individuals of different species, and assuming that ecological interactions emerge randomly, we demonstrate that a spatially explicit multitrophic neutral model is able to capture the recurrent structural patterns of mutualistic networks (i.e., degree distribution, connectance, nestedness, and phylogenetic signal of species interactions). Nonrandom species distribution, caused by probabilistic events of migration and speciation, create nonrandom network patterns. These findings have broad implications for the interpretation of niche-based processes as drivers of ecological networks, as well as for the integration of network structures with demographic stochasticity.

Knowledge diffusion in complex networks by considering time-varying information channels

NASA Astrophysics Data System (ADS)

Zhu, He; Ma, Jing

2018-03-01

In this article, based on a model of epidemic spreading, we explore the knowledge diffusion process with an innovative mechanism for complex networks by considering time-varying information channels. To cover the knowledge diffusion process in homogeneous and heterogeneous networks, two types of networks (the BA network and the ER network) are investigated. The mean-field theory is used to theoretically draw the knowledge diffusion threshold. Numerical simulation demonstrates that the knowledge diffusion threshold is almost linearly correlated with the mean of the activity rate. In addition, under the influence of the activity rate and distinct from the classic Susceptible-Infected-Susceptible (SIS) model, the density of knowers almost linearly grows with the spreading rate. Finally, in consideration of the ubiquitous mechanism of innovation, we further study the evolution of knowledge in our proposed model. The results suggest that compared with the effect of the spreading rate, the average knowledge version of the population is affected more by the innovation parameter and the mean of the activity rate. Furthermore, in the BA network, the average knowledge version of individuals with higher degree is always newer than those with lower degree.

Recurrent rewiring and emergence of RNA regulatory networks.

PubMed

Wilinski, Daniel; Buter, Natascha; Klocko, Andrew D; Lapointe, Christopher P; Selker, Eric U; Gasch, Audrey P; Wickens, Marvin

2017-04-04

Alterations in regulatory networks contribute to evolutionary change. Transcriptional networks are reconfigured by changes in the binding specificity of transcription factors and their cognate sites. The evolution of RNA-protein regulatory networks is far less understood. The PUF (Pumilio and FBF) family of RNA regulatory proteins controls the translation, stability, and movements of hundreds of mRNAs in a single species. We probe the evolution of PUF-RNA networks by direct identification of the mRNAs bound to PUF proteins in budding and filamentous fungi and by computational analyses of orthologous RNAs from 62 fungal species. Our findings reveal that PUF proteins gain and lose mRNAs with related and emergent biological functions during evolution. We demonstrate at least two independent rewiring events for PUF3 orthologs, independent but convergent evolution of PUF4/5 binding specificity and the rewiring of the PUF4/5 regulons in different fungal lineages. These findings demonstrate plasticity in RNA regulatory networks and suggest ways in which their rewiring occurs.

Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks.

PubMed

Kim, Inhae; Lee, Heetak; Han, Seong Kyu; Kim, Sanguk

2014-10-01

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

Dynamic Evolution of Financial Network and its Relation to Economic Crises

NASA Astrophysics Data System (ADS)

Gao, Ya-Chun; Wei, Zong-Wen; Wang, Bing-Hong

2013-02-01

The static topology properties of financial networks have been widely investigated since the work done by Mantegna, yet their dynamic evolution with time is little considered. In this paper, we comprehensively study the dynamic evolution of financial network by a sliding window technique. The vertices and edges of financial network are represented by the stocks from S&P500 components and correlations between pairs of daily returns of price fluctuation, respectively. Furthermore, the duration of stock price fluctuation, spanning from January 4, 1985 to September 14, 2009, makes us to carefully observe the relation between the dynamic topological properties and big financial crashes. The empirical results suggest that the financial network has the robust small-world property when the time evolves, and the topological structure drastically changes when the big financial crashes occur. This correspondence between the dynamic evolution of financial network and big financial crashes may provide a novel view to understand the origin of economic crisis.

Phase Transitions of an Epidemic Spreading Model in Small-World Networks

NASA Astrophysics Data System (ADS)

Hua, Da-Yin; Gao, Ke

2011-06-01

We propose a modified susceptible-infected-refractory-susceptible (SIRS) model to investigate the global oscillations of the epidemic spreading in Watts—Strogatz (WS) small-world networks. It is found that when an individual immunity does not change or decays slowly in an immune period, the system can exhibit complex transition from an infecting stationary state to a large amplitude sustained oscillation or an absorbing state with no infection. When the immunity decays rapidly in the immune period, the transition to the global oscillation disappears and there is no oscillation. Furthermore, based on the spatio-temporal evolution patterns and the phase diagram, it is disclosed that a long immunity period takes an important role in the emergence of the global oscillation in small-world networks.

The nature of intermediate-range order in Ge-As-S glasses : results from reverse Monte Carlo modeling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soyer-Uzun, S.; Benmore, C. J.; Siewenie, J. E.

2010-01-01

The experimental neutron and x-ray diffraction data for stoichiometric and S-deficient Ge{sub x}As{sub x}S{sub 100-2x} glasses with x = 18.2, 25.0, and 33.3 at.% have been modeled simultaneously using the reverse Monte Carlo (RMC) technique. Nearest-neighbor coordination environments, as obtained in previous x-ray absorption spectroscopy and diffraction experiments, have been employed as short-range order constraints in these simulations. The large scale three-dimensional structural models thus obtained from RMC simulation are used to investigate the nature and compositional evolution of intermediate-range structural order in these ternary glasses. The intermediate-range structural order is controlled by (1) a corner-shared three-dimensional network of AsS{submore » 3} pyramids and GeS{sub 4} tetrahedra in the stoichiometric Ge{sub 18.2}As{sub 18.2}S{sub 63.6} glass, (2) a heterogeneous structure that consists of homopolar bonded As-rich regions coexisting with a GeS{sub 2} network in the S-deficient Ge{sub 25}As{sub 25}S{sub 50} glass, and (3) a homogeneous structure resulting from the disruption of the topological continuity of the GeS{sub 2} network and As-rich clusters regions due to the formation of Ge-As bonds in the most S-deficient Ge{sub 33.3}As{sub 33.3}S{sub 33.3} glass. This scenario of the compositional evolution of intermediate-range structural order is consistent with and provides an atomistic explanation of the corresponding evolution in the position, width and intensity of the first sharp diffraction peak and the magnitude of small angle scattering in these glasses.« less

Multi Sensor Fusion Using Fitness Adaptive Differential Evolution

NASA Astrophysics Data System (ADS)

Giri, Ritwik; Ghosh, Arnob; Chowdhury, Aritra; Das, Swagatam

The rising popularity of multi-source, multi-sensor networks supports real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on a modified version of Differential Evolution (DE), called Fitness Adaptive Differential Evolution (FiADE). FiADE treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed approach is formulated to produce good result for the problems that are high-dimensional, highly nonlinear, and random. The proposed approach gives better result in case of optimal allocation of sensors. The performance of the proposed approach is compared with an evolutionary algorithm coordination generalized particle model (C-GPM).

