Universal fractal scaling of self-organized networks

There is an abundance of literature on complex networks describing a variety of relationships among units in social, biological, and technological systems. Such networks, consisting of interconnected nodes, are often self-organized, naturally emerging without any overarching designs on topological structure yet enabling efficient interactions among nodes. Here we show that the number of nodes and the density of connections in such self-organized networks exhibit a power law relationship. We examined the size and connection density of 47 self-organizing networks of various biological, social, and technological origins, and found that the size-density relationship follows a fractal relationship spanning over 6 orders of magnitude. This finding indicates that there is an optimal connection density in self-organized networks following fractal scaling regardless of their sizes.     

Classes of complex networks defined by role-to-role connectivity profiles

In physical, biological, technological and social systems, interactions between units give rise to intricate networks. These-typically non-trivial-structures, in turn, critically affect the dynamics and properties of the system. The focus of most current research on complex networks is, still, on global network properties. A caveat of this approach is that the relevance of global properties hinges on the premise that networks are homogeneous, whereas most real-world networks have a markedly modular structure. Here, we report that networks with different functions, including the Internet, metabolic, air transportation and protein interaction networks, have distinct patterns of connections among nodes with different roles, and that, as a consequence, complex networks can be classified into two distinct functional classes on the basis of their link type frequency. Importantly, we demonstrate that these structural features cannot be captured by means of often studied global properties.     

Signal analysis of behavioral and molecular cycles

Background  

Circadian clocks are biological oscillators that regulate molecular, physiological, and behavioral rhythms in a wide variety of organisms. While behavioral rhythms are typically monitored over many cycles, a similar approach to molecular rhythms was not possible until recently; the advent of real-time analysis using transgenic reporters now permits the observations of molecular rhythms over many cycles as well. This development suggests that new details about the relationship between molecular and behavioral rhythms may be revealed. Even so, behavioral and molecular rhythmicity have been analyzed using different methods, making such comparisons difficult to achieve. To address this shortcoming, among others, we developed a set of integrated analytical tools to unify the analysis of biological rhythms across modalities.     

Topological structures enhance the presence of dynamical regimes in synthetic networks

Genetic and protein networks, through their underlying dynamical behavior, characterize structural and functional cellular processes, and are thus regarded as "driving forces" of all living systems. Understanding the rhythm generation mechanisms that emerge from such complex networks has benefited in recent years by synthetic approaches, through which simpler network modules (e.g., switches and oscillators) have been built. In this manner, a significant attention to date has been focused on the dynamical behavior of these isolated synthetic circuits, and the occurrence of unifying rhythms in systems of globally coupled genetic units. In contrast to this, we address here the question: Could topologically distinct structures enhance the presence of various dynamical regimes in synthetic networks? We show that an intercellular mechanism, engineered to operate on a local scale, will inevitably lead to multirhythmicity, and to the appearance of several coexisting (complex) dynamical regimes, if certain preconditions regarding the dynamical structure of the synthetic circuits are met. Moreover, we discuss the importance of regime enhancement in synthetic structures in terms of memory storage and computation capabilities.     

Canalizing Kauffman networks: nonergodicity and its effect on their critical behavior

Boolean networks have been used to study numerous phenomena, including gene regulation, neural networks, social interactions, and biological evolution. Here, we propose a general method for determining the critical behavior of Boolean systems built from arbitrary ensembles of Boolean functions. In particular, we solve the critical condition for systems of units operating according to canalizing functions and present strong numerical evidence that our approach correctly predicts the phase transition from order to chaos in such systems.     

Robust prediction of complex spatiotemporal states through machine learning with sparse sensing

Complex spatiotemporal states arise frequently in material as well as biological systems consisting of multiple interacting units. A specific, but rather ubiquitous and interesting example is that of “chimeras”, existing in the edge between order and chaos. We use Machine Learning methods involving “observers” to predict the evolution of a system of coupled lasers, comprising turbulent chimera states and of a less chaotic biological one, of modular neuronal networks containing states that are synchronized across the networks. We demonstrated the necessity of using “observers” to improve the performance of Feed-Forward Networks in such complex systems. The robustness of the forecasting capabilities of the “Observer Feed-Forward Networks” versus the distribution of the observers, including equidistant and random, and the motion of them, including stationary and moving was also investigated. We conclude that the method has broader applicability in dynamical system context when partial dynamical information about the system is available.     

Fluctuation of biological rhythm in finger tapping

By analyzing biological rhythms obtained from finger tapping, we have investigated the differences of two biological rhythms between healthy and handicapped persons caused by Parkinson, brain infraction, car accident and so on. In this study, we have observed the motion of handedness of all subjects and obtained a slope a which characterizes a power-law relation between frequency and amplitude of finger-tapping rhythm. From our results, we have estimated that the slope a=0.06 is a rough criterion in order to distinguish healthy and handicapped persons.     

Complex Networks: from Graph Theory to Biology

The aim of this text is to show the central role played by networks in complex system science. A remarkable feature of network studies is to lie at the crossroads of different disciplines, from mathematics (graph theory, combinatorics, probability theory) to physics (statistical physics of networks) to computer science (network generating algorithms, combinatorial optimization) to biological issues (regulatory networks). New paradigms recently appeared, like that of ‘scale-free networks’ providing an alternative to the random graph model introduced long ago by Erdös and Renyi. With the notion of statistical ensemble and methods originally introduced for percolation networks, statistical physics is of high relevance to get a deep account of topological and statistical properties of a network. Then their consequences on the dynamics taking place in the network should be investigated. Impact of network theory is huge in all natural sciences, especially in biology with gene networks, metabolic networks, neural networks or food webs. I illustrate this brief overview with a recent work on the influence of network topology on the dynamics of coupled excitable units, and the insights it provides about network emerging features, robustness of network behaviors, and the notion of static or dynamic motif.     

Heisenberg and the German bomb

Many natural and human-made nonlinear oscillators exhibit the ability to adjust their rhythms due to weak interaction: two lasers, being coupled, start to generate with a common frequency; cardiac pacemaker cells fire simultaneously; violinists in an orchestra play in unison. Such coordination of rhythms is a manifestation of a fundamental nonlinear phenomenon--synchronization. Discovered in the 17th century by Christiaan Huygens, it was observed in physics, chemistry, biology and even social behaviour, and found practical applications in engineering and medicine. The notion of synchronization has been recently extended to cover the adjustment of rhythms in chaotic systems, large ensembles of oscillating units, rotating objects, continuous media, etc. In spite of essential progress in theoretical and experimental studies, synchronization remains a challenging problem of nonlinear sciences.     

Magic number 7+/-2 in networks of threshold dynamics

Information processing by random feed-forward networks consisting of units with a sigmoidal input-output response is studied by focusing on the dependence of its outputs on the number of parallel paths M. It is found that the system leads to a combination of on-off outputs when M less or similar to 7, while for M greater or similar to 7, chaotic dynamics arises, resulting in a continuous distribution of outputs. This universality of the critical number M approximately 7 is explained by a combinatorial explosion, i.e., the dominance of factorial over exponential increase. The relevance of the result to biological problems is briefly discussed.     

Breakdown of order preservation in symmetric oscillator networks with pulse-coupling

Symmetric networks of coupled dynamical units exhibit invariant subspaces with two or more units synchronized. In time-continuously coupled systems, these invariant sets constitute barriers for the dynamics. For networks of units with local dynamics defined on the real line, this implies that the units' ordering is preserved and that their winding number is identical. Here, we show that in permutation-symmetric networks with pulse-coupling, the order is often no longer preserved. We analytically study a class of pulse-coupled oscillators (characterizing for instance the dynamics of spiking neural networks) and derive quantitative conditions for the breakdown of order preservation. We find that in general pulse-coupling yields additional dimensions to the state space such that units may change their order by avoiding the invariant sets. We identify a system of two symmetrically pulse-coupled identical oscillators where, contrary to intuition, the oscillators' average frequencies and thus their winding numbers are different.     

Noise-induced synchronous stochastic oscillations in small scale cultured heart-cell networks

This paper reports that the synchronous integer multiple oscillations of heart-cell networks or clusters are observed in the biology experiment.The behaviour of the integer multiple rhythm is a transition between super-and subthreshold oscillations,the stochastic mechanism of the transition is identified.The similar synchronized oscillations are theoretically reproduced in the stochastic network composed of heterogeneous cells whose behaviours are chosen as excitable or oscillatory states near a Hopf bifurcation point.The parameter regions of coupling strength and noise density that the complex oscillatory rhythms can be simulated are identified.The results show that the rhythm results from a simple stochastic alternating process between super-and sub-threshold oscillations.Studies on single heart cells forming these clusters reveal excitable or oscillatory state nearby a Hopf bifurcation point underpinning the stochastic alternation.In discussion,the results are related to some abnormal heartbeat rhythms such as the sinus arrest.     

Multiscale Information Propagation in Emergent Functional Networks

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for the information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.     

An efficient agent-based algorithm for overlapping community detection using nodes’ closeness

Communities are groups of nodes forming tightly connected units in networks. Some nodes can be shared between different communities of a network. The presence of overlapping nodes and their associated membership diversity is a common characteristic of social networks. Analyzing these overlapping structures can reveal valuable information about the intrinsic features of realistic complex networks, especially social networks.     

Metachronal waves in a chain of rowers with hydrodynamic interactions

Poly(ε-caprolactone)/poly(hydroxyethyl acrylate) networks have been investigated by thermally stimulated depolarization currents (TSDC) and differential scanning calorimetry (DSC). The introduction of hydrophilic units (HEA) in the system aiming at tailoring the hydrophilicity of the system results in a series of copolymer networks with microphase separation into hydrophobic/hydrophilic domains. Polycaprolactone (PCL) crystallization is prevented by the topological constraints HEA units imposed in such heterogeneous domains. Moreover, the mobility of the amorphous PCL chains is enhanced as revealed by the main relaxation process which becomes faster. The glass transition of PHEA-rich domains shifts to lower temperatures, as the total amount of PCL in the copolymer increases, due to the presence of PCL units within the same region. The behaviour of the copolymer networks swollen with different content of water has been investigated to analyze the interaction between water molecules and hydrophobic/hydrophilic domains and provide further insights into the molecular structure of the system.     

A general fractional-order dynamical network: synchronization behavior and state tuning

A general fractional-order dynamical network model for synchronization behavior is proposed. Different from previous integer-order dynamical networks, the model is made up of coupled units described by fractional differential equations, where the connections between individual units are nondiffusive and nonlinear. We show that the synchronous behavior of such a network cannot only occur, but also be dramatically different from the behavior of its constituent units. In particular, we find that simple behavior can emerge as synchronized dynamics although the isolated units evolve chaotically. Conversely, individually simple units can display chaotic attractors when the network synchronizes. We also present an easily checked criterion for synchronization depending only on the eigenvalues distribution of a decomposition matrix and the fractional orders. The analytic results are complemented with numerical simulations for two networks whose nodes are governed by fractional-order Lorenz dynamics and fractional-order Ro?ssler dynamics, respectively.     

Entrainment Versus Chaos in a Model for a Circadian Oscillator Driven by Light-Dark Cycles

Circadian rhythms occur in nearly all living organisms with a period close to 24 h. These rhythms constitute an important class of biological oscillators which present the characteristic of being naturally subjected to forcing by light-dark (LD) cycles. In order to investigate the conditions in which such a forcing might lead to chaos, we consider a model for a circadian limit cycle oscillator and assess its dynamic behavior when a light-sensitive parameter is periodically forced by LD cycles. We determine as a function of the forcing period and of the amplitude of the light-induced changes in the light-sensitive parameter the occurrence of various modes of dynamic behavior such as quasi-periodicity, entrainment, period-doubling and chaos. The type of oscillatory behavior markedly depends on the forcing waveform; thus the domain of entrainment grows at the expense of the domain of chaos as the forcing function progressively goes from a square wave to a sine wave. Also studied is the dependence of the phase of periodic or aperiodic oscillations on the amplitude of the light-induced changes in the control parameter. The results are discussed with respect to the main physiological role of circadian rhythms which is to allow organisms to adapt to their periodically varying environment by entrainment to the natural LD cycle.     

Fast convergence of spike sequences to periodic patterns in recurrent networks

The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks.     

Explosive first-order transition to synchrony in networked chaotic oscillators

Critical phenomena in complex networks, and the emergence of dynamical abrupt transitions in the macroscopic state of the system are currently a subject of the outmost interest. We report evidence of an explosive phase synchronization in networks of chaotic units. Namely, by means of both extensive simulations of networks made up of chaotic units, and validation with an experiment of electronic circuits in a star configuration, we demonstrate the existence of a first-order transition towards synchronization of the phases of the networked units. Our findings constitute the first prove of this kind of synchronization in practice, thus opening the path to its use in real-world applications.