Dynamical Properties of Discrete Reaction Networks
Paulevé, Loïc; Craciun, Gheorghe; Koeppl, Heinz
2013-01-01
Reaction networks are commonly used to model the dynamics of populations subject to transformations that follow an imposed stoichiometry. This paper focuses on the efficient characterisation of dynamical properties of Discrete Reaction Networks (DRNs). DRNs can be seen as modeling the underlying discrete nondeterministic transitions of stochastic models of reaction networks. In that sense, a proof of non-reachability in a given DRN has immediate implications for any concrete stochastic model based on that DRN, independent of the choice of kinetic laws and constants. Moreover, if we assume that stochastic kinetic rates are given by the mass-action law (or any other kinetic law that gives non-vanishing probability to each reaction if the required number of interacting substrates is present), then reachability properties are equivalent in the two settings. The analysis of two types of global dynamical properties of DRNs is addressed: irreducibility, i.e., the ability to reach any discrete state from any other state; and recurrence, i.e., the ability to return to any initial state. Our results consider both the verification of such properties when species are present in a large copy number, and in the general case. The necessary and sufficient conditions obtained involve algebraic conditions on the network reactions which in most cases can be verified using linear programming. Finally, the relationship of DRN irreducibility and recurrence with dynamical properties of stochastic and continuous models of reaction networks is discussed. PMID:23722628
Markovian dynamics on complex reaction networks
NASA Astrophysics Data System (ADS)
Goutsias, J.; Jenkinson, G.
2013-08-01
Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.
Reduction of dynamical biochemical reactions networks in computational biology
Radulescu, O.; Gorban, A. N.; Zinovyev, A.; Noel, V.
2012-01-01
Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler models, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state (QSS) and quasi-equilibrium approximations (QE), and provide practical recipes for model reduction of linear and non-linear networks. We also discuss the application of model reduction to the problem of parameter identification, via backward pruning machine learning techniques. PMID:22833754
Translated chemical reaction networks.
Johnston, Matthew D
2014-05-01
Many biochemical and industrial applications involve complicated networks of simultaneously occurring chemical reactions. Under the assumption of mass action kinetics, the dynamics of these chemical reaction networks are governed by systems of polynomial ordinary differential equations. The steady states of these mass action systems have been analyzed via a variety of techniques, including stoichiometric network analysis, deficiency theory, and algebraic techniques (e.g., Gröbner bases). In this paper, we present a novel method for characterizing the steady states of mass action systems. Our method explicitly links a network's capacity to permit a particular class of steady states, called toric steady states, to topological properties of a generalized network called a translated chemical reaction network. These networks share their reaction vectors with their source network but are permitted to have different complex stoichiometries and different network topologies. We apply the results to examples drawn from the biochemical literature.
A molecular dynamics study of bond exchange reactions in covalent adaptable networks.
Yang, Hua; Yu, Kai; Mu, Xiaoming; Shi, Xinghua; Wei, Yujie; Guo, Yafang; Qi, H Jerry
2015-08-21
Covalent adaptable networks are polymers that can alter the arrangement of network connections by bond exchange reactions where an active unit attaches to an existing bond then kicks off its pre-existing peer to form a new bond. When the polymer is stretched, bond exchange reactions lead to stress relaxation and plastic deformation, or the so-called reforming. In addition, two pieces of polymers can be rejoined together without introducing additional monomers or chemicals on the interface, enabling welding and reprocessing. Although covalent adaptable networks have been researched extensively in the past, knowledge about the macromolecular level network alternations is limited. In this study, molecular dynamics simulations are used to investigate the macromolecular details of bond exchange reactions in a recently reported epoxy system. An algorithm for bond exchange reactions is first developed and applied to study a crosslinking network formed by epoxy resin DGEBA with the crosslinking agent tricarballylic acid. The trace of the active units is tracked to show the migration of these units within the network. Network properties, such as the distance between two neighboring crosslink sites, the chain angle, and the initial modulus, are examined after each iteration of the bond exchange reactions to provide detailed information about how material behaviors and macromolecular structure evolve. Stress relaxation simulations are also conducted. It is found that even though bond exchange reactions change the macroscopic shape of the network, microscopic network characteristic features, such as the distance between two neighboring crosslink sites and the chain angle, relax back to the unstretched isotropic state. Comparison with a recent scaling theory also shows good agreement.
NASA Astrophysics Data System (ADS)
Saakian, David B.; Schwartz, Laurent
2012-12-01
We investigate the catalytic reactions model used in cell modeling. The reaction kinetics is defined through the energies of different species of molecules following a random independent distribution. The related statistical physics model has three phases and these three phases emerge in the dynamics: fast dynamics phase, slow dynamic phase and ultra-slow dynamic phase. The phenomenon we found is rather general, and does not depend on the details of the model. We assume as a hypothesis that the transition among these phases (glassiness degrees) is related to cancer. The imbalance in the rate of processes between key aspects of the cell (gene regulation, protein-protein interaction, metabolical networks) creates a change in the fine tuning among these key aspects, affects the logics of the cell and initiates cancer. It is probable that cancer is a change of phase resulting from increased and deregulated metabolic reactions.
Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement
NASA Astrophysics Data System (ADS)
Srinivas, Niranjan
Over the last century, the silicon revolution has enabled us to build faster, smaller and more sophisticated computers. Today, these computers control phones, cars, satellites, assembly lines, and other electromechanical devices. Just as electrical wiring controls electromechanical devices, living organisms employ "chemical wiring" to make decisions about their environment and control physical processes. Currently, the big difference between these two substrates is that while we have the abstractions, design principles, verification and fabrication techniques in place for programming with silicon, we have no comparable understanding or expertise for programming chemistry. In this thesis we take a small step towards the goal of learning how to systematically engineer prescribed non-equilibrium dynamical behaviors in chemical systems. We use the formalism of chemical reaction networks (CRNs), combined with mass-action kinetics, as our programming language for specifying dynamical behaviors. Leveraging the tools of nucleic acid nanotechnology (introduced in Chapter 1), we employ synthetic DNA molecules as our molecular architecture and toehold-mediated DNA strand displacement as our reaction primitive. Abstraction, modular design and systematic fabrication can work only with well-understood and quantitatively characterized tools. Therefore, we embark on a detailed study of the "device physics" of DNA strand displacement (Chapter 2). We present a unified view of strand displacement biophysics and kinetics by studying the process at multiple levels of detail, using an intuitive model of a random walk on a 1-dimensional energy landscape, a secondary structure kinetics model with single base-pair steps, and a coarse-grained molecular model that incorporates three-dimensional geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Our findings are consistent with previously measured or inferred rates for
Bellesia, Giovanni; Bales, Benjamin B
2016-10-01
We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.
NASA Astrophysics Data System (ADS)
Bellesia, Giovanni; Bales, Benjamin B.
2016-10-01
We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.
Concordant Chemical Reaction Networks
Shinar, Guy; Feinberg, Martin
2015-01-01
We describe a large class of chemical reaction networks, those endowed with a subtle structural property called concordance. We show that the class of concordant networks coincides precisely with the class of networks which, when taken with any weakly monotonic kinetics, invariably give rise to kinetic systems that are injective — a quality that, among other things, precludes the possibility of switch-like transitions between distinct positive steady states. We also provide persistence characteristics of concordant networks, instability implications of discordance, and consequences of stronger variants of concordance. Some of our results are in the spirit of recent ones by Banaji and Craciun, but here we do not require that every species suffer a degradation reaction. This is especially important in studying biochemical networks, for which it is rare to have all species degrade. PMID:22659063
Liang, Xiao; Wang, Linshan; Wang, Yangfan; Wang, Ruili
2016-09-01
In this paper, we focus on the long time behavior of the mild solution to delayed reaction-diffusion Hopfield neural networks (DRDHNNs) driven by infinite dimensional Wiener processes. We analyze the existence, uniqueness, and stability of this system under the local Lipschitz function by constructing an appropriate Lyapunov-Krasovskii function and utilizing the semigroup theory. Some easy-to-test criteria affecting the well-posedness and stability of the networks, such as infinite dimensional noise and diffusion effect, are obtained. The criteria can be used as theoretic guidance to stabilize DRDHNNs in practical applications when infinite dimensional noise is taken into consideration. Meanwhile, considering the fact that the standard Brownian motion is a special case of infinite dimensional Wiener process, we undertake an analysis of the local Lipschitz condition, which has a wider range than the global Lipschitz condition. Two samples are given to examine the availability of the results in this paper. Simulations are also given using the MATLAB.
Zhao, Ziqing W; Xie, X Sunney; Ge, Hao
2016-03-24
Nucleotide-induced conformational closing of the finger domain of DNA polymerase is crucial for its catalytic action during DNA replication. Such large-amplitude molecular motion is often not fully accessible to either direct experimental monitoring or molecular dynamics simulations. However, a coarse-grained model can offer an informative alternative, especially for probing the relationship between conformational dynamics and catalysis. Here we investigate the dynamics of T7 DNA polymerase catalysis using a Langevin-type elastic network model incorporating detailed structural information on the open conformation without the substrate bound. Such a single-parameter model remarkably captures the induced conformational dynamics of DNA polymerase upon dNTP binding, and reveals its close coupling to the advancement toward transition state along the coordinate of the target reaction, which contributes to significant lowering of the activation energy barrier. Furthermore, analysis of stochastic catalytic rates suggests that when the activation energy barrier has already been significantly lowered and nonequilibrium relaxation toward the closed form dominates the catalytic rate, one must appeal to a picture of two-dimensional free energy surface in order to account for the full spectrum of catalytic modes. Our semiquantitative study illustrates the general role of conformational dynamics in achieving transition-state stabilization, and suggests that such an elastic network model, albeit simplified, possesses the potential to furnish significant mechanistic insights into the functioning of a variety of enzymatic systems.
Photochemical reaction dynamics
Moore, B.C.
1993-12-01
The purpose of the program is to develop a fundamental understanding of unimolecular and bimolecular reaction dynamics with application in combustion and energy systems. The energy dependence in ketene isomerization, ketene dissociation dynamics, and carbonyl substitution on organometallic rhodium complexes in liquid xenon have been studied. Future studies concerning unimolecular processes in ketene as well as energy transfer and kinetic studies of methylene radicals are discussed.
Concordant chemical reaction networks and the Species-Reaction Graph.
Shinar, Guy; Feinberg, Martin
2013-01-01
In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network's Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring significant dynamical information directly from standard biochemical reaction diagrams.
Concordant Chemical Reaction Networks and the Species-Reaction Graph
Shinar, Guy; Feinberg, Martin
2015-01-01
In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network’s Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring significant dynamical information directly from standard biochemical reaction diagrams. PMID:22940368
Kinematically complete chemical reaction dynamics
NASA Astrophysics Data System (ADS)
Trippel, S.; Stei, M.; Otto, R.; Hlavenka, P.; Mikosch, J.; Eichhorn, C.; Lourderaj, U.; Zhang, J. X.; Hase, W. L.; Weidemüller, M.; Wester, R.
2009-11-01
Kinematically complete studies of molecular reactions offer an unprecedented level of insight into the dynamics and the different mechanisms by which chemical reactions occur. We have developed a scheme to study ion-molecule reactions by velocity map imaging at very low collision energies. Results for the elementary nucleophilic substitution (SN2) reaction Cl- + CH3I → ClCH3 + I- are presented and compared to high-level direct dynamics trajectory calculations. Furthermore, an improved design of the crossed-beam imaging spectrometer with full three-dimensional measurement capabilities is discussed and characterization measurements using photoionization of NH3 and photodissociation of CH3I are presented.
Deciphering Time Scale Hierarchy in Reaction Networks.
Nagahata, Yutaka; Maeda, Satoshi; Teramoto, Hiroshi; Horiyama, Takashi; Taketsugu, Tetsuya; Komatsuzaki, Tamiki
2016-03-03
Markovian dynamics on complex reaction networks are one of the most intriguing subjects in a wide range of research fields including chemical reactions, biological physics, and ecology. To represent the global kinetics from one node (corresponding to a basin on an energy landscape) to another requires information on multiple pathways that directly or indirectly connect these two nodes through the entire network. In this paper we present a scheme to extract a hierarchical set of global transition states (TSs) over a discrete-time Markov chain derived from first-order rate equations. The TSs can naturally take into account the multiple pathways connecting any pair of nodes. We also propose a new type of disconnectivity graph (DG) to capture the hierarchical organization of different time scales of reactions that can capture changes in the network due to changes in the time scale of observation. The crux is the introduction of the minimum conductance cut (MCC) in graph clustering, corresponding to the dividing surface across the network having the "smallest" transition probability between two disjoint subnetworks (superbasins on the energy landscape) in the network. We present a new combinatorial search algorithm for finding this MCC. We apply our method to a reaction network of Claisen rearrangement of allyl vinyl ether that consists of 23 nodes and 66 links (saddles on the energy landscape) connecting them. We compare the kinetic properties of our DG to those of the transition matrix of the rate equations and show that our graph can properly reveal the hierarchical organization of time scales in a network.
Structural simplification of chemical reaction networks in partial steady states.
Madelaine, Guillaume; Lhoussaine, Cédric; Niehren, Joachim; Tonello, Elisa
2016-11-01
We study the structural simplification of chemical reaction networks with partial steady state semantics assuming that the concentrations of some but not all species are constant. We present a simplification rule that can eliminate intermediate species that are in partial steady state, while preserving the dynamics of all other species. Our simplification rule can be applied to general reaction networks with some but few restrictions on the possible kinetic laws. We can also simplify reaction networks subject to conservation laws. We prove that our simplification rule is correct when applied to a module of a reaction network, as long as the partial steady state is assumed with respect to the complete network. Michaelis-Menten's simplification rule for enzymatic reactions falls out as a special case. We have implemented an algorithm that applies our simplification rules repeatedly and applied it to reaction networks from systems biology.
Structural control of reaction-diffusion networks
NASA Astrophysics Data System (ADS)
Xuan, Qi; Du, Fang; Dong, Hui; Yu, Li; Chen, Guanrong
2011-09-01
Recent studies revealed that reaction-diffusion (RD) dynamics can be significantly influenced by the structure of the underlying network. In this paper, a framework is established to study a closely related problem, i.e., to control the proportion of active particles in an RD process by adjusting the structure of the underlying diffusion network. Both distributed and centralized rewiring and reweighting control schemes are proposed for unweighted and weighted networks, respectively. Simulations show that the proportion of active particles can indeed be controlled to a certain extent even when the distributed control mechanism is totally random, while quite high precision can be achieved by centralized control schemes. More interestingly, it is found that the reactants in heterogeneous networks have wider controllable ranges than those in homogeneous networks with similar numbers of nodes and links, if only the weights of links are changed with a fixed bound. Therefore, it is believed that heterogeneous networks fit the changeable environment better, which provides another explanation for some common observations on many heterogeneous real-world networks.
Reaction Dynamics at Liquid Interfaces
NASA Astrophysics Data System (ADS)
Benjamin, Ilan
2015-04-01
The liquid interface is a narrow, highly anisotropic region, characterized by rapidly varying density, polarity, and molecular structure. I review several aspects of interfacial solvation and show how these affect reactivity at liquid/liquid interfaces. I specifically consider ion transfer, electron transfer, and SN2 reactions, showing that solvent effects on these reactions can be understood by examining the unique structure and dynamics of the liquid interface region.
Reaction dynamics at liquid interfaces.
Benjamin, Ilan
2015-04-01
The liquid interface is a narrow, highly anisotropic region, characterized by rapidly varying density, polarity, and molecular structure. I review several aspects of interfacial solvation and show how these affect reactivity at liquid/liquid interfaces. I specifically consider ion transfer, electron transfer, and SN2 reactions, showing that solvent effects on these reactions can be understood by examining the unique structure and dynamics of the liquid interface region.
Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions
NASA Astrophysics Data System (ADS)
Semenov, Sergey N.; Kraft, Lewis J.; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E.; Kang, Kyungtae; Fox, Jerome M.; Whitesides, George M.
2016-09-01
Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving
Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions.
Semenov, Sergey N; Kraft, Lewis J; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E; Kang, Kyungtae; Fox, Jerome M; Whitesides, George M
2016-09-29
Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving
Dynamics in scheduled networks
NASA Astrophysics Data System (ADS)
Zanin, Massimiliano; Lacasa, Lucas; Cea, Miguel
2009-06-01
When studying real or virtual systems through complex networks theories, usually time restrictions are neglected, and a static structure is defined to characterize which node is connected to which other. However, this approach is oversimplified, as real networks are indeed dynamically modified by external mechanisms. In order to bridge the gap, in this work we present a scheduled network formalism, which takes into account such dynamical modifications by including generic time restrictions in the structure of an extended adjacency matrix. We present some of its properties and apply this formalism to the specific case of the air transportation network in order to analyze its efficiency. Real data are used at this point. We finally discuss on the applicability of this formalism to other complex systems.
Revealing networks from dynamics: an introduction
NASA Astrophysics Data System (ADS)
Timme, Marc; Casadiego, Jose
2014-08-01
What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.
Modular verification of chemical reaction network encodings via serializability analysis.
Lakin, Matthew R; Stefanovic, Darko; Phillips, Andrew
2016-06-13
Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a "commit reaction" that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of "extra tolerance", which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of any system constructed using verified encodings. As an example, we show how this approach may be used to verify two- and four-domain DNA strand displacement encodings of chemical reaction networks, and we generalize our result to the limit where the populations of helper species are unlimited.
Some concepts in reaction dynamics.
Polanyi, J C
1987-05-08
The objective in this work has been one which I have shared with the two other 1986 Nobel lecturers in chemistry, D. R. Herschbach and Y. T. Lee, as well as with a wide group of colleagues and co-workers who have been responsible for bringing this field to its current state. That state is summarized in the title; we now have some concepts relevant to the motions of atoms and molecules in simple reactions, and some examples of the application of these concepts. We are, however, richer in vocabulary than in literature. The great epics of reaction dynamics remain to be written. I shall confine myself to some simple stories.
NASA Astrophysics Data System (ADS)
Orbach, R.
1986-02-01
Random structures often exhibit fractal geometry, defined in terms of the mass scaling exponent, D, the fractal dimension. The vibrational dynamics of fractal networks are expressed in terms of the exponent d double bar, the fracton dimensionality. The eigenstates on a fractal network are spatially localized for d double bar less than or equal to 2. The implications of fractal geometry are discussed for thermal transport on fractal networks. The electron-fracton interaction is developed, with a brief outline given for the time dependence of the electronic relaxation on fractal networks. It is suggested that amorphous or glassy materials may exhibit fractal properties at short length scales or, equivalently, at high energies. The calculations of physical properties can be used to test the fractal character of the vibrational excitations in these materials.
Dynamical model of surrogate reactions
Aritomo, Y.; Chiba, S.; Nishio, K.
2011-08-15
A new dynamical model is developed to describe the whole process of surrogate reactions: Transfer of several nucleons at an initial stage, thermal equilibration of residues leading to washing out of shell effects, and decay of populated compound nuclei are treated in a unified framework. Multidimensional Langevin equations are employed to describe time evolution of collective coordinates with a time-dependent potential energy surface corresponding to different stages of surrogate reactions. The new model is capable of calculating spin distributions of the compound nuclei, one of the most important quantities in the surrogate technique. Furthermore, various observables of surrogate reactions can be calculated, for example, energy and angular distribution of ejectile and mass distributions of fission fragments. These features are important to assess validity of the proposed model itself, to understand mechanisms of the surrogate reactions, and to determine unknown parameters of the model. It is found that spin distributions of compound nuclei produced in {sup 18}O+{sup 238}U{yields}{sup 16}O+{sup 240}*U and {sup 18}O+{sup 236}U{yields}{sup 16}O+{sup 238}*U reactions are equivalent and much less than 10({h_bar}/2{pi}) and therefore satisfy conditions proposed by Chiba and Iwamoto [Phys. Rev. C 81, 044604 (2010)] if they are used as a pair in the surrogate ratio method.
Competition and cooperation in dynamic replication networks.
Dadon, Zehavit; Wagner, Nathaniel; Alasibi, Samaa; Samiappan, Manickasundaram; Mukherjee, Rakesh; Ashkenasy, Gonen
2015-01-07
The simultaneous replication of six coiled-coil peptide mutants by reversible thiol-thioester exchange reactions is described. Experimental analysis of the time dependent evolution of networks formed by the peptides under different conditions reveals a complex web of molecular interactions and consequent mutant replication, governed by competition for resources and by autocatalytic and/or cross-catalytic template-assisted reactions. A kinetic model, first of its kind, is then introduced, allowing simulation of varied network behaviour as a consequence of changing competition and cooperation scenarios. We suggest that by clarifying the kinetic description of these relatively complex dynamic networks, both at early stages of the reaction far from equilibrium and at later stages approaching equilibrium, one lays the foundation for studying dynamic networks out-of-equilibrium in the near future.
Modeling the Dynamics of Compromised Networks
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
NASA Astrophysics Data System (ADS)
Ribeiro, Andre S.
2007-06-01
Genetic toggle switches (TSs) are one of the best studied small gene regulatory networks (GRNs), due to their simplicity and relevant role. They have been interpreted as decision circuits in cell differentiation, a process long hypothesized to be bistable [1], or as cellular memory units [2]. In these contexts, they must be reliable. Once a “decision” is made, the system must remain stable. One way to gain stability is by duplicating the genes of a TS and coupling the two TSs. Using a recent modeling strategy of GRNs, driven by a delayed stochastic simulation algorithm (delayed SSA) that allows modeling transcription and translation as multidelayed reactions, we analyze the stability of systems of coupled TSs. For this, we introduce the coupling strength (C) , a parameter to characterize the GRN structure, against which we compare the GRN stability (S) . We first show that time delays in transcription, associated to the promoter region release, ensure bistability of a TS, given no cooperative binding or self-activation reactions. Next, we couple two TSs and measure their toggling frequencies as C varies. Three dynamical regimes are observed: (i) for weak coupling, high frequency synchronized oscillations, (ii) for average coupling, low frequency synchronized oscillations, and (iii) for strong coupling the system becomes stable after a transient, in one of two steady states. The system stability, S , goes through a first order phase transition as C increases, in the average coupling regime. After, we study the effects of spatial separation in two compartments on the dynamics of two coupled TSs, where spatial separation is modeled as normally distributed random time delayed reactions. The phase transition of S , as C increases, occurs for lower values of C than when the two TSs are in the same compartment. Finally, we couple weakly and homogeneously several TSs within a single compartment and observe that as the number of coupled TSs increases, the system goes
A Networks Approach to Modeling Enzymatic Reactions.
Imhof, P
2016-01-01
Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes.
Reaction-diffusion processes and metapopulation models on duplex networks
NASA Astrophysics Data System (ADS)
Xuan, Qi; Du, Fang; Yu, Li; Chen, Guanrong
2013-03-01
Reaction-diffusion processes, used to model various spatially distributed dynamics such as epidemics, have been studied mostly on regular lattices or complex networks with simplex links that are identical and invariant in transferring different kinds of particles. However, in many self-organized systems, different particles may have their own private channels to keep their purities. Such division of links often significantly influences the underlying reaction-diffusion dynamics and thus needs to be carefully investigated. This article studies a special reaction-diffusion process, named susceptible-infected-susceptible (SIS) dynamics, given by the reaction steps β→α and α+β→2β, on duplex networks where links are classified into two groups: α and β links used to transfer α and β particles, which, along with the corresponding nodes, consist of an α subnetwork and a β subnetwork, respectively. It is found that the critical point of particle density to sustain reaction activity is independent of the network topology if there is no correlation between the degree sequences of the two subnetworks, and this critical value is suppressed or extended if the two degree sequences are positively or negatively correlated, respectively. Based on the obtained results, it is predicted that epidemic spreading may be promoted on positive correlated traffic networks but may be suppressed on networks with modules composed of different types of diffusion links.
Efficient parameter sensitivity computation for spatially extended reaction networks
NASA Astrophysics Data System (ADS)
Lester, C.; Yates, C. A.; Baker, R. E.
2017-01-01
Reaction-diffusion models are widely used to study spatially extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on the stochastic models of spatially extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios.
Minimal Increase Network Coding for Dynamic Networks.
Zhang, Guoyin; Fan, Xu; Wu, Yanxia
2016-01-01
Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.
Minimal Increase Network Coding for Dynamic Networks
Wu, Yanxia
2016-01-01
Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery. PMID:26867211
Networks in metapopulation dynamics
NASA Astrophysics Data System (ADS)
Vuorinen, V.; Peltomäki, M.; Rost, M.; Alava, M. J.
2004-03-01
The behavior of spatially inhomogeneous populations in networks of habitats provides examples of dynamical systems on random graphs with structure. A particular example is a butterfly species inhabiting the Åland archipelago. A metapopulation description of the patch occupancies is here mapped to a quenched graph, using the empirical ecology-based incidence function description as a starting point. Such graphs are shown to have interesting features that both reflect the probably “self-organized” nature of a metapopulation that can survive and the geographical details of the landscape. Simulations of the Susceptible-Infected-Susceptible model, to mimick the time-dependent population dynamics relate to the graph features: lack of a typical scale, large connectivity per vertex, and the existence of independent subgraphs. Finally, ideas related to the application and extension of scale-free graphs to metapopulations are discussed.
Adaptive Dynamic Bayesian Networks
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Mochizuki, Atsushi; Fiedler, Bernold
2015-02-21
In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle.
Multilayer Network Analysis of Nuclear Reactions
Zhu, Liang; Ma, Yu-Gang; Chen, Qu; Han, Ding-Ding
2016-01-01
The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however, we adopt the methods from both multilayer and reaction networks, and obtain a distinctive view by mapping all the nuclear reactions in JINA REACLIB database into a directed network with 4 layers: neutron, proton, 4He and the remainder. The layer names correspond to reaction types decided by the currency particles consumed. This combined approach reveals that, in the remainder layer, the β-stability has high correlation with node degree difference and overlapping coefficient. Moreover, when reaction rates are considered as node strength, we find that, at lower temperatures, nuclide half-life scales reciprocally with its out-strength. The connection between physical properties and topological characteristics may help to explore the boundary of the nuclide chart. PMID:27558995
Law of Localization in Chemical Reaction Networks
NASA Astrophysics Data System (ADS)
Okada, Takashi; Mochizuki, Atsushi
2016-07-01
In living cells, chemical reactions are connected by sharing their products and substrates, and form complex networks, e.g., metabolic pathways. Here we developed a theory to predict the sensitivity, i.e., the responses of concentrations and fluxes to perturbations of enzymes, from network structure alone. Nonzero response patterns turn out to exhibit two characteristic features, localization and hierarchy. We present a general theorem connecting sensitivity with network topology that explains these characteristic patterns. Our results imply that network topology is an origin of biological robustness. Finally, we suggest a strategy to determine real networks from experimental measurements.
Computational functions in biochemical reaction networks.
Arkin, A; Ross, J
1994-01-01
In prior work we demonstrated the implementation of logic gates, sequential computers (universal Turing machines), and parallel computers by means of the kinetics of chemical reaction mechanisms. In the present article we develop this subject further by first investigating the computational properties of several enzymatic (single and multiple) reaction mechanisms: we show their steady states are analogous to either Boolean or fuzzy logic gates. Nearly perfect digital function is obtained only in the regime in which the enzymes are saturated with their substrates. With these enzymatic gates, we construct combinational chemical networks that execute a given truth-table. The dynamic range of a network's output is strongly affected by "input/output matching" conditions among the internal gate elements. We find a simple mechanism, similar to the interconversion of fructose-6-phosphate between its two bisphosphate forms (fructose-1,6-bisphosphate and fructose-2,6-bisphosphate), that functions analogously to an AND gate. When the simple model is supplanted with one in which the enzyme rate laws are derived from experimental data, the steady state of the mechanism functions as an asymmetric fuzzy aggregation operator with properties akin to a fuzzy AND gate. The qualitative behavior of the mechanism does not change when situated within a large model of glycolysis/gluconeogenesis and the TCA cycle. The mechanism, in this case, switches the pathway's mode from glycolysis to gluconeogenesis in response to chemical signals of low blood glucose (cAMP) and abundant fuel for the TCA cycle (acetyl coenzyme A). Images FIGURE 3 FIGURE 4 FIGURE 5 FIGURE 7 FIGURE 10 FIGURE 12 FIGURE 13 FIGURE 14 FIGURE 15 FIGURE 16 PMID:7948674
Disease dynamics in a dynamic social network
NASA Astrophysics Data System (ADS)
Christensen, Claire; Albert, István; Grenfell, Bryan; Albert, Réka
2010-07-01
We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.
Li, Jun; Guo, Hua E-mail: hguo@unm.edu; Chen, Jun; Zhang, Dong H. E-mail: hguo@unm.edu
2014-01-28
A permutationally invariant global potential energy surface for the HOCO system is reported by fitting a larger number of high-level ab initio points using the newly proposed permutation invariant polynomial-neural network method. The small fitting error (∼5 meV) indicates a faithful representation of the potential energy surface over a large configuration space. Full-dimensional quantum and quasi-classical trajectory studies of the title reaction were performed on this potential energy surface. While the results suggest that the differences between this and an earlier neural network fits are small, discrepancies with state-to-state experimental data remain significant.
Timescale analysis of rule-based biochemical reaction networks
Klinke, David J.; Finley, Stacey D.
2012-01-01
The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150
Dynamic Reaction Figures: An Integrative Vehicle for Understanding Chemical Reactions
ERIC Educational Resources Information Center
Schultz, Emeric
2008-01-01
A highly flexible learning tool, referred to as a dynamic reaction figure, is described. Application of these figures can (i) yield the correct chemical equation by simply following a set of menu driven directions; (ii) present the underlying "mechanism" in chemical reactions; and (iii) help to solve quantitative problems in a number of different…
Genotype networks in metabolic reaction spaces
2010-01-01
Background A metabolic genotype comprises all chemical reactions an organism can catalyze via enzymes encoded in its genome. A genotype is viable in a given environment if it is capable of producing all biomass components the organism needs to survive and reproduce. Previous work has focused on the properties of individual genotypes while little is known about how genome-scale metabolic networks with a given function can vary in their reaction content. Results We here characterize spaces of such genotypes. Specifically, we study metabolic genotypes whose phenotype is viability in minimal chemical environments that differ in their sole carbon sources. We show that regardless of the number of reactions in a metabolic genotype, the genotypes of a given phenotype typically form vast, connected, and unstructured sets -- genotype networks -- that nearly span the whole of genotype space. The robustness of metabolic phenotypes to random reaction removal in such spaces has a narrow distribution with a high mean. Different carbon sources differ in the number of metabolic genotypes in their genotype network; this number decreases as a genotype is required to be viable on increasing numbers of carbon sources, but much less than if metabolic reactions were used independently across different chemical environments. Conclusions Our work shows that phenotype-preserving genotype networks have generic organizational properties and that these properties are insensitive to the number of reactions in metabolic genotypes. PMID:20302636
Some Concepts in Reaction Dynamics
NASA Technical Reports Server (NTRS)
Polannyi, John C.
1972-01-01
In 1929 London 1 published a very approximate solution of the Schroedinger equation for a system of chemical interest: H3. To the extent that chemistry can be regarded as existing separately from physics, this was a landmark in the history of chemistry, comparable in importance to the landmark in the history of physics marked by the appearance of the Heitler-London equation for H2. The expression for H3, was, of necessity, even less accurate than that for H2, but chemists, like the habitual poor, were accustomed to this sort of misfortune. Together with the physicists they enjoyed the sensation of living in a renaissance. The physicists still could not calculate a great deal that was of interest to them, and the chemists could calculate less, but both could now dream. It would be too easy to say that their dreams were dreams of unlimited computer time. Their dreams were a lot more productive than that. Two years after London published his equation, H. Eyring and M. Polanyi obtained the first numerical energy surface for H3. They infused the London equation with a measure of empiricism to produce an energy surface which, whether or not it was correct in its details, provided a basis for further speculations of an important sort. The existence of a tangible energy surface in 1931 stimulated speculation along two different lines. The following year Pelzer and Wigner used this London-Eyring-Polanyi (LEP) energy surface for a thermodynamic treatment of the reaction rate in H + H2. This important development reached its full flowering a few years later. In these remarks I shall be concerned with another line of development. A second more-or-less distinct category of speculation that began with (and, indeed, in) the 1931 paper has to do with the dynamics of individual reactive encounters under the influence of specified interaction potentials.
Reconstructing Weighted Networks from Dynamics
NASA Astrophysics Data System (ADS)
Ching, Emily S. C.; Lai, P. Y.; Leung, C. Y.
2015-03-01
The knowledge of how the different nodes of a network interact or link with one another is crucial for the understanding of the collective behavior and the functionality of the network. We have recently developed a method that can reconstruct both the links and their relative coupling strength of bidirectional weighted networks. Our method requires only measurements of node dynamics as input and is based on a relation between the pseudo-inverse of the matrix of the correlation of the node dynamics and the Laplacian matrix of the weighted network. Using several examples of different dynamics, we demonstrate that our method can accurately reconstruct the connectivity as well as the weights of the links for weighted random and weighted scale-free networks with both linear and nonlinear dynamics. The work of ESCC and CYL has been supported by the Hong Kong Research Grants Council under Grant No. CUHK 14300914.
Stochastic thermodynamics of chemical reaction networks.
Schmiedl, Tim; Seifert, Udo
2007-01-28
For chemical reaction networks in a dilute solution described by a master equation, the authors define energy and entropy on a stochastic trajectory and develop a consistent nonequilibrium thermodynamic description along a single stochastic trajectory of reaction events. A first-law like energy balance relates internal energy, applied (chemical) work, and dissipated heat for every single reaction. Entropy production along a single trajectory involves a sum over changes in the entropy of the network itself and the entropy of the medium. The latter is given by the exchanged heat identified through the first law. Total entropy production is constrained by an integral fluctuation theorem for networks arbitrarily driven by time-dependent rates and a detailed fluctuation theorem for networks in the steady state. Further exact relations such as a generalized Jarzynski relation and a generalized Clausius inequality are discussed. The authors illustrate these results for a three-species cyclic reaction network which exhibits nonequilibrium steady states as well as transitions between different steady states.
Dynamics and thermodynamics of open chemical networks
NASA Astrophysics Data System (ADS)
Esposito, Massimiliano
Open chemical networks (OCN) are large sets of coupled chemical reactions where some of the species are chemostated (i.e. continuously restored from the environment). Cell metabolism is a notable example of OCN. Two results will be presented. First, dissipation in OCN operating in nonequilibrium steady-states strongly depends on the network topology (algebraic properties of the stoichiometric matrix). An application to oligosaccharides exchange dynamics performed by so-called D-enzymes will be provided. Second, at low concentration the dissipation of OCN is in general inaccurately predicted by deterministic dynamics (i.e. nonlinear rate equations for the species concentrations). In this case a description in terms of the chemical master equation is necessary. A notable exception is provided by so-called deficiency zero networks, i.e. chemical networks with no hidden cycles present in the graph of reactant complexes.
Directed network discovery with dynamic network modelling.
Anzellotti, Stefano; Kliemann, Dorit; Jacoby, Nir; Saxe, Rebecca
2017-02-16
Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modelling technique for network discovery (Dynamic Network Modelling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modelling introducing three key changes: (1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, (2) the tests take into account the magnitude and sign of each influence, and (3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face.
Nonlinear Dynamics on Interconnected Networks
NASA Astrophysics Data System (ADS)
Arenas, Alex; De Domenico, Manlio
2016-06-01
Networks of dynamical interacting units can represent many complex systems, from the human brain to transportation systems and societies. The study of these complex networks, when accounting for different types of interactions has become a subject of interest in the last few years, especially because its representational power in the description of users' interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.) [1], or in representing different transportation modes in urban networks [2,3]. The general name coined for these networks is multilayer networks, where each layer accounts for a type of interaction (see Fig. 1).
Structurally Dynamic Spin Market Networks
NASA Astrophysics Data System (ADS)
Horváth, Denis; Kuscsik, Zoltán
The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.
Nonequilibrium Thermodynamics of Chemical Reaction Networks: Wisdom from Stochastic Thermodynamics
NASA Astrophysics Data System (ADS)
Rao, Riccardo; Esposito, Massimiliano
2016-10-01
We build a rigorous nonequilibrium thermodynamic description for open chemical reaction networks of elementary reactions. Their dynamics is described by deterministic rate equations with mass action kinetics. Our most general framework considers open networks driven by time-dependent chemostats. The energy and entropy balances are established and a nonequilibrium Gibbs free energy is introduced. The difference between this latter and its equilibrium form represents the minimal work done by the chemostats to bring the network to its nonequilibrium state. It is minimized in nondriven detailed-balanced networks (i.e., networks that relax to equilibrium states) and has an interesting information-theoretic interpretation. We further show that the entropy production of complex-balanced networks (i.e., networks that relax to special kinds of nonequilibrium steady states) splits into two non-negative contributions: one characterizing the dissipation of the nonequilibrium steady state and the other the transients due to relaxation and driving. Our theory lays the path to study time-dependent energy and information transduction in biochemical networks.
Dynamic and interacting complex networks
NASA Astrophysics Data System (ADS)
Dickison, Mark E.
This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible
Solution of Chemical Master Equations for Nonlinear Stochastic Reaction Networks.
Smadbeck, Patrick; Kaznessis, Yiannis N
2014-08-01
Stochasticity in the dynamics of small reacting systems requires discrete-probabilistic models of reaction kinetics instead of traditional continuous-deterministic ones. The master probability equation is a complete model of randomly evolving molecular populations. Because of its ambitious character, the master equation remained unsolved for all but the simplest of molecular interaction networks. With the first solution of chemical master equations, a wide range of experimental observations of small-system interactions may be mathematically conceptualized.
Solution of Chemical Master Equations for Nonlinear Stochastic Reaction Networks
Smadbeck, Patrick; Kaznessis, Yiannis N.
2014-01-01
Stochasticity in the dynamics of small reacting systems requires discrete-probabilistic models of reaction kinetics instead of traditional continuous-deterministic ones. The master probability equation is a complete model of randomly evolving molecular populations. Because of its ambitious character, the master equation remained unsolved for all but the simplest of molecular interaction networks. With the first solution of chemical master equations, a wide range of experimental observations of small-system interactions may be mathematically conceptualized. PMID:25215268
Cascading dynamics in modular networks
NASA Astrophysics Data System (ADS)
Galstyan, Aram; Cohen, Paul
2007-03-01
In this paper we study a simple cascading process in a structured heterogeneous population, namely, a network composed of two loosely coupled communities. We demonstrate that under certain conditions the cascading dynamics in such a network has a two-tiered structure that characterizes activity spreading at different rates in the communities. We study the dynamics of the model using both simulations and an analytical approach based on annealed approximation and obtain good agreement between the two. Our results suggest that network modularity might have implications in various applications, such as epidemiology and viral marketing.
Realistic control of network dynamics.
Cornelius, Sean P; Kath, William L; Motter, Adilson E
2013-01-01
The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response and cell reprogramming. A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behaviour or fail. Here we show that it is possible to exploit the same principle to control network behaviour. Our approach accounts for the nonlinear dynamics inherent to real systems, and allows bringing the system to a desired target state even when this state is not directly accessible due to constraints that limit the allowed interventions. Applications show that this framework permits reprogramming a network to a desired task, as well as rescuing networks from the brink of failure-which we illustrate through the mitigation of cascading failures in a power-grid network and the identification of potential drug targets in a signalling network of human cancer.
Realistic Control of Network Dynamics
Cornelius, Sean P.; Kath, William L.; Motter, Adilson E.
2014-01-01
The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response, and cell reprogramming. A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. Here, we show that it is possible to exploit the same principle to control network behavior. Our approach accounts for the nonlinear dynamics inherent to real systems, and allows bringing the system to a desired target state even when this state is not directly accessible due to constraints that limit the allowed interventions. Applications show that this framework permits reprogramming a network to a desired task as well as rescuing networks from the brink of failure—which we illustrate through the mitigation of cascading failures in a power-grid network and the identification of potential drug targets in a signaling network of human cancer. PMID:23803966
Communication Dynamics of Blog Networks
NASA Astrophysics Data System (ADS)
Goldberg, Mark; Kelley, Stephen; Magdon-Ismail, Malik; Mertsalov, Konstantin; Wallace, William (Al)
We study the communication dynamics of Blog networks, focusing on the Russian section of LiveJournal as a case study. Communication (blogger-to-blogger links) in such online communication networks is very dynamic: over 60% of the links in the network are new from one week to the next, though the set of bloggers remains approximately constant. Two fundamental questions are: (i) what models adequately describe such dynamic communication behavior; and (ii) how does one detect the phase transitions, i.e. the changes that go beyond the standard high-level dynamics? We approach these questions through the notion of stable statistics. We give strong experimental evidence to the fact that, despite the extreme amount of communication dynamics, several aggregate statistics are remarkably stable. We use stable statistics to test our models of communication dynamics postulating that any good model should produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe models of the communication dynamics in large social networks based on the principle of locality of communication: a node's communication energy is spent mostly within its own "social area," the locality of the node.
The quantum dynamics of chemical reactions
NASA Astrophysics Data System (ADS)
Kuppermann, A.
1983-03-01
In this project, we developed accurate and approximate methods for calculating cross sections of elementary reactions. These methods were applied to systems of importance for the fundamental aspects of chemical dynamics and for advanced technologies of interest to the United States Air Force. The application included calculations of three-atom exchange reactions, break-up and three-body recombination collisions and vibrational quenching by reaction. These calculations improved our understanding of such processes and permitted an assessment of some approximate methods.
Using dynamic sensitivities to characterize metabolic reaction systems.
Sriyudthsak, Kansuporn; Uno, Harumi; Gunawan, Rudiyanto; Shiraishi, Fumihide
2015-11-01
Metabolite concentrations in cells are governed by enzyme kinetics in the metabolic reaction system. One can analyze how these concentrations depend on system variables such as enzyme activities by computing system sensitivities, which generally vary with time. Dynamic sensitivities, i.e., time-varying sensitivities, reflect the time-dependent response of the metabolic reaction network to perturbations. Unfortunately, dynamic sensitivity profiles are not commonly used in the analysis of metabolic reaction systems. In the present study, we demonstrate the use of dynamic logarithmic gains, i.e., normalized time-varying sensitivities, to gain insights into the dynamic behavior of metabolic networks. A biosynthetic reaction model of aromatic amino acids proposed by other researchers is used as a case study. The model system is analyzed using the dynamic logarithmic gains in parallel with simulations of the time-transient behavior of metabolite concentrations and metabolic fluxes. The result indicates that the influences of independent variables are most pronounced just after perturbations and the effects of perturbations on metabolite concentration at early times can be larger than those at steady state. These findings suggest that it is important to perform dynamic sensitivity analysis in addition to steady-state analysis. Furthermore, the rankings of the bottleneck ranking indicators, defined as the product of dynamic logarithmic gain and metabolite concentration, for three desired amino acids reveal that the degree of bottleneck for each enzyme changes with time. In conclusion, the dynamic logarithmic gains are not only useful for analyzing metabolic reaction systems but also can offer additional insights on the transient behavior of the system over steady state sensitivities, leading to a proper design of metabolic systems.
Perspective: chemical dynamics simulations of non-statistical reaction dynamics.
Ma, Xinyou; Hase, William L
2017-04-28
Non-statistical chemical dynamics are exemplified by disagreements with the transition state (TS), RRKM and phase space theories of chemical kinetics and dynamics. The intrinsic reaction coordinate (IRC) is often used for the former two theories, and non-statistical dynamics arising from non-IRC dynamics are often important. In this perspective, non-statistical dynamics are discussed for chemical reactions, with results primarily obtained from chemical dynamics simulations and to a lesser extent from experiment. The non-statistical dynamical properties discussed are: post-TS dynamics, including potential energy surface bifurcations, product energy partitioning in unimolecular dissociation and avoiding exit-channel potential energy minima; non-RRKM unimolecular decomposition; non-IRC dynamics; direct mechanisms for bimolecular reactions with pre- and/or post-reaction potential energy minima; non-TS theory barrier recrossings; and roaming dynamics.This article is part of the themed issue 'Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces'.
Jouanin, Jean-Claude; Pérès, Michel; Ducorps, Antoine; Renault, Bernard
2009-02-01
Magnetoencephalographic, electromyographic (EMG), work, and reaction time (RT) were recorded from nine subjects during visually triggered intermittent isometric contractions of the middle finger under two conditions: unloaded and loaded (30% of maximal voluntary contraction). The effect of muscle fatigue was studied over three consecutive periods under both conditions. In the loaded condition, the motor evoked field triggered by the EMG onset decreased with fatigue, whereas movement-evoked fields (MEFs) increased (P < 0.01). Fatigue was demonstrated in the loaded condition, since (i) RT increased due to an increase in the electromechanical delay (P < 0.002); (ii) work decreased from Periods 1 to 3 (P < 0.005), while (iii) the myoelectric RMS amplitude of both flexor digitorum superficialis and extensor muscles increased (P < 0.003) and (iv) during Period 3, the spectral deflection of the EMG median frequency of the FDS muscle decreased (P < 0.001). In the unloaded condition and at the beginning of the loaded condition, a parallel network including M1-S1, posterior SII-insular, and posterior cingulate cortices accounted for the MEF activities. However, under the effect of fatigue, medial insular and posterior cingulate cortices drove this network. Moreover, changes in the location of insular and M1-S1 activations were significantly correlated with muscle fatigue (increase of RMS-EMG; P < 0.03 and P < 0.01, respectively). These results demonstrate that a plastic network controls the strength of the motor command as fatigue occurs: sensory information, pain, and exhaustion act through activation of the medial insular and posterior cingulate cortices to decrease the motor command in order to preserve muscle efficiency and integrity.
NASA Astrophysics Data System (ADS)
Vijaykumar, Adithya; Ouldridge, Thomas E.; ten Wolde, Pieter Rein; Bolhuis, Peter G.
2017-03-01
The modeling of complex reaction-diffusion processes in, for instance, cellular biochemical networks or self-assembling soft matter can be tremendously sped up by employing a multiscale algorithm which combines the mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit stochastic Brownian, Langevin, or deterministic molecular dynamics to treat reactants at the microscopic scale [A. Vijaykumar, P. G. Bolhuis, and P. R. ten Wolde, J. Chem. Phys. 143, 214102 (2015)]. Here we extend this multiscale MD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We present the novel algorithm focusing on Brownian dynamics only, although the methodology is generic. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm, we discuss its performance. The rotational Brownian dynamics MD-GFRD multiscale method will open up the possibility for large scale simulations of protein signalling networks.
Dynamical detection of network communities.
Quiles, Marcos G; Macau, Elbert E N; Rubido, Nicolás
2016-05-09
A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.
Dynamical detection of network communities
Quiles, Marcos G.; Macau, Elbert E. N.; Rubido, Nicolás
2016-01-01
A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance. PMID:27158092
Dynamical detection of network communities
NASA Astrophysics Data System (ADS)
Quiles, Marcos G.; Macau, Elbert E. N.; Rubido, Nicolás
2016-05-01
A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.
Stochastic dynamics of macromolecular-assembly networks.
NASA Astrophysics Data System (ADS)
Saiz, Leonor; Vilar, Jose
2006-03-01
The formation and regulation of macromolecular complexes provides the backbone of most cellular processes, including gene regulation and signal transduction. The inherent complexity of assembling macromolecular structures makes current computational methods strongly limited for understanding how the physical interactions between cellular components give rise to systemic properties of cells. Here we present a stochastic approach to study the dynamics of networks formed by macromolecular complexes in terms of the molecular interactions of their components [1]. Exploiting key thermodynamic concepts, this approach makes it possible to both estimate reaction rates and incorporate the resulting assembly dynamics into the stochastic kinetics of cellular networks. As prototype systems, we consider the lac operon and phage λ induction switches, which rely on the formation of DNA loops by proteins [2] and on the integration of these protein-DNA complexes into intracellular networks. This cross-scale approach offers an effective starting point to move forward from network diagrams, such as those of protein-protein and DNA-protein interaction networks, to the actual dynamics of cellular processes. [1] L. Saiz and J.M.G. Vilar, submitted (2005). [2] J.M.G. Vilar and L. Saiz, Current Opinion in Genetics & Development, 15, 136-144 (2005).
Spontaneous recovery in dynamical networks
NASA Astrophysics Data System (ADS)
Majdandzic, Antonio; Podobnik, Boris; Buldyrev, Sergey V.; Kenett, Dror Y.; Havlin, Shlomo; Eugene Stanley, H.
2014-01-01
Much research has been carried out to explore the structural properties and vulnerability of complex networks. Of particular interest are abrupt dynamic events that cause networks to irreversibly fail. However, in many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic `phase-flipping' phenomena. As the network is of finite size and is stochastic, the fraction of active nodes z switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behaviour analogous to phase transitions near a critical point. We present real-world network data exhibiting phase switching behaviour in accord with the predictions of the model.
Dynamic interactions in neural networks
Arbib, M.A. ); Amari, S. )
1989-01-01
The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.
Recent advances in symmetric and network dynamics
NASA Astrophysics Data System (ADS)
Golubitsky, Martin; Stewart, Ian
2015-09-01
We summarize some of the main results discovered over the past three decades concerning symmetric dynamical systems and networks of dynamical systems, with a focus on pattern formation. In both of these contexts, extra constraints on the dynamical system are imposed, and the generic phenomena can change. The main areas discussed are time-periodic states, mode interactions, and non-compact symmetry groups such as the Euclidean group. We consider both dynamics and bifurcations. We summarize applications of these ideas to pattern formation in a variety of physical and biological systems, and explain how the methods were motivated by transferring to new contexts René Thom's general viewpoint, one version of which became known as "catastrophe theory." We emphasize the role of symmetry-breaking in the creation of patterns. Topics include equivariant Hopf bifurcation, which gives conditions for a periodic state to bifurcate from an equilibrium, and the H/K theorem, which classifies the pairs of setwise and pointwise symmetries of periodic states in equivariant dynamics. We discuss mode interactions, which organize multiple bifurcations into a single degenerate bifurcation, and systems with non-compact symmetry groups, where new technical issues arise. We transfer many of the ideas to the context of networks of coupled dynamical systems, and interpret synchrony and phase relations in network dynamics as a type of pattern, in which space is discretized into finitely many nodes, while time remains continuous. We also describe a variety of applications including animal locomotion, Couette-Taylor flow, flames, the Belousov-Zhabotinskii reaction, binocular rivalry, and a nonlinear filter based on anomalous growth rates for the amplitude of periodic oscillations in a feed-forward network.
A chemical reaction network solver for the astrophysics code NIRVANA
NASA Astrophysics Data System (ADS)
Ziegler, U.
2016-02-01
Context. Chemistry often plays an important role in astrophysical gases. It regulates thermal properties by changing species abundances and via ionization processes. This way, time-dependent cooling mechanisms and other chemistry-related energy sources can have a profound influence on the dynamical evolution of an astrophysical system. Modeling those effects with the underlying chemical kinetics in realistic magneto-gasdynamical simulations provide the basis for a better link to observations. Aims: The present work describes the implementation of a chemical reaction network solver into the magneto-gasdynamical code NIRVANA. For this purpose a multispecies structure is installed, and a new module for evolving the rate equations of chemical kinetics is developed and coupled to the dynamical part of the code. A small chemical network for a hydrogen-helium plasma was constructed including associated thermal processes which is used in test problems. Methods: Evolving a chemical network within time-dependent simulations requires the additional solution of a set of coupled advection-reaction equations for species and gas temperature. Second-order Strang-splitting is used to separate the advection part from the reaction part. The ordinary differential equation (ODE) system representing the reaction part is solved with a fourth-order generalized Runge-Kutta method applicable for stiff systems inherent to astrochemistry. Results: A series of tests was performed in order to check the correctness of numerical and technical implementation. Tests include well-known stiff ODE problems from the mathematical literature in order to confirm accuracy properties of the solver used as well as problems combining gasdynamics and chemistry. Overall, very satisfactory results are achieved. Conclusions: The NIRVANA code is now ready to handle astrochemical processes in time-dependent simulations. An easy-to-use interface allows implementation of complex networks including thermal processes
Raman spectroscopic study of reaction dynamics
NASA Astrophysics Data System (ADS)
MacPhail, R. A.
1990-12-01
The Raman spectra of reacting molecules in liquids can yield information about various aspects of the reaction dynamics. The author discusses the analysis of Raman spectra for three prototypical unimolecular reactions, the rotational isomerization of n-butane and 1,2-difluoroethane, and the barrierless exchange of axial and equatorial hydrogens in cyclopentane via pseudorotation. In the first two cases the spectra are sensitive to torsional oscillations of the gauche conformer, and yield estimates of the torsional solvent friction. In the case of cyclopentane, the spectra can be used to discriminate between different stochastic models of the pseudorotation dynamics, and to determine the relevant friction coefficients.
Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks
Ziaul Huque
2007-08-31
This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.
Competitive Dynamics on Complex Networks
NASA Astrophysics Data System (ADS)
Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan
2014-07-01
We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.
Competitive Dynamics on Complex Networks
Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan
2014-01-01
We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition. PMID:25068622
Morphisms of reaction networks that couple structure to function
2014-01-01
Background The mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. However, similarity of mechanism is better revealed by relationships between network structures. Results We define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm. Conclusions Structural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks. PMID:25128194
Unifying evolutionary and network dynamics
NASA Astrophysics Data System (ADS)
Swarup, Samarth; Gasser, Les
2007-06-01
Many important real-world networks manifest small-world properties such as scale-free degree distributions, small diameters, and clustering. The most common model of growth for these networks is preferential attachment, where nodes acquire new links with probability proportional to the number of links they already have. We show that preferential attachment is a special case of the process of molecular evolution. We present a single-parameter model of network growth that unifies varieties of preferential attachment with the quasispecies equation (which models molecular evolution), and also with the Erdős-Rényi random graph model. We suggest some properties of evolutionary models that might be applied to the study of networks. We also derive the form of the degree distribution resulting from our algorithm, and we show through simulations that the process also models aspects of network growth. The unification allows mathematical machinery developed for evolutionary dynamics to be applied in the study of network dynamics, and vice versa.
Stochastic analysis of complex reaction networks using binomial moment equations.
Barzel, Baruch; Biham, Ofer
2012-09-01
The stochastic analysis of complex reaction networks is a difficult problem because the number of microscopic states in such systems increases exponentially with the number of reactive species. Direct integration of the master equation is thus infeasible and is most often replaced by Monte Carlo simulations. While Monte Carlo simulations are a highly effective tool, equation-based formulations are more amenable to analytical treatment and may provide deeper insight into the dynamics of the network. Here, we present a highly efficient equation-based method for the analysis of stochastic reaction networks. The method is based on the recently introduced binomial moment equations [Barzel and Biham, Phys. Rev. Lett. 106, 150602 (2011)]. The binomial moments are linear combinations of the ordinary moments of the probability distribution function of the population sizes of the interacting species. They capture the essential combinatorics of the reaction processes reflecting their stoichiometric structure. This leads to a simple and transparent form of the equations, and allows a highly efficient and surprisingly simple truncation scheme. Unlike ordinary moment equations, in which the inclusion of high order moments is prohibitively complicated, the binomial moment equations can be easily constructed up to any desired order. The result is a set of equations that enables the stochastic analysis of complex reaction networks under a broad range of conditions. The number of equations is dramatically reduced from the exponential proliferation of the master equation to a polynomial (and often quadratic) dependence on the number of reactive species in the binomial moment equations. The aim of this paper is twofold: to present a complete derivation of the binomial moment equations; to demonstrate the applicability of the moment equations for a representative set of example networks, in which stochastic effects play an important role.
Reaction networks and kinetics of biochemical systems.
Arceo, Carlene Perpetua P; Jose, Editha C; Lao, Angelyn R; Mendoza, Eduardo R
2017-01-01
This paper further develops the connection between Chemical Reaction Network Theory (CRNT) and Biochemical Systems Theory (BST) that we recently introduced [1]. We first use algebraic properties of kinetic sets to study the set of complex factorizable kinetics CFK(N) on a CRN, which shares many characteristics with its subset of mass action kinetics. In particular, we extend the Theorem of Feinberg-Horn [9] on the coincidence of the kinetic and stoichiometric subsets of a mass action system to CF kinetics, using the concept of span surjectivity. We also introduce the branching type of a network, which determines the availability of kinetics on it and allows us to characterize the networks for which all kinetics are complex factorizable: A "Kinetics Landscape" provides an overview of kinetics sets, their algebraic properties and containment relationships. We then apply our results and those (of other CRNT researchers) reviewed in [1] to fifteen BST models of complex biological systems and discover novel network and kinetic properties that so far have not been widely studied in CRNT. In our view, these findings show an important benefit of connecting CRNT and BST modeling efforts.
Reaction dynamics in polyatomic molecular systems
Miller, W.H.
1993-12-01
The goal of this program is the development of theoretical methods and models for describing the dynamics of chemical reactions, with specific interest for application to polyatomic molecular systems of special interest and relevance. There is interest in developing the most rigorous possible theoretical approaches and also in more approximate treatments that are more readily applicable to complex systems.
Mode-Specific SN2 Reaction Dynamics.
Wang, Yan; Song, Hongwei; Szabó, István; Czakó, Gábor; Guo, Hua; Yang, Minghui
2016-09-01
Despite its importance in chemistry, the microscopic dynamics of bimolecular nucleophilic substitution (SN2) reactions is still not completely elucidated. In this publication, the dynamics of a prototypical SN2 reaction (F(-) + CH3Cl → CH3F + Cl(-)) is investigated using a high-dimensional quantum mechanical model on an accurate potential energy surface (PES) and further analyzed by quasi-classical trajectories on the same PES. While the indirect mechanism dominates at low collision energies, the direct mechanism makes a significant contribution. The reactivity is found to depend on the specific reactant vibrational mode excitation. The mode specificity, which is more prevalent in the direct reaction, is rationalized by a transition-state-based model.
Criticality in finite dynamical networks
NASA Astrophysics Data System (ADS)
Rohlf, Thimo; Gulbahce, Natali; Teuscher, Christof
2007-03-01
It has been shown analytically and experimentally that both random boolean and random threshold networks show a transition from ordered to chaotic dynamics at a critical average connectivity Kc in the thermodynamical limit [1]. By looking at the statistical distributions of damage spreading (damage sizes), we go beyond this extensively studied mean-field approximation. We study the scaling properties of damage size distributions as a function of system size N and initial perturbation size d(t=0). We present numerical evidence that another characteristic point, Kd exists for finite system sizes, where the expectation value of damage spreading in the network is independent of the system size N. Further, the probability to obtain critical networks is investigated for a given system size and average connectivity k. Our results suggest that, for finite size dynamical networks, phase space structure is very complex and may not exhibit a sharp order-disorder transition. Finally, we discuss the implications of our findings for evolutionary processes and learning applied to networks which solve specific computational tasks. [1] Derrida, B. and Pomeau, Y. (1986), Europhys. Lett., 1, 45-49
Asynchronous networks: modularization of dynamics theorem
NASA Astrophysics Data System (ADS)
Bick, Christian; Field, Michael
2017-02-01
Building on the first part of this paper, we develop the theory of functional asynchronous networks. We show that a large class of functional asynchronous networks can be (uniquely) represented as feedforward networks connecting events or dynamical modules. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network: the modularization of dynamics theorem. We give examples to illustrate the main results.
Chemical Reaction Networks for Computing Polynomials.
Salehi, Sayed Ahmad; Parhi, Keshab K; Riedel, Marc D
2017-01-20
Chemical reaction networks (CRNs) provide a fundamental model in the study of molecular systems. Widely used as formalism for the analysis of chemical and biochemical systems, CRNs have received renewed attention as a model for molecular computation. This paper demonstrates that, with a new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these functions must map the unit interval to itself. These polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. In the proposed encoding approach, each variable is represented using two molecular types: a type-0 and a type-1. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of type-0 and type-1 molecules. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. Molecular encoders for converting any input in a standard representation to the fractional representation as well as decoders for converting the computed output from the fractional to a standard representation are presented. The method is illustrated first for generic CRNs; then chemical reactions designed for an example are mapped to DNA strand-displacement reactions.
Asynchronous networks and event driven dynamics
NASA Astrophysics Data System (ADS)
Bick, Christian; Field, Michael
2017-02-01
Real-world networks in technology, engineering and biology often exhibit dynamics that cannot be adequately reproduced using network models given by smooth dynamical systems and a fixed network topology. Asynchronous networks give a theoretical and conceptual framework for the study of network dynamics where nodes can evolve independently of one another, be constrained, stop, and later restart, and where the interaction between different components of the network may depend on time, state, and stochastic effects. This framework is sufficiently general to encompass a wide range of applications ranging from engineering to neuroscience. Typically, dynamics is piecewise smooth and there are relationships with Filippov systems. In this paper, we give examples of asynchronous networks, and describe the basic formalism and structure. In the following companion paper, we make the notion of a functional asynchronous network rigorous, discuss the phenomenon of dynamical locks, and present a foundational result on the spatiotemporal factorization of the dynamics for a large class of functional asynchronous networks.
Pedestrian dynamics via Bayesian networks
NASA Astrophysics Data System (ADS)
Venkat, Ibrahim; Khader, Ahamad Tajudin; Subramanian, K. G.
2014-06-01
Studies on pedestrian dynamics have vital applications in crowd control management relevant to organizing safer large scale gatherings including pilgrimages. Reasoning pedestrian motion via computational intelligence techniques could be posed as a potential research problem within the realms of Artificial Intelligence. In this contribution, we propose a "Bayesian Network Model for Pedestrian Dynamics" (BNMPD) to reason the vast uncertainty imposed by pedestrian motion. With reference to key findings from literature which include simulation studies, we systematically identify: What are the various factors that could contribute to the prediction of crowd flow status? The proposed model unifies these factors in a cohesive manner using Bayesian Networks (BNs) and serves as a sophisticated probabilistic tool to simulate vital cause and effect relationships entailed in the pedestrian domain.
Quantum effects in unimolecular reaction dynamics
Gezelter, Joshua Daniel
1995-12-01
This work is primarily concerned with the development of models for the quantum dynamics of unimolecular isomerization and photodissociation reactions. We apply the rigorous quantum methodology of a Discrete Variable Representation (DVR) with Absorbing Boundary Conditions (ABC) to these models in an attempt to explain some very surprising results from a series of experiments on vibrationally excited ketene. Within the framework of these models, we are able to identify the experimental signatures of tunneling and dynamical resonances in the energy dependence of the rate of ketene isomerization. Additionally, we investigate the step-like features in the energy dependence of the rate of dissociation of triplet ketene to form ^{3}B_{1} CH_{2} + ^{1}σ^{+} CO that have been observed experimentally. These calculations provide a link between ab initio calculations of the potential energy surfaces and the experimentally observed dynamics on these surfaces. Additionally, we develop an approximate model for the partitioning of energy in the products of photodissociation reactions of large molecules with appreciable barriers to recombination. In simple bond cleavage reactions like CH_{3}COCl → CH_{3}CO + Cl, the model does considerably better than other impulsive and statistical models in predicting the energy distribution in the products. We also investigate ways of correcting classical mechanics to include the important quantum mechanical aspects of zero-point energy. The method we investigate is found to introduce a number of undesirable dynamical artifacts including a reduction in the above-threshold rates for simple reactions, and a strong mixing of the chaotic and regular energy domains for some model problems. We conclude by discussing some of the directions for future research in the field of theoretical chemical dynamics.
Theoretical studies of chemical reaction dynamics
Schatz, G.C.
1993-12-01
This collaborative program with the Theoretical Chemistry Group at Argonne involves theoretical studies of gas phase chemical reactions and related energy transfer and photodissociation processes. Many of the reactions studied are of direct relevance to combustion; others are selected they provide important examples of special dynamical processes, or are of relevance to experimental measurements. Both classical trajectory and quantum reactive scattering methods are used for these studies, and the types of information determined range from thermal rate constants to state to state differential cross sections.
Molecular beam studies of reaction dynamics
Lee, Y.T.
1987-03-01
Purpose of this research project is two-fold: (1) to elucidate detailed dynamics of simple elementary reactions which are theoretically important and to unravel the mechanism of complex chemical reactions or photo chemical processes which play an important role in many macroscopic processes and (2) to determine the energetics of polyatomic free radicals using microscopic experimental methods. Most of the information is derived from measurement of the product fragment translational energy and angular distributions using unique molecular beam apparati designed for these purposes.
Data modeling of network dynamics
NASA Astrophysics Data System (ADS)
Jaenisch, Holger M.; Handley, James W.; Faucheux, Jeffery P.; Harris, Brad
2004-01-01
This paper highlights Data Modeling theory and its use for text data mining as a graphical network search engine. Data Modeling is then used to create a real-time filter capable of monitoring network traffic down to the port level for unusual dynamics and changes in business as usual. This is accomplished in an unsupervised fashion without a priori knowledge of abnormal characteristics. Two novel methods for converting streaming binary data into a form amenable to graphics based search and change detection are introduced. These techniques are then successfully applied to 1999 KDD Cup network attack data log-on sessions to demonstrate that Data Modeling can detect attacks without prior training on any form of attack behavior. Finally, two new methods for data encryption using these ideas are proposed.
Network dynamics and systems biology
NASA Astrophysics Data System (ADS)
Norrell, Johannes A.
The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior. In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects---an asymmetry between on and off states, and a decaying memory of events in each element's inputs---that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors. Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the
Molecular beam studies of reaction dynamics
Lee, Y.T.
1993-12-01
The major thrust of this research project is to elucidate detailed dynamics of simple elementary reactions that are theoretically important and to unravel the mechanism of complex chemical reactions or photochemical processes that play important roles in many macroscopic processes. Molecular beams of reactants are used to study individual reactive encounters between molecules or to monitor photodissociation events in a collision-free environment. Most of the information is derived from measurement of the product fragment energy, angular, and state distributions. Recent activities are centered on the mechanisms of elementary chemical reactions involving oxygen atoms with unsaturated hydrocarbons, the dynamics of endothermic substitution reactions, the dependence of the chemical reactivity of electronically excited atoms on the alignment of excited orbitals, the primary photochemical processes of polyatomic molecules, intramolecular energy transfer of chemically activated and locally excited molecules, the energetics of free radicals that are important to combustion processes, the infrared-absorption spectra of carbonium ions and hydrated hydronium ions, and bond-selective photodissociation through electric excitation.
Molecular beam studies of reaction dynamics
Lee, Yuan T.
1991-03-01
The major thrust of this research project is to elucidate detailed dynamics of simple elementary reactions that are theoretically important and to unravel the mechanism of complex chemical reactions or photochemical processes that play important roles in many macroscopic processes. Molecular beams of reactants are used to study individual reactive encounters between molecules or to monitor photodissociation events in a collision-free environment. Most of the information is derived from measurement of the product fragment energy, angular, and state distributions. Recent activities are centered on the mechanisms of elementary chemical reactions involving oxygen atoms with unsaturated hydrocarbons, the dynamics of endothermic substitution reactions, the dependence of the chemical reactivity of electronically excited atoms on the alignment of excited orbitals, the primary photochemical processes of polyatomic molecules, intramolecular energy transfer of chemically activated and locally excited molecules, the energetics of free radicals that are important to combustion processes, the infrared-absorption spectra of carbonium ions and hydrated hydronium ions, and bond-selective photodissociation through electric excitation.
Anomaly Detection in Dynamic Networks
Turcotte, Melissa
2014-10-14
Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the
Leveraging modeling approaches: reaction networks and rules.
Blinov, Michael L; Moraru, Ion I
2012-01-01
We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks - the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.
Network dynamics in nanofilled polymers
Baeza, Guilhem P.; Dessi, Claudia; Costanzo, Salvatore; Zhao, Dan; Gong, Shushan; Alegria, Angel; Colby, Ralph H.; Rubinstein, Michael; Vlassopoulos, Dimitris; Kumar, Sanat K.
2016-01-01
It is well accepted that adding nanoparticles (NPs) to polymer melts can result in significant property improvements. Here we focus on the causes of mechanical reinforcement and present rheological measurements on favourably interacting mixtures of spherical silica NPs and poly(2-vinylpyridine), complemented by several dynamic and structural probes. While the system dynamics are polymer-like with increased friction for low silica loadings, they turn network-like when the mean face-to-face separation between NPs becomes smaller than the entanglement tube diameter. Gel-like dynamics with a Williams–Landel–Ferry temperature dependence then result. This dependence turns particle dominated, that is, Arrhenius-like, when the silica loading increases to ∼31 vol%, namely, when the average nearest distance between NP faces becomes comparable to the polymer's Kuhn length. Our results demonstrate that the flow properties of nanocomposites are complex and can be tuned via changes in filler loading, that is, the character of polymer bridges which ‘tie' NPs together into a network. PMID:27109062
Network dynamics in nanofilled polymers
NASA Astrophysics Data System (ADS)
Baeza, Guilhem P.; Dessi, Claudia; Costanzo, Salvatore; Zhao, Dan; Gong, Shushan; Alegria, Angel; Colby, Ralph H.; Rubinstein, Michael; Vlassopoulos, Dimitris; Kumar, Sanat K.
2016-04-01
It is well accepted that adding nanoparticles (NPs) to polymer melts can result in significant property improvements. Here we focus on the causes of mechanical reinforcement and present rheological measurements on favourably interacting mixtures of spherical silica NPs and poly(2-vinylpyridine), complemented by several dynamic and structural probes. While the system dynamics are polymer-like with increased friction for low silica loadings, they turn network-like when the mean face-to-face separation between NPs becomes smaller than the entanglement tube diameter. Gel-like dynamics with a Williams-Landel-Ferry temperature dependence then result. This dependence turns particle dominated, that is, Arrhenius-like, when the silica loading increases to ~31 vol%, namely, when the average nearest distance between NP faces becomes comparable to the polymer's Kuhn length. Our results demonstrate that the flow properties of nanocomposites are complex and can be tuned via changes in filler loading, that is, the character of polymer bridges which `tie' NPs together into a network.
Reduction of chemical reaction networks through delay distributions
NASA Astrophysics Data System (ADS)
Barrio, Manuel; Leier, André; Marquez-Lago, Tatiana T.
2013-03-01
Accurate modelling and simulation of dynamic cellular events require two main ingredients: an adequate description of key chemical reactions and simulation of such chemical events in reasonable time spans. Quite logically, posing the right model is a crucial step for any endeavour in Computational Biology. However, more often than not, it is the associated computational costs which actually limit our capabilities of representing complex cellular behaviour. In this paper, we propose a methodology aimed at representing chains of chemical reactions by much simpler, reduced models. The abridgement is achieved by generation of model-specific delay distribution functions, consecutively fed to a delay stochastic simulation algorithm. We show how such delay distributions can be analytically described whenever the system is solely composed of consecutive first-order reactions, with or without additional "backward" bypass reactions, yielding an exact reduction. For models including other types of monomolecular reactions (constitutive synthesis, degradation, or "forward" bypass reactions), we discuss why one must adopt a numerical approach for its accurate stochastic representation, and propose two alternatives for this. In these cases, the accuracy depends on the respective numerical sample size. Our model reduction methodology yields significantly lower computational costs while retaining accuracy. Quite naturally, computational costs increase alongside network size and separation of time scales. Thus, we expect our model reduction methodologies to significantly decrease computational costs in these instances. We anticipate the use of delays in model reduction will greatly alleviate some of the current restrictions in simulating large sets of chemical reactions, largely applicable in pharmaceutical and biological research.
Dynamics and pattern formation in a cancer network with diffusion
NASA Astrophysics Data System (ADS)
Zheng, Qianqian; Shen, Jianwei
2015-10-01
Diffusion is ubiquitous inside cells, and it is capable of inducing spontaneous pattern formation in reaction-diffusion systems on a spatially homogeneous domain. In this paper, we investigate the dynamics of a diffusive cancer network regulated by microRNA and obtain the condition that the network undergoes a Hopf bifurcation and a Turing pattern bifurcation. In addition, we also develop the amplitude equation of the network model by using Taylor series expansion, multi-scaling and further expansion in powers of a small parameter. As a result of these analyses, we obtain the explicit condition on how the dynamics of the diffusive cancer network evolve. These results reveal that this system has rich dynamics, such as spotted stripe and hexagon patterns. The bifurcation diagram helps us understand the biological mechanism in the cancer network. Finally, numerical simulations confirm our analytical results.
Dynamics of and on complex networks
NASA Astrophysics Data System (ADS)
Halu, Arda
Complex networks are dynamic, evolving structures that can host a great number of dynamical processes. In this thesis, we address current challenges regarding the dynamics of and dynamical processes on complex networks. First, we study complex network dynamics from the standpoint of network growth. As a quantitative measure of the complexity and information content of networks generated by growing network models, we define and evaluate their entropy rate. We propose stochastic growth models inspired by the duplication-divergence mechanism to generate epistatic interaction networks and find that they exhibit the property of monochromaticity as a result of their dynamical evolution. Second, we explore the dynamics of quantum mechanical processes on complex networks. We investigate the Bose-Hubbard model on annealed and quenched scale-free networks as well as Apollonian networks and show that their phase diagram changes significantly in the presence of complex topologies, depending on the second degree of the degree distribution and the maximal eigenvalue of the adjacency matrix. We then study the Jaynes-Cummings-Hubbard model on various complex topologies and demonstrate the importance of the maximal eigenvalue of the hopping matrix in determining the phase diagram of the model. Third, we investigate dynamical processes on interacting and multiplex networks. We study opinion dynamics in a simulated setting of two antagonistically interacting networks and recover the importance of connectivity and committed agents. We propose a multiplex centrality measure that takes into account the connectivity patterns within and across different layers and find that the dynamics of biased random walks on multiplex networks gives rise to a centrality ranking that is different from univariate centrality measures. Finally, we study the statistical mechanics of multilayered spatial networks and demonstrate the emergence of significant link overlap and improved navigability in
Quantum dynamics of fast chemical reactions
Light, J.C.
1993-12-01
The aims of this research are to explore, develop, and apply theoretical methods for the evaluation of the dynamics of gas phase collision processes, primarily chemical reactions. The primary theoretical tools developed for this work have been quantum scattering theory, both in time dependent and time independent forms. Over the past several years, the authors have developed and applied methods for the direct quantum evaluation of thermal rate constants, applying these to the evaluation of the hydrogen isotopic exchange reactions, applied wave packet propagation techniques to the dissociation of Rydberg H{sub 3}, incorporated optical potentials into the evaluation of thermal rate constants, evaluated the use of optical potentials for state-to-state reaction probability evaluations, and, most recently, have developed quantum approaches for electronically non-adiabatic reactions which may be applied to simplify calculations of reactive, but electronically adiabatic systems. Evaluation of the thermal rate constants and the dissociation of H{sub 3} were reported last year, and have now been published.
New methods for quantum mechanical reaction dynamics
Thompson, Ward Hugh
1996-12-01
Quantum mechanical methods are developed to describe the dynamics of bimolecular chemical reactions. We focus on developing approaches for directly calculating the desired quantity of interest. Methods for the calculation of single matrix elements of the scattering matrix (S-matrix) and initial state-selected reaction probabilities are presented. This is accomplished by the use of absorbing boundary conditions (ABC) to obtain a localized (L^{2}) representation of the outgoing wave scattering Green`s function. This approach enables the efficient calculation of only a single column of the S-matrix with a proportionate savings in effort over the calculation of the entire S-matrix. Applying this method to the calculation of the initial (or final) state-selected reaction probability, a more averaged quantity, requires even less effort than the state-to-state S-matrix elements. It is shown how the same representation of the Green`s function can be effectively applied to the calculation of negative ion photodetachment intensities. Photodetachment spectroscopy of the anion ABC^{-} can be a very useful method for obtaining detailed information about the neutral ABC potential energy surface, particularly if the ABC^{-} geometry is similar to the transition state of the neutral ABC. Total and arrangement-selected photodetachment spectra are calculated for the H_{3}O^{-} system, providing information about the potential energy surface for the OH + H_{2} reaction when compared with experimental results. Finally, we present methods for the direct calculation of the thermal rate constant from the flux-position and flux-flux correlation functions. The spirit of transition state theory is invoked by concentrating on the short time dynamics in the area around the transition state that determine reactivity. These methods are made efficient by evaluating the required quantum mechanical trace in the basis of eigenstates of the
Networks of Dynamic Allostery Regulate Enzyme Function
Holliday, Michael Joseph; Camilloni, Carlo; Armstrong, Geoffrey Stuart; Vendruscolo, Michele; Eisenmesser, Elan Zohar
2017-01-01
SUMMARY Many protein systems rely on coupled dynamic networks to allosterically regulate function. However, the broad conformational space sampled by non-coherently dynamic systems has precluded detailed analysis of their communication mechanisms. Here, we have developed a methodology that combines the high sensitivity afforded by nuclear magnetic resonance relaxation techniques and single-site multiple mutations, termed RASSMM, to identify two allosterically coupled dynamic networks within the non-coherently dynamic enzyme cyclophilin A. Using this methodology, we discovered two key hotspot residues, Val6 and Val29, that communicate through these networks, the mutation of which altered active-site dynamics, modulating enzymatic turnover of multiple substrates. Finally, we utilized molecular dynamics simulations to identify the mechanism by which one of these hotspots is coupled to the larger dynamic networks. These studies confirm a link between enzyme dynamics and the catalytic cycle of cyclophilin A and demonstrate how dynamic allostery may be engineered to tune enzyme function. PMID:28089447
Networks of Dynamic Allostery Regulate Enzyme Function.
Holliday, Michael Joseph; Camilloni, Carlo; Armstrong, Geoffrey Stuart; Vendruscolo, Michele; Eisenmesser, Elan Zohar
2017-02-07
Many protein systems rely on coupled dynamic networks to allosterically regulate function. However, the broad conformational space sampled by non-coherently dynamic systems has precluded detailed analysis of their communication mechanisms. Here, we have developed a methodology that combines the high sensitivity afforded by nuclear magnetic resonance relaxation techniques and single-site multiple mutations, termed RASSMM, to identify two allosterically coupled dynamic networks within the non-coherently dynamic enzyme cyclophilin A. Using this methodology, we discovered two key hotspot residues, Val6 and Val29, that communicate through these networks, the mutation of which altered active-site dynamics, modulating enzymatic turnover of multiple substrates. Finally, we utilized molecular dynamics simulations to identify the mechanism by which one of these hotspots is coupled to the larger dynamic networks. These studies confirm a link between enzyme dynamics and the catalytic cycle of cyclophilin A and demonstrate how dynamic allostery may be engineered to tune enzyme function.
Lattice based Kinetic Monte Carlo Simulations of a complex chemical reaction network
NASA Astrophysics Data System (ADS)
Danielson, Thomas; Savara, Aditya; Hin, Celine
Lattice Kinetic Monte Carlo (KMC) simulations offer a powerful alternative to using ordinary differential equations for the simulation of complex chemical reaction networks. Lattice KMC provides the ability to account for local spatial configurations of species in the reaction network, resulting in a more detailed description of the reaction pathway. In KMC simulations with a large number of reactions, the range of transition probabilities can span many orders of magnitude, creating subsets of processes that occur more frequently or more rarely. Consequently, processes that have a high probability of occurring may be selected repeatedly without actually progressing the system (i.e. the forward and reverse process for the same reaction). In order to avoid the repeated occurrence of fast frivolous processes, it is necessary to throttle the transition probabilities in such a way that avoids altering the overall selectivity. Likewise, as the reaction progresses, new frequently occurring species and reactions may be introduced, making a dynamic throttling algorithm a necessity. We present a dynamic steady-state detection scheme with the goal of accurately throttling rate constants in order to optimize the KMC run time without compromising the selectivity of the reaction network. The algorithm has been applied to a large catalytic chemical reaction network, specifically that of methanol oxidative dehydrogenation, as well as additional pathways on CeO2(111) resulting in formaldehyde, CO, methanol, CO2, H2 and H2O as gas products.
Matching metabolites and reactions in different metabolic networks.
Qi, Xinjian; Ozsoyoglu, Z Meral; Ozsoyoglu, Gultekin
2014-10-01
Comparing and identifying matching metabolites, reactions, and compartments in genome-scale reconstructed metabolic networks can be difficult due to inconsistent naming in different networks. In this paper, we propose metabolite and reaction matching techniques for matching metabolites and reactions in a given metabolic network to metabolites and reactions in another metabolic network. We employ a variety of techniques that include approximate string matching, similarity score functions and multi-step filtering techniques, all enhanced by a set of rules based on the underlying metabolic biochemistry. The proposed techniques are evaluated by an empirical study on four pairs of metabolic networks, and significant accuracy gains are achieved using the proposed metabolite and reaction identification techniques.
Ganscha, Stefan; Claassen, Manfred
2016-01-01
Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso. PMID:27923064
Reaction dynamics and photochemistry of divalent systems
Davis, H.F.
1992-05-01
Results are presented of molecular beam studies of bimolecular and unimolecular reactions of Ba. Chapter 1 discusses the reaction Ba + NO{sub 2}. Formation of the dominant BaO({sup 1}{Sigma}) + NO products resulted primarily from decay of long-lived Ba{sup +}NO{sub 2}{sup {minus}} collision complexes. Secondary mechanisms led to formation of forward scattered, internally excited BaO, and BaNO + O. D{sub o}(Ba-NO) = 65{plus_minus}20 kcal/mol. Reactions of ground state and electronically excited Ba with water and alcohols are examined in Chapter 2. Reaction of Ba({sup 1}S) + H{sup 2}O led to BaO + H{sub 2}, whereas excited state Ba({sup 1}D) + H{sub 2}O reacted to form BaOH + H. Collisions between Ba and CH{sub 3}OH led to BaOCH{sub 3} + H. Radical channels involve H-atom migration and are promoted by excitation of the incident Ba atom. In Chapter 3, reactions of Ba({sup 1}S) with ClO{sub 2}2 and O{sub 3} are discussed. Again, direct and complex mechanisms were observed. Formation of BaCl + O{sub 2} from decomposition of Ba{sup +}ClO{sub 2}{sup {minus}} accounted for 10% of total reaction crass section. Although Ba + O{sub 3} {yields} BaO + 0{sub 2} occurs primarily by direct reaction mechanisms, the secondary channel Ba + 0{sub 3} {yields} BaO{sub 2} + 0 involved decay of long lived Ba{sup +}O{sub 3}{sup {minus}} intermediates. D{sub o}(Ba{minus}O{sub 2}) = 120 {plus_minus}20 kcal/mol. Photodissociation dynamics of NO{sub 3} is explored in chapter 4. Visible excitation leads to formation of NO + 0{sub 2} and NO{sub 2} + O. Wavelength dependence of branching ratios is investigated. D{sub o}(O-NO{sub 2}) = 48.55 kcal/mole ;and calculate {Delta}H{sub f}(NO{sub 3}) = 17.75 kcal/mole (298K). Chapter 5 discusses the photodissociation of OClO in a molecular beam. Although ClO({sup 2}II) + O({sup 3}P) is dominant, Cl({sup 2}P) + O{sub 2} also forms, with a max yield of 3.9{plus_minus}0.8% near 404nm.
Le, Hung M; Raff, Lionel M
2010-01-14
The classical reaction dynamics of a four-body, bimolecular reaction on a neural network (NN) potential-energy surface (PES) fitted to a database obtained solely from ab initio MP2/6-311G(d,p) calculations are reported. The present work represents the first reported application of ab initio NN methods to a four-body, bimolecular, gas-phase reaction where bond extensions reach 8.1 A for the BeH + H(2) --> BeH(2) + H reaction. A modified, iterative novelty sampling method is used to select data points based on classical trajectories computed on temporary NN surfaces. After seven iterations, the sampling process is found to converge after selecting 9604 configurations. Incorporation of symmetry increases this to 19 208 BeH(3) configurations. The analytic PES for the system is obtained from the ensemble average of a five-member (6-60-1) NN committee. The mean absolute error (MAE) for the committee is 0.0046 eV (0.44 kJ mol(-1)). The total energy range of the BeH(3) database is 147.0 kJ mol(-1). Therefore, this MAE represents a percent energy error of 0.30%. Since it is the gradient of the PES that constitutes the most important quantity in molecular dynamics simulations, the paper also reports mean absolute error for the gradient. This result is 0.026 eV A(-1) (2.51 kJ mol(-1) A(-1)). Since the gradient magnitudes span a range of 15.32 eV A(-1) over the configuration space tested, this mean absolute gradient error represents a percent error of 0.17%. The mean percent absolute relative gradient error is 4.67%. The classically computed reaction cross sections generally increase with total energy. They vary from 0.007 to 0.030 A(2) when H(2) is at ground state, and from 0.05 to 0.10 A(2) when H(2) is in the first excited state. Trajectory integration is very fast using the five-member NN PES. The average trajectory integration time is 1.07 s on a CPU with a clock speed of 2.4 GHz. Zero angular momentum collisions are also investigated and compared with previously reported
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.
Photochemical Reactions of Cyclohexanone: Mechanisms and Dynamics.
Shemesh, Dorit; Nizkorodov, Sergey A; Gerber, R Benny
2016-09-15
Photochemistry of carbonyl compounds is of major importance in atmospheric and organic chemistry. The photochemistry of cyclohexanone is studied here using on-the-fly molecular dynamics simulations on a semiempirical multireference configuration interaction potential-energy surface to predict the distribution of photoproducts and time scales for their formation. Rich photochemistry is predicted to occur on a picosecond time scale following the photoexcitation of cyclohexanone to the first singlet excited state. The main findings include: (1) Reaction channels found experimentally are confirmed by the theoretical simulations, and a new reaction channel is predicted. (2) The majority (87%) of the reactive trajectories start with a ring opening via C-Cα bond cleavage, supporting observations of previous studies. (3) Mechanistic details, time scales, and yields are predicted for all reaction channels. These benchmark results shed light on the photochemistry of isolated carbonyl compounds in the atmosphere and can be extended in the future to photochemistry of more complex atmospherically relevant carbonyl compounds in both gaseous and condensed-phase environments.
Dynamic information routing in complex networks
Kirst, Christoph; Timme, Marc; Battaglia, Demian
2016-01-01
Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257
Dynamic information routing in complex networks
NASA Astrophysics Data System (ADS)
Kirst, Christoph; Timme, Marc; Battaglia, Demian
2016-04-01
Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.
Community dynamics in social networks
NASA Astrophysics Data System (ADS)
Palla, Gergely; Barabási, Albert-László; Vicsek, Tamás
2007-06-01
We study the statistical properties of community dynamics in large social networks, where the evolving communities are obtained from subsequent snapshots of the modular structure. Such cohesive groups of people can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We find significant difference between the behaviour of smaller collaborative or friendship circles and larger communities, eg. institutions. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.
Stochastic averaging and sensitivity analysis for two scale reaction networks
NASA Astrophysics Data System (ADS)
Hashemi, Araz; Núñez, Marcel; Plecháč, Petr; Vlachos, Dionisios G.
2016-02-01
In the presence of multiscale dynamics in a reaction network, direct simulation methods become inefficient as they can only advance the system on the smallest scale. This work presents stochastic averaging techniques to accelerate computations for obtaining estimates of expected values and sensitivities with respect to the steady state distribution. A two-time-scale formulation is used to establish bounds on the bias induced by the averaging method. Further, this formulation provides a framework to create an accelerated "averaged" version of most single-scale sensitivity estimation methods. In particular, we propose the use of a centered ergodic likelihood ratio method for steady state estimation and show how one can adapt it to accelerated simulations of multiscale systems. Finally, we develop an adaptive "batch-means" stopping rule for determining when to terminate the micro-equilibration process.
Automated Discovery of Complex Reaction Networks: Reaction Topology, Thermochemistry and Kinetics
2015-07-08
characterization of chemical reaction networks in combustion, minimize human input, and avoid on-the -fly thermochemical estimation. Based on the...transition states of different types of chemical reactions . 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF... chemical reaction networks in combustion, minimize human input, and avoid on-the-fly thermochemical estimation. Based on the enumerated elementary
Graph fibrations and symmetries of network dynamics
NASA Astrophysics Data System (ADS)
Nijholt, Eddie; Rink, Bob; Sanders, Jan
2016-11-01
Dynamical systems with a network structure can display remarkable phenomena such as synchronisation and anomalous synchrony breaking. A methodology for classifying patterns of synchrony in networks was developed by Golubitsky and Stewart. They showed that the robustly synchronous dynamics of a network is determined by its quotient networks. This result was recently reformulated by DeVille and Lerman, who pointed out that the reduction from a network to a quotient is an example of a graph fibration. The current paper exploits this observation and demonstrates the importance of self-fibrations of network graphs. Self-fibrations give rise to symmetries in the dynamics of a network. We show that every network admits a lift with a semigroup or semigroupoid of self-fibrations. The resulting symmetries impact the global dynamics of the network and can therefore be used to explain and predict generic scenarios for synchrony breaking. Also, when the network has a trivial symmetry groupoid, then every robust synchrony in the lift is determined by symmetry. We finish this paper with a discussion of networks with interior symmetries and nonhomogeneous networks.
Model Of Neural Network With Creative Dynamics
NASA Technical Reports Server (NTRS)
Zak, Michail; Barhen, Jacob
1993-01-01
Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.
A system dynamics model for communications networks
NASA Astrophysics Data System (ADS)
Awcock, A. J.; King, T. E. G.
1985-09-01
An abstract model of a communications network in system dynamics terminology is developed as implementation of this model by a FORTRAN program package developed at RSRE is discussed. The result of this work is a high-level simulation package in which the performance of adaptive routing algorithms and other network controls may be assessed for a network of arbitrary topology.
Combining molecular dynamics with mesoscopic Green’s function reaction dynamics simulations
Vijaykumar, Adithya; Bolhuis, Peter G.; Rein ten Wolde, Pieter
2015-12-07
In many reaction-diffusion processes, ranging from biochemical networks, catalysis, to complex self-assembly, the spatial distribution of the reactants and the stochastic character of their interactions are crucial for the macroscopic behavior. The recently developed mesoscopic Green’s Function Reaction Dynamics (GFRD) method enables efficient simulation at the particle level provided the microscopic dynamics can be integrated out. Yet, many processes exhibit non-trivial microscopic dynamics that can qualitatively change the macroscopic behavior, calling for an atomistic, microscopic description. We propose a novel approach that combines GFRD for simulating the system at the mesoscopic scale where particles are far apart, with a microscopic technique such as Langevin dynamics or Molecular Dynamics (MD), for simulating the system at the microscopic scale where reactants are in close proximity. This scheme defines the regions where the particles are close together and simulated with high microscopic resolution and those where they are far apart and simulated with lower mesoscopic resolution, adaptively on the fly. The new multi-scale scheme, called MD-GFRD, is generic and can be used to efficiently simulate reaction-diffusion systems at the particle level.
Mélykúti, Bence; Hespanha, João P.; Khammash, Mustafa
2014-01-01
Many biochemical reaction networks are inherently multiscale in time and in the counts of participating molecular species. A standard technique to treat different time scales in the stochastic kinetics framework is averaging or quasi-steady-state analysis: it is assumed that the fast dynamics reaches its equilibrium (stationary) distribution on a time scale where the slowly varying molecular counts are unlikely to have changed. We derive analytic equilibrium distributions for various simple biochemical systems, such as enzymatic reactions and gene regulation models. These can be directly inserted into simulations of the slow time-scale dynamics. They also provide insight into the stimulus–response of these systems. An important model for which we derive the analytic equilibrium distribution is the binding of dimer transcription factors (TFs) that first have to form from monomers. This gene regulation mechanism is compared to the cases of the binding of simple monomer TFs to one gene or to multiple copies of a gene, and to the cases of the cooperative binding of two or multiple TFs to a gene. The results apply equally to ligands binding to enzyme molecules. PMID:24920118
Forced synchronization of autonomous dynamical Boolean networks
NASA Astrophysics Data System (ADS)
Rivera-Durón, R. R.; Campos-Cantón, E.; Campos-Cantón, I.; Gauthier, Daniel J.
2015-08-01
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Using Network Dynamical Influence to Drive Consensus
NASA Astrophysics Data System (ADS)
Punzo, Giuliano; Young, George F.; MacDonald, Malcolm; Leonard, Naomi E.
2016-05-01
Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.
Forced synchronization of autonomous dynamical Boolean networks.
Rivera-Durón, R R; Campos-Cantón, E; Campos-Cantón, I; Gauthier, Daniel J
2015-08-01
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Using Network Dynamical Influence to Drive Consensus
Punzo, Giuliano; Young, George F.; Macdonald, Malcolm; Leonard, Naomi E.
2016-01-01
Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks. PMID:27210291
Forced synchronization of autonomous dynamical Boolean networks
Rivera-Durón, R. R. Campos-Cantón, E.; Campos-Cantón, I.; Gauthier, Daniel J.
2015-08-15
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Self-organized criticality of a catalytic reaction network under flow.
Awazu, Akinori; Kaneko, Kunihiko
2009-07-01
Self-organized critical behavior in a catalytic reaction network system induced by smallness in the molecule number is reported. The system under a flow of chemicals is shown to undergo a transition from a stationary to an intermittent reaction phase when the flow rate is decreased. In the intermittent reaction phase, two temporal regimes with active and halted reactions alternate. The number frequency of reaction events at each active regime and its duration time are shown to obey a universal power law with the exponents 4/3 and 3/2, respectively, independently of the parameters and network structure. These power laws are explained by a one-dimensional random-walk representation of the number of catalytically active chemicals. Possible relevance of the result to reaction dynamics in artificial and biological cells is briefly discussed.
Dynamics and topology of species networks
NASA Astrophysics Data System (ADS)
Bastolla, Ugo; Laessig, Michael; Manrubia, Susanna C.; Valleriani, Angelo
We study communities formed by a large number of species, which are an example of dynamical networks in biology. Interactions between species, such as prey-predator relationships and mutual competition, define the links of these networks. They also govern the dynamics of their population sizes. This dynamics acts as a selection mechanism, which can lead to the extinction of species. Adaptive changes of the interactions or the generation of new species involve random mutations as well as selection. We show how this dynamics determines key topological characteristics of species networks. The results are in agreement with observations.
Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks
Nelson Butuk
2006-09-21
This is an annual technical report for the work done over the last year (period ending 9/30/2005) on the project titled ''Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks''. The aim of the project is to develop an efficient chemistry model for combustion simulations. The reduced chemistry model will be developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) will be used via a new network topology know as Non-linear Principal Components Analysis (NPCA). We report on the significant development made in developing a truly meshfree computational fluid dynamics (CFD) flow solver to be coupled to NPCA. First, the procedure of obtaining nearly analytic accurate first order derivatives using the complex step method (CSM) is extended to include computation of accurate meshfree second order derivatives via a theorem described in this report. Next, boosted generalized regression neural network (BGRNN), described in our previous report is combined with CSM and used to obtain complete solution of a hard to solve wave dominated sample second order partial differential equation (PDE): the cubic Schrodinger equation. The resulting algorithm is a significant improvement of the meshfree technique of smooth particle hydrodynamics method (SPH). It is suggested that the demonstrated meshfree technique be termed boosted smooth particle hydrodynamics method (BSPH). Some of the advantages of BSPH over other meshfree methods include; it is of higher order accuracy than SPH; compared to other meshfree methods, it is completely meshfree and does not require any background meshes; It does not involve any construction of shape function with their associated solution of possibly ill conditioned matrix equations; compared to some SPH techniques, no equation for the smoothing parameter is required; finally it is easy to program.
Charge transport network dynamics in molecular aggregates
Jackson, Nicholas E.; Chen, Lin X.; Ratner, Mark A.
2016-07-20
Due to the nonperiodic nature of charge transport in disordered systems, generating insight into static charge transport networks, as well as analyzing the network dynamics, can be challenging. Here, we apply time-dependent network analysis to scrutinize the charge transport networks of two representative molecular semiconductors: a rigid n-type molecule, perylenediimide, and a flexible p-type molecule, bBDT(TDPP)2. Simulations reveal the relevant timescale for local transfer integral decorrelation to be ~100 fs, which is shown to be faster than that of a crystalline morphology of the same molecule. Using a simple graph metric, global network changes are observed over timescales competitive with charge carrier lifetimes. These insights demonstrate that static charge transport networks are qualitatively inadequate, whereas average networks often overestimate network connectivity. Finally, a simple methodology for tracking dynamic charge transport properties is proposed.
Charge transport network dynamics in molecular aggregates
Jackson, Nicholas E.; Chen, Lin X.; Ratner, Mark A.
2016-01-01
Due to the nonperiodic nature of charge transport in disordered systems, generating insight into static charge transport networks, as well as analyzing the network dynamics, can be challenging. Here, we apply time-dependent network analysis to scrutinize the charge transport networks of two representative molecular semiconductors: a rigid n-type molecule, perylenediimide, and a flexible p-type molecule, bBDT(TDPP)2. Simulations reveal the relevant timescale for local transfer integral decorrelation to be ∼100 fs, which is shown to be faster than that of a crystalline morphology of the same molecule. Using a simple graph metric, global network changes are observed over timescales competitive with charge carrier lifetimes. These insights demonstrate that static charge transport networks are qualitatively inadequate, whereas average networks often overestimate network connectivity. Finally, a simple methodology for tracking dynamic charge transport properties is proposed. PMID:27439871
Using Visualizations to Explore Network Dynamics
Chu, Kar-Hai; Wipfli, Heather; Valente, Thomas W.
2014-01-01
Network analysis has become a popular tool to examine data from online social networks to politics to ecological systems. As more computing power has become available, new technology-driven methods and tools are being developed that can support larger and richer network data, including dynamic network analysis. This timely merger of abundant data and cutting edge techniques affords researchers the ability to better understand networks over time, accurately show how they evolve, find patterns of growth, or study models such as the diffusion of innovation. We combine traditional methods in social network analysis with new innovative visualizations and methods in dynamic network studies to explore an online tobacco-control community called GLOBALink, using almost twenty years of longitudinal data. We describe the methods used for the study, and perform an exploratory network study that links empirical results to real-world events. PMID:25285051
Financial networks with static and dynamic thresholds
NASA Astrophysics Data System (ADS)
Qiu, Tian; Zheng, Bo; Chen, Guang
2010-04-01
Based on the daily data of the American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross correlation of individual stock prices and leads to a stable topological structure in the dynamic evolution. Long-range time correlations are revealed for the average clustering coefficient, average degree and cross correlation of degrees. The dynamic network shows a two-peak behavior in the degree distribution.
Dynamic information routing in complex networks
NASA Astrophysics Data System (ADS)
Kirst, Christoph; Timme, Marc; Battaglia, Demian
2015-03-01
Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how information may be specifically communicated and dynamically routed in these systems is not well understood. Here we demonstrate that collective dynamical states systematically control patterns of information sharing and transfer in networks, as measured by delayed mutual information and transfer entropies between activities of a network's units. For oscillatory networks we analyze how individual unit properties, the connectivity structure and external inputs all provide means to flexibly control information routing. For multi-scale, modular architectures, we resolve communication patterns at all levels and show how local interventions within one sub-network may remotely control the non-local network-wide routing of information. This theory helps understanding information routing patterns across systems where collective dynamics co-occurs with a communication function.
Psychology and social networks: a dynamic network theory perspective.
Westaby, James D; Pfaff, Danielle L; Redding, Nicholas
2014-04-01
Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Quasispecies dynamics with network constraints.
Barbosa, Valmir C; Donangelo, Raul; Souza, Sergio R
2012-11-07
A quasispecies is a set of interrelated genotypes that have reached a stationary state while evolving according to the usual Darwinian principles of selection and mutation. Quasispecies studies invariably assume that it is possible for any genotype to mutate into any other, but recent finds indicate that this assumption is not necessarily true. Here we revisit the traditional quasispecies theory by adopting a network structure to constrain the occurrence of mutations. Such structure is governed by a random-graph model, whose single parameter (a probability p) controls both the graph's density and the dynamics of mutation. We contribute two further modifications to the theory, one to account for the fact that different loci in a genotype may be differently susceptible to the occurrence of mutations, the other to allow for a more plausible description of the transition from adaptation to degeneracy of the quasispecies as p is increased. We give analytical and simulation results for the usual case of binary genotypes, assuming the fitness landscape in which a genotype's fitness decays exponentially with its Hamming distance to the wild type. These results support the theory's assertions regarding the adaptation of the quasispecies to the fitness landscape and also its possible demise as a function of p.
Recent Advances in Quantum Dynamics of Bimolecular Reactions
NASA Astrophysics Data System (ADS)
Zhang, Dong H.; Guo, Hua
2016-05-01
In this review, we survey the latest advances in theoretical understanding of bimolecular reaction dynamics in the past decade. The remarkable recent progress in this field has been driven by more accurate and efficient ab initio electronic structure theory, effective potential-energy surface fitting techniques, and novel quantum scattering algorithms. Quantum mechanical characterization of bimolecular reactions continues to uncover interesting dynamical phenomena in atom-diatom reactions and beyond, reaching an unprecedented level of sophistication. In tandem with experimental explorations, these theoretical developments have greatly advanced our understanding of key issues in reaction dynamics, such as microscopic reaction mechanisms, mode specificity, product energy disposal, influence of reactive resonances, and nonadiabatic effects.
Stochastic Generator of Chemical Structure. 3. Reaction Network Generation
FAULON,JEAN-LOUP; SAULT,ALLEN G.
2000-07-15
A new method to generate chemical reaction network is proposed. The particularity of the method is that network generation and mechanism reduction are performed simultaneously using sampling techniques. Our method is tested for hydrocarbon thermal cracking. Results and theoretical arguments demonstrate that our method scales in polynomial time while other deterministic network generator scale in exponential time. This finding offers the possibility to investigate complex reacting systems such as those studied in petroleum refining and combustion.
Functional Motifs in Biochemical Reaction Networks
Tyson, John J.; Novák, Béla
2013-01-01
The signal-response characteristics of a living cell are determined by complex networks of interacting genes, proteins, and metabolites. Understanding how cells respond to specific challenges, how these responses are contravened in diseased cells, and how to intervene pharmacologically in the decision-making processes of cells requires an accurate theory of the information-processing capabilities of macromolecular regulatory networks. Adopting an engineer’s approach to control systems, we ask whether realistic cellular control networks can be decomposed into simple regulatory motifs that carry out specific functions in a cell. We show that such functional motifs exist and review the experimental evidence that they control cellular responses as expected. PMID:20055671
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.
Arampatzis, Georgios; Katsoulakis, Markos A; Pantazis, Yannis
2015-01-01
Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the
A probability generating function method for stochastic reaction networks
NASA Astrophysics Data System (ADS)
Kim, Pilwon; Lee, Chang Hyeong
2012-06-01
In this paper we present a probability generating function (PGF) approach for analyzing stochastic reaction networks. The master equation of the network can be converted to a partial differential equation for PGF. Using power series expansion of PGF and Padé approximation, we develop numerical schemes for finding probability distributions as well as first and second moments. We show numerical accuracy of the method by simulating chemical reaction examples such as a binding-unbinding reaction, an enzyme-substrate model, Goldbeter-Koshland ultrasensitive switch model, and G2/M transition model.
Equilibriumlike behavior in chemical reaction networks far from equilibrium.
Lubensky, David K
2010-06-01
In an equilibrium chemical reaction mixture, the number of molecules present obeys a Poisson distribution. We report that, surprisingly, the same is true of a large class of nonequilibrium reaction networks. In particular, we show that certain topological features imply a Poisson distribution, whatever the reaction rates. Such driven systems also obey an analog of the fluctuation-dissipation theorem. Our results shed light on the fundamental question of when equilibrium concepts might apply to nonequilibrium systems and may have applications to models of noise in biochemical networks.
Matsuura, Tomoaki; Tanimura, Naoki; Hosoda, Kazufumi; Yomo, Tetsuya; Shimizu, Yoshihiro
2017-01-01
To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions. PMID:28167777
Matsuura, Tomoaki; Tanimura, Naoki; Hosoda, Kazufumi; Yomo, Tetsuya; Shimizu, Yoshihiro
2017-02-21
To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions.
Symmetry in Critical Random Boolean Networks Dynamics
NASA Astrophysics Data System (ADS)
Bassler, Kevin E.; Hossein, Shabnam
2014-03-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used to both greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. Classes of functions occur at the same frequency. These classes are orbits of the controlling symmetry group. We find the nature of the symmetry that controls the dynamics of critical random Boolean networks by determining the frequency of output functions utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using symmetry to characterize complex network dynamics, and introduce a novel approach to the analysis of heterogeneous complex systems. This work was supported by the NSF through grants DMR-0908286 and DMR-1206839, and by the AFSOR and DARPA through grant FA9550-12-1-0405.
DNA reaction networks: Providing a panoramic view
NASA Astrophysics Data System (ADS)
Wang, Fei; Fan, Chunhai
2016-08-01
A quantitative understanding of the functional landscape of a biochemical circuit can reveal the design rules required to optimize the circuit. Now, a high-throughput droplet-based microfluidic platform has been developed which enables high-resolution mapping of bifurcation diagrams for two nonlinear DNA networks.
Uncertainty quantification for quantum chemical models of complex reaction networks.
Proppe, Jonny; Husch, Tamara; Simm, Gregor N; Reiher, Markus
2016-12-22
For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.
Restoration of rhythmicity in diffusively coupled dynamical networks.
Zou, Wei; Senthilkumar, D V; Nagao, Raphael; Kiss, István Z; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen
2015-07-15
Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks.
Krumholz, Elias W; Libourel, Igor G L
2015-07-31
Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.
Dynamical properties of transportation on complex networks
NASA Astrophysics Data System (ADS)
Shen, Bo; Gao, Zi-You
2008-02-01
We study the dynamical properties of transportation considering the topology structure of networks and congestion effects, based on a proposed simple model. We analyze the behavior of the model for finding out the relationship between the properties of transportation and the structure of network. Analysis and numerical results demonstrate that the transition from free flow to congested regime can be observed for both single link load and network load, but it is discontinuous for single link and continuous for network. We also find that networks with large average degree have small average link betweenness and are more tolerant to congestion, and networks with homogeneous structure can hold more vehicles in stationary state at the subcritical region. Furthermore, by allotting capacity with different mode to links, a manner of enhancing the performance of networks is introduced, which should be helpful in the design of traffic networks.
Optimal dynamic bandwidth allocation for complex networks
NASA Astrophysics Data System (ADS)
Jiang, Zhong-Yuan; Liang, Man-Gui; Li, Qian; Guo, Dong-Chao
2013-03-01
Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.
2008-02-01
investigate the simulation of a biomolecular reaction network with BNS, a simple model of a generic self-assembling catalytic ligation reaction in a...Amino Acid Pools Nucleotide Triphosphate Pools Nucleotide Monophosphate Pools Ligation Reaction 1551 517 7 RESULTS Simulation of exemplar...and reaction r8 is the catalytic ligation reaction . In figures 5(B) through 5(F), both the time-averaged event rate for a single simulation run
Algorithms for radio networks with dynamic topology
NASA Astrophysics Data System (ADS)
Shacham, Nachum; Ogier, Richard; Rutenburg, Vladislav V.; Garcia-Luna-Aceves, Jose
1991-08-01
The objective of this project was the development of advanced algorithms and protocols that efficiently use network resources to provide optimal or nearly optimal performance in future communication networks with highly dynamic topologies and subject to frequent link failures. As reflected by this report, we have achieved our objective and have significantly advanced the state-of-the-art in this area. The research topics of the papers summarized include the following: efficient distributed algorithms for computing shortest pairs of disjoint paths; minimum-expected-delay alternate routing algorithms for highly dynamic unreliable networks; algorithms for loop-free routing; multipoint communication by hierarchically encoded data; efficient algorithms for extracting the maximum information from event-driven topology updates; methods for the neural network solution of link scheduling and other difficult problems arising in communication networks; and methods for robust routing in networks subject to sophisticated attacks.
Chaos synchronization of general complex dynamical networks
NASA Astrophysics Data System (ADS)
Lü, Jinhu; Yu, Xinghuo; Chen, Guanrong
2004-03-01
Recently, it has been demonstrated that many large-scale complex dynamical networks display a collective synchronization motion. Here, we introduce a time-varying complex dynamical network model and further investigate its synchronization phenomenon. Based on this new complex network model, two network chaos synchronization theorems are proved. We show that the chaos synchronization of a time-varying complex network is determined by means of the inner coupled link matrix, the eigenvalues and the corresponding eigenvectors of the coupled configuration matrix, rather than the conventional eigenvalues of the coupled configuration matrix for a uniform network. Especially, we do not assume that the coupled configuration matrix is symmetric and its off-diagonal elements are nonnegative, which in a way generalizes the related results existing in the literature.
Can distributed delays perfectly stabilize dynamical networks?
NASA Astrophysics Data System (ADS)
Omi, Takahiro; Shinomoto, Shigeru
2008-04-01
Signal transmission delays tend to destabilize dynamical networks leading to oscillation, but their dispersion contributes oppositely toward stabilization. We analyze an integrodifferential equation that describes the collective dynamics of a neural network with distributed signal delays. With the Γ distributed delays less dispersed than exponential distribution, the system exhibits reentrant phenomena, in which the stability is once lost but then recovered as the mean delay is increased. With delays dispersed more highly than exponential, the system never destabilizes.
Network analysis of human heartbeat dynamics
NASA Astrophysics Data System (ADS)
Shao, Zhi-Gang
2010-02-01
We construct the complex networks of human heartbeat dynamics and investigate their statistical properties, using the visibility algorithm proposed by Lacasa and co-workers [Proc. Natl. Acad. Sci. U.S.A. 105, 4972 (2008)]. Our results show that the associated networks for the time series of heartbeat interval are always scale-free, high clustering, hierarchy, and assortative mixing. In particular, the assortative coefficient of associated networks could distinguish between healthy subjects and patients with congestive heart failure.
Amplitude dynamics favors synchronization in complex networks
Gambuzza, Lucia Valentina; Gómez-Gardeñes, Jesus; Frasca, Mattia
2016-01-01
In this paper we study phase synchronization in random complex networks of coupled periodic oscillators. In particular, we show that, when amplitude dynamics is not negligible, phase synchronization may be enhanced. To illustrate this, we compare the behavior of heterogeneous units with both amplitude and phase dynamics and pure (Kuramoto) phase oscillators. We find that in small network motifs the behavior crucially depends on the topology and on the node frequency distribution. Surprisingly, the microscopic structures for which the amplitude dynamics improves synchronization are those that are statistically more abundant in random complex networks. Thus, amplitude dynamics leads to a general lowering of the synchronization threshold in arbitrary random topologies. Finally, we show that this synchronization enhancement is generic of oscillators close to Hopf bifurcations. To this aim we consider coupled FitzHugh-Nagumo units modeling neuron dynamics. PMID:27108847
Amplitude dynamics favors synchronization in complex networks
NASA Astrophysics Data System (ADS)
Gambuzza, Lucia Valentina; Gómez-Gardeñes, Jesus; Frasca, Mattia
2016-04-01
In this paper we study phase synchronization in random complex networks of coupled periodic oscillators. In particular, we show that, when amplitude dynamics is not negligible, phase synchronization may be enhanced. To illustrate this, we compare the behavior of heterogeneous units with both amplitude and phase dynamics and pure (Kuramoto) phase oscillators. We find that in small network motifs the behavior crucially depends on the topology and on the node frequency distribution. Surprisingly, the microscopic structures for which the amplitude dynamics improves synchronization are those that are statistically more abundant in random complex networks. Thus, amplitude dynamics leads to a general lowering of the synchronization threshold in arbitrary random topologies. Finally, we show that this synchronization enhancement is generic of oscillators close to Hopf bifurcations. To this aim we consider coupled FitzHugh-Nagumo units modeling neuron dynamics.
Symmetry in critical random Boolean network dynamics.
Hossein, Shabnam; Reichl, Matthew D; Bassler, Kevin E
2014-04-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce an alternative approach to the analysis of heterogeneous complex systems.
Symmetry in critical random Boolean network dynamics
NASA Astrophysics Data System (ADS)
Hossein, Shabnam; Reichl, Matthew D.; Bassler, Kevin E.
2014-04-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce an alternative approach to the analysis of heterogeneous complex systems.
Semiclassical methods in chemical reaction dynamics
Keshavamurthy, Srihari
1994-12-01
Semiclassical approximations, simple as well as rigorous, are formulated in order to be able to describe gas phase chemical reactions in large systems. We formulate a simple but accurate semiclassical model for incorporating multidimensional tunneling in classical trajectory simulations. This model is based on the existence of locally conserved actions around the saddle point region on a multidimensional potential energy surface. Using classical perturbation theory and monitoring the imaginary action as a function of time along a classical trajectory we calculate state-specific unimolecular decay rates for a model two dimensional potential with coupling. Results are in good comparison with exact quantum results for the potential over a wide range of coupling constants. We propose a new semiclassical hybrid method to calculate state-to-state S-matrix elements for bimolecular reactive scattering. The accuracy of the Van Vleck-Gutzwiller propagator and the short time dynamics of the system make this method self-consistent and accurate. We also go beyond the stationary phase approximation by doing the resulting integrals exactly (numerically). As a result, classically forbidden probabilties are calculated with purely real time classical trajectories within this approach. Application to the one dimensional Eckart barrier demonstrates the accuracy of this approach. Successful application of the semiclassical hybrid approach to collinear reactive scattering is prevented by the phenomenon of chaotic scattering. The modified Filinov approach to evaluating the integrals is discussed, but application to collinear systems requires a more careful analysis. In three and higher dimensional scattering systems, chaotic scattering is suppressed and hence the accuracy and usefulness of the semiclassical method should be tested for such systems.
Theoretical approaches for the dynamics of complex biological systems from information of networks
MOCHIZUKI, Atsushi
2016-01-01
Modern biology has provided many examples of large networks describing the interactions between multiple species of bio-molecules. It is believed that the dynamics of molecular activities based on such networks are the origin of biological functions. On the other hand, we have a limited understanding for dynamics of molecular activity based on networks. To overcome this problem, we have developed two structural theories, by which the important aspects of the dynamical properties of the system are determined only from information on the network structure, without assuming other quantitative details. The first theory, named Linkage Logic, determines a subset of molecules in regulatory networks, by which any long-term dynamical behavior of the whole system can be identified/controlled. The second theory, named Structural Sensitivity Analysis, determines the sensitivity responses of the steady state of chemical reaction networks to perturbations of the reaction rate. The first and second theories investigate the dynamical properties of regulatory and reaction networks, respectively. The first theory targets the attractors of the regulatory network systems, whereas the second theory applies only to the steady states of the reaction network systems, but predicts their detailed behavior. To demonstrate the utility of our methods several biological network systems, and show they are practically useful to analyze behaviors of biological systems. PMID:27725468
Network Physiology: How Organ Systems Dynamically Interact.
Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch
2015-01-01
We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.
Predicting changes of reaction networks with partial kinetic information.
Niehren, Joachim; Versari, Cristian; John, Mathias; Coutte, François; Jacques, Philippe
2016-11-01
We wish to predict changes of reaction networks with partial kinetic information that lead to target changes of their steady states. The changes may be either increases or decreases of influxes, reaction knockouts, or multiple changes of these two kinds. Our prime applications are knockout prediction tasks for metabolic and regulation networks. In a first step, we propose a formal modeling language for reaction networks with partial kinetic information. The modeling language has a graphical syntax reminiscent to Petri nets. Each reaction in a model comes with a partial description of its kinetics, based on a similarity relation on kinetic functions that we introduce. Such partial descriptions are able to model the regulation of existing metabolic networks for which precise kinetic knowledge is usually not available. In a second step, we develop prediction algorithms that can be applied to any reaction network modeled in our language. These algorithms perform qualitative reasoning based on abstract interpretation, by which the kinetic unknowns are abstracted away. Given a reaction network, abstract interpretation produces a finite domain constraint in a novel class. We show how to solve these finite domain constraints with an existing finite domain constraint solver, and how to interpret the solution sets as predictions of multiple reaction knockouts that lead to a desired change of the steady states. We have implemented the prediction algorithm and integrated it into a prediction tool. This journal article extends the two conference papers John et al. (2013) and Niehren et al. (2015) while adding a new prediction algorithm for multiple gene knockouts. An application to single gene knockout prediction for surfactin overproduction was presented in Coutte et al. (2015). It illustrates the adequacy of the model-based predictions made by our algorithm in the wet lab.
Sharper Graph-Theoretical Conditions for the Stabilization of Complex Reaction Networks
Knight, Daniel; Shinar, Guy; Feinberg, Martin
2015-01-01
Across the landscape of all possible chemical reaction networks there is a surprising degree of stable behavior, despite what might be substantial complexity and nonlinearity in the governing differential equations. At the same time there are reaction networks, in particular those that arise in biology, for which richer behavior is exhibited. Thus, it is of interest to understand network-structural features whose presence enforces dull, stable behavior and whose absence permits the dynamical richness that might be necessary for life. We present conditions on a network’s Species-Reaction Graph that ensure a high degree of stable behavior, so long as the kinetic rate functions satisfy certain weak and natural constraints. These graph-theoretical conditions are considerably more incisive than those reported earlier. PMID:25600138
Gupta, Ankit; Briat, Corentin; Khammash, Mustafa
2014-06-01
Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed.
Khammash, Mustafa
2014-01-01
Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed. PMID:24968191
Dynamic optimization of metabolic networks coupled with gene expression.
Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander
2015-01-21
The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle.
Wickramasinghe, Mahesh; Kiss, István Z
2013-01-01
Dynamical processes in many engineered and living systems take place on complex networks of discrete dynamical units. We present laboratory experiments with a networked chemical system of nickel electrodissolution in which synchronization patterns are recorded in systems with smooth periodic, relaxation periodic, and chaotic oscillators organized in networks composed of up to twenty dynamical units and 140 connections. The reaction system formed domains of synchronization patterns that are strongly affected by the architecture of the network. Spatially organized partial synchronization could be observed either due to densely connected network nodes or through the 'chimera' symmetry breaking mechanism. Relaxation periodic and chaotic oscillators formed structures by dynamical differentiation. We have identified effects of network structure on pattern selection (through permutation symmetry and coupling directness) and on formation of hierarchical and 'fuzzy' clusters. With chaotic oscillators we provide experimental evidence that critical coupling strengths at which transition to identical synchronization occurs can be interpreted by experiments with a pair of oscillators and analysis of the eigenvalues of the Laplacian connectivity matrix. The experiments thus provide an insight into the extent of the impact of the architecture of a network on self-organized synchronization patterns.
Complexity, dynamic cellular network, and tumorigenesis.
Waliszewski, P
1997-01-01
A holistic approach to tumorigenesis is proposed. The main element of the model is the existence of dynamic cellular network. This network comprises a molecular and an energetistic structure of a cell connected through the multidirectional flow of information. The interactions within dynamic cellular network are complex, stochastic, nonlinear, and also involve quantum effects. From this non-reductionist perspective, neither tumorigenesis can be limited to the genetic aspect, nor the initial event must be of molecular nature, nor mutations and epigenetic factors are mutually exclusive, nor a link between cause and effect can be established. Due to complexity, an unstable stationary state of dynamic cellular network rather than a group of unrelated genes determines the phenotype of normal and transformed cells. This implies relativity of tumor suppressor genes and oncogenes. A bifurcation point is defined as an unstable state of dynamic cellular network leading to the other phenotype-stationary state. In particular, the bifurcation point may be determined by a change of expression of a single gene. Then, the gene is called bifurcation point gene. The unstable stationary state facilitates the chaotic dynamics. This may result in a fractal dimension of both normal and tumor tissues. The co-existence of chaotic dynamics and complexity is the essence of cellular processes and shapes differentiation, morphogenesis, and tumorigenesis. In consequence, tumorigenesis is a complex, unpredictable process driven by the interplay between self-organisation and selection.
Competing dynamic phases of active polymer networks
NASA Astrophysics Data System (ADS)
Freedman, Simon; Banerjee, Shiladitya; Dinner, Aaron R.
Recent experiments on in-vitro reconstituted assemblies of F-actin, myosin-II motors, and cross-linking proteins show that tuning local network properties can changes the fundamental biomechanical behavior of the system. For example, by varying cross-linker density and actin bundle rigidity, one can switch between contractile networks useful for reshaping cells, polarity sorted networks ideal for directed molecular transport, and frustrated networks with robust structural properties. To efficiently investigate the dynamic phases of actomyosin networks, we developed a coarse grained non-equilibrium molecular dynamics simulation of model semiflexible filaments, molecular motors, and cross-linkers with phenomenologically defined interactions. The simulation's accuracy was verified by benchmarking the mechanical properties of its individual components and collective behavior against experimental results at the molecular and network scales. By adjusting the model's parameters, we can reproduce the qualitative phases observed in experiment and predict the protein characteristics where phase crossovers could occur in collective network dynamics. Our model provides a framework for understanding cells' multiple uses of actomyosin networks and their applicability in materials research. Supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.
Origin and Structure of Dynamic Cooperative Networks
Wardil, Lucas; Hauert, Christoph
2014-01-01
Societies are built on social interactions among individuals. Cooperation represents the simplest form of a social interaction: one individual provides a benefit to another one at a cost to itself. Social networks represent a dynamical abstraction of social interactions in a society. The behaviour of an individual towards others and of others towards the individual shape the individual's neighbourhood and hence the local structure of the social network. Here we propose a simple theoretical framework to model dynamic social networks by focussing on each individual's actions instead of interactions between individuals. This eliminates the traditional dichotomy between the strategy of individuals and the structure of the population and easily complements empirical studies. As a consequence, altruists, egoists and fair types are naturally determined by the local social structures, while globally egalitarian networks or stratified structures arise. Cooperative interactions drive the emergence and shape the structure of social networks. PMID:25030202
Origin and Structure of Dynamic Cooperative Networks
NASA Astrophysics Data System (ADS)
Wardil, Lucas; Hauert, Christoph
2014-07-01
Societies are built on social interactions among individuals. Cooperation represents the simplest form of a social interaction: one individual provides a benefit to another one at a cost to itself. Social networks represent a dynamical abstraction of social interactions in a society. The behaviour of an individual towards others and of others towards the individual shape the individual's neighbourhood and hence the local structure of the social network. Here we propose a simple theoretical framework to model dynamic social networks by focussing on each individual's actions instead of interactions between individuals. This eliminates the traditional dichotomy between the strategy of individuals and the structure of the population and easily complements empirical studies. As a consequence, altruists, egoists and fair types are naturally determined by the local social structures, while globally egalitarian networks or stratified structures arise. Cooperative interactions drive the emergence and shape the structure of social networks.
Dynamic fracture toughnesses of reaction-bonded silicon nitride
NASA Technical Reports Server (NTRS)
Kobayashi, A. S.; Emery, A. F.; Liaw, B. M.
1983-01-01
The room-temperature dynamic fracture response of reaction-bonded silicon nitride is investigated using a hybrid experimental-numerical procedure. In this procedure, experimentally determined crack velocities are utilized to drive a dynamic finite-element code or dynamic finite-difference code in its generation mode in order to extract numerically the dynamic stress intensity factor of the fracturing specimen. Results show that the dynamic fracture toughness vs crack velocity relations of the two reaction-bonded silicon nitrides do not follow the general trend in those relations of brittle polymers and steel. A definite slow crack velocity during the initial phase of dynamic crack propagation is observed in reaction-bonded silicon nitride, which results in a nonunique dynamic fracture toughness vs crack velocity relation. In addition, it is found that a propagating crack will continue to propagate under a static stress intensity factor substantially lower than K(IC).
Memory dynamics in attractor networks.
Li, Guoqi; Ramanathan, Kiruthika; Ning, Ning; Shi, Luping; Wen, Changyun
2015-01-01
As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented to the network, and its states converge to one of stable equilibrium points. Consequently, the existence of the spurious points, that is, local maxima, saddle points, or other local minima which are undesired memory patterns, can be avoided. The simulation results show the effectiveness of the proposed method.
Fluctuations in interbank network dynamics
NASA Astrophysics Data System (ADS)
Cajueiro, Daniel O.; Tabak, Benjamin M.; Andrade, Roberto F. S.
2009-03-01
This work investigates the scaling properties of fluctuations in the flux of individual agents with respect to their average flux in an interbank network. The analyzed data provide information on daily values of fiasset , the credit provided by bank i in the interbank network, and filiab , the credit received by bank i from the other banks of the network. The investigation focuses on the scaling properties of the fluctuations in the raw data fiasset , filiab , and fR,iext(t)=fiasset-filiab , as well as on similar properties internal and external fluctuations fiint and fiext , which are derived according to a recently proposed methodology [M. Argollo de Menezes and A. L. Barabasi, Phys. Rev. Lett. 93, 068701 (2004)]. Finally, a “rolling sampling” approach is introduced in order to deal with the nonstationarity of the fluxes. The results suggest that exponents are time varying, hinting that the considered interbank network is changing with time.
Stochastic analysis of biochemical reaction networks with absolute concentration robustness
Anderson, David F.; Enciso, Germán A.; Johnston, Matthew D.
2014-01-01
It has recently been shown that structural conditions on the reaction network, rather than a ‘fine-tuning’ of system parameters, often suffice to impart ‘absolute concentration robustness’ (ACR) on a wide class of biologically relevant, deterministically modelled mass-action systems. We show here that fundamentally different conclusions about the long-term behaviour of such systems are reached if the systems are instead modelled with stochastic dynamics and a discrete state space. Specifically, we characterize a large class of models that exhibit convergence to a positive robust equilibrium in the deterministic setting, whereas trajectories of the corresponding stochastic models are necessarily absorbed by a set of states that reside on the boundary of the state space, i.e. the system undergoes an extinction event. If the time to extinction is large relative to the relevant timescales of the system, the process will appear to settle down to a stationary distribution long before the inevitable extinction will occur. This quasi-stationary distribution is considered for two systems taken from the literature, and results consistent with ACR are recovered by showing that the quasi-stationary distribution of the robust species approaches a Poisson distribution. PMID:24522780
Phase transitions in complex network dynamics
NASA Astrophysics Data System (ADS)
Squires, Shane
Two phase transitions in complex networks are analyzed. The first of these is a percolation transition, in which the network develops a macroscopic connected component as edges are added to it. Recent work has shown that if edges are added "competitively" to an undirected network, the onset of percolation is abrupt or "explosive." A new variant of explosive percolation is introduced here for directed networks, whose critical behavior is explored using numerical simulations and finite-size scaling theory. This process is also characterized by a very rapid percolation transition, but it is not as sudden as in undirected networks. The second phase transition considered here is the emergence of instability in Boolean networks, a class of dynamical systems that are widely used to model gene regulation. The dynamics, which are determined by the network topology and a set of update rules, may be either stable or unstable, meaning that small perturbations to the state of the network either die out or grow to become macroscopic. Here, this transition is analytically mapped onto a well-studied percolation problem, which can be used to predict the average steady-state distance between perturbed and unperturbed trajectories. This map applies to specific Boolean networks with few restrictions on network topology, but can only be applied to two commonly used types of update rules. Finally, a method is introduced for predicting the stability of Boolean networks with a much broader range of update rules. The network is assumed to have a given complex topology, subject only to a locally tree-like condition, and the update rules may be correlated with topological features of the network. While past work has addressed the separate effects of topology and update rules on stability, the present results are the first widely applicable approach to studying how these effects interact. Numerical simulations agree with the theory and show that such correlations between topology and update
Employing Deceptive Dynamic Network Topology Through Software-Defined Networking
2014-03-01
Government. IRB Protocol Number: N/A. 12a. DISTRIBUTION / AVAILABILITY STATEMENT Approved for public release; distribution is unlimited 12b...and the OpenFlow protocol , we constructed a custom-built SDN controller to listen for network probes and craft customized deceptive replies to those...expand- ing OpenFlow protocol show strong potential for constructing an enterprise-grade dynamic deceptive network topology solution to protect computer
Mathematics of small stochastic reaction networks: A boundary layer theory for eigenstate analysis
Mjolsness, Eric; Prasad, Upendra
2013-01-01
We study and analyze the stochastic dynamics of a reversible bimolecular reaction A + B ↔ C called the “trivalent reaction.” This reaction is of a fundamental nature and is part of many biochemical reaction networks. The stochastic dynamics is given by the stochastic master equation, which is difficult to solve except when the equilibrium state solution is desired. We present a novel way of finding the eigenstates of this system of difference-differential equations, using perturbation analysis of ordinary differential equations arising from approximation of the difference equations. The time evolution of the state probabilities can then be expressed in terms of the eigenvalues and the eigenvectors. PMID:23514469
Dynamic simulation of regulatory networks using SQUAD
Di Cara, Alessandro; Garg, Abhishek; De Micheli, Giovanni; Xenarios, Ioannis; Mendoza, Luis
2007-01-01
Background The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. Results We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. Conclusion The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or
Piecewise linear and Boolean models of chemical reaction networks
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-01-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions (xn/(Jn + xn)). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent n is large. However, while the case of small constant J appears in practice, it is not well understood. We provide a mathematical analysis of this limit, and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator. PMID:25412739
In situ exploration of large dynamic networks.
Hadlak, Steffen; Schulz, Hans-Jörg; Schumann, Heidrun
2011-12-01
The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.
Understanding Modularity in Molecular Networks Requires Dynamics
Alexander, Roger P.; Kim, Philip M.; Emonet, Thierry; Gerstein, Mark B.
2014-01-01
The era of genome sequencing has produced long lists of the molecular parts from which cellular machines are constructed. A fundamental goal in systems biology is to understand how cellular behavior emerges from the interaction in time and space of genetically encoded molecular parts, as well as non-genetically encoded small molecules. Networks provide a natural framework for the organization and quantitative representation of all the available data about molecular interactions. The structural and dynamic properties of molecular networks have been the subject of intense research. Despite major advances, bridging network structure to dynamics – and therefore to behavior – remains challenging. A key concept of modern engineering that recurs in the functional analysis of biological networks is modularity. Most approaches to molecular network analysis rely to some extent on the assumption that molecular networks are modular – that is, they are separable and can be studied to some degree in isolation. We describe recent advances in the analysis of modularity in biological networks, focusing on the increasing realization that a dynamic perspective is essential to grouping molecules into modules and determining their collective function. PMID:19638611
Dynamics of Bottlebrush Networks: A Computational Study
NASA Astrophysics Data System (ADS)
Dobrynin, Andrey; Cao, Zhen; Sheiko, Sergei
We study dynamics of deformation of bottlebrush networks using molecular dynamics simulations and theoretical calculations. Analysis of our simulation results show that the dynamics of bottlebrush network deformation can be described by a Rouse model for polydisperse networks with effective Rouse time of the bottlebrush network strand, τR =τ0Ns2 (Nsc + 1) where, Ns is the number-average degree of polymerization of the bottlebrush backbone strands between crosslinks, Nsc is the degree of polymerization of the side chains and τ0is a characteristic monomeric relaxation time. At time scales t smaller than the Rouse time, t <τR , the time dependent network shear modulus decays with time as G (t) ~ ρkB T(τ0 / t) 1 / 2 , where ρis the monomer number density. However, at the time scale t larger than the Rouse time of the bottlebrush strands between crosslinks, the network response is pure elastic with shear modulus G (t) =G0 , where G0 is the equilibrium shear modulus at small deformation. The stress evolution in the bottlebrush networks can be described by a universal function of t /τR . NSF DMR-1409710.
Neural Networks for the Prediction of Organic Chemistry Reactions
2016-01-01
Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, “learn” from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook. PMID:27800555
Neural Networks for the Prediction of Organic Chemistry Reactions.
Wei, Jennifer N; Duvenaud, David; Aspuru-Guzik, Alán
2016-10-26
Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.
Dynamics-based centrality for directed networks
NASA Astrophysics Data System (ADS)
Masuda, Naoki; Kori, Hiroshi
2010-11-01
Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.
The dynamics of transmission and the dynamics of networks.
Farine, Damien
2017-05-01
A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]. Springer, A., Kappeler, P.M. & Nunn, C.L. (2017) Dynamic vs. static social networks in models of parasite transmission: Predicting Cryptosporidium spread in wild lemurs. Journal of Animal Ecology, 86, 419-433. The spread of disease or information through networks can be affected by several factors. Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn () investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors
Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.
Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán
2014-03-11
While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.
Chemical and genomic evolution of enzyme-catalyzed reaction networks.
Kanehisa, Minoru
2013-09-02
There is a tendency that a unit of enzyme genes in an operon-like structure in the prokaryotic genome encodes enzymes that catalyze a series of consecutive reactions in a metabolic pathway. Our recent analysis shows that this and other genomic units correspond to chemical units reflecting chemical logic of organic reactions. From all known metabolic pathways in the KEGG database we identified chemical units, called reaction modules, as the conserved sequences of chemical structure transformation patterns of small molecules. The extracted patterns suggest co-evolution of genomic units and chemical units. While the core of the metabolic network may have evolved with mechanisms involving individual enzymes and reactions, its extension may have been driven by modular units of enzymes and reactions.
Self-Organized Stationary Patterns in Networks of Bistable Chemical Reactions.
Kouvaris, Nikos E; Sebek, Michael; Mikhailov, Alexander S; Kiss, István Z
2016-10-10
Experiments with networks of discrete reactive bistable electrochemical elements organized in regular and nonregular tree networks are presented to confirm an alternative to the Turing mechanism for the formation of self-organized stationary patterns. The results show that the pattern formation can be described by the identification of domains that can be activated individually or in combinations. The method also enabled the localization of chemical reactions to network substructures and the identification of critical sites whose activation results in complete activation of the system. Although the experiments were performed with a specific nickel electrodissolution system, they reproduced all the salient dynamic behavior of a general network model with a single nonlinearity parameter. Thus, the considered pattern-formation mechanism is very robust, and similar behavior can be expected in other natural or engineered networked systems that exhibit, at least locally, a treelike structure.
Molecular codes in biological and chemical reaction networks.
Görlich, Dennis; Dittrich, Peter
2013-01-01
Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.
Transportation dynamics on networks of mobile agents.
Yang, Han-Xin; Wang, Wen-Xu; Xie, Yan-Bo; Lai, Ying-Cheng; Wang, Bing-Hong
2011-01-01
Most existing works on transportation dynamics focus on networks of a fixed structure, but networks whose nodes are mobile have become widespread, such as cell-phone networks. We introduce a model to explore the basic physics of transportation on mobile networks. Of particular interest is the dependence of the throughput on the speed of agent movement and the communication range. Our computations reveal a hierarchical dependence for the former, while an algebraic power law is found between the throughput and the communication range with the exponent determined by the speed. We develop a physical theory based on the Fokker-Planck equation to explain these phenomena. Our findings provide insights into complex transportation dynamics arising commonly in natural and engineering systems.
Learning biological networks: from modules to dynamics.
Bonneau, Richard
2008-11-01
Learning regulatory networks from genomics data is an important problem with applications spanning all of biology and biomedicine. Functional genomics projects offer a cost-effective means of greatly expanding the completeness of our regulatory models, and for some prokaryotic organisms they offer a means of learning accurate models that incorporate the majority of the genome. There are, however, several reasons to believe that regulatory network inference is beyond our current reach, such as (i) the combinatorics of the problem, (ii) factors we can't (or don't often) collect genome-wide measurements for and (iii) dynamics that elude cost-effective experimental designs. Recent works have demonstrated the ability to reconstruct large fractions of prokaryotic regulatory networks from compendiums of genomics data; they have also demonstrated that these global regulatory models can be used to predict the dynamics of the transcriptome. We review an overall strategy for the reconstruction of global networks based on these results in microbial systems.
Transportation dynamics on networks of mobile agents
NASA Astrophysics Data System (ADS)
Yang, Han-Xin; Wang, Wen-Xu; Xie, Yan-Bo; Lai, Ying-Cheng; Wang, Bing-Hong
2011-01-01
Most existing works on transportation dynamics focus on networks of a fixed structure, but networks whose nodes are mobile have become widespread, such as cell-phone networks. We introduce a model to explore the basic physics of transportation on mobile networks. Of particular interest is the dependence of the throughput on the speed of agent movement and the communication range. Our computations reveal a hierarchical dependence for the former, while an algebraic power law is found between the throughput and the communication range with the exponent determined by the speed. We develop a physical theory based on the Fokker-Planck equation to explain these phenomena. Our findings provide insights into complex transportation dynamics arising commonly in natural and engineering systems.
Communication Dynamics in Finite Capacity Social Networks
NASA Astrophysics Data System (ADS)
Haerter, Jan O.; Jamtveit, Bjørn; Mathiesen, Joachim
2012-10-01
In communication networks, structure and dynamics are tightly coupled. The structure controls the flow of information and is itself shaped by the dynamical process of information exchanged between nodes. In order to reconcile structure and dynamics, a generic model, based on the local interaction between nodes, is considered for the communication in large social networks. In agreement with data from a large human organization, we show that the flow is non-Markovian and controlled by the temporal limitations of individuals. We confirm the versatility of our model by predicting simultaneously the degree-dependent node activity, the balance between information input and output of nodes, and the degree distribution. Finally, we quantify the limitations to network analysis when it is based on data sampled over a finite period of time.
Single-molecule analysis of chirality in a multicomponent reaction network
NASA Astrophysics Data System (ADS)
Steffensen, Mackay B.; Rotem, Dvir; Bayley, Hagan
2014-07-01
Single-molecule approaches to chemical reaction analysis can provide information that is not accessible by studying ensemble systems. Changes in the molecular structures of compounds tethered to the inner wall of a protein pore are known to affect the current carried through the pore by aqueous ions under a fixed applied potential. Here, we use this approach to study the substitution reactions of arsenic(III) compounds with thiols, stretching the limits of the protein pore technology to track the interconversion of seven reaction components in a network that comprises interconnected Walden cycles. Single-molecule pathway analysis of ‘allowed’ and ‘forbidden’ reactions reveals that sulfur-sulfur substitution occurs with stereochemical inversion at the arsenic centre. Hence, we demonstrate that the nanoreactor approach can be a valuable technique for the analysis of dynamic reaction systems of relevance to biology.
Failure and recovery in dynamical networks
Böttcher, L.; Luković, M.; Nagler, J.; Havlin, S.; Herrmann, H. J.
2017-01-01
Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network’s components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component. We identify a metastable domain in the global network phase diagram spanned by the model’s control parameters where dramatic hysteresis effects and random switching between two coexisting states are observed. This dynamics depends on the characteristic link length of the embedded system. For the Euclidean lattice in particular, hysteresis and switching only occur in an extremely narrow region of the parameter space compared to random networks. We develop a unifying theory which links the dynamics of our model to contact processes. Our unifying framework may help to better understand controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in dynamical networks. PMID:28155876
Failure and recovery in dynamical networks
NASA Astrophysics Data System (ADS)
Böttcher, L.; Luković, M.; Nagler, J.; Havlin, S.; Herrmann, H. J.
2017-02-01
Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network’s components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component. We identify a metastable domain in the global network phase diagram spanned by the model’s control parameters where dramatic hysteresis effects and random switching between two coexisting states are observed. This dynamics depends on the characteristic link length of the embedded system. For the Euclidean lattice in particular, hysteresis and switching only occur in an extremely narrow region of the parameter space compared to random networks. We develop a unifying theory which links the dynamics of our model to contact processes. Our unifying framework may help to better understand controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in dynamical networks.
Targeting the dynamics of complex networks
Gutiérrez, Ricardo; Sendiña-Nadal, Irene; Zanin, Massimiliano; Papo, David; Boccaletti, Stefano
2012-01-01
We report on a generic procedure to steer (target) a network's dynamics towards a given, desired evolution. The problem is here tackled through a Master Stability Function approach, assessing the stability of the aimed dynamics, and through a selection of nodes to be targeted. We show that the degree of a node is a crucial element in this selection process, and that the targeting mechanism is most effective in heterogeneous scale-free architectures. This makes the proposed approach applicable to the large majority of natural and man-made networked systems. PMID:22563525
Passivity of Directed and Undirected Complex Dynamical Networks With Adaptive Coupling Weights.
Wang, Jin-Liang; Wu, Huai-Ning; Huang, Tingwen; Ren, Shun-Yan; Wu, Jigang
2016-05-05
A complex dynamical network consisting of $N$ identical neural networks with reaction-diffusion terms is considered in this paper. First, several passivity definitions for the systems with different dimensions of input and output are given. By utilizing some inequality techniques, several criteria are presented, ensuring the passivity of the complex dynamical network under the designed adaptive law. Then, we discuss the relationship between the synchronization and output strict passivity of the proposed network model. Furthermore, these results are extended to the case when the topological structure of the network is undirected. Finally, two examples with numerical simulations are provided to illustrate the correctness and effectiveness of the proposed results.
Roaming dynamics in radical addition-elimination reactions.
Joalland, Baptiste; Shi, Yuanyuan; Kamasah, Alexander; Suits, Arthur G; Mebel, Alexander M
2014-06-06
Radical addition-elimination reactions are a major pathway for transformation of unsaturated hydrocarbons. In the gas phase, these reactions involve formation of a transient strongly bound intermediate. However, the detailed mechanism and dynamics for these reactions remain unclear. Here we show, for reaction of chlorine atoms with butenes, that the Cl addition-HCl elimination pathway occurs from an abstraction-like Cl-H-C geometry rather than a conventional three-centre or four-centre transition state. Furthermore, access to this geometry is attained by roaming excursions of the Cl atom from the initially formed adduct. In effect, the alkene π cloud serves to capture the Cl atom and hold it, allowing many subsequent opportunities for the energized intermediate to find a suitable approach to the abstraction geometry. These bimolecular roaming reactions are closely related to the roaming radical dynamics recently discovered to play an important role in unimolecular reactions.
Neural network with formed dynamics of activity
Dunin-Barkovskii, V.L.; Osovets, N.B.
1995-03-01
The problem of developing a neural network with a given pattern of the state sequence is considered. A neural network structure and an algorithm, of forming its bond matrix which lead to an approximate but robust solution of the problem are proposed and discussed. Limiting characteristics of the serviceability of the proposed structure are studied. Various methods of visualizing dynamic processes in a neural network are compared. Possible applications of the results obtained for interpretation of neurophysiological data and in neuroinformatics systems are discussed.
Dynamics of overlapping structures in modular networks.
Almendral, J A; Leyva, I; Li, D; Sendiña-Nadal, I; Havlin, S; Boccaletti, S
2010-07-01
Modularity is a fundamental feature of real networks, being intimately bounded to their functionality, i.e., to their capability of performing parallel tasks in a coordinated way. Although the modular structure of real graphs has been intensively studied, very little is known on the interactions between functional modules of a graph. Here, we present a general method based on synchronization of networking oscillators, that is able to detect overlapping structures in multimodular environments. We furthermore report the full analytical and theoretical description on the relationship between the overlapping dynamics and the underlying network topology. The method is illustrated by means of a series of applications.
NASA Astrophysics Data System (ADS)
Stocco, Gabriel; Savell, Robert; Cybenko, George
2010-04-01
In many security environments, the textual content of communications may be unavailable. In these instances, it is often desirable to infer the status of the network and its component entities from patterns of communication flow. Conversational dynamics among entities in the network may provide insight into important aspects of the underlying social network such as the formational dynamics of group structures, the active state of these groups, individuals' roles within groups, and the likelihood of individual participation in conversations. To gain insight into the use of conversational dynamics to facilitate Dynamic Social Network Analysis, we explore the use of interevent timings to associate entities in the Twitter social networking and micro-blogging environment. Specifically, we use message timings to establish inter-nodal relationships among participants. In addition, we demonstrate a new visualization technique for tracking levels of coordination or synchronization within the community via measures of socio-temporal coherence of the participants.
On the Dynamics of Random Neuronal Networks
NASA Astrophysics Data System (ADS)
Robert, Philippe; Touboul, Jonathan
2016-11-01
We study the mean-field limit and stationary distributions of a pulse-coupled network modeling the dynamics of a large neuronal assemblies. Our model takes into account explicitly the intrinsic randomness of firing times, contrasting with the classical integrate-and-fire model. The ergodicity properties of the Markov process associated to finite networks are investigated. We derive the large network size limit of the distribution of the state of a neuron, and characterize their invariant distributions as well as their stability properties. We show that the system undergoes transitions as a function of the averaged connectivity parameter, and can support trivial states (where the network activity dies out, which is also the unique stationary state of finite networks in some cases) and self-sustained activity when connectivity level is sufficiently large, both being possibly stable.
A stronger necessary condition for the multistationarity of chemical reaction networks.
Soliman, Sylvain
2013-11-01
Biochemical reaction networks grow bigger and bigger, fed by the high-throughput data provided by biologists and bred in open repositories of models allowing merging and evolution. Nevertheless, since the available data is still very far from permitting the identification of the increasing number of kinetic parameters of such models, the necessity of structural analyses for describing the dynamics of chemical networks appears stronger every day. Using the structural information, notably from the stoichiometric matrix, of a biochemical reaction system, we state a more strict version of the famous Thomas' necessary condition for multistationarity. In particular, the obvious cases where Thomas' condition was trivially satisfied, mutual inhibition due to a multimolecular reaction and mutual activation due to a reversible reaction, can now easily be ruled out. This more strict condition shall not be seen as some version of Thomas' circuit functionality for the continuous case but rather as related and complementary to the whole domain of the structural analysis of (bio)chemical reaction systems, as pioneered by the chemical reaction network theory.
Transient dynamics increasing network vulnerability to cascading failures.
Simonsen, Ingve; Buzna, Lubos; Peters, Karsten; Bornholdt, Stefan; Helbing, Dirk
2008-05-30
We study cascading failures in networks using a dynamical flow model based on simple conservation and distribution laws. It is found that considering the flow dynamics may imply reduced network robustness compared to previous static overload failure models. This is due to the transient oscillations or overshooting in the loads, when the flow dynamics adjusts to the new (remaining) network structure. The robustness of networks showing cascading failures is generally given by a complex interplay between the network topology and flow dynamics.
Cytoskeletal Network Morphology Regulates Intracellular Transport Dynamics.
Ando, David; Korabel, Nickolay; Huang, Kerwyn Casey; Gopinathan, Ajay
2015-10-20
Intracellular transport is essential for maintaining proper cellular function in most eukaryotic cells, with perturbations in active transport resulting in several types of disease. Efficient delivery of critical cargos to specific locations is accomplished through a combination of passive diffusion and active transport by molecular motors that ballistically move along a network of cytoskeletal filaments. Although motor-based transport is known to be necessary to overcome cytoplasmic crowding and the limited range of diffusion within reasonable timescales, the topological features of the cytoskeletal network that regulate transport efficiency and robustness have not been established. Using a continuum diffusion model, we observed that the time required for cellular transport was minimized when the network was localized near the nucleus. In simulations that explicitly incorporated network spatial architectures, total filament mass was the primary driver of network transit times. However, filament traps that redirect cargo back to the nucleus caused large variations in network transport. Filament polarity was more important than filament orientation in reducing average transit times, and transport properties were optimized in networks with intermediate motor on and off rates. Our results provide important insights into the functional constraints on intracellular transport under which cells have evolved cytoskeletal structures, and have potential applications for enhancing reactions in biomimetic systems through rational transport network design.
NASA Astrophysics Data System (ADS)
Zañudo, Jorge G. T.; Albert, Réka
2013-06-01
Discrete dynamic models are a powerful tool for the understanding and modeling of large biological networks. Although a lot of progress has been made in developing analysis tools for these models, there is still a need to find approaches that can directly relate the network structure to its dynamics. Of special interest is identifying the stable patterns of activity, i.e., the attractors of the system. This is a problem for large networks, because the state space of the system increases exponentially with network size. In this work, we present a novel network reduction approach that is based on finding network motifs that stabilize in a fixed state. Notably, we use a topological criterion to identify these motifs. Specifically, we find certain types of strongly connected components in a suitably expanded representation of the network. To test our method, we apply it to a dynamic network model for a type of cytotoxic T cell cancer and to an ensemble of random Boolean networks of size up to 200. Our results show that our method goes beyond reducing the network and in most cases can actually predict the dynamical repertoire of the nodes (fixed states or oscillations) in the attractors of the system.
Structural and dynamical properties of complex networks
NASA Astrophysics Data System (ADS)
Ghoshal, Gourab
Recent years have witnessed a substantial amount of interest within the physics community in the properties of networks. Techniques from statistical physics coupled with the widespread availability of computing resources have facilitated studies ranging from large scale empirical analysis of the worldwide web, social networks, biological systems, to the development of theoretical models and tools to explore the various properties of these systems. Following these developments, in this dissertation, we present and solve for a diverse set of new problems, investigating the structural and dynamical properties of both model and real world networks. We start by defining a new metric to measure the stability of network structure to disruptions, and then using a combination of theory and simulation study its properties in detail on artificially generated networks; we then compare our results to a selection of networks from the real world and find good agreement in most cases. In the following chapter, we propose a mathematical model that mimics the structure of popular file-sharing websites such as Flickr and CiteULike and demonstrate that many of its properties can solved exactly in the limit of large network size. The remaining part of the dissertation primarily focuses on the dynamical properties of networks. We first formulate a model of a network that evolves under the addition and deletion of vertices and edges, and solve for the equilibrium degree distribution for a variety of cases of interest. We then consider networks whose structure can be manipulated by adjusting the rules by which vertices enter and leave the network. We focus in particular on degree distributions and show that, with some mild constraints, it is possible by a suitable choice of rules to arrange for the network to have any degree distribution we desire. In addition we define a simple local algorithm by which appropriate rules can be implemented in practice. Finally, we conclude our
On a theory of stability for nonlinear stochastic chemical reaction networks
Smadbeck, Patrick; Kaznessis, Yiannis N.
2015-05-14
We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms.
On a theory of stability for nonlinear stochastic chemical reaction networks
Smadbeck, Patrick; Kaznessis, Yiannis N.
2015-01-01
We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms. PMID:25978877
Fundamental structures of dynamic social networks.
Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune
2016-09-06
Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.
Fundamental structures of dynamic social networks
Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune
2016-01-01
Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision. PMID:27555584
Nuclear Reactions and Stellar Evolution: Unified Dynamics
Bauer, W.; Strother, T.
2007-10-26
Motivated by the success of kinetic theory in the description of observables in intermediate and high energy heavy ion collisions, we use kinetic theory to model the dynamics of collapsing iron cores in type II supernova explosions. The algorithms employed to model the collapse, some preliminary results and predictions, and the future of the code are discussed.
On correlated reaction sets and coupled reaction sets in metabolic networks.
Marashi, Sayed-Amir; Hosseini, Zhaleh
2015-08-01
Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.
Spreading dynamics in complex networks
NASA Astrophysics Data System (ADS)
Pei, Sen; Makse, Hernán A.
2013-12-01
Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.
Chemical reaction network approaches to Biochemical Systems Theory.
Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R
2015-11-01
This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed.
Dynamic Origin of the Stereoselectivity of a Nucleophilic Substitution Reaction
Bogle, Xavier S.; Singleton, Daniel A.
2012-01-01
A nucleophilic substitution on a dichlorovinyl ketone was studied experimentally and computationally. A mixture of products is observed experimentally, but a conventional computational analysis does not account for the formation of the minor stereoisomer. Instead, the product mixture is predicted accurately from a dynamic trajectory study on a bifurcating energy surface. The dynamic origin of the stereoselectivity of the reaction is discussed. PMID:22540965
Solvable non-Markovian dynamic network
NASA Astrophysics Data System (ADS)
Georgiou, Nicos; Kiss, Istvan Z.; Scalas, Enrico
2015-10-01
Non-Markovian processes are widespread in natural and human-made systems, yet explicit modeling and analysis of such systems is underdeveloped. We consider a non-Markovian dynamic network with random link activation and deletion (RLAD) and heavy-tailed Mittag-Leffler distribution for the interevent times. We derive an analytically and computationally tractable system of Kolmogorov-like forward equations utilizing the Caputo derivative for the probability of having a given number of active links in the network and solve them. Simulations for the RLAD are also studied for power-law interevent times and we show excellent agreement with the Mittag-Leffler model. This agreement holds even when the RLAD network dynamics is coupled with the susceptible-infected-susceptible spreading dynamics. Thus, the analytically solvable Mittag-Leffler model provides an excellent approximation to the case when the network dynamics is characterized by power-law-distributed interevent times. We further discuss possible generalizations of our result.
Dynamical networks with topological self-organization
NASA Technical Reports Server (NTRS)
Zak, M.
2001-01-01
Coupled evolution of state and topology of dynamical networks is introduced. Due to the well organized tensor structure, the governing equations are presented in a canonical form, and required attractors as well as their basins can be easily implanted and controlled.
Reaction Event Counting Statistics of Biopolymer Reaction Systems with Dynamic Heterogeneity.
Lim, Yu Rim; Park, Seong Jun; Park, Bo Jung; Cao, Jianshu; Silbey, Robert J; Sung, Jaeyoung
2012-04-10
We investigate the reaction event counting statistics (RECS) of an elementary biopolymer reaction in which the rate coefficient is dependent on states of the biopolymer and the surrounding environment and discover a universal kinetic phase transition in the RECS of the reaction system with dynamic heterogeneity. From an exact analysis for a general model of elementary biopolymer reactions, we find that the variance in the number of reaction events is dependent on the square of the mean number of the reaction events when the size of measurement time is small on the relaxation time scale of rate coefficient fluctuations, which does not conform to renewal statistics. On the other hand, when the size of the measurement time interval is much greater than the relaxation time of rate coefficient fluctuations, the variance becomes linearly proportional to the mean reaction number in accordance with renewal statistics. Gillespie's stochastic simulation method is generalized for the reaction system with a rate coefficient fluctuation. The simulation results confirm the correctness of the analytic results for the time dependent mean and variance of the reaction event number distribution. On the basis of the obtained results, we propose a method of quantitative analysis for the reaction event counting statistics of reaction systems with rate coefficient fluctuations, which enables one to extract information about the magnitude and the relaxation times of the fluctuating reaction rate coefficient, without a bias that can be introduced by assuming a particular kinetic model of conformational dynamics and the conformation dependent reactivity. An exact relationship is established between a higher moment of the reaction event number distribution and the multitime correlation of the reaction rate for the reaction system with a nonequilibrium initial state distribution as well as for the system with the equilibrium initial state distribution.
Power Aware Dynamic Provisioning of HPC Networks
Groves, Taylor; Grant, Ryan
2015-10-01
Future exascale systems are under increased pressure to find power savings. The network, while it consumes a considerable amount of power is often left out of the picture when discussing total system power. Even when network power is being considered, the references are frequently a decade or older and rely on models that lack validation on modern inter- connects. In this work we explore how dynamic mechanisms of an Infiniband network save power and at what granularity we can engage these features. We explore this within the context of the host controller adapter (HCA) on the node and for the fabric, i.e. switches, using three different mechanisms of dynamic link width, frequency and disabling of links for QLogic and Mellanox systems. Our results show that while there is some potential for modest power savings, real world systems need to improved responsiveness to adjustments in order to fully leverage these savings. This page intentionally left blank.
Shape, connectedness and dynamics in neuronal networks.
Comin, Cesar Henrique; da Fontoura Costa, Luciano
2013-11-15
The morphology of neurons is directly related to several aspects of the nervous system, including its connectedness, health, development, evolution, dynamics and, ultimately, behavior. Such interplays of the neuronal morphology can be understood within the more general shape-function paradigm. The current article reviews, in an introductory way, some key issues regarding the role of neuronal morphology in the nervous system, with emphasis on works developed in the authors' group. The following topics are addressed: (a) characterization of neuronal shape; (b) stochastic synthesis of neurons and neuronal systems; (c) characterization of the connectivity of neuronal networks by using complex networks concepts; and (d) investigations of influences of neuronal shape on network dynamics. The presented concepts and methods are useful also for several other multiple object systems, such as protein-protein interaction, tissues, aggregates and polymers.
Aggregate Dynamics in AN Evolutionary Network Model
NASA Astrophysics Data System (ADS)
Seufert, Adrian M.; Schweitzer, Frank
We analyze a model of interacting agents (e.g. prebiotic chemical species) which are represented by nodes of a network, whereas their interactions are mapped onto directed links between these nodes. On a fast time scale, each agent follows an eigendynamics based on catalytic support from other nodes, whereas on a much slower time scale the network evolves through selection and mutation of its nodes-agent. In the first part of the paper, we explain the dynamics of the model by means of characteristic snapshots of the network evolution and confirm earlier findings on crashes and recoveries in the network structure. In the second part, we focus on the aggregate behavior of the network dynamics. We show that the disruptions in the network structure are smoothed out, so that the average evolution can be described by a growth regime followed by a saturation regime, without an initial random regime. For the saturation regime, we obtain a logarithmic scaling between the average connectivity per node
A multi-time-scale analysis of chemical reaction networks: II. Stochastic systems.
Kan, Xingye; Lee, Chang Hyeong; Othmer, Hans G
2016-11-01
We consider stochastic descriptions of chemical reaction networks in which there are both fast and slow reactions, and for which the time scales are widely separated. We develop a computational algorithm that produces the generator of the full chemical master equation for arbitrary systems, and show how to obtain a reduced equation that governs the evolution on the slow time scale. This is done by applying a state space decomposition to the full equation that leads to the reduced dynamics in terms of certain projections and the invariant distributions of the fast system. The rates or propensities of the reduced system are shown to be the rates of the slow reactions conditioned on the expectations of fast steps. We also show that the generator of the reduced system is a Markov generator, and we present an efficient stochastic simulation algorithm for the slow time scale dynamics. We illustrate the numerical accuracy of the approximation by simulating several examples. Graph-theoretic techniques are used throughout to describe the structure of the reaction network and the state-space transitions accessible under the dynamics.
Transverse flow reactor studies of the dynamics of radical reactions
Macdonald, R.G.
1993-12-01
Radical reactions are in important in combustion chemistry; however, little state-specific information is available for these reactions. A new apparatus has been constructed to measure the dynamics of radical reactions. The unique feature of this apparatus is a transverse flow reactor in which an atom or radical of known concentration will be produced by pulsed laser photolysis of an appropriate precursor molecule. The time dependence of individual quantum states or products and/or reactants will be followed by rapid infrared laser absorption spectroscopy. The reaction H + O{sub 2} {yields} OH + O will be studied.
A dynamical systems view of network centrality
Grindrod, Peter; Higham, Desmond J.
2014-01-01
To gain insights about dynamic networks, the dominant paradigm is to study discrete snapshots, or timeslices, as the interactions evolve. Here, we develop and test a new mathematical framework where network evolution is handled over continuous time, giving an elegant dynamical systems representation for the important concept of node centrality. The resulting system allows us to track the relative influence of each individual. This new setting is natural in many digital applications, offering both conceptual and computational advantages. The novel differential equations approach is convenient for modelling and analysis of network evolution and gives rise to an interesting application of the matrix logarithm function. From a computational perspective, it avoids the awkward up-front compromises between accuracy, efficiency and redundancy required in the prevalent discrete-time setting. Instead, we can rely on state-of-the-art ODE software, where discretization takes place adaptively in response to the prevailing system dynamics. The new centrality system generalizes the widely used Katz measure, and allows us to identify and track, at any resolution, the most influential nodes in terms of broadcasting and receiving information through time-dependent links. In addition to the classical static network notion of attenuation across edges, the new ODE also allows for attenuation over time, as information becomes stale. This allows ‘running measures’ to be computed, so that networks can be monitored in real time over arbitrarily long intervals. With regard to computational efficiency, we explain why it is cheaper to track good receivers of information than good broadcasters. An important consequence is that the overall broadcast activity in the network can also be monitored efficiently. We use two synthetic examples to validate the relevance of the new measures. We then illustrate the ideas on a large-scale voice call network, where key features are discovered that are
Water dynamics in rigid ionomer networks
NASA Astrophysics Data System (ADS)
Osti, N. C.; Etampawala, T. N.; Shrestha, U. M.; Aryal, D.; Tyagi, M.; Diallo, S. O.; Mamontov, E.; Cornelius, C. J.; Perahia, D.
2016-12-01
The dynamics of water within ionic polymer networks formed by sulfonated poly(phenylene) (SPP), as revealed by quasi-elastic neutron scattering (QENS), is presented. These polymers are distinguished from other ionic macromolecules by their rigidity and therefore in their network structure. QENS measurements as a function of temperature as the fraction of ionic groups and humidity were varied have shown that the polymer molecules are immobile while absorbed water molecules remain dynamic. The water molecules occupy multiple sites, either bound or loosely constrained, and bounce between the two. With increasing temperature and hydration levels, the system becomes more dynamic. Water molecules remain mobile even at subzero temperatures, illustrating the applicability of the SPP membrane for selective transport over a broad temperature range.
Complex Dynamics in Information Sharing Networks
NASA Astrophysics Data System (ADS)
Cronin, Bruce
This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.
Dynamics on modular networks with heterogeneous correlations
Melnik, Sergey; Porter, Mason A.; Mucha, Peter J.; Gleeson, James P.
2014-06-15
We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each pair of modules. We present an analytical approach that allows one to analyze several types of binary dynamics operating on such networks, and we illustrate our approach using bond percolation, site percolation, and the Watts threshold model. The new network ensemble generalizes existing models (e.g., the well-known configuration model and Lancichinetti-Fortunato-Radicchi networks) by allowing a heterogeneous distribution of degree-degree correlations across modules, which is important for the consideration of nonidentical interacting networks.
Petri Nets - A Mathematical Formalism to Analyze Chemical Reaction Networks.
Koch, Ina
2010-12-17
In this review we introduce and discuss Petri nets - a mathematical formalism to describe and analyze chemical reaction networks. Petri nets were developed to describe concurrency in general systems. We find most applications to technical and financial systems, but since about twenty years also in systems biology to model biochemical systems. This review aims to give a short informal introduction to the basic formalism illustrated by a chemical example, and to discuss possible applications to the analysis of chemical reaction networks, including cheminformatics. We give a short overview about qualitative as well as quantitative modeling Petri net techniques useful in systems biology, summarizing the state-of-the-art in that field and providing the main literature references. Finally, we discuss advantages and limitations of Petri nets and give an outlook to further development.
Nonparametric inference of network structure and dynamics
NASA Astrophysics Data System (ADS)
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong
2016-07-15
This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.
Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions
NASA Astrophysics Data System (ADS)
Omori, Toshiaki; Kuwatani, Tatsu; Okamoto, Atsushi; Hukushima, Koji
2016-09-01
It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagation method and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.
Dynamic functional network connectivity using distance correlation
NASA Astrophysics Data System (ADS)
Rudas, Jorge; Guaje, Javier; Demertzi, Athena; Heine, Lizette; Tshibanda, Luaba; Soddu, Andrea; Laureys, Steven; Gómez, Francisco
2015-01-01
Investigations about the intrinsic brain organization in resting-state are critical for the understanding of healthy, pathological and pharmacological cerebral states. Recent studies on fMRI suggest that resting state activity is organized on large scale networks of coordinated activity, in the so called, Resting State Networks (RSNs). The assessment of the interactions among these functional networks plays an important role for the understanding of different brain pathologies. Current methods to quantify these interactions commonly assume that the underlying coordination mechanisms are stationary and linear through the whole recording of the resting state phenomena. Nevertheless, recent evidence suggests that rather than stationary, these mechanisms may exhibit a rich set of time-varying repertoires. In addition, these approaches do not consider possible non-linear relationships maybe linked to feed-back communication mechanisms between RSNs. In this work, we introduce a novel approach for dynamical functional network connectivity for functional magnetic resonance imaging (fMRI) resting activity, which accounts for non-linear dynamic relationships between RSNs. The proposed method is based on a windowed distance correlations computed on resting state time-courses extracted at single subject level. We showed that this strategy is complementary to the current approaches for dynamic functional connectivity and will help to enhance the discrimination capacity of patients with disorder of consciousness.
Dynamic protoneural networks in plants
Debono, Marc-Williams
2013-01-01
Taking as a basis of discussion Kalanchoe’s spontaneous and evoked extracellular activities recorded at the whole plant level, we put the challenging questions: do these low-voltage variations, together with endocellular events, reflect integrative properties and complex behavior in plants? Does it reflect common perceptive systems in animal and plant species? Is the ability of plants to treat short-term variations and information transfer without nervous system relevant? Is a protoneural construction of the world by lower organisms possible? More generally, the aim of this paper is to reevaluate the probably underestimated role of plant surface potentials in the plant relation life, carefully comparing the biogenesis of both animal and plant organisms in the era of plant neurobiology. Knowing that surface potentials participate at least to morphogenesis, cell to cell coupling, long distance transmission and transduction of stimuli, some hypothesis are given indicating that plants have to be studied as environmental biosensors and non linear dynamic systems able to detect transitional states between perception and response to stimuli. This study is conducted in the frame of the “plasticity paradigm,” which gives a theoretical model of evolutionary processes and suggests some hypothesis about the nature of complexity, information and behavior. PMID:23603975
Dynamic motifs in socio-economic networks
NASA Astrophysics Data System (ADS)
Zhang, Xin; Shao, Shuai; Stanley, H. Eugene; Havlin, Shlomo
2014-12-01
Socio-economic networks are of central importance in economic life. We develop a method of identifying and studying motifs in socio-economic networks by focusing on “dynamic motifs,” i.e., evolutionary connection patterns that, because of “node acquaintances” in the network, occur much more frequently than random patterns. We examine two evolving bi-partite networks: i) the world-wide commercial ship chartering market and ii) the ship build-to-order market. We find similar dynamic motifs in both bipartite networks, even though they describe different economic activities. We also find that “influence” and “persistence” are strong factors in the interaction behavior of organizations. When two companies are doing business with the same customer, it is highly probable that another customer who currently only has business relationship with one of these two companies, will become customer of the second in the future. This is the effect of influence. Persistence means that companies with close business ties to customers tend to maintain their relationships over a long period of time.
Motion detection based on recurrent network dynamics
Joukes, Jeroen; Hartmann, Till S.; Krekelberg, Bart
2014-01-01
The detection of visual motion requires temporal delays to compare current with earlier visual input. Models of motion detection assume that these delays reside in separate classes of slow and fast thalamic cells, or slow and fast synaptic transmission. We used a data-driven modeling approach to generate a model that instead uses recurrent network dynamics with a single, fixed temporal integration window to implement the velocity computation. This model successfully reproduced the temporal response dynamics of a population of motion sensitive neurons in macaque middle temporal area (MT) and its constituent parts matched many of the properties found in the motion processing pathway (e.g., Gabor-like receptive fields (RFs), simple and complex cells, spatially asymmetric excitation and inhibition). Reverse correlation analysis revealed that a simplified network based on first and second order space-time correlations of the recurrent model behaved much like a feedforward motion energy (ME) model. The feedforward model, however, failed to capture the full speed tuning and direction selectivity properties based on higher than second order space-time correlations typically found in MT. These findings support the idea that recurrent network connectivity can create temporal delays to compute velocity. Moreover, the model explains why the motion detection system often behaves like a feedforward ME network, even though the anatomical evidence strongly suggests that this network should be dominated by recurrent feedback. PMID:25565992
Dynamics of deceptive interactions in social networks.
Barrio, Rafael A; Govezensky, Tzipe; Dunbar, Robin; Iñiguez, Gerardo; Kaski, Kimmo
2015-11-06
In this paper, we examine the role of lies in human social relations by implementing some salient characteristics of deceptive interactions into an opinion formation model, so as to describe the dynamical behaviour of a social network more realistically. In this model, we take into account such basic properties of social networks as the dynamics of the intensity of interactions, the influence of public opinion and the fact that in every human interaction it might be convenient to deceive or withhold information depending on the instantaneous situation of each individual in the network. We find that lies shape the topology of social networks, especially the formation of tightly linked, small communities with loose connections between them. We also find that agents with a larger proportion of deceptive interactions are the ones that connect communities of different opinion, and, in this sense, they have substantial centrality in the network. We then discuss the consequences of these results for the social behaviour of humans and predict the changes that could arise due to a varying tolerance for lies in society.
Modeling Insurgent Network Structure and Dynamics
NASA Astrophysics Data System (ADS)
Gabbay, Michael; Thirkill-Mackelprang, Ashley
2010-03-01
We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.
Dynamic network mechanisms of relational integration.
Parkin, Beth L; Hellyer, Peter J; Leech, Robert; Hampshire, Adam
2015-05-20
A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support "relational integration" (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional "domain-general" resources when processing more difficult problems in general as opposed to RI specifically. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain-general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.
Hybrid discrete/continuum algorithms for stochastic reaction networks
Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; ...
2014-10-22
Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker-Planck equation. The Fokker-Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components to avoid negative probability values. The numerical construction at the interface between the discretemore » and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. As a result, the performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.« less
Hybrid discrete/continuum algorithms for stochastic reaction networks
Safta, Cosmin Sargsyan, Khachik Debusschere, Bert Najm, Habib N.
2015-01-15
Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker–Planck equation. The Fokker–Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components. The numerical construction at the interface between the discrete and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. The performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.
Hybrid discrete/continuum algorithms for stochastic reaction networks
Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; Najm, Habib N.
2014-10-22
Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker-Planck equation. The Fokker-Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components to avoid negative probability values. The numerical construction at the interface between the discrete and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. As a result, the performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.
Traffic chaotic dynamics modeling and analysis of deterministic network
NASA Astrophysics Data System (ADS)
Wu, Weiqiang; Huang, Ning; Wu, Zhitao
2016-07-01
Network traffic is an important and direct acting factor of network reliability and performance. To understand the behaviors of network traffic, chaotic dynamics models were proposed and helped to analyze nondeterministic network a lot. The previous research thought that the chaotic dynamics behavior was caused by random factors, and the deterministic networks would not exhibit chaotic dynamics behavior because of lacking of random factors. In this paper, we first adopted chaos theory to analyze traffic data collected from a typical deterministic network testbed — avionics full duplex switched Ethernet (AFDX, a typical deterministic network) testbed, and found that the chaotic dynamics behavior also existed in deterministic network. Then in order to explore the chaos generating mechanism, we applied the mean field theory to construct the traffic dynamics equation (TDE) for deterministic network traffic modeling without any network random factors. Through studying the derived TDE, we proposed that chaotic dynamics was one of the nature properties of network traffic, and it also could be looked as the action effect of TDE control parameters. A network simulation was performed and the results verified that the network congestion resulted in the chaotic dynamics for a deterministic network, which was identical with expectation of TDE. Our research will be helpful to analyze the traffic complicated dynamics behavior for deterministic network and contribute to network reliability designing and analysis.
Personality traits and ego-network dynamics
Centellegher, Simone; López, Eduardo; Saramäki, Jari; Lepri, Bruno
2017-01-01
Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot of effort on a few high-intensity relationships while maintaining larger numbers of less close relationships. It has also been shown that there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others strongly focus on their closest relationships. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how personality traits of individuals affect this picture, using mobile phone calls records and survey data from the Mobile Territorial Lab (MTL) study. In particular, we look at the relationship between personality traits and the (i) persistence of social signatures, namely the similarity of the social signature shape of an individual measured in different time intervals; (ii) the turnover in egocentric networks, that is, differences in the set of alters present at two consecutive temporal intervals; and (iii) the rank dynamics defined as the variation of alter rankings in egocentric networks in consecutive intervals. We observe that some traits have effects on the stability of the social signatures as well as network turnover and rank dynamics. As an example, individuals who score highly in the Openness to Experience trait tend to have higher levels of network turnover and larger alter rank variations. On broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks. PMID:28253333
Personality traits and ego-network dynamics.
Centellegher, Simone; López, Eduardo; Saramäki, Jari; Lepri, Bruno
2017-01-01
Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot of effort on a few high-intensity relationships while maintaining larger numbers of less close relationships. It has also been shown that there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others strongly focus on their closest relationships. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how personality traits of individuals affect this picture, using mobile phone calls records and survey data from the Mobile Territorial Lab (MTL) study. In particular, we look at the relationship between personality traits and the (i) persistence of social signatures, namely the similarity of the social signature shape of an individual measured in different time intervals; (ii) the turnover in egocentric networks, that is, differences in the set of alters present at two consecutive temporal intervals; and (iii) the rank dynamics defined as the variation of alter rankings in egocentric networks in consecutive intervals. We observe that some traits have effects on the stability of the social signatures as well as network turnover and rank dynamics. As an example, individuals who score highly in the Openness to Experience trait tend to have higher levels of network turnover and larger alter rank variations. On broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks.
Dynamic Attractors and Basin Class Capacity in Binary Neural Networks
1994-12-21
The wide repertoire of attractors and basins of attraction that appear in dynamic neural networks not only serve as models of brain activity patterns...limitations of static neural networks by use of dynamic attractors and their basins. The results show that dynamic networks have a high capacity for
Dynamic Trust Management for Mobile Networks and Its Applications
ERIC Educational Resources Information Center
Bao, Fenye
2013-01-01
Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we "design" and "validate" a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management…
Single-molecule chemical reaction reveals molecular reaction kinetics and dynamics.
Zhang, Yuwei; Song, Ping; Fu, Qiang; Ruan, Mingbo; Xu, Weilin
2014-06-25
Understanding the microscopic elementary process of chemical reactions, especially in condensed phase, is highly desirable for improvement of efficiencies in industrial chemical processes. Here we show an approach to gaining new insights into elementary reactions in condensed phase by combining quantum chemical calculations with a single-molecule analysis. Elementary chemical reactions in liquid-phase, revealed from quantum chemical calculations, are studied by tracking the fluorescence of single dye molecules undergoing a reversible redox process. Statistical analyses of single-molecule trajectories reveal molecular reaction kinetics and dynamics of elementary reactions. The reactivity dynamic fluctuations of single molecules are evidenced and probably arise from either or both of the low-frequency approach of the molecule to the internal surface of the SiO2 nanosphere or the molecule diffusion-induced memory effect. This new approach could be applied to other chemical reactions in liquid phase to gain more insight into their molecular reaction kinetics and the dynamics of elementary steps.
Identification of simple reaction coordinates from complex dynamics
NASA Astrophysics Data System (ADS)
McGibbon, Robert T.; Husic, Brooke E.; Pande, Vijay S.
2017-01-01
Reaction coordinates are widely used throughout chemical physics to model and understand complex chemical transformations. We introduce a definition of the natural reaction coordinate, suitable for condensed phase and biomolecular systems, as a maximally predictive one-dimensional projection. We then show that this criterion is uniquely satisfied by a dominant eigenfunction of an integral operator associated with the ensemble dynamics. We present a new sparse estimator for these eigenfunctions which can search through a large candidate pool of structural order parameters and build simple, interpretable approximations that employ only a small number of these order parameters. Example applications with a small molecule's rotational dynamics and simulations of protein conformational change and folding show that this approach can filter through statistical noise to identify simple reaction coordinates from complex dynamics.
Organization of Excitable Dynamics in Hierarchical Biological Networks
Müller-Linow, Mark; Hilgetag, Claus C.; Hütt, Marc-Thorsten
2008-01-01
This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks. PMID:18818769
Study of the structure and dynamics of complex biological networks
NASA Astrophysics Data System (ADS)
Samal, Areejit
2008-12-01
In this thesis, we have studied the large scale structure and system level dynamics of certain biological networks using tools from graph theory, computational biology and dynamical systems. We study the structure and dynamics of large scale metabolic networks inside three organisms, Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus. We also study the dynamics of the large scale genetic network controlling E. coli metabolism. We have tried to explain the observed system level dynamical properties of these networks in terms of their underlying structure. Our studies of the system level dynamics of these large scale biological networks provide a different perspective on their functioning compared to that obtained from purely structural studies. Our study also leads to some new insights on features such as robustness, fragility and modularity of these large scale biological networks. We also shed light on how different networks inside the cell such as metabolic networks and genetic networks are interrelated to each other.
NASA Astrophysics Data System (ADS)
Chushak, Yaroslav; Foy, Brent; Frazier, John
2008-03-01
At the functional level, all biological processes in cells can be represented as a series of biochemical reactions that are stochastic in nature. We have developed a software package called Biomolecular Network Simulator (BNS) that uses a stochastic approach to model and simulate complex biomolecular reaction networks. Two simulation algorithms - the exact Gillespie stochastic simulation algorithm and the approximate adaptive tau-leaping algorithm - are implemented for generating Monte Carlo trajectories that describe the evolution of a system of biochemical reactions. The software uses a combination of MATLAB and C-coded functions and is parallelized with the Message Passing Interface (MPI) library to run on multiprocessor architectures. We will present a brief description of the Biomolecular Network Simulator software along with some examples.
Dynamics of photoinduced reactions at oxide surfaces
NASA Astrophysics Data System (ADS)
Al-Shamery, K.
1996-11-01
This report summarizes our work on UV-laser induced desorption of small molecules and atoms from transition metal oxides. The systems presented serve as examples for a simple photochemical reaction, the fission of the molecule surface bond. State resolved detection methods were used to record the final state distributions of the desorbing neutral molecules. Detailed results on the systems NO/NiO(1 1 1) and CO/Cr2O3(0 0 0 1) are presented. The experiments include investigations on stereodynamic aspects like the angular distributions of the desorbing molecules and, in the case of CO desorption, the rotational alignment with respect to the surface normal. Large desorption cross sections of (6±1) ṡ 10-17 cm2 for NO and (3.5±1) ṡ 10-17 cm2 for CO have been found for the desorption at 6.4 eV. The wavelength dependence indicates that the primary excitation step is substrate induced. The final state distributions show a high degree of translational, rotational and vibrational excitation and are clearly nonthermal of origin. The results are consistent with the formation of a negative ion intermediate state of the adsorbate. This observation is supported from a comparison to former results on NO/NiO(1 0 0) for which extensive ab initio calculations including electronically excited states exist. A spin state dependence of the vibrational excitation of NO could only be observed for NO/NiO(1 1 1) and is absent for NO/NiO(1 0 0). We attribute this observation to a spin state dependent coupling of the desorbing molecule to the surface in case the spin lattice orientation of the surface shows a preferential orientation. In the (1 1 1) plane the spin orientation is parallel within neighbour nickel ions while it is alternating in the (1 0 0) plane. For both systems studied the velocity component parallel to the surface is constant leading to a strong peaking along the surface normal for the fast molecules. The change from a preferred helicopter rotation (angular momentum
Evolutionary epistemology and dynamical virtual learning networks.
Giani, Umberto
2004-01-01
This paper is an attempt to define the main features of a new educational model aimed at satisfying the needs of a rapidly changing society. The evolutionary epistemology paradigm of culture diffusion in human groups could be the conceptual ground for the development of this model. Multidimensionality, multi-disciplinarity, complexity, connectivity, critical thinking, creative thinking, constructivism, flexible learning, contextual learning, are the dimensions that should characterize distance learning models aimed at increasing the epistemological variability of learning communities. Two multimedia educational software, Dynamic Knowledge Networks (DKN) and Dynamic Virtual Learning Networks (DVLN) are described. These two complementary tools instantiate these dimensions, and were tested in almost 150 online courses. Even if the examples are framed in the medical context, the analysis of the shortcomings of the traditional educational systems and the proposed solutions can be applied to the vast majority of the educational contexts.
Adaptive Networks Foundations: Modeling, Dynamics, and Applications
2013-02-13
22-Mar. 2, 2012. • Shaw, L.B., Long, Y., and Gross, T. Simultaneous spread of infection and information in adaptive networks. Casablanca ...International Workshop on Mathematical Biology, Casablanca , Morocco, Jun. 20-24, 2011. • Tunc, I. and Shaw, L.B. Dynamics of infection spreading in adaptive...Defense The number of undergraduates funded by your agreement who graduated during this period and will receive scholarships or fellowships for further
Hidden hysteresis – population dynamics can obscure gene network dynamics
2013-01-01
Background Positive feedback is a common motif in gene regulatory networks. It can be used in synthetic networks as an amplifier to increase the level of gene expression, as well as a nonlinear module to create bistable gene networks that display hysteresis in response to a given stimulus. Using a synthetic positive feedback-based tetracycline sensor in E. coli, we show that the population dynamics of a cell culture has a profound effect on the observed hysteretic response of a population of cells with this synthetic gene circuit. Results The amount of observable hysteresis in a cell culture harboring the gene circuit depended on the initial concentration of cells within the culture. The magnitude of the hysteresis observed was inversely related to the dilution procedure used to inoculate the subcultures; the higher the dilution of the cell culture, lower was the observed hysteresis of that culture at steady state. Although the behavior of the gene circuit in individual cells did not change significantly in the different subcultures, the proportion of cells exhibiting high levels of steady-state gene expression did change. Although the interrelated kinetics of gene expression and cell growth are unpredictable at first sight, we were able to resolve the surprising dilution-dependent hysteresis as a result of two interrelated phenomena - the stochastic switching between the ON and OFF phenotypes that led to the cumulative failure of the gene circuit over time, and the nonlinear, logistic growth of the cell in the batch culture. Conclusions These findings reinforce the fact that population dynamics cannot be ignored in analyzing the dynamics of gene networks. Indeed population dynamics may play a significant role in the manifestation of bistability and hysteresis, and is an important consideration when designing synthetic gene circuits intended for long-term application. PMID:23800122
Dynamic congestion control mechanisms for MPLS networks
NASA Astrophysics Data System (ADS)
Holness, Felicia; Phillips, Chris I.
2001-02-01
Considerable interest has arisen in congestion control through traffic engineering from the knowledge that although sensible provisioning of the network infrastructure is needed, together with sufficient underlying capacity, these are not sufficient to deliver the Quality of Service required for new applications. This is due to dynamic variations in load. In operational Internet Protocol (IP) networks, it has been difficult to incorporate effective traffic engineering due to the limited capabilities of the IP technology. In principle, Multiprotocol Label Switching (MPLS), which is a connection-oriented label swapping technology, offers new possibilities in addressing the limitations by allowing the operator to use sophisticated traffic control mechanisms. This paper presents a novel scheme to dynamically manage traffic flows through the network by re-balancing streams during periods of congestion. It proposes management-based algorithms that will allow label switched routers within the network to utilize mechanisms within MPLS to indicate when flows are starting to experience frame/packet loss and then to react accordingly. Based upon knowledge of the customer's Service Level Agreement, together with instantaneous flow information, the label edge routers can then instigate changes to the LSP route and circumvent congestion that would hitherto violate the customer contacts.
Failure dynamics of the global risk network.
Szymanski, Boleslaw K; Lin, Xin; Asztalos, Andrea; Sreenivasan, Sameet
2015-06-18
Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological, and include difficult to quantify risks, such as geo-political and social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.
Failure dynamics of the global risk network
NASA Astrophysics Data System (ADS)
Szymanski, Boleslaw K.; Lin, Xin; Asztalos, Andrea; Sreenivasan, Sameet
2015-06-01
Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological, and include difficult to quantify risks, such as geo-political and social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.
Failure dynamics of the global risk network
Szymanski, Boleslaw K.; Lin, Xin; Asztalos, Andrea; Sreenivasan, Sameet
2015-01-01
Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological, and include difficult to quantify risks, such as geo-political and social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network. PMID:26087020
Dynamics of synchrotron VUV-induced intracluster reactions
Grover, J.R.
1993-12-01
Photoionization mass spectrometry (PIMS) using the tunable vacuum ultraviolet radiation available at the National Synchrotron Light Source is being exploited to study photoionization-induced reactions in small van der Waals mixed complexes. The information gained includes the observation and classification of reaction paths, the measurement of onsets, and the determination of relative yields of competing reactions. Additional information is obtained by comparison of the properties of different reacting systems. Special attention is given to finding unexpected features, and most of the reactions investigated to date display such features. However, understanding these reactions demands dynamical information, in addition to what is provided by PIMS. Therefore the program has been expanded to include the measurement of kinetic energy release distributions.
Vehicle dynamic analysis using neuronal network algorithms
NASA Astrophysics Data System (ADS)
Oloeriu, Florin; Mocian, Oana
2014-06-01
Theoretical developments of certain engineering areas, the emergence of new investigation tools, which are better and more precise and their implementation on-board the everyday vehicles, all these represent main influence factors that impact the theoretical and experimental study of vehicle's dynamic behavior. Once the implementation of these new technologies onto the vehicle's construction had been achieved, it had led to more and more complex systems. Some of the most important, such as the electronic control of engine, transmission, suspension, steering, braking and traction had a positive impact onto the vehicle's dynamic behavior. The existence of CPU on-board vehicles allows data acquisition and storage and it leads to a more accurate and better experimental and theoretical study of vehicle dynamics. It uses the information offered directly by the already on-board built-in elements of electronic control systems. The technical literature that studies vehicle dynamics is entirely focused onto parametric analysis. This kind of approach adopts two simplifying assumptions. Functional parameters obey certain distribution laws, which are known in classical statistics theory. The second assumption states that the mathematical models are previously known and have coefficients that are not time-dependent. Both the mentioned assumptions are not confirmed in real situations: the functional parameters do not follow any known statistical repartition laws and the mathematical laws aren't previously known and contain families of parameters and are mostly time-dependent. The purpose of the paper is to present a more accurate analysis methodology that can be applied when studying vehicle's dynamic behavior. A method that provides the setting of non-parametrical mathematical models for vehicle's dynamic behavior is relying on neuronal networks. This method contains coefficients that are time-dependent. Neuronal networks are mostly used in various types' system controls, thus
Constraints on Reciprocal Flux Sensitivities in Biochemical Reaction Networks
Shinar, Guy; Mayo, Avi; Ji, Haixia; Feinberg, Martin
2011-01-01
We identify a connection between the structural features of mass-action networks and the robustness of their steady-state fluxes against rate constant variations. We find that in all positive steady states of so-called injective networks—networks that arise, for example, in metabolic and gene regulation contexts—there are certain firm bounds on the flux control coefficients. In particular, the control coefficient of the flux of a reaction, with respect to variation in its own rate constant, is delimited in a precise way. Moreover, for each pair of reactions, the flux of at least one of them must have a precisely delimited control coefficient with respect to variation in the rate constant of the other. The derived bounds can, however, be violated in noninjective networks, so for them a more pronounced lack of robustness could be exhibited. These results, which indicate a mechanism by which some degree of robustness is induced in the injective setting, also shed light on how robustness might evolve. PMID:21402019
Bootstrapping least-squares estimates in biochemical reaction networks.
Linder, Daniel F; Rempała, Grzegorz A
2015-01-01
The paper proposes new computational methods of computing confidence bounds for the least-squares estimates (LSEs) of rate constants in mass action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large-volume limit of a reaction network, to network's partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large-volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods.
Interdisciplinary applications of network dynamics: From microscopic to Macroscopic
NASA Astrophysics Data System (ADS)
Jeong, Hawoong
``Everything touches everything.'' We are living in a connected world, which has been modeled successfully by complex networks. Ever since, network science becomes new paradigm for understanding our connected yet complex world. After investigating network structure itself, our focus naturally moved to dynamics of/on the network because our connected world is not static but dynamic. In this presentation, we will briefly review the historical development of network science and show some applications of network dynamics ranging from microscopic (metabolic engineering, PNAS, 104 13638) to macroscopic scale (price of anarchy in transportation network, Phys.Rev.Lett. 101 128701). Supported by National Research Foundation of Korea through Grant No. 2011-0028908.
Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
Kaltenbacher, Barbara; Hasenauer, Jan
2017-01-01
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351
Dithioacetal Exchange: A New Reversible Reaction for Dynamic Combinatorial Chemistry.
Orrillo, A Gastón; Escalante, Andrea M; Furlan, Ricardo L E
2016-05-10
Reversibility of dithioacetal bond formation is reported under acidic mild conditions. Its utility for dynamic combinatorial chemistry was explored by combining it with orthogonal disulfide exchange. In such a setup, thiols are positioned at the intersection of both chemistries, constituting a connecting node between temporally separated networks.
NASA Astrophysics Data System (ADS)
Jablonski, Piotr; Poe, Gina R.; Zochowski, Michal
2007-01-01
The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.
NASA Astrophysics Data System (ADS)
Trinidad Pérez-Rivera, Danilo; Romani, Paul N.; Lopez-Encarnacion, Juan Manuel
2016-10-01
Titan's atmosphere is arguably the atmosphere of greatest interest that we have an abundance of data for from both ground based and spacecraft observations. As we have learned more about Titan's atmospheric composition, the presence of pre-biotic molecules in its atmosphere has generated more and more fascination about the photochemical process and pathways it its atmosphere. Our computational laboratory has been extensively working throughout the past year characterizing nitrile synthesis reactions, making significant progress on the energetics and dynamics of the reactions of .CN with the hydrocarbons acetylene (C2H2), propylene (CH3CCH), and benzene (C6H6), developing a clear picture of the mechanistic aspects through which these three reactions proceed. Specifically, first principles calculations of the reaction profiles and molecular dynamics studies for gas-phase reactions of .CN and C2H2, .CN and CH3CCH, and .CN and C6H6 have been carried out. A very accurate determination of potential energy surfaces of these reactions will allow us to compute the reaction rates which are indispensable for photochemical modeling of Titan's atmosphere.The work at University of Puerto Rico at Cayey was supported by Puerto Rico NASA EPSCoR IDEAS-ER program (2015-2016) and DTPR was sponsored by the Puerto Rico NASA Space Grant Consortium Fellowship. *E-mail: juan.lopez15@upr.edu
On lateral competition in dynamic neural networks
Bellyustin, N.S.
1995-02-01
Artificial neural networks connected homogeneously, which use retinal image processing methods, are considered. We point out that there are probably two different types of lateral inhibition for each neural element by the neighboring ones-due to the negative connection coefficients between elements and due to the decreasing neuron`s response to a too high input signal. The first case characterized by stable dynamics, which is given by the Lyapunov function, while in the second case, stability is absent and two-dimensional dynamic chaos occurs if the time step in the integration of model equations is large enough. The continuous neural medium approximation is used for analytical estimation in both cases. The result is the partition of the parameter space into domains with qualitatively different dynamic modes. Computer simulations confirm the estimates and show that joining two-dimensional chaos with symmetries provided by the initial and boundary conditions may produce patterns which are genuine pieces of art.
Zhang, Hao; Sheng, Yin; Zeng, Zhigang
2017-03-15
This paper investigates the synchronization issue of coupled reaction-diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results.
Reaction Dynamics and Spectroscopy of Hydrocarbons in Plasma
Braams, Bastiaan J.
2014-03-24
This grant supported research in theoretical and computational Chemical Physics that resulted in numerous publications on fitting ab initio potential energy surfaces and dipole moment surfaces of polyatomic molecules and cations. This work made use of novel fitting methods that ensures that these surfaces are invariant with respect to all permutations of like atoms. The surfaces were used in various dynamics calculations, ranging from quantum vibrational dynamics to(quasi)classical trajectory calculations of reaction dynamics. A number of these studies were done in collaboration with experimental groups where the theoretical analyses turned out to be essential to give a proper understanding of the experimental results.
Quantum Molecular Dynamics Simulations of Nanotube Tip Assisted Reactions
NASA Technical Reports Server (NTRS)
Menon, Madhu
1998-01-01
In this report we detail the development and application of an efficient quantum molecular dynamics computational algorithm and its application to the nanotube-tip assisted reactions on silicon and diamond surfaces. The calculations shed interesting insights into the microscopic picture of tip surface interactions.
Geometrical Models of the Phase Space Structures Governing Reaction Dynamics
2009-08-01
s.wiggins@bristol.ac.uk Abstract Hamiltonian dynamical systems possessing equilibria of saddle × centre × · · · × centre stability type display...definition of the phase space structures in the normal form coordinates . . . . . . . . 6 2.3 The foliation of the reaction region by Lagrangian ...McGehee representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4 Implications for Nonlinear Hamiltonian Vector Fields
Explicit Integration of Extremely Stiff Reaction Networks: Partial Equilibrium Methods
Guidry, Mike W; Billings, J. J.; Hix, William Raphael
2013-01-01
In two preceding papers [1,2] we have shown that, when reaction networks are well removed from equilibrium, explicit asymptotic and quasi-steady-state approximations can give algebraically stabilized integration schemes that rival standard implicit methods in accuracy and speed for extremely stiff systems. However, we also showed that these explicit methods remain accurate but are no longer competitive in speed as the network approaches equilibrium. In this paper we analyze this failure and show that it is associated with the presence of fast equilibration timescales that neither asymptotic nor quasi-steady-state approximations are able to remove efficiently from the numerical integration. Based on this understanding, we develop a partial equilibrium method to deal effectively with the new partial equilibrium methods, give an integration scheme that plausibly can deal with the stiffest networks, even in the approach to equilibrium, with accuracy and speed competitive with that of implicit methods. Thus we demonstrate that algebraically stabilized explicit methods may offer alternatives to implicit integration of even extremely stiff systems, and that these methods may permit integration of much larger networks than have been feasible previously in a variety of fields.
Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks
Nelson Butuk
2005-12-01
This is an annual technical report for the work done over the last year (period ending 9/30/2005) on the project titled ''Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks''. The aim of the project is to develop an efficient chemistry model for combustion simulations. The reduced chemistry model will be developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) will be used via a new network topology know as Non-linear Principal Components Analysis (NPCA). We report on the development of a novel procedure to speed up the training of NPCA. The same procedure termed L{sub 2}Boost can be used to increase the order of approximation of the Generalized Regression Neural Network (GRNN). It is pointed out that GRNN is a basic procedure for the emerging mesh free CFD. Also reported is an efficient simple approach of computing the derivatives of GRNN function approximation using complex variables or the Complex Step Method (CSM). The results presented demonstrate the significance of the methods developed and will be useful in many areas of applied science and engineering.
Coriolis coupling and nonadiabaticity in chemical reaction dynamics.
Wu, Emilia L
2010-12-01
The nonadiabatic quantum dynamics and Coriolis coupling effect in chemical reaction have been reviewed, with emphasis on recent progress in using the time-dependent wave packet approach to study the Coriolis coupling and nonadiabatic effects, which was done by K. L. Han and his group. Several typical chemical reactions, for example, H+D(2), F+H(2)/D(2)/HD, D(+)+H(2), O+H(2), and He+H(2)(+), have been discussed. One can find that there is a significant role of Coriolis coupling in reaction dynamics for the ion-molecule collisions of D(+)+H(2), Ne+H(2)(+), and He+H(2)(+) in both adiabatic and nonadiabatic context.
Dynamic Privacy Management in Pervasive Sensor Networks
NASA Astrophysics Data System (ADS)
Gong, Nan-Wei; Laibowitz, Mathew; Paradiso, Joseph A.
This paper describes the design and implementation of a dynamic privacy management system aimed at enabling tangible privacy control and feedback in a pervasive sensor network. Our work began with the development of a potentially invasive sensor network (with high resolution video, audio, and motion tracking capabilities) featuring different interactive applications that created incentive for accepting this network as an extension of people's daily social space. A user study was then conducted to evaluate several privacy management approaches - an active badge system for both online and on-site control, on/off power switches for physically disabling the hardware, and touch screen input control. Results from a user study indicated that an active badge for on-site privacy control is the most preferable method among all provided options. We present a set of results that yield insight into the privacy/benefit tradeoff from various sensing capabilities in pervasive sensor networks and how privacy settings and user behavior relate in these environments.
Optimizing Dynamical Network Structure for Pinning Control
NASA Astrophysics Data System (ADS)
Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo
2016-04-01
Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.
Electronic neural network for dynamic resource allocation
NASA Technical Reports Server (NTRS)
Thakoor, A. P.; Eberhardt, S. P.; Daud, T.
1991-01-01
A VLSI implementable neural network architecture for dynamic assignment is presented. The resource allocation problems involve assigning members of one set (e.g. resources) to those of another (e.g. consumers) such that the global 'cost' of the associations is minimized. The network consists of a matrix of sigmoidal processing elements (neurons), where the rows of the matrix represent resources and columns represent consumers. Unlike previous neural implementations, however, association costs are applied directly to the neurons, reducing connectivity of the network to VLSI-compatible 0 (number of neurons). Each row (and column) has an additional neuron associated with it to independently oversee activations of all the neurons in each row (and each column), providing a programmable 'k-winner-take-all' function. This function simultaneously enforces blocking (excitatory/inhibitory) constraints during convergence to control the number of active elements in each row and column within desired boundary conditions. Simulations show that the network, when implemented in fully parallel VLSI hardware, offers optimal (or near-optimal) solutions within only a fraction of a millisecond, for problems up to 128 resources and 128 consumers, orders of magnitude faster than conventional computing or heuristic search methods.
Stochastic dynamics for idiotypic immune networks
NASA Astrophysics Data System (ADS)
Barra, Adriano; Agliari, Elena
2010-12-01
In this work we introduce and analyze the stochastic dynamics obeyed by a model of an immune network recently introduced by the authors. We develop Fokker-Planck equations for the single lymphocyte behavior and coarse grained Langevin schemes for the averaged clone behavior. After showing agreement with real systems (as a short path Jerne cascade), we suggest, both with analytical and numerical arguments, explanations for the generation of (metastable) memory cells, improvement of the secondary response (both in the quality and quantity) and bell shaped modulation against infections as a natural behavior. The whole emerges from the model without being postulated a-priori as it often occurs in second generation immune networks: so the aim of the work is to present some out-of-equilibrium features of this model and to highlight mechanisms which can replace a-priori assumptions in view of further detailed analysis in theoretical systemic immunology.
Creative Cognition and Brain Network Dynamics.
Beaty, Roger E; Benedek, Mathias; Silvia, Paul J; Schacter, Daniel L
2016-02-01
Creative thinking is central to the arts, sciences, and everyday life. How does the brain produce creative thought? A series of recently published papers has begun to provide insight into this question, reporting a strikingly similar pattern of brain activity and connectivity across a range of creative tasks and domains, from divergent thinking to poetry composition to musical improvisation. This research suggests that creative thought involves dynamic interactions of large-scale brain systems, with the most compelling finding being that the default and executive control networks, which can show an antagonistic relation, tend to cooperate during creative cognition and artistic performance. These findings have implications for understanding how brain networks interact to support complex cognitive processes, particularly those involving goal-directed, self-generated thought.
Stochastic epidemic dynamics on extremely heterogeneous networks
NASA Astrophysics Data System (ADS)
Parra-Rojas, César; House, Thomas; McKane, Alan J.
2016-12-01
Networks of contacts capable of spreading infectious diseases are often observed to be highly heterogeneous, with the majority of individuals having fewer contacts than the mean, and a significant minority having relatively very many contacts. We derive a two-dimensional diffusion model for the full temporal behavior of the stochastic susceptible-infectious-recovered (SIR) model on such a network, by making use of a time-scale separation in the deterministic limit of the dynamics. This low-dimensional process is an accurate approximation to the full model in the limit of large populations, even for cases when the time-scale separation is not too pronounced, provided the maximum degree is not of the order of the population size.
Creative Cognition and Brain Network Dynamics
Beaty, Roger E.; Benedek, Mathias; Silvia, Paul J.; Schacter, Daniel L.
2015-01-01
Creative thinking is central to the arts, sciences, and everyday life. How does the brain produce creative thought? A series of recently published papers has begun to provide insight into this question, reporting a strikingly similar pattern of brain activity and connectivity across a range of creative tasks and domains, from divergent thinking to poetry composition to musical improvisation. This research suggests that creative thought involves dynamic interactions of large-scale brain systems, with the most compelling finding being that the default and executive control networks, which can show an antagonistic relationship, actually cooperate during creative cognition and artistic performance. These findings have implications for understanding how brain networks interact to support complex cognitive processes, particularly those involving goal-directed, self-generated thought. PMID:26553223
Weighted-ensemble Brownian dynamics simulations for protein association reactions.
Huber, G A; Kim, S
1996-01-01
A new method, weighted-ensemble Brownian dynamics, is proposed for the simulation of protein-association reactions and other events whose frequencies of outcomes are constricted by free energy barriers. The method features a weighted ensemble of trajectories in configuration space with energy levels dictating the proper correspondence between "particles" and probability. Instead of waiting a very long time for an unlikely event to occur, the probability packets are split, and small packets of probability are allowed to diffuse almost immediately into regions of configuration space that are less likely to be sampled. The method has been applied to the Northrup and Erickson (1992) model of docking-type diffusion-limited reactions and yields reaction rate constants in agreement with those obtained by direct Brownian simulation, but at a fraction of the CPU time (10(-4) to 10(-3), depending on the model). Because the method is essentially a variant of standard Brownian dynamics algorithms, it is anticipated that weighted-ensemble Brownian dynamics, in conjunction with biophysical force models, can be applied to a large class of association reactions of interest to the biophysics community. Images FIGURE 4 PMID:8770190
Quantum dynamics of the abstraction reaction of H with cyclopropane.
Shan, Xiao; Clary, David C
2014-10-30
The dynamics of the abstraction reaction of H atoms with the cyclopropane molecule is studied using quantum mechanical scattering theory. The quantum scattering calculations are performed in hyperspherical coordinates with a two-dimensional (2D) potential energy surface. The ab initio energy calculations are carried out with CCSD(T)-F12a/cc-pVTZ-F12 level of theory with the geometry and frequency calculations at the MP2/cc-pVTZ level. The contribution to the potential energy surface from the spectator modes is included as the projected zero-point energy correction to the ab initio energy. The 2D surface is fitted with a 29-parameter double Morse potential. An R-matrix propagation scheme is carried out to solve the close-coupled equations. The adiabatic energy barrier and reaction enthalpy are compared with high level computational calculations as well as experimental data. The calculated reaction rate constants shows very good agreement when compared with the experimental data, especially at lower temperature highlighting the importance of quantum tunnelling. The reaction probabilities are also presented and discussed. The special features of performing quantum dynamics calculation on the chemical reaction of a cyclic molecule are discussed.
Dynamic Spectrum Management for Military Wireless Networks
2010-09-01
IEEE Communications Magazine , May 2004, vol. 42, No. 5, pp. 72-81. [4] I.F. Akyildiz, W.Y. Lee, M.C. Vuran and S. Mohanty, NeXt Generation / Dynamic...Mission-Critical Networks, IEEE Communications Magazine , October 2009, vol. 47, No. 10, pp. 64-71. [8] W. Heisey i in., Automated Spectrum Plan Advisor... Communications Magazine , January 2009, vol. 47, No. 1, pp. 130-138. [7] O. Younis, L. Kant, K. Chang, K. Young, C. Graff: Cognitive MANET Design for
Unfolding Dynamic Networks for Visual Exploration.
Bach, Benjamin
2016-01-01
Visualization builds on the human capacity to quickly process parallel visual inputs and to offload cognitive processes. Interactive visualizations can become interfaces between humans, their data, and the phenomena represented by the data. Such interfaces can allow for exploration, enable serendipitous discoveries, and serve as a mental metaphor to help us remember and reason about the data. This article gives a brief overview of the author's dissertation research, which is concerned with creating and evaluating novel interfaces to explore dynamic networks, supporting analysts in formulating hypotheses, and discussing proper analysis methods.
NASA Astrophysics Data System (ADS)
Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko
In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.
Relaxation dynamics of a multihierarchical polymer network
NASA Astrophysics Data System (ADS)
Jurjiu, Aurel; Biter, Teodor Lucian; Turcu, Flaviu
2017-01-01
In this work, we study the relaxation dynamics of a multihierarchical polymer network built by replicating the Vicsek fractal in dendrimer shape. The relaxation dynamics is investigated in the framework of the generalized Gaussian structure model by employing both Rouse and Zimm approaches. In the Rouse-type approach, we show the iterative procedure whereby the whole eigenvalue spectrum of the connectivity matrix of the multihierarchical structure can be obtained. Remarkably, the general picture that emerges from both approaches, even though we have a mixed growth algorithm, is that the obtained multihierarchical structure preserves the individual relaxation behaviors of its components. The theoretical findings with respect to the splitting of the intermediate domain of the relaxation quantities are well supported by experimental results.
Mauguière, Frédéric A L; Collins, Peter; Ezra, Gregory S; Farantos, Stavros C; Wiggins, Stephen
2014-04-07
A model Hamiltonian for the reaction CH4(+) -> CH3(+) + H, parametrized to exhibit either early or late inner transition states, is employed to investigate the dynamical characteristics of the roaming mechanism. Tight/loose transition states and conventional/roaming reaction pathways are identified in terms of time-invariant objects in phase space. These are dividing surfaces associated with normally hyperbolic invariant manifolds (NHIMs). For systems with two degrees of freedom NHIMS are unstable periodic orbits which, in conjunction with their stable and unstable manifolds, unambiguously define the (locally) non-recrossing dividing surfaces assumed in statistical theories of reaction rates. By constructing periodic orbit continuation/bifurcation diagrams for two values of the potential function parameter corresponding to late and early transition states, respectively, and using the total energy as another parameter, we dynamically assign different regions of phase space to reactants and products as well as to conventional and roaming reaction pathways. The classical dynamics of the system are investigated by uniformly sampling trajectory initial conditions on the dividing surfaces. Trajectories are classified into four different categories: direct reactive and non-reactive trajectories, which lead to the formation of molecular and radical products respectively, and roaming reactive and non-reactive orbiting trajectories, which represent alternative pathways to form molecular and radical products. By analysing gap time distributions at several energies, we demonstrate that the phase space structure of the roaming region, which is strongly influenced by nonlinear resonances between the two degrees of freedom, results in nonexponential (nonstatistical) decay.
Crossed molecular beam studies of atmospheric chemical reaction dynamics
Zhang, Jingsong
1993-04-01
The dynamics of several elementary chemical reactions that are important in atmospheric chemistry are investigated. The reactive scattering of ground state chlorine or bromine atoms with ozone molecules and ground state chlorine atoms with nitrogen dioxide molecules is studied using a crossed molecular beams apparatus with a rotatable mass spectrometer detector. The Cl + O_{3} → ClO + O_{2} reaction has been studied at four collision energies ranging from 6 kcal/mole to 32 kcal/mole. The derived product center-of-mass angular and translational energy distributions show that the reaction has a direct reaction mechanism and that there is a strong repulsion on the exit channel. The ClO product is sideways and forward scattered with respect to the Cl atom, and the translational energy release is large. The Cl atom is most likely to attack the terminal oxygen atom of the ozone molecule. The Br + O_{3} → ClO + O_{2} reaction has been studied at five collision energies ranging from 5 kcal/mole to 26 kcal/mole. The derived product center-of-mass angular and translational energy distributions are quite similar to those in the Cl + O_{3} reaction. The Br + O_{3} reaction has a direct reaction mechanism similar to that of the Cl + O_{3} reaction. The electronic structure of the ozone molecule seems to play the central role in determining the reaction mechanism in atomic radical reactions with the ozone molecule. The Cl + NO_{2} → ClO + NO reaction has been studied at three collision energies ranging from 10.6 kcal/mole to 22.4 kcal/mole. The center-of-mass angular distribution has some forward-backward symmetry, and the product translational energy release is quite large. The reaction proceeds through a short-lived complex whose lifetime is less than one rotational period. The experimental results seem to show that the Cl atom mainly attacks the oxygen atom instead of the nitrogen atom of the NO_{2 }
Dynamic Processes in Network Goods: Modeling, Analysis and Applications
ERIC Educational Resources Information Center
Paothong, Arnut
2013-01-01
The network externality function plays a very important role in the study of economic network industries. Moreover, the consumer group dynamic interactions coupled with network externality concept is going to play a dominant role in the network goods in the 21st century. The existing literature is stemmed on a choice of externality function with…
Molecular Dynamics Simulations of Network Glasses
NASA Astrophysics Data System (ADS)
Drabold, David A.
The following sections are included: * Introduction and Background * History and use of MD * The role of the potential * Scope of the method * Use of a priori information * Appraising a model * MD Method * Equations of motion * Energy minimization and equilibration * Deeper or global minima * Simulated annealing * Genetic algorithms * Activation-relaxation technique * Alternate dynamics * Modeling infinite systems: Periodic boundary conditions * The Interatomic Interactions * Overview * Empirical classical potentials * Potentials from electronic structure * The tight-binding method * Approximate methods based on tight-binding * First principles * Local basis: "ab initio tight binding" * Plane-waves: Car-Parrinello methods * Efficient ab initio methods for large systems * The need for locality of electron states in real space * Avoiding explicit orthogonalization * Connecting Simulation to Experiment * Structure * Network dynamics * Computing the harmonic modes * Dynamical autocorrelation functions * Dynamical structure factor * Electronic structure * Density of states * Thermal modulation of the electron states * Transport * Applications * g-GeSe2 * g-GexSe1-x glasses * Amorphous carbon surface * Where to Get Codes to Get Started * Acknowledgments * References
A Reaction-Diffusion Model of ROS-Induced ROS Release in a Mitochondrial Network
Zhou, Lufang; Aon, Miguel A.; Almas, Tabish; Cortassa, Sonia; Winslow, Raimond L.; O'Rourke, Brian
2010-01-01
Loss of mitochondrial function is a fundamental determinant of cell injury and death. In heart cells under metabolic stress, we have previously described how the abrupt collapse or oscillation of the mitochondrial energy state is synchronized across the mitochondrial network by local interactions dependent upon reactive oxygen species (ROS). Here, we develop a mathematical model of ROS-induced ROS release (RIRR) based on reaction-diffusion (RD-RIRR) in one- and two-dimensional mitochondrial networks. The nodes of the RD-RIRR network are comprised of models of individual mitochondria that include a mechanism of ROS-dependent oscillation based on the interplay between ROS production, transport, and scavenging; and incorporating the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and Ca2+ handling. Local mitochondrial interaction is mediated by superoxide (O2 .−) diffusion and the O2 .−-dependent activation of an inner membrane anion channel (IMAC). In a 2D network composed of 500 mitochondria, model simulations reveal ΔΨm depolarization waves similar to those observed when isolated guinea pig cardiomyocytes are subjected to a localized laser-flash or antioxidant depletion. The sensitivity of the propagation rate of the depolarization wave to O2.− diffusion, production, and scavenging in the reaction-diffusion model is similar to that observed experimentally. In addition, we present novel experimental evidence, obtained in permeabilized cardiomyocytes, confirming that ΔΨm depolarization is mediated specifically by O2 .−. The present work demonstrates that the observed emergent macroscopic properties of the mitochondrial network can be reproduced in a reaction-diffusion model of RIRR. Moreover, the findings have uncovered a novel aspect of the synchronization mechanism, which is that clusters of mitochondria that are oscillating can entrain mitochondria that would otherwise display stable dynamics. The work identifies the fundamental mechanisms
The photodissociation and reaction dynamics of vibrationally excited molecules
Crim, F.F.
1993-12-01
This research determines the nature of highly vibrationally excited molecules, their unimolecular reactions, and their photodissociation dynamics. The goal is to characterize vibrationally excited molecules and to exploit that understanding to discover and control their chemical pathways. Most recently the author has used a combination of vibrational overtone excitation and laser induced fluorescence both to characterize vibrationally excited molecules and to study their photodissociation dynamics. The author has also begun laser induced grating spectroscopy experiments designed to obtain the electronic absorption spectra of highly vibrationally excited molecules.
Chemical memory reactions induced bursting dynamics in gene expression.
Tian, Tianhai
2013-01-01
Memory is a ubiquitous phenomenon in biological systems in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many memorial phenomena are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict to the extant stochastic approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. To tackle the challenge, I proposed a novel theory consisting of the memory chemical master equations and memory stochastic simulation algorithm. A stochastic model for single-gene expression was proposed to illustrate the key function of memory reactions in inducing bursting dynamics of gene expression that has been observed in experiments recently. The importance of memory reactions has been further validated by the stochastic model of the p53-MDM2 core module. Simulations showed that memory reactions is a major mechanism for realizing both sustained oscillations of p53 protein numbers in single cells and damped oscillations over a population of cells. These successful applications of the memory modeling framework suggested that this innovative theory is an effective and powerful tool to study memory process and conditional chemical reactions in a wide range of complex biological systems.
Imaging complex nutrient dynamics in mycelial networks.
Fricker, M D; Lee, J A; Bebber, D P; Tlalka, M; Hynes, J; Darrah, P R; Watkinson, S C; Boddy, L
2008-08-01
techniques shows that as the colony forms, it self-organizes into well demarcated domains that are identifiable by differences in the phase relationship of the pulses. On the centimetre to metre scale, we have begun to use techniques borrowed from graph theory to characterize the development and dynamics of the network, and used these abstracted network models to predict the transport characteristics, resilience, and cost of the network.
Dynamic DNA nanotechnology using strand-displacement reactions
NASA Astrophysics Data System (ADS)
Zhang, David Yu; Seelig, Georg
2011-02-01
The specificity and predictability of Watson-Crick base pairing make DNA a powerful and versatile material for engineering at the nanoscale. This has enabled the construction of a diverse and rapidly growing set of DNA nanostructures and nanodevices through the programmed hybridization of complementary strands. Although it had initially focused on the self-assembly of static structures, DNA nanotechnology is now also becoming increasingly attractive for engineering systems with interesting dynamic properties. Various devices, including circuits, catalytic amplifiers, autonomous molecular motors and reconfigurable nanostructures, have recently been rationally designed to use DNA strand-displacement reactions, in which two strands with partial or full complementarity hybridize, displacing in the process one or more pre-hybridized strands. This mechanism allows for the kinetic control of reaction pathways. Here, we review DNA strand-displacement-based devices, and look at how this relatively simple mechanism can lead to a surprising diversity of dynamic behaviour.
Effect of Coriolis coupling in chemical reaction dynamics.
Chu, Tian-Shu; Han, Ke-Li
2008-05-14
It is essential to evaluate the role of Coriolis coupling effect in molecular reaction dynamics. Here we consider Coriolis coupling effect in quantum reactive scattering calculations in the context of both adiabaticity and nonadiabaticity, with particular emphasis on examining the role of Coriolis coupling effect in reaction dynamics of triatomic molecular systems. We present the results of our own calculations by the time-dependent quantum wave packet approach for H + D2 and F(2P3/2,2P1/2) + H2 as well as for the ion-molecule collisions of He + H2 +, D(-) + H2, H(-) + D2, and D+ + H2, after reviewing in detail other related research efforts on this issue.
Information diversity in structure and dynamics of simulated neuronal networks.
Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena
2011-01-01
Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.
Functional Network Dynamics of the Language System
Chai, Lucy R.; Mattar, Marcelo G.; Blank, Idan Asher; Fedorenko, Evelina; Bassett, Danielle S.
2016-01-01
During linguistic processing, a set of brain regions on the lateral surfaces of the left frontal, temporal, and parietal cortices exhibit robust responses. These areas display highly correlated activity while a subject rests or performs a naturalistic language comprehension task, suggesting that they form an integrated functional system. Evidence suggests that this system is spatially and functionally distinct from other systems that support high-level cognition in humans. Yet, how different regions within this system might be recruited dynamically during task performance is not well understood. Here we use network methods, applied to fMRI data collected from 22 human subjects performing a language comprehension task, to reveal the dynamic nature of the language system. We observe the presence of a stable core of brain regions, predominantly located in the left hemisphere, that consistently coactivate with one another. We also observe the presence of a more flexible periphery of brain regions, predominantly located in the right hemisphere, that coactivate with different regions at different times. However, the language functional ROIs in the angular gyrus and the anterior temporal lobe were notable exceptions to this trend. By highlighting the temporal dimension of language processing, these results suggest a trade-off between a region's specialization and its capacity for flexible network reconfiguration. PMID:27550868
Motif analysis for small-number effects in chemical reaction dynamics.
Saito, Nen; Sughiyama, Yuki; Kaneko, Kunihiko
2016-09-07
The number of molecules involved in a cell or subcellular structure is sometimes rather small. In this situation, ordinary macroscopic-level fluctuations can be overwhelmed by non-negligible large fluctuations, which results in drastic changes in chemical-reaction dynamics and statistics compared to those observed under a macroscopic system (i.e., with a large number of molecules). In order to understand how salient changes emerge from fluctuations in molecular number, we here quantitatively define small-number effect by focusing on a "mesoscopic" level, in which the concentration distribution is distinguishable both from micro- and macroscopic ones and propose a criterion for determining whether or not such an effect can emerge in a given chemical reaction network. Using the proposed criterion, we systematically derive a list of motifs of chemical reaction networks that can show small-number effects, which includes motifs showing emergence of the power law and the bimodal distribution observable in a mesoscopic regime with respect to molecule number. The list of motifs provided herein is helpful in the search for candidates of biochemical reactions with a small-number effect for possible biological functions, as well as for designing a reaction system whose behavior can change drastically depending on molecule number, rather than concentration.
Motif analysis for small-number effects in chemical reaction dynamics
NASA Astrophysics Data System (ADS)
Saito, Nen; Sughiyama, Yuki; Kaneko, Kunihiko
2016-09-01
The number of molecules involved in a cell or subcellular structure is sometimes rather small. In this situation, ordinary macroscopic-level fluctuations can be overwhelmed by non-negligible large fluctuations, which results in drastic changes in chemical-reaction dynamics and statistics compared to those observed under a macroscopic system (i.e., with a large number of molecules). In order to understand how salient changes emerge from fluctuations in molecular number, we here quantitatively define small-number effect by focusing on a "mesoscopic" level, in which the concentration distribution is distinguishable both from micro- and macroscopic ones and propose a criterion for determining whether or not such an effect can emerge in a given chemical reaction network. Using the proposed criterion, we systematically derive a list of motifs of chemical reaction networks that can show small-number effects, which includes motifs showing emergence of the power law and the bimodal distribution observable in a mesoscopic regime with respect to molecule number. The list of motifs provided herein is helpful in the search for candidates of biochemical reactions with a small-number effect for possible biological functions, as well as for designing a reaction system whose behavior can change drastically depending on molecule number, rather than concentration.
Decentralized track fusion in dynamic networks
NASA Astrophysics Data System (ADS)
Nicholson, David; Deaves, Rob H.
2000-07-01
Decentralized systems merit a detailed analysis in view of the potential advantages that they offer. These include significant improvements in fault tolerance, modularity and scalability. Such attributes are required by a number of systems that are currently being planned within the defence and civil aerosense sectors. A recognized difficulty with the decentralized network architecture is the potential it creates for redundant data to proliferate as a result of cyclic information flows. This can lead to estimation biases and divergence. Solutions which require the network information sources to be tagged in some way are not generally possible without relaxing some of the constraints on which the decentralized paradigm is founded. This paper consequently investigates a different approach. Specifically, it examines the application of the Covariance Intersection (CI) data fusion technique. CI is relevant to the redundant data problem because it guarantees consistent estimates without requiring correlations to be maintained. The estimation performance of CI is compared here, with respect to a restricted Kalman approach, for a dynamic multi-platform network example. It is concluded that a hybrid CI/Kalman approach offers the best solution, since it exploits known independent information and unknown correlated information without having to relax the decentralized constraints.
Dynamic Social Networks in Recovery Homes
Jason, Leonard A.; Light, John M.; Stevens, Edward B.; Beers, Kimberly
2013-01-01
Acute treatment aftercare in the form of sober living environments—i.e., recovery houses—provide an inexpensive and effective medium-term treatment alternative for many with substance use disorders. Limited evidence suggests that house-situated social relationships and associated social support are critical determinants of how successful these residential experiences are for their members, but little is known about the mechanisms underlying these relationships. This study explored the feasibility of using dynamic social network modeling to understand house-situated longitudinal associations among individual Alcoholics Anonymous (AA) related recovery behaviors, length of residence, dyadic interpersonal trust, and dyadic confidant relationship formation processes. Trust and confidant relationships were measured 3 months apart in U.S. urban-area recovery houses, all of which were part of a network of substance use recovery homes. A stochastic actor-based model was successfully estimated from this data set. Results suggest that confidant relationships are predicted by trust, while trust is affected by recovery behaviors and length of residence. Conceptualizing recovery houses as a set of independent, evolving social networks that can be modeled jointly appears to be a promising direction for research. PMID:24217855
Reaction dynamics studies for the system 7Be+58Ni
NASA Astrophysics Data System (ADS)
Torresi, D.; Mazzocco, M.; Acosta, L.; Boiano, A.; Boiano, C.; Diaz-Torres, A.; Fierro, N.; Glodariu, T.; Grilj, L.; Guglielmetti, A.; Keeley, N.; La Commara, M.; Martel, I.; Mazzocchi, C.; Molini, P.; Pakou, A.; Parascandolo, C.; Parkar, V. V.; Patronis, N.; Pierroutsakou, D.; Romoli, M.; Rusek, K.; Sanchez-Benitez, A. M.; Sandoli, M.; Signorini, C.; Silvestri, R.; Soramel, F.; Stiliaris, E.; Strano, E.; Stroe, L.; Zerva, K.
2015-04-01
The study of reactions induced by exotic weakly bound nuclei at energies around the Coulomb barrier had attracted a large interest in the last decade, since the features of these nuclei can deeply affect the reaction dynamics. The discrimination between different reaction mechanisms is, in general, a rather difficult task. It can be achieved by using detector arrays covering high solid angle and with high granularity that allow to measure the reaction products and, possibly, coincidences between them, as, for example, recently done for stable weakly bound nuclei [1, 2]. We investigated the collision of the weakly bound nucleus 7Be on a 58Ni target at the beam energy of 1.1 times the Coulomb barrier, measuring the elastic scattering angular distribution and the energy and angular distributions of 3He and 4He. The 7Be radioactive ion beam was produced by the facility EXOTIC at INFN-LNL with an energy of 22 MeV and an intensity of ~3×105 pps. Results showed that the 4He yeld is about 4 times larger than 3He yield, suggesting that reaction mechanisms other than the break-up mostly produce the He isotopes. Theoretical calculations for transfer channels and compound nucleus reactions suggest that complete fusion accounts for (41±5%) of the total reaction cross section extracted from optical model analysis of the elastic scattering data, and that 3He and 4He stripping are the most populated reaction channels among direct processes. Eventually estimation of incomplete fusion contributions to the 3,4He production cross sections was performed through semi-classical calculations with the code PLATYPUS [3].
A Dynamic Game on Network Topology for Counterinsurgency Applications
2015-03-26
A Dynamic Game on Network Topology for Counterinsurgency Applications THESIS MARCH 2015 Jared K. Nystrom, Major, USA AFIT-ENS-MS-15-M-144 DEPARTMENT...Government and is not subject to copyright protection in the United States. AFIT-ENS-MS-15-M-144 A DYNAMIC GAME ON NETWORK TOPOLOGY FOR COUNTERINSURGENCY...15-M-144 A DYNAMIC GAME ON NETWORK TOPOLOGY FOR COUNTERINSURGENCY APPLICATIONS THESIS Jared K. Nystrom, BS, BA, MA Major, USA Committee Membership
A dynamical study of the Si(+) + H(2)O reaction.
Flores, Jesús R
2007-11-01
A dynamical study of the Si(+) + H(2)O reaction has been carried out by means of a quasiclassical trajectory method that decomposes the reaction into a capture step, for which an accurate analytical potential is employed, and an unimolecular step, in which the evolution of the collision complex is studied through a direct dynamics BHandHLYP/6-31G(d,p) method. The capture rate coefficient has been computed for thermal conditions corresponding to temperatures ranging from 50 to 1000 K. It is concluded that the main reason why the reaction rate is about 10 times smaller than the capture rate (at T = 298 K) is the topology of the potential energy surface of the ground state. It is also concluded that the ratio between the rates of product and reactant generation from the collision complex decreases quite steeply with increasing temperature, and therefore, the reaction rate decreases even more sharply. Exciting the stretching normal modes of water substantially increases that ratio, and moderate rotational excitation does not appear to have a relevant effect. The collision complex is always initially SiOH(2)(+), but in some trajectories, it becomes HSiOH(+), which generates the products, although the former species is the main intermediate.
Dynamic treatment of invariant and univariant reactions in metamorphic systems
Lasaga, A.C.; Luettge, A.; Rye, D.M.; Bolton, E.W.
2000-03-01
A simple model is presented that incorporates the essential dynamics of metamorphic processes leading to reactions along univariant curves and up to and beyond the invariant point. The model includes both heat flow by conduction and convection as well as fluid flow in and out of a representative volume. Overall mineral reactions can then take place within this rock volume in response to internal and external factors. The paper derives a simple back-of-the-envelope expression for the steady state reached by the system. The steady state composition of the fluid and the steady state temperature are then compared with the composition and temperature predicted by the assumption of thermodynamic equilibrium. Expressions for the amount of fluid passing through the system based on the kinetic model are compared with previous calculations of the mass of fluid added to the system using the equilibrium assumptions. The approach to this steady state is also analyzed and an analytical solution is obtained for the time evolution up to the steady state. Both the steady state and the time evolution solution are then applied to an understanding of the dynamics involved in obtaining T-X-t paths in nature. The results of the kinetic approach lead to major revisions in many of the previously held concepts used in petrologic fluid flow models. These include the expected reaction pathway, the role of metastable reactions, the calculation of fluid flux, the role of the invariant point, and the interpretation of mineral textures and modal abundances of minerals.
2006-09-01
Therefore, dynamic quantities of reaction mixtures such as the velocity autocorrelation functions and the diffusion coefficients can be accurately...using the virial expression [25]. A standard NVT molecular dynamics method was em- ployed with the equations of motion solved using the Verlet leapfrog...configurational energy, pressure, and species concen- trations) are compared to quantities calculated by the RxMC approach. Second , the dynamic quantities
Samarasinghe, S; Ling, H
2017-02-04
In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced
Spreading of infection in a two species reaction-diffusion process in networks.
Korosoglou, Paschalis; Kittas, Aristotelis; Argyrakis, Panos
2010-12-01
We study the dynamics of the infection of a two mobile species reaction from a single infected agent in a population of healthy agents. Historically, the main focus for infection propagation has been through spreading phenomena, where a random location of the system is initially infected and then propagates by successfully infecting its neighbor sites. Here both the infected and healthy agents are mobile, performing classical random walks. This may be a more realistic picture to such epidemiological models, such as the spread of a virus in communication networks of routers, where data travel in packets, the communication time of stations in ad hoc mobile networks, information spreading (such as rumor spreading) in social networks, etc. We monitor the density of healthy particles ρ(t), which we find in all cases to be an exponential function in the long-time limit in two-dimensional and three-dimensional lattices and Erdős-Rényi (ER) and scale-free (SF) networks. We also investigate the scaling of the crossover time t(c) from short- to long-time exponential behavior, which we find to be a power law in lattices and ER networks. This crossover is shown to be absent in SF networks, where we reveal the role of the connectivity of the network in the infection process. We compare this behavior to ER networks and lattices and highlight the significance of various connectivity patterns, as well as the important differences of this process in the various underlying geometries, revealing a more complex behavior of ρ(t).
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.
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.
Google matrix, dynamical attractors, and Ulam networks
NASA Astrophysics Data System (ADS)
Shepelyansky, D. L.; Zhirov, O. V.
2010-03-01
We study the properties of the Google matrix generated by a coarse-grained Perron-Frobenius operator of the Chirikov typical map with dissipation. The finite-size matrix approximant of this operator is constructed by the Ulam method. This method applied to the simple dynamical model generates directed Ulam networks with approximate scale-free scaling and characteristics being in certain features similar to those of the world wide web with approximate scale-free degree distributions as well as two characteristics similar to the web: a power-law decay in PageRank that mirrors the decay of PageRank on the world wide web and a sensitivity to the value α in PageRank. The simple dynamical attractors play here the role of popular websites with a strong concentration of PageRank. A variation in the Google parameter α or other parameters of the dynamical map can drive the PageRank of the Google matrix to a delocalized phase with a strange attractor where the Google search becomes inefficient.
Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli
2006-01-01
A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411
Prosocial Orientation Alters Network Dynamics and Fosters Cooperation.
Melamed, David; Simpson, Brent; Harrell, Ashley
2017-03-23
Dynamic networks have been shown to increase cooperation, but prior findings are compatible with two different mechanisms for the evolution of cooperation. It may be that dynamic networks promote cooperation even in networks composed entirely of egoists, who strategically cooperate to attract and maintain profitable interaction partners. Alternatively, drawing on recent insights into heterogeneous social preferences, we expect that dynamic networks will increase cooperation only when nodes are occupied by persons with more prosocial preferences, who tend to attract and keep more cooperative partners relative to egoists. Our experiment used a standard procedure to classify participants a priori as egoistic or prosocial and then embedded them in homogeneous networks of all prosocials or all egoists, or in heterogeneous networks (50/50). Participants then interacted in repeated prisoner's dilemma games with alters in both static and dynamic networks. In both heterogeneous and homogeneous networks, we find dynamic networks only promote cooperation among prosocials. Resulting from their greater cooperation, prosocials' relations are more stable, yielding substantially higher fitness compared to egoists in both heterogeneous and homogeneous dynamic networks. Our results suggest that a key to the evolution and stability of cooperation is the ability of those with prosocial preferences to alter their networks.
In silico evolution of oscillatory dynamics in biochemical networks
NASA Astrophysics Data System (ADS)
Ali, Md Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan
2015-03-01
We are studying in silico evolution of complex, oscillatory network dynamics within the framework of a minimal mutational model of protein-protein interactions. In our model we consider two different types of proteins, kinase (activator) and phosphatase(inhibitor). In our model. each protein can either be phosphorylated(active) or unphospphorylated (inactive), represented by binary strings. Active proteins can modify their target based on the Michaelis-Menten kinetics of chemical equation. Reaction rate constants are directly related to sequence dependent protein-protein interaction energies. This model can be stuided for non-trivial behavior e.g. oscillations, chaos, multiple stable states. We focus here on biochemical oscillators; some questions we will address within our framework include how the oscillatory dynamics depends on number of protein species, connectivity of the network, whether evolution can readily converge on a stable oscillator if we start with random intitial parameters, neutral evolution with additional protein components and general questions of robustness and evolavability.
Modeling the network dynamics of pulse-coupled neurons
NASA Astrophysics Data System (ADS)
Chandra, Sarthak; Hathcock, David; Crain, Kimberly; Antonsen, Thomas M.; Girvan, Michelle; Ott, Edward
2017-03-01
We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions and degree correlations (assortativity). Using the ansatz of Ott and Antonsen [Chaos 18, 037113 (2008)], we obtain a reduced system of ordinary differential equations describing the mean-field dynamics, with significantly lower dimensionality compared with the complete set of dynamical equations for the system. We find that, for sufficiently large networks and degrees, the dynamical behavior of the reduced system agrees well with that of the full network. This dimensional reduction allows for an efficient characterization of system phase transitions and attractors. For networks with tightly peaked degree distributions, the macroscopic behavior closely resembles that of fully connected networks previously studied by others. In contrast, networks with highly skewed degree distributions exhibit different macroscopic dynamics due to the emergence of degree dependent behavior of different oscillators. For nonassortative networks (i.e., networks without degree correlations), we observe the presence of a synchronously firing phase that can be suppressed by the presence of either assortativity or disassortativity in the network. We show that the results derived here can be used to analyze the effects of network topology on macroscopic behavior in neuronal networks in a computationally efficient fashion.
Topological stabilization for synchronized dynamics on networks
NASA Astrophysics Data System (ADS)
Cencetti, Giulia; Bagnoli, Franco; Battistelli, Giorgio; Chisci, Luigi; Di Patti, Francesca; Fanelli, Duccio
2017-01-01
A general scheme is proposed and tested to control the symmetry breaking instability of a homogeneous solution of a spatially extended multispecies model, defined on a network. The inherent discreteness of the space makes it possible to act on the topology of the inter-nodes contacts to achieve the desired degree of stabilization, without altering the dynamical parameters of the model. Both symmetric and asymmetric couplings are considered. In this latter setting the web of contacts is assumed to be balanced, for the homogeneous equilibrium to exist. The performance of the proposed method are assessed, assuming the Complex Ginzburg-Landau equation as a reference model. In this case, the implemented control allows one to stabilize the synchronous limit cycle, hence time-dependent, uniform solution. A system of coupled real Ginzburg-Landau equations is also investigated to obtain the topological stabilization of a homogeneous and constant fixed point.
Attractor dynamics in local neuronal networks
Thivierge, Jean-Philippe; Comas, Rosa; Longtin, André
2014-01-01
Patterns of synaptic connectivity in various regions of the brain are characterized by the presence of synaptic motifs, defined as unidirectional and bidirectional synaptic contacts that follow a particular configuration and link together small groups of neurons. Recent computational work proposes that a relay network (two populations communicating via a third, relay population of neurons) can generate precise patterns of neural synchronization. Here, we employ two distinct models of neuronal dynamics and show that simulated neural circuits designed in this way are caught in a global attractor of activity that prevents neurons from modulating their response on the basis of incoming stimuli. To circumvent the emergence of a fixed global attractor, we propose a mechanism of selective gain inhibition that promotes flexible responses to external stimuli. We suggest that local neuronal circuits may employ this mechanism to generate precise patterns of neural synchronization whose transient nature delimits the occurrence of a brief stimulus. PMID:24688457
Dynamic biochemical reaction process analysis and pathway modification predictions.
Conejeros, R; Vassiliadis, V S
2000-05-05
Recently, the area of model predictive modification of biochemical pathways has received attention with the aim to increase the productivity of microbial systems. In this study, we present a generalization of previous work, where, using a sensitivity study over the fermentation as a dynamic system, the optimal selection of reaction steps for modification (amplification or attenuation) is determined. The influence of metabolites in the activity of enzymes has also been considered (through activation or inhibition). We further introduce a new concept in the dynamic modeling of biochemical reaction systems including a generalized continuous superstructure in which two artificial multiplicative terms are included to account for: (a) enzyme overexpression or underexpression (attenuation or amplification) for the whole enzyme pool; and (b) modification of the apparent order of a kinetic expression with respect to the concentration of a metabolite or any subset of metabolites participating in the pathway. This new formulation allows the prediction of the sensitivity of the pathway performance index (objective function) with respect to the concentration of the enzyme, as well as the interaction of the enzyme with other metabolites. Using this framework, a case study for the production of penicillin V is analyzed, obtaining the most sensitive reaction steps (or bottlenecks) and the most significant regulations of the system, due to the effect of concentration of intracellular metabolites on the activity of each enzyme.
Filtering in Hybrid Dynamic Bayesian Networks
NASA Technical Reports Server (NTRS)
Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin
2004-01-01
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2 - T i e Slice DBN (2T-DBN) from [Koller & Lerner, 20001 to model fault detection in a watertank system. In [Koller & Lerner, 20001 a generic Particle Filter (PF) is used for inference. We extend the experiment and perform approximate inference using The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Furthermore, we combine these techniques in a 'non-strict' Rao-Blackwellisation framework and apply it to the watertank system. We show that UKF and UKF in a PF framework outperfom the generic PF, EKF and EKF in a PF framework with respect to accuracy and robustness in terms of estimation RMSE. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. We also show that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the water[ank simulation. Theory and implementation is based on the theory presented.
Choice Shift in Opinion Network Dynamics
NASA Astrophysics Data System (ADS)
Gabbay, Michael
Choice shift is a phenomenon associated with small group dynamics whereby group discussion causes group members to shift their opinions in a more extreme direction so that the mean post-discussion opinion exceeds the mean pre-discussion opinion. Also known as group polarization, choice shift is a robust experimental phenomenon and has been well-studied within social psychology. In opinion network models, shifts toward extremism are typically produced by the presence of stubborn agents at the extremes of the opinion axis, whose opinions are much more resistant to change than moderate agents. However, we present a model in which choice shift can arise without the assumption of stubborn agents; the model evolves member opinions and uncertainties using coupled nonlinear differential equations. In addition, we briefly describe the results of a recent experiment conducted involving online group discussion concerning the outcome of National Football League games are described. The model predictions concerning the effects of network structure, disagreement level, and team choice (favorite or underdog) are in accord with the experimental results. This research was funded by the Office of Naval Research and the Defense Threat Reduction Agency.
The Dynamics of Initiative in Communication Networks.
Mollgaard, Anders; Mathiesen, Joachim
2016-01-01
Human social interaction is often intermittent. Two acquainted persons can have extended periods without social interaction punctuated by periods of repeated interaction. In this case, the repeated interaction can be characterized by a seed initiative by either of the persons and a number of follow-up interactions. The tendency to initiate social interaction plays an important role in the formation of social networks and is in general not symmetric between persons. In this paper, we study the dynamics of initiative by analysing and modeling a detailed call and text message network sampled from a group of 700 individuals. We show that in an average relationship between two individuals, one part is almost twice as likely to initiate communication compared to the other part. The asymmetry has social consequences and ultimately might lead to the discontinuation of a relationship. We explain the observed asymmetry by a positive feedback mechanism where individuals already taking initiative are more likely to take initiative in the future. In general, people with many initiatives receive attention from a broader spectrum of friends than people with few initiatives. Lastly, we compare the likelihood of taking initiative with the basic personality traits of the five factor model.
The Dynamics of Initiative in Communication Networks
Mollgaard, Anders; Mathiesen, Joachim
2016-01-01
Human social interaction is often intermittent. Two acquainted persons can have extended periods without social interaction punctuated by periods of repeated interaction. In this case, the repeated interaction can be characterized by a seed initiative by either of the persons and a number of follow-up interactions. The tendency to initiate social interaction plays an important role in the formation of social networks and is in general not symmetric between persons. In this paper, we study the dynamics of initiative by analysing and modeling a detailed call and text message network sampled from a group of 700 individuals. We show that in an average relationship between two individuals, one part is almost twice as likely to initiate communication compared to the other part. The asymmetry has social consequences and ultimately might lead to the discontinuation of a relationship. We explain the observed asymmetry by a positive feedback mechanism where individuals already taking initiative are more likely to take initiative in the future. In general, people with many initiatives receive attention from a broader spectrum of friends than people with few initiatives. Lastly, we compare the likelihood of taking initiative with the basic personality traits of the five factor model. PMID:27124493
Memory and burstiness in dynamic networks
NASA Astrophysics Data System (ADS)
Colman, Ewan R.; Vukadinović Greetham, Danica
2015-07-01
A discrete-time random process is described, which can generate bursty sequences of events. A Bernoulli process, where the probability of an event occurring at time t is given by a fixed probability x , is modified to include a memory effect where the event probability is increased proportionally to the number of events that occurred within a given amount of time preceding t . For small values of x the interevent time distribution follows a power law with exponent -2 -x . We consider a dynamic network where each node forms, and breaks connections according to this process. The value of x for each node depends on the fitness distribution, ρ (x ) , from which it is drawn; we find exact solutions for the expectation of the degree distribution for a variety of possible fitness distributions, and for both cases where the memory effect either is, or is not present. This work can potentially lead to methods to uncover hidden fitness distributions from fast changing, temporal network data, such as online social communications and fMRI scans.
Filtering in Hybrid Dynamic Bayesian Networks
NASA Technical Reports Server (NTRS)
Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin
2000-01-01
We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).
Exact probability distributions of selected species in stochastic chemical reaction networks.
López-Caamal, Fernando; Marquez-Lago, Tatiana T
2014-09-01
Chemical reactions are discrete, stochastic events. As such, the species' molecular numbers can be described by an associated master equation. However, handling such an equation may become difficult due to the large size of reaction networks. A commonly used approach to forecast the behaviour of reaction networks is to perform computational simulations of such systems and analyse their outcome statistically. This approach, however, might require high computational costs to provide accurate results. In this paper we opt for an analytical approach to obtain the time-dependent solution of the Chemical Master Equation for selected species in a general reaction network. When the reaction networks are composed exclusively of zeroth and first-order reactions, this analytical approach significantly alleviates the computational burden required by simulation-based methods. By building upon these analytical solutions, we analyse a general monomolecular reaction network with an arbitrary number of species to obtain the exact marginal probability distribution for selected species. Additionally, we study two particular topologies of monomolecular reaction networks, namely (i) an unbranched chain of monomolecular reactions with and without synthesis and degradation reactions and (ii) a circular chain of monomolecular reactions. We illustrate our methodology and alternative ways to use it for non-linear systems by analysing a protein autoactivation mechanism. Later, we compare the computational load required for the implementation of our results and a pure computational approach to analyse an unbranched chain of monomolecular reactions. Finally, we study calcium ions gates in the sarco/endoplasmic reticulum mediated by ryanodine receptors.
Coriolis-coupled wave packet dynamics of H + HLi reaction.
Padmanaban, R; Mahapatra, S
2006-05-11
We investigated the effect of Coriolis coupling (CC) on the initial state-selected dynamics of H+HLi reaction by a time-dependent wave packet (WP) approach. Exact quantum scattering calculations were obtained by a WP propagation method based on the Chebyshev polynomial scheme and ab initio potential energy surface of the reacting system. Partial wave contributions up to the total angular momentum J=30 were found to be necessary for the scattering of HLi in its vibrational and rotational ground state up to a collision energy approximately 0.75 eV. For each J value, the projection quantum number K was varied from 0 to min (J, K(max)), with K(max)=8 until J=20 and K(max)=4 for further higher J values. This is because further higher values of K do not have much effect on the dynamics and also because one wishes to maintain the large computational overhead for each calculation within the affordable limit. The initial state-selected integral reaction cross sections and thermal rate constants were calculated by summing up the contributions from all partial waves. These were compared with our previous results on the title system, obtained within the centrifugal sudden and J-shifting approximations, to demonstrate the impact of CC on the dynamics of this system.
Rumor diffusion in an interests-based dynamic social network.
Tang, Mingsheng; Mao, Xinjun; Guessoum, Zahia; Zhou, Huiping
2013-01-01
To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1) positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2) with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3) a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4) a network with a smaller clustering coefficient has a larger efficiency.
Topological effects of network structure on long-term social network dynamics in a wild mammal.
Ilany, Amiyaal; Booms, Andrew S; Holekamp, Kay E
2015-07-01
Social structure influences ecological processes such as dispersal and invasion, and affects survival and reproductive success. Recent studies have used static snapshots of social networks, thus neglecting their temporal dynamics, and focused primarily on a limited number of variables that might be affecting social structure. Here, instead we modelled effects of multiple predictors of social network dynamics in the spotted hyena, using observational data collected during 20 years of continuous field research in Kenya. We tested the hypothesis that the current state of the social network affects its long-term dynamics. We employed stochastic agent-based models that allowed us to estimate the contribution of multiple factors to network changes. After controlling for environmental and individual effects, we found that network density and individual centrality affected network dynamics, but that social bond transitivity consistently had the strongest effects. Our results emphasise the significance of structural properties of networks in shaping social dynamics.
Imaging the dynamics of chlorine atom reactions with alkenes
NASA Astrophysics Data System (ADS)
Estillore, Armando D.; Visger, Laura M.; Suits, Arthur G.
2010-08-01
We report a study of chlorine atom reactions with a series of target monounsaturated alkene molecules: 1-pentene, 1-hexene, 2-hexene, and cyclohexene. These reactions were studied using crossed-beam dc slice ion imaging at collision energies of 4 and 7 kcal/mol. Images of the reactively scattered alkenyl radical products were obtained via single photon ionization at 157 nm. The angular distributions at low collision energy are largely isotropic, suggesting the formation of a complex that has a lifetime comparable to or longer than its rotational period, followed by HCl elimination. At high collision energy, the distributions show a sharp forward peak superimposed on the isotropic component accounting for ˜13% of the product flux. The translational energy distributions peak near zero for the backscattered product, in sharp contrast to the results for alkanes. In the forward direction, the translational energy distributions change dramatically with collision energy. At the high collision energy, a sharp forward peak at ˜80% of the collision energy appears, quite reminiscent of results of our recent study of Cl+pentane reactions. The scattering distributions for all target molecules are similar, suggesting similarity of the reaction dynamics among these molecules. Ab initio calculations of the energetics and ionization energies for the various product channels were performed at the CBS-QB3 level to aid in interpreting the results.
Molecular-dynamics study of detonation. II. The reaction mechanism
NASA Astrophysics Data System (ADS)
Rice, Betsy M.; Mattson, William; Grosh, John; Trevino, S. F.
1996-01-01
In this work, we investigate mechanisms of chemical reactions that sustain an unsupported detonation. The chemical model of an energetic crystal used in this study consists of heteronuclear diatomic molecules that, at ambient pressure, dissociate endothermically. Subsequent association of the products to form homonuclear diatomic molecules provides the energy release that sustains the detonation. A many-body interaction is used to simulate changes in the electronic bonding as a function of local atomic environment. The consequence of the many-body interaction in this model is that the intramolecular bond is weakened with increasing density. The mechanism of the reaction for this model was extracted by investigating the details of the molecular properties in the reaction zone with two-dimensional molecular dynamics. The mechanism for the initiation of the reaction in this model is pressure-induced atomization. There was no evidence of excitation of vibrational modes to dissociative states. This particular result is directly attributable to the functional form and choice of parameters for this model, but might also have more general applicability.
Crossed-beam studies of the dynamics of radical reactions
Liu, K.
1993-12-01
The objective of this program is to characterize the detailed dynamics of elementary radical reactions and to provide a better understanding of radical reactivity in general. The radical beam is typically generated by a laser photolysis method. After colliding with the reacting molecule in a crossed-beam apparatus, the reaction product state distribution is interrogated by laser spectroscopic techniques. Several radicals of combustion significance, such as O, CH, OH, CN and NCO have been successfully generated and their collisional behavior at the state-to-state integral cross section level of detail has been studied in this manner. During the past year, the detection system has been converted from LIF to REMPI schemes, and the emphasis of this program shifted to investigate the product angular distributions. Both inelastic and reactive processes have been studied.
Global sensitivity analysis in stochastic simulators of uncertain reaction networks.
Navarro Jimenez, M; Le Maître, O P; Knio, O M
2016-12-28
Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
Towards blueprints for network architecture, biophysical dynamics and signal transduction.
Coombes, Stephen; Doiron, Brent; Josić, Kresimir; Shea-Brown, Eric
2006-12-15
We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.
The quantum dynamics of electronically nonadiabatic chemical reactions
NASA Technical Reports Server (NTRS)
Truhlar, Donald G.
1993-01-01
Considerable progress was achieved on the quantum mechanical treatment of electronically nonadiabatic collisions involving energy transfer and chemical reaction in the collision of an electronically excited atom with a molecule. In the first step, a new diabatic representation for the coupled potential energy surfaces was created. A two-state diabatic representation was developed which was designed to realistically reproduce the two lowest adiabatic states of the valence bond model and also to have the following three desirable features: (1) it is more economical to evaluate; (2) it is more portable; and (3) all spline fits are replaced by analytic functions. The new representation consists of a set of two coupled diabatic potential energy surfaces plus a coupling surface. It is suitable for dynamics calculations on both the electronic quenching and reaction processes in collisions of Na(3p2p) with H2. The new two-state representation was obtained by a three-step process from a modified eight-state diatomics-in-molecules (DIM) representation of Blais. The second step required the development of new dynamical methods. A formalism was developed for treating reactions with very general basis functions including electronically excited states. Our formalism is based on the generalized Newton, scattered wave, and outgoing wave variational principles that were used previously for reactive collisions on a single potential energy surface, and it incorporates three new features: (1) the basis functions include electronic degrees of freedom, as required to treat reactions involving electronic excitation and two or more coupled potential energy surfaces; (2) the primitive electronic basis is assumed to be diabatic, and it is not assumed that it diagonalizes the electronic Hamiltonian even asymptotically; and (3) contracted basis functions for vibrational-rotational-orbital degrees of freedom are included in a very general way, similar to previous prescriptions for locally
Global Dynamical Properties of the Yeast Cell Cycle Network
NASA Astrophysics Data System (ADS)
Tang, Chao
2004-03-01
The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the network regulating the cell division (cell cycle) of the budding yeast. With the use of both discrete (Boolean) and continuous (ODEs) dynamical models, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state--the G1 state--is a global attractor of the dynamics. The biological pathway--the cell-cycle sequence of protein states--is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.
Dynamic tubulation of mitochondria drives mitochondrial network formation.
Wang, Chong; Du, Wanqing; Su, Qian Peter; Zhu, Mingli; Feng, Peiyuan; Li, Ying; Zhou, Yichen; Mi, Na; Zhu, Yueyao; Jiang, Dong; Zhang, Senyan; Zhang, Zerui; Sun, Yujie; Yu, Li
2015-10-01
Mitochondria form networks. Formation of mitochondrial networks is important for maintaining mitochondrial DNA integrity and interchanging mitochondrial material, whereas disruption of the mitochondrial network affects mitochondrial functions. According to the current view, mitochondrial networks are formed by fusion of individual mitochondria. Here, we report a new mechanism for formation of mitochondrial networks through KIF5B-mediated dynamic tubulation of mitochondria. We found that KIF5B pulls thin, highly dynamic tubules out of mitochondria. Fusion of these dynamic tubules, which is mediated by mitofusins, gives rise to the mitochondrial network. We further demonstrated that dynamic tubulation and fusion is sufficient for mitochondrial network formation, by reconstituting mitochondrial networks in vitro using purified fusion-competent mitochondria, recombinant KIF5B, and polymerized microtubules. Interestingly, KIF5B only controls network formation in the peripheral zone of the cell, indicating that the mitochondrial network is divided into subzones, which may be constructed by different mechanisms. Our data not only uncover an essential mechanism for mitochondrial network formation, but also reveal that different parts of the mitochondrial network are formed by different mechanisms.
Algebraic Stabilization of Explicit Numerical Integration for Extremely Stiff Reaction Networks
Guidry, Mike W
2012-01-01
In contrast to the prevailing view in the literature, it is shown that even extremely stiff sets of ordinary differential equations may be solved efficiently by explicit methods if limiting algebraic solutions are used to stabilize the numerical integration. The stabilizing algebra differs essentially for systems well removed from equilibrium and those near equilibrium. Explicit asymptotic and quasi-steady-state methods that are appropriate when the system is only weakly equilibrated are examined first. These methods are then extended to the case of close approach to equilibrium through a new implementation of partial equilibrium approximations. Using stringent tests with astrophysical thermonuclear networks, evidence is provided that these methods can deal with the stiffest networks, even in the approach to equilibrium, with accuracy and integration timestepping comparable to that of implicit methods. Because explicit methods can execute a timestep faster and scale more favorably with network size than implicit algorithms, our results suggest that algebraically stabilized explicit methods might enable integration of larger reaction networks coupled to fluid dynamics than has been feasible previously for a variety of disciplines.
Bimolecular reaction dynamics from photoelectron spectroscopy of negative ions
Bradforth, S.E.
1992-11-01
The transition state region of a neutral bimolecular reaction may be experimentally investigated by photoelectron spectroscopy of an appropriate negative ion. The photoelectron spectrum provides information on the spectroscopy and dynamics of the short lived transition state and may be used to develop model potential energy surfaces that are semi-quantitative in this important region. The principles of bound [yields] bound negative ion photoelectron spectroscopy are illustrated by way of an example: a full analysis of the photoelectron bands of CN[sup [minus
Dynamic Evolution Model Based on Social Network Services
NASA Astrophysics Data System (ADS)
Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen
2013-11-01
Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.
Stochastic Simulation and Analysis of Biomolecular Reaction Networks
2009-01-01
a discrete stochastic system, a hypothetical model of a generic two gene, self- assembling catalytic ligation reaction in a cell-free tran- scription...ligation reactions and the tRNA charging reactions terminate. Third, the first metabolic ligation reaction terminated when Sub_1 was depleted at about...2900 sec and subsequently, the second metabolic ligation reaction would have terminated when all of Prod_A formed by the first ligation reaction was
Dynamic simulation of multicomponent reaction transport in water distribution systems.
Munavalli, G R; Mohan Kumar, M S M S
2004-04-01
Given the presence of nutrients, regrowth of bacteria within a distribution system is possible. The bacterial growth phenomena, which can be studied by developing a multicomponent (substrate, biomass and disinfectant) reaction transport model, is governed by its relationship with the substrate (organic carbon) and disinfectant (chlorine). The multicomponent reaction transport model developed in the present study utilizes the simplified expressions for the basic processes (in bulk flow and at pipe wall) such as bacterial growth and decay, attachment to and detachment from the surface, substrate utilization and disinfectant action involved in the model. The usefulness of the model is further enhanced by the incorporation of an expression for bulk reaction parameter relating it with the organic carbon. The model is validated and applied to study the sensitive behavior of the components using a hypothetical network. The developed model is able to simulate the biodegradable organic carbon threshold in accordance with the values reported in the literature. The spread of contaminant intruded into the system at any location can also be simulated by the model. The multicomponent model developed is useful for water supply authorities in identifying the locations with high substrate concentrations, bacterial growth and lower chlorine residuals.
Modeling dynamics of adaptive complex systems: From gene regulatory networks to financial markets
NASA Astrophysics Data System (ADS)
Liu, Min
This dissertation aims to model the dynamics of two types of adaptive complex systems: gene regulatory networks and financial markets. In modeling gene regulatory networks, a dynamics-driven rewiring mechanism is introduced to Boolean networks and it is found that a critical state emerges spontaneously resulting from the interplay between topology and dynamics during evolution. For biologically realized network sizes, significant finite-size effects are observed. In networks of competing Boolean nodes, we find that in small networks, the evolutionary dynamics selects for input inverting functions rather than canalizing functions in infinitely large networks. It is found that finite sizes can cause symmetry breaking in the evolutionary dynamics. Using the Polya theorem, we show the number of the function classes increases to 46, in contrast to 14 in infinitely large networks, due to the reduced symmetry which matches our simulation results well. In addition, we find that an optimum amount of stochastic noise in the signals exchanged between nodes can result in maximum excess canalization. In modeling financial markets, we simulate a double-auction virtual market by utilizing reaction-diffusion processes to describe the dynamics of limit orders. We find that the log-returns produced have a dynamical scaling exponent of 1/4 and nonstationary, negatively autocorrelated increments. By investigating the microstructure of the virtual market, we find that the mean interarrival time between transactions satisfies an increasing power-law function of time. We propose an inhomogeneous compound Poisson process with a decreasing power-law intensity rate function and demonstrate that this purely jump process captures the essential macroscopic dynamics of the virtual market.
Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach**
Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian
2014-01-01
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241
Dynamical isospin effects in nucleon-induced reactions
Ou Li; Li Zhuxia; Wu Xizhen
2008-10-15
The isospin effects in proton-induced reactions on isotopes of {sup 112-132}Sn and the corresponding {beta}-stable isobars are studied by means of the improved quantum molecular dynamics model and some sensitive probes for the density dependence of the symmetry energy at subnormal densities are proposed. The beam energy range is chosen to be 100-300 MeV. Our study shows that the system size dependence of the reaction cross sections for p+{sup 112-132}Sn deviates from the Carlson's empirical expression obtained by fitting the reaction cross sections for proton on nuclei along the {beta}-stability line and sensitively depends on the stiffness of the symmetry energy. We also find that the angular distribution of elastic scattering for p+{sup 132}Sn at large impact parameters is very sensitive to the density dependence of the symmetry energy, which is uniquely due to the effect of the symmetry potential with no mixture of the effect from the isospin dependence of the nucleon-nucleon cross sections. The isospin effects in neutron-induced reactions are also studied and it is found that the effects are just opposite to that in proton-induced reactions. We find that the difference between the peaks of the angular distributions of elastic scattering for p+{sup 132}Sn and n+{sup 132}Sn at E{sub p,n}=100 MeV and b=7.5 fm is positive for soft symmetry energy U{sub sym}{sup sf} and negative for super-stiff symmetry energy U{sub sym}{sup nlin} and close to zero for linear density dependent symmetry energy U{sub sym}{sup lin}, which seems very useful for constraining the density dependence of the symmetry energy at subnormal densities.
Major component analysis of dynamic networks of physiologic organ interactions
NASA Astrophysics Data System (ADS)
Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch
2015-09-01
The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.
Defect reaction network in Si-doped InAs. Numerical predictions.
Schultz, Peter A.
2015-05-01
This Report characterizes the defects in the def ect reaction network in silicon - doped, n - type InAs predicted with first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si - doped InAs , until culminating in immobile reaction p roducts. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon - related defects in the reaction network. This Report serves to extend the results for the properties of intrinsic defects in bulk InAs as colla ted in SAND 2013 - 2477 : Simple intrinsic defects in InAs : Numerical predictions to include Si - containing simple defects likely to be present in a radiation - induced defect reaction sequence . This page intentionally left blank
Defect reaction network in Si-doped InP : numerical predictions.
Schultz, Peter Andrew