An incoherent feedforward loop facilitates adaptive tuning of gene expression.

PubMed

Hong, Jungeui; Brandt, Nathan; Abdul-Rahman, Farah; Yang, Ally; Hughes, Tim; Gresham, David

2018-04-05

We studied adaptive evolution of gene expression using long-term experimental evolution of Saccharomyces cerevisiae in ammonium-limited chemostats. We found repeated selection for non-synonymous variation in the DNA binding domain of the transcriptional activator, GAT1, which functions with the repressor, DAL80 in an incoherent type-1 feedforward loop (I1-FFL) to control expression of the high affinity ammonium transporter gene, MEP2. Missense mutations in the DNA binding domain of GAT1 reduce its binding to the GATAA consensus sequence. However, we show experimentally, and using mathematical modeling, that decreases in GAT1 binding result in increased expression of MEP2 as a consequence of properties of I1-FFLs. Our results show that I1-FFLs, one of the most commonly occurring network motifs in transcriptional networks, can facilitate adaptive tuning of gene expression through modulation of transcription factor binding affinities. Our findings highlight the importance of gene regulatory architectures in the evolution of gene expression. © 2018, Hong et al.

Multi-agent fare optimization model of two modes problem and its analysis based on edge of chaos

NASA Astrophysics Data System (ADS)

Li, Xue-yan; Li, Xue-mei; Li, Xue-wei; Qiu, He-ting

2017-03-01

This paper proposes a new framework of fare optimization & game model for studying the competition between two travel modes (high speed railway and civil aviation) in which passengers' group behavior is taken into consideration. The small-world network is introduced to construct the multi-agent model of passengers' travel mode choice. The cumulative prospect theory is adopted to depict passengers' bounded rationality, the heterogeneity of passengers' reference point is depicted using the idea of group emotion computing. The conceptions of "Langton parameter" and "evolution entropy" in the theory of "edge of chaos" are introduced to create passengers' "decision coefficient" and "evolution entropy of travel mode choice" which are used to quantify passengers' group behavior. The numerical simulation and the analysis of passengers' behavior show that (1) the new model inherits the features of traditional model well and the idea of self-organizing traffic flow evolution fully embodies passengers' bounded rationality, (2) compared with the traditional model (logit model), when passengers are in the "edge of chaos" state, the total profit of the transportation system is higher.

Informatics technology mimics ecology: dense, mutualistic collaboration networks are associated with higher publication rates.

PubMed

Sorani, Marco D

2012-01-01

Information technology (IT) adoption enables biomedical research. Publications are an accepted measure of research output, and network models can describe the collaborative nature of publication. In particular, ecological networks can serve as analogies for publication and technology adoption. We constructed network models of adoption of bioinformatics programming languages and health IT (HIT) from the literature.We selected seven programming languages and four types of HIT. We performed PubMed searches to identify publications since 2001. We calculated summary statistics and analyzed spatiotemporal relationships. Then, we assessed ecological models of specialization, cooperativity, competition, evolution, biodiversity, and stability associated with publications.Adoption of HIT has been variable, while scripting languages have experienced rapid adoption. Hospital systems had the largest HIT research corpus, while Perl had the largest language corpus. Scripting languages represented the largest connected network components. The relationship between edges and nodes was linear, though Bioconductor had more edges than expected and Perl had fewer. Spatiotemporal relationships were weak. Most languages shared a bioinformatics specialization and appeared mutualistic or competitive. HIT specializations varied. Specialization was highest for Bioconductor and radiology systems. Specialization and cooperativity were positively correlated among languages but negatively correlated among HIT. Rates of language evolution were similar. Biodiversity among languages grew in the first half of the decade and stabilized, while diversity among HIT was variable but flat. Compared with publications in 2001, correlation with publications one year later was positive while correlation after ten years was weak and negative.Adoption of new technologies can be unpredictable. Spatiotemporal relationships facilitate adoption but are not sufficient. As with ecosystems, dense, mutualistic, specialized co-habitation is associated with faster growth. There are rapidly changing trends in external technological and macroeconomic influences. We propose that a better understanding of how technologies are adopted can facilitate their development.

Reciprocity of weighted networks

PubMed Central

Squartini, Tiziano; Picciolo, Francesco; Ruzzenenti, Franco; Garlaschelli, Diego

2013-01-01

In directed networks, reciprocal links have dramatic effects on dynamical processes, network growth, and higher-order structures such as motifs and communities. While the reciprocity of binary networks has been extensively studied, that of weighted networks is still poorly understood, implying an ever-increasing gap between the availability of weighted network data and our understanding of their dyadic properties. Here we introduce a general approach to the reciprocity of weighted networks, and define quantities and null models that consistently capture empirical reciprocity patterns at different structural levels. We show that, counter-intuitively, previous reciprocity measures based on the similarity of mutual weights are uninformative. By contrast, our measures allow to consistently classify different weighted networks according to their reciprocity, track the evolution of a network's reciprocity over time, identify patterns at the level of dyads and vertices, and distinguish the effects of flux (im)balances or other (a)symmetries from a true tendency towards (anti-)reciprocation. PMID:24056721

Reciprocity of weighted networks.

PubMed

Squartini, Tiziano; Picciolo, Francesco; Ruzzenenti, Franco; Garlaschelli, Diego

2013-01-01

In directed networks, reciprocal links have dramatic effects on dynamical processes, network growth, and higher-order structures such as motifs and communities. While the reciprocity of binary networks has been extensively studied, that of weighted networks is still poorly understood, implying an ever-increasing gap between the availability of weighted network data and our understanding of their dyadic properties. Here we introduce a general approach to the reciprocity of weighted networks, and define quantities and null models that consistently capture empirical reciprocity patterns at different structural levels. We show that, counter-intuitively, previous reciprocity measures based on the similarity of mutual weights are uninformative. By contrast, our measures allow to consistently classify different weighted networks according to their reciprocity, track the evolution of a network's reciprocity over time, identify patterns at the level of dyads and vertices, and distinguish the effects of flux (im)balances or other (a)symmetries from a true tendency towards (anti-)reciprocation.

Experimental evolution of protein–protein interaction networks

PubMed Central

Kaçar, Betül; Gaucher, Eric A.

2013-01-01

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

Diversifying mechanisms in the on-farm evolution of crop mixtures.

PubMed

Thomas, Mathieu; Thépot, Stéphanie; Galic, Nathalie; Jouanne-Pin, Sophie; Remoué, Carine; Goldringer, Isabelle

2015-06-01

While modern agriculture relies on genetic homogeneity, diversifying practices associated with seed exchange and seed recycling may allow crops to adapt to their environment. This socio-genetic model is an original experimental evolution design referred to as on-farm dynamic management of crop diversity. Investigating such model can help in understanding how evolutionary mechanisms shape crop diversity submitted to diverse agro-environments. We studied a French farmer-led initiative where a mixture of four wheat landraces called 'Mélange de Touselles' (MDT) was created and circulated within a farmers' network. The 15 sampled MDT subpopulations were simultaneously submitted to diverse environments (e.g. altitude, rainfall) and diverse farmers' practices (e.g. field size, sowing and harvesting date). Twenty-one space-time samples of 80 individuals each were genotyped using 17 microsatellite markers and characterized for their heading date in a 'common-garden' experiment. Gene polymorphism was studied using four markers located in earliness genes. An original network-based approach was developed to depict the particular and complex genetic structure of the landraces composing the mixture. Rapid differentiation among populations within the mixture was detected, larger at the phenotypic and gene levels than at the neutral genetic level, indicating potential divergent selection. We identified two interacting selection processes: variation in the mixture component frequencies, and evolution of within-variety diversity, that shaped the standing variability available within the mixture. These results confirmed that diversifying practices and environments maintain genetic diversity and allow for crop evolution in the context of global change. Including concrete measurements of farmers' practices is critical to disentangle crop evolution processes. © 2015 John Wiley & Sons Ltd.

A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks

NASA Technical Reports Server (NTRS)

Shaw, Harry

2012-01-01

Users are pushing for greater physical mobility with their network and Internet access. Mobile ad hoc networks (MANET) can provide an efficient mobile network architecture, but security is a key concern. A figure summarizes differences in the state of network security for MANET and fixed networks. MANETs require the ability to distinguish trusted peers, and tolerate the ingress/egress of nodes on an unscheduled basis. Because the networks by their very nature are mobile and self-organizing, use of a Public Key Infra structure (PKI), X.509 certificates, RSA, and nonce ex changes becomes problematic if the ideal of MANET is to be achieved. Molecular biology models such as DNA evolution can provide a basis for a proprietary security architecture that achieves high degrees of diffusion and confusion, and resistance to cryptanalysis. A proprietary encryption mechanism was developed that uses the principles of DNA replication and steganography (hidden word cryptography) for confidentiality and authentication. The foundation of the approach includes organization of coded words and messages using base pairs organized into genes, an expandable genome consisting of DNA-based chromosome keys, and a DNA-based message encoding, replication, and evolution and fitness. In evolutionary computing, a fitness algorithm determines whether candidate solutions, in this case encrypted messages, are sufficiently encrypted to be transmitted. The technology provides a mechanism for confidential electronic traffic over a MANET without a PKI for authenticating users.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network.

PubMed

Alvarez-Ponce, David; Feyertag, Felix; Chakraborty, Sandip

2017-06-01

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein-protein interaction data set and the human signal transduction network-a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Dynamic stiffness of chemically and physically ageing rubber vibration isolators in the audible frequency range. Part 1: constitutive equations

NASA Astrophysics Data System (ADS)

Kari, Leif

2017-09-01

The constitutive equations of chemically and physically ageing rubber in the audible frequency range are modelled as a function of ageing temperature, ageing time, actual temperature, time and frequency. The constitutive equations are derived by assuming nearly incompressible material with elastic spherical response and viscoelastic deviatoric response, using Mittag-Leffler relaxation function of fractional derivative type, the main advantage being the minimum material parameters needed to successfully fit experimental data over a broad frequency range. The material is furthermore assumed essentially entropic and thermo-mechanically simple while using a modified William-Landel-Ferry shift function to take into account temperature dependence and physical ageing, with fractional free volume evolution modelled by a nonlinear, fractional differential equation with relaxation time identical to that of the stress response and related to the fractional free volume by Doolittle equation. Physical ageing is a reversible ageing process, including trapping and freeing of polymer chain ends, polymer chain reorganizations and free volume changes. In contrast, chemical ageing is an irreversible process, mainly attributed to oxygen reaction with polymer network either damaging the network by scission or reformation of new polymer links. The chemical ageing is modelled by inner variables that are determined by inner fractional evolution equations. Finally, the model parameters are fitted to measurements results of natural rubber over a broad audible frequency range, and various parameter studies are performed including comparison with results obtained by ordinary, non-fractional ageing evolution differential equations.

Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease.

PubMed

Vivekanandan, T; Sriman Narayana Iyengar, N Ch

2017-11-01

Enormous data growth in multiple domains has posed a great challenge for data processing and analysis techniques. In particular, the traditional record maintenance strategy has been replaced in the healthcare system. It is vital to develop a model that is able to handle the huge amount of e-healthcare data efficiently. In this paper, the challenging tasks of selecting critical features from the enormous set of available features and diagnosing heart disease are carried out. Feature selection is one of the most widely used pre-processing steps in classification problems. A modified differential evolution (DE) algorithm is used to perform feature selection for cardiovascular disease and optimization of selected features. Of the 10 available strategies for the traditional DE algorithm, the seventh strategy, which is represented by DE/rand/2/exp, is considered for comparative study. The performance analysis of the developed modified DE strategy is given in this paper. With the selected critical features, prediction of heart disease is carried out using fuzzy AHP and a feed-forward neural network. Various performance measures of integrating the modified differential evolution algorithm with fuzzy AHP and a feed-forward neural network in the prediction of heart disease are evaluated in this paper. The accuracy of the proposed hybrid model is 83%, which is higher than that of some other existing models. In addition, the prediction time of the proposed hybrid model is also evaluated and has shown promising results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Network news: innovations in 21st century systems biology.

PubMed

Arkin, Adam P; Schaffer, David V

2011-03-18

A decade ago, seminal perspectives and papers set a strong vision for the field of systems biology, and a number of these themes have flourished. Here, we describe key technologies and insights that have elucidated the evolution, architecture, and function of cellular networks, ultimately leading to the first predictive genome-scale regulatory and metabolic models of organisms. Can systems approaches bridge the gap between correlative analysis and mechanistic insights? Copyright © 2011 Elsevier Inc. All rights reserved.

A Unified Access Model for Interconnecting Heterogeneous Wireless Networks

DTIC Science & Technology

2015-05-01

Defined Networking, OpenFlow, WiFi, LTE 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 18 19a. NAME OF...Machine Configurations with WiFi and LTE 4 2.3 Three Virtual Machine Configurations with WiFi and LTE 5 3. Results and Discussion 5 4. Summary and...WiFi and long-term evolution ( LTE ), and created a communication pathway between them via a central controller node. Our simulation serves as a

From the grid to the smart grid, topologically

NASA Astrophysics Data System (ADS)

Pagani, Giuliano Andrea; Aiello, Marco

2016-05-01

In its more visionary acceptation, the smart grid is a model of energy management in which the users are engaged in producing energy as well as consuming it, while having information systems fully aware of the energy demand-response of the network and of dynamically varying prices. A natural question is then: to make the smart grid a reality will the distribution grid have to be upgraded? We assume a positive answer to the question and we consider the lower layers of medium and low voltage to be the most affected by the change. In our previous work, we analyzed samples of the Dutch distribution grid (Pagani and Aiello, 2011) and we considered possible evolutions of these using synthetic topologies modeled after studies of complex systems in other technological domains (Pagani and Aiello, 2014). In this paper, we take an extra important step by defining a methodology for evolving any existing physical power grid to a good smart grid model, thus laying the foundations for a decision support system for utilities and governmental organizations. In doing so, we consider several possible evolution strategies and apply them to the Dutch distribution grid. We show how increasing connectivity is beneficial in realizing more efficient and reliable networks. Our proposal is topological in nature, enhanced with economic considerations of the costs of such evolutions in terms of cabling expenses and economic benefits of evolving the grid.

Evolution of the Intelligent Telecommunications Network.

ERIC Educational Resources Information Center

Mayo, John S.

1982-01-01

Discusses the evolution of the nationwide telecommunications network, including key technologies (transistors, communications satellites, and lasers), putting these technologies together, current and future services, and challenges for the future. (JN)

How synapses can enhance sensibility of a neural network

NASA Astrophysics Data System (ADS)

Protachevicz, P. R.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Baptista, M. S.; Viana, R. L.; Lameu, E. L.; Macau, E. E. N.; Batista, A. M.

2018-02-01

In this work, we study the dynamic range in a neural network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. The learning rules are related to neuroplasticity that describes change to the neural connections in the brain. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.

CHANNEL EVOLUTION IN MODIFIED ALLUVIAL STREAMS.

USGS Publications Warehouse

Simon, Andrew; Hupp, Cliff R.

1987-01-01

This study (a) assesses the channel changes and network trends of bed level response after modifications between 1959 and 1972 of alluvial channels in western Tennessee and (b) develops a conceptual model of bank slope development to qualitatively assess bank stability and potential channel widening. A six-step, semiquantitative model of channel evolution in disturbed channels was developed by quantifying bed level trends and recognizing qualitative stages of bank slope development. Development of the bank profile is defined in terms of three dynamic and observable surfaces: (a) vertical face (70 to 90 degrees), (b) upper bank (25 to 50 degrees), and (c) slough line (20 to 25 degrees).

From sMOOC to tMOOC, Learning towards Professional Transference: ECO European Project

ERIC Educational Resources Information Center

Osuna-Acedo, Sara; Marta-Lazo, Carmen; Frau-Meigs, Divina

2018-01-01

The evolution of MOOCs in the last decade has been constant and dynamic. The first cMOOC and xMOOC models eventually evolved into different post-MOOC modalities, such as sMOOC, which conjugates interaction among students with a participation model based on social networks. This work is focused on carrying out a systematic review of the…

Relevance of phenotypic noise to adaptation and evolution.

PubMed

Kaneko, K; Furusawa, C

2008-09-01

Biological processes are inherently noisy, as highlighted in recent measurements of stochasticity in gene expression. Here, the authors show that such phenotypic noise is essential to the adaptation of organisms to a variety of environments and also to the evolution of robustness against mutations. First, the authors show that for any growing cell showing stochastic gene expression, the adaptive cellular state is inevitably selected by noise, without the use of a specific signal transduction network. In general, changes in any protein concentration in a cell are products of its synthesis minus dilution and degradation, both of which are proportional to the rate of cell growth. In an adaptive state, both the synthesis and dilution terms of proteins are large, and so the adaptive state is less affected by stochasticity in gene expression, whereas for a non-adaptive state, both terms are smaller, and so cells are easily knocked out of their original state by noise. This leads to a novel, generic mechanism for the selection of adaptive states. The authors have confirmed this selection by model simulations. Secondly, the authors consider the evolution of gene networks to acquire robustness of the phenotype against noise and mutation. Through simulations using a simple stochastic gene expression network that undergoes mutation and selection, the authors show that a threshold level of noise in gene expression is required for the network to acquire both types of robustness. The results reveal how the noise that cells encounter during growth and development shapes any network's robustness, not only to noise but also to mutations. The authors also establish a relationship between developmental and mutational robustness.

Evolution of Cooperation in Continuous Prisoner's Dilemma Games on Barabasi—Albert Networks with Degree-Dependent Guilt Mechanism

NASA Astrophysics Data System (ADS)

Wang, Xian-Jia; Quan, Ji; Liu, Wei-Bing

2012-05-01

This paper studies the continuous prisoner's dilemma games (CPDG) on Barabasi—Albert (BA) networks. In the model, each agent on a vertex of the networks makes an investment and interacts with all of his neighboring agents. Making an investment is costly, but which benefits its neighboring agents, where benefit and cost depend on the level of investment made. The payoff of each agent is given by the sum of payoffs it receives in its interactions with all its neighbors. Not only payoff, individual's guilty emotion in the games has also been considered. The negative guilty emotion produced in comparing with its neighbors can reduce the utility of individuals directly. We assume that the reduction amount depends on the individual's degree and a baseline level parameter. The group's cooperative level is characterized by the average investment of the population. Each player makes his investment in the next step based on a convex combination of the investment of his best neighbors in the last step, his best history strategies in the latest steps which number is controlled by a memory length parameter, and a uniformly distributed random number. Simulation results show that this degree-dependent guilt mechanism can promote the evolution of cooperation dramatically comparing with degree-independent guilt or no guilt cases. Imitation, memory, uncertainty coefficients and network structure also play determinant roles in the cooperation level of the population. All our results may shed some new light on studying the evolution of cooperation based on network reciprocity mechanisms.

Mean-field approximation for the Sznajd model in complex networks

NASA Astrophysics Data System (ADS)

Araújo, Maycon S.; Vannucchi, Fabio S.; Timpanaro, André M.; Prado, Carmen P. C.

2015-02-01

This paper studies the Sznajd model for opinion formation in a population connected through a general network. A master equation describing the time evolution of opinions is presented and solved in a mean-field approximation. Although quite simple, this approximation allows us to capture the most important features regarding the steady states of the model. When spontaneous opinion changes are included, a discontinuous transition from consensus to polarization can be found as the rate of spontaneous change is increased. In this case we show that a hybrid mean-field approach including interactions between second nearest neighbors is necessary to estimate correctly the critical point of the transition. The analytical prediction of the critical point is also compared with numerical simulations in a wide variety of networks, in particular Barabási-Albert networks, finding reasonable agreement despite the strong approximations involved. The same hybrid approach that made it possible to deal with second-order neighbors could just as well be adapted to treat other problems such as epidemic spreading or predator-prey systems.

Ising-based model of opinion formation in a complex network of interpersonal interactions

NASA Astrophysics Data System (ADS)

Grabowski, A.; Kosiński, R. A.

2006-03-01

In our work the process of opinion formation in the human population, treated as a scale-free network, is modeled and investigated numerically. The individuals (nodes of the network) are characterized by their authorities, which influence the interpersonal interactions in the population. Hierarchical, two-level structures of interpersonal interactions and spatial localization of individuals are taken into account. The effect of the mass media, modeled as an external stimulation acting on the social network, on the process of opinion formation is investigated. It was found that in the time evolution of opinions of individuals critical phenomena occur. The first one is observed in the critical temperature of the system TC and is connected with the situation in the community, which may be described by such quantifiers as the economic status of people, unemployment or crime wave. Another critical phenomenon is connected with the influence of mass media on the population. As results from our computations, under certain circumstances the mass media can provoke critical rebuilding of opinions in the population.

Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter

NASA Astrophysics Data System (ADS)

Raitoharju, Matti; Nurminen, Henri; Piché, Robert

2015-12-01

Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.

Emergence of Multiplex Communities in Collaboration Networks.

PubMed

Battiston, Federico; Iacovacci, Jacopo; Nicosia, Vincenzo; Bianconi, Ginestra; Latora, Vito

2016-01-01

Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

PubMed

Vo, Tommy V; Das, Jishnu; Meyer, Michael J; Cordero, Nicolas A; Akturk, Nurten; Wei, Xiaomu; Fair, Benjamin J; Degatano, Andrew G; Fragoza, Robert; Liu, Lisa G; Matsuyama, Akihisa; Trickey, Michelle; Horibata, Sachi; Grimson, Andrew; Yamano, Hiroyuki; Yoshida, Minoru; Roth, Frederick P; Pleiss, Jeffrey A; Xia, Yu; Yu, Haiyuan

2016-01-14

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution. Copyright © 2016 Elsevier Inc. All rights reserved.

Opinion diversity and community formation in adaptive networks

NASA Astrophysics Data System (ADS)

Yu, Y.; Xiao, G.; Li, G.; Tay, W. P.; Teoh, H. F.

2017-10-01

It is interesting and of significant importance to investigate how network structures co-evolve with opinions. In this article, we show that, a simple model integrating consensus formation, link rewiring, and opinion change allows complex system dynamics to emerge, driving the system into a dynamic equilibrium with the co-existence of diversified opinions. Specifically, similar opinion holders may form into communities yet with no strict community consensus; and rather than being separated into disconnected communities, different communities are connected by a non-trivial proportion of inter-community links. More importantly, we show that the complex dynamics may lead to different numbers of communities at the steady state with a given tolerance between different opinion holders. We construct a framework for theoretically analyzing the co-evolution process. Theoretical analysis and extensive simulation results reveal some useful insights into the complex co-evolution process, including the formation of dynamic equilibrium, the transition between different steady states with different numbers of communities, and the dynamics between opinion distribution and network modularity.

Evolving autonomous learning in cognitive networks.

PubMed

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design

NASA Astrophysics Data System (ADS)

Kaminski, Thomas; Rayner, Peter Julian

2017-10-01

Various observational data streams have been shown to provide valuable constraints on the state and evolution of the global carbon cycle. These observations have the potential to reduce uncertainties in past, current, and predicted natural and anthropogenic surface fluxes. In particular such observations provide independent information for verification of actions as requested by the Paris Agreement. It is, however, difficult to decide which variables to sample, and how, where, and when to sample them, in order to achieve an optimal use of the observational capabilities. Quantitative network design (QND) assesses the impact of a given set of existing or hypothetical observations in a modelling framework. QND has been used to optimise in situ networks and assess the benefit to be expected from planned space missions. This paper describes recent progress and highlights aspects that are not yet sufficiently addressed. It demonstrates the advantage of an integrated QND system that can simultaneously evaluate a multitude of observational data streams and assess their complementarity and redundancy.

On the role of sparseness in the evolution of modularity in gene regulatory networks

PubMed Central

2018-01-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases. PMID:29775459

On the role of sparseness in the evolution of modularity in gene regulatory networks.

PubMed

Espinosa-Soto, Carlos

2018-05-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

The evolution to transparent optical networking

NASA Astrophysics Data System (ADS)

Wrage, Marc; Kirstaedter, Andreas; Rohde, Harald

2005-02-01

Optical data transmission has undergone a tremendous evolution. Starting with unrepeated point-to-point transmission in the 80s the inventions of wavelength division multiplexing (WDM) and erbium doped fiber amplifiers (EDFAs) have let to an explosion of system capacity as well as of system reach. After the steep downturn of recent years network operators have now regained the strength to upgrade their networks and to implement new services. This paper will review current and upcoming technologies in the long haul (LH) and ultra long haul (ULH) data transmission. It will further discuss the future evolution of transparent optical networks towards dynamically routed meshed optical networks with respect to operator"s technical operational and economical requirements. Upgradeability turns out as a key issue as it on the one hand side facilitates low front investments for network providers and on the other hand side enables organic and flexible network growth.

Individual heterogeneity generating explosive system network dynamics.

PubMed

Manrique, Pedro D; Johnson, Neil F

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Formation of Common Investment Networks by Project Establishment between Agents

NASA Astrophysics Data System (ADS)

Navarro-Barrientos, Jesús Emeterio

We present an investment model integrated with trust and reputation mechanisms where agents interact with each other to establish investment projects. We investigate the establishment of investment projects, the influence of the interaction between agents in the evolution of the distribution of wealth as well as the formation of common investment networks and some of their properties. Simulation results show that the wealth distribution presents a power law in its tail. Also, it is shown that the trust and reputation mechanism proposed leads to the establishment of networks among agents, presenting some of the typical characteristics of real-life networks like a high clustering coefficient and short average path length.

Social influence in small-world networks

NASA Astrophysics Data System (ADS)

Sun, Kai; Mao, Xiao-Ming; Ouyang, Qi

2002-12-01

We report on our numerical studies of the Axelrod model for social influence in small-world networks. Our simulation results show that the topology of the network has a crucial effect on the evolution of cultures. As the randomness of the network increases, the system undergoes a transition from a highly fragmented phase to a uniform phase. We also find that the power-law distribution at the transition point, reported by Castellano et al, is not a critical phenomenon; it exists not only at the onset of transition but also for almost any control parameters. All these power-law distributions are stable against perturbations. A mean-field theory is developed to explain these phenomena.

Static, Dynamic and Semantic Dimensions: Towards a Multidisciplinary Approach of Social Networks Analysis

NASA Astrophysics Data System (ADS)

Thovex, Christophe; Trichet, Francky

The objective of our work is to extend static and dynamic models of Social Networks Analysis (SNA), by taking conceptual aspects of enterprises and institutions social graph into account. The originality of our multidisciplinary work is to introduce abstract notions of electro-physic to define new measures in SNA, for new decision-making functions dedicated to Human Resource Management (HRM). This paper introduces a multidimensional system and new measures: (1) a tension measure for social network analysis, (2) an electrodynamic, predictive and semantic system for recommendations on social graphs evolutions and (3) a reactance measure used to evaluate the individual stress at work of the members of a social network.

Individual heterogeneity generating explosive system network dynamics

NASA Astrophysics Data System (ADS)

Manrique, Pedro D.; Johnson, Neil F.

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Spatial networks

NASA Astrophysics Data System (ADS)

Barthélemy, Marc

2011-02-01

Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.

A non-affine micro-macro approach to strain-crystallizing rubber-like materials

NASA Astrophysics Data System (ADS)

Rastak, Reza; Linder, Christian

2018-02-01

Crystallization can occur in rubber materials at large strains due to a phenomenon called strain-induced crystallization. We propose a multi-scale polymer network model to capture this process in rubber-like materials. At the microscopic scale, we present a chain formulation by studying the thermodynamic behavior of a polymer chain and its crystallization mechanism inside a stretching polymer network. The chain model accounts for the thermodynamics of crystallization and presents a rate-dependent evolution law for crystallization based on the gradient of the free energy with respect to the crystallinity variables to ensures the dissipation is always non-negative. The multiscale framework allows the anisotropic crystallization of rubber which has been observed experimentally. Two different approaches for formulating the orientational distribution of crystallinity are studied. In the first approach, the algorithm tracks the crystallization at a finite number of orientations. In contrast, the continuous distribution describes the crystallization for all polymer chain orientations and describes its evolution with only a few distribution parameters. To connect the deformation of the micro with that of the macro scale, our model combines the recently developed maximal advance path constraint with the principal of minimum average free energy, resulting in a non-affine deformation model for polymer chains. Various aspects of the proposed model are validated by existing experimental results, including the stress response, crystallinity evolution during loading and unloading, crystallinity distribution, and the rotation of the principal crystallization direction. As a case study, we simulate the formation of crystalline regions around a pre-existing notch in a 3D rubber block and we compare the results with experimental data.

Random walks on activity-driven networks with attractiveness

NASA Astrophysics Data System (ADS)

Alessandretti, Laura; Sun, Kaiyuan; Baronchelli, Andrea; Perra, Nicola

2017-05-01

Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodes (attractiveness) are heterogeneously distributed. Here, we present a time-varying network model where each node and the dynamical formation of ties are characterized by these two features. We study how these properties affect random-walk processes unfolding on the network when the time scales describing the process and the network evolution are comparable. We derive analytical solutions for the stationary state and the mean first-passage time of the process, and we study cases informed by empirical observations of social networks. Our work shows that previously disregarded properties of real social systems, such as heterogeneous distributions of activity and attractiveness as well as the correlations between them, substantially affect the dynamical process unfolding on the network.

Models for the modern power grid

NASA Astrophysics Data System (ADS)

Nardelli, Pedro H. J.; Rubido, Nicolas; Wang, Chengwei; Baptista, Murilo S.; Pomalaza-Raez, Carlos; Cardieri, Paulo; Latva-aho, Matti

2014-10-01

This article reviews different kinds of models for the electric power grid that can be used to understand the modern power system, the smart grid. From the physical network to abstract energy markets, we identify in the literature different aspects that co-determine the spatio-temporal multilayer dynamics of power system. We start our review by showing how the generation, transmission and distribution characteristics of the traditional power grids are already subject to complex behaviour appearing as a result of the the interplay between dynamics of the nodes and topology, namely synchronisation and cascade effects. When dealing with smart grids, the system complexity increases even more: on top of the physical network of power lines and controllable sources of electricity, the modernisation brings information networks, renewable intermittent generation, market liberalisation, prosumers, among other aspects. In this case, we forecast a dynamical co-evolution of the smart grid and other kind of networked systems that cannot be understood isolated. This review compiles recent results that model electric power grids as complex systems, going beyond pure technological aspects. From this perspective, we then indicate possible ways to incorporate the diverse co-evolving systems into the smart grid model using, for example, network theory and multi-agent simulation.

Emergent spectral properties of river network topology: an optimal channel network approach.

PubMed

Abed-Elmdoust, Armaghan; Singh, Arvind; Yang, Zong-Liang

2017-09-13

Characterization of river drainage networks has been a subject of research for many years. However, most previous studies have been limited to quantities which are loosely connected to the topological properties of these networks. In this work, through a graph-theoretic formulation of drainage river networks, we investigate the eigenvalue spectra of their adjacency matrix. First, we introduce a graph theory model for river networks and explore the properties of the network through its adjacency matrix. Next, we show that the eigenvalue spectra of such complex networks follow distinct patterns and exhibit striking features including a spectral gap in which no eigenvalue exists as well as a finite number of zero eigenvalues. We show that such spectral features are closely related to the branching topology of the associated river networks. In this regard, we find an empirical relation for the spectral gap and nullity in terms of the energy dissipation exponent of the drainage networks. In addition, the eigenvalue distribution is found to follow a finite-width probability density function with certain skewness which is related to the drainage pattern. Our results are based on optimal channel network simulations and validated through examples obtained from physical experiments on landscape evolution. These results suggest the potential of the spectral graph techniques in characterizing and modeling river networks.

How People Interact in Evolving Online Affiliation Networks

NASA Astrophysics Data System (ADS)

Gallos, Lazaros K.; Rybski, Diego; Liljeros, Fredrik; Havlin, Shlomo; Makse, Hernán A.

2012-07-01

The study of human interactions is of central importance for understanding the behavior of individuals, groups, and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links, and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We show that an accurate estimation of these probabilistic tendencies can be achieved only by following the time evolution of the network. Inferences about the reason for the existence of links using statistical analysis of network snapshots must therefore be made with great caution. Here, we start by characterizing every single link when the tie was established in the network. This information allows us to describe the probabilistic tendencies of tie formation and extract meaningful sociological conclusions. We also find significant differences in behavioral traits in the social tendencies among individuals according to their degree of activity, gender, age, popularity, and other attributes. For instance, in the particular data sets analyzed here, we find that women reciprocate connections 3 times as much as men and that this difference increases with age. Men tend to connect with the most popular people more often than women do, across all ages. On the other hand, triangular tie tendencies are similar, independent of gender, and show an increase with age. These results require further validation in other social settings. Our findings can be useful to build models of realistic social network structures and to discover the underlying laws that govern establishment of ties in evolving social networks.

Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes

NASA Astrophysics Data System (ADS)

Caglar, Mehmet Umut; Pal, Ranadip

2011-03-01

Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.

Evolutionary prisoner's dilemma games coevolving on adaptive networks.

PubMed

Lee, Hsuan-Wei; Malik, Nishant; Mucha, Peter J

2018-02-01

We study a model for switching strategies in the Prisoner's Dilemma game on adaptive networks of player pairings that coevolve as players attempt to maximize their return. We use a node-based strategy model wherein each player follows one strategy at a time (cooperate or defect) across all of its neighbors, changing that strategy and possibly changing partners in response to local changes in the network of player pairing and in the strategies used by connected partners. We compare and contrast numerical simulations with existing pair approximation differential equations for describing this system, as well as more accurate equations developed here using the framework of approximate master equations. We explore the parameter space of the model, demonstrating the relatively high accuracy of the approximate master equations for describing the system observations made from simulations. We study two variations of this partner-switching model to investigate the system evolution, predict stationary states, and compare the total utilities and other qualitative differences between these two model variants.

Quantitative analysis of factors that affect oil pipeline network accident based on Bayesian networks: A case study in China

NASA Astrophysics Data System (ADS)

Zhang, Chao; Qin, Ting Xin; Huang, Shuai; Wu, Jian Song; Meng, Xin Yan

2018-06-01

Some factors can affect the consequences of oil pipeline accident and their effects should be analyzed to improve emergency preparation and emergency response. Although there are some qualitative analysis models of risk factors' effects, the quantitative analysis model still should be researched. In this study, we introduce a Bayesian network (BN) model of risk factors' effects analysis in an oil pipeline accident case that happened in China. The incident evolution diagram is built to identify the risk factors. And the BN model is built based on the deployment rule for factor nodes in BN and the expert knowledge by Dempster-Shafer evidence theory. Then the probabilities of incident consequences and risk factors' effects can be calculated. The most likely consequences given by this model are consilient with the case. Meanwhile, the quantitative estimations of risk factors' effects may provide a theoretical basis to take optimal risk treatment measures for oil pipeline management, which can be used in emergency preparation and emergency response.

A 1D thermomechanical network transition constitutive model coupled with multiple structural relaxation for shape memory polymers

NASA Astrophysics Data System (ADS)

Zeng, Hao; Xie, Zhimin; Gu, Jianping; Sun, Huiyu

2018-03-01

A new thermomechanical network transition constitutive model is proposed in the study to describe the viscoelastic behavior of shape memory polymers (SMPs). Based on the microstructure of semi-crystalline SMPs, a new simplified transformation equation is proposed to describe the transform of transient networks. And the generalized fractional Maxwell model is introduced in the paper to estimate the temperature-dependent storage modulus. In addition, a neo-KAHR theory with multiple discrete relaxation processes is put forward to study the structural relaxation of the nonlinear thermal strain in cooling/heating processes. The evolution equations of the time- and temperature-dependent stress and strain response are developed. In the model, the thermodynamical and mechanical characteristics of SMPs in the typical thermomechanical cycle are described clearly and the irreversible deformation is studied in detail. Finally, the typical thermomechanical cycles are simulated using the present constitutive model, and the simulation results agree well with the experimental results.

Topology and evolution of technology innovation networks

NASA Astrophysics Data System (ADS)

Valverde, Sergi; Solé, Ricard V.; Bedau, Mark A.; Packard, Norman

2007-11-01

The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the U.S. Patent and Trademark Office. We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such an attachment kernel is shared by scientific citation networks, thus indicating a universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.

Population Fluctuation Promotes Cooperation in Networks

PubMed Central

Miller, Steve; Knowles, Joshua

2015-01-01

We consider the problem of explaining the emergence and evolution of cooperation in dynamic network-structured populations. Building on seminal work by Poncela et al., which shows how cooperation (in one-shot prisoner’s dilemma) is supported in growing populations by an evolutionary preferential attachment (EPA) model, we investigate the effect of fluctuations in the population size. We find that a fluctuating model – based on repeated population growth and truncation – is more robust than Poncela et al.’s in that cooperation flourishes for a wider variety of initial conditions. In terms of both the temptation to defect, and the types of strategies present in the founder network, the fluctuating population is found to lead more securely to cooperation. Further, we find that this model will also support the emergence of cooperation from pre-existing non-cooperative random networks. This model, like Poncela et al.’s, does not require agents to have memory, recognition of other agents, or other cognitive abilities, and so may suggest a more general explanation of the emergence of cooperation in early evolutionary transitions, than mechanisms such as kin selection, direct and indirect reciprocity. PMID:26061705

Radial restricted solid-on-solid and etching interface-growth models

NASA Astrophysics Data System (ADS)

Alves, Sidiney G.

2018-03-01

An approach to generate radial interfaces is presented. A radial network recursively obtained is used to implement discrete model rules designed originally for the investigation in flat substrates. I used the restricted solid-on-solid and etching models as to test the proposed scheme. The results indicate the Kardar, Parisi, and Zhang conjecture is completely verified leading to a good agreement between the interface radius fluctuation distribution and the Gaussian unitary ensemble. The evolution of the radius agrees well with the generalized conjecture, and the two-point correlation function exhibits also a good agreement with the covariance of the Airy2 process. The approach can be used to investigate radial interfaces evolution for many other classes of universality.

Radial restricted solid-on-solid and etching interface-growth models.

PubMed

Alves, Sidiney G

2018-03-01

An approach to generate radial interfaces is presented. A radial network recursively obtained is used to implement discrete model rules designed originally for the investigation in flat substrates. I used the restricted solid-on-solid and etching models as to test the proposed scheme. The results indicate the Kardar, Parisi, and Zhang conjecture is completely verified leading to a good agreement between the interface radius fluctuation distribution and the Gaussian unitary ensemble. The evolution of the radius agrees well with the generalized conjecture, and the two-point correlation function exhibits also a good agreement with the covariance of the Airy_{2} process. The approach can be used to investigate radial interfaces evolution for many other classes of universality.

Earth Regime Network Evolution Study (ERNESt)

NASA Technical Reports Server (NTRS)

Menrad, Bob

2016-01-01

Speaker and Presenter at the Lincoln Laboratory Communications Workshop on April 5, 2016 at the Massachusetts Institute of Technology Lincoln Laboratory in Lexington, MA. A visual presentation titled Earth Regimes Network Evolution Study (ERNESt).

Deployment strategy for battery energy storage system in distribution network based on voltage violation regulation

NASA Astrophysics Data System (ADS)

Wu, H.; Zhou, L.; Xu, T.; Fang, W. L.; He, W. G.; Liu, H. M.

2017-11-01

In order to improve the situation of voltage violation caused by the grid-connection of photovoltaic (PV) system in a distribution network, a bi-level programming model is proposed for battery energy storage system (BESS) deployment. The objective function of inner level programming is to minimize voltage violation, with the power of PV and BESS as the variables. The objective function of outer level programming is to minimize the comprehensive function originated from inner layer programming and all the BESS operating parameters, with the capacity and rated power of BESS as the variables. The differential evolution (DE) algorithm is applied to solve the model. Based on distribution network operation scenarios with photovoltaic generation under multiple alternative output modes, the simulation results of IEEE 33-bus system prove that the deployment strategy of BESS proposed in this paper is well adapted to voltage violation regulation invariable distribution network operation scenarios. It contributes to regulating voltage violation in distribution network, as well as to improve the utilization of PV systems.

Burstiness and tie activation strategies in time-varying social networks.

PubMed

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-13

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Netgram: Visualizing Communities in Evolving Networks

PubMed Central

Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

2015-01-01

Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems. PMID:26356538

Evolution of eumetazoan nervous systems: insights from cnidarians.

PubMed

Kelava, Iva; Rentzsch, Fabian; Technau, Ulrich

2015-12-19

Cnidarians, the sister group to bilaterians, have a simple diffuse nervous system. This morphological simplicity and their phylogenetic position make them a crucial group in the study of the evolution of the nervous system. The development of their nervous systems is of particular interest, as by uncovering the genetic programme that underlies it, and comparing it with the bilaterian developmental programme, it is possible to make assumptions about the genes and processes involved in the development of ancestral nervous systems. Recent advances in sequencing methods, genetic interference techniques and transgenic technology have enabled us to get a first glimpse into the molecular network underlying the development of a cnidarian nervous system-in particular the nervous system of the anthozoan Nematostella vectensis. It appears that much of the genetic network of the nervous system development is partly conserved between cnidarians and bilaterians, with Wnt and bone morphogenetic protein (BMP) signalling, and Sox genes playing a crucial part in the differentiation of neurons. However, cnidarians possess some specific characteristics, and further studies are necessary to elucidate the full regulatory network. The work on cnidarian neurogenesis further accentuates the need to study non-model organisms in order to gain insights into processes that shaped present-day lineages during the course of evolution. © 2015 The Authors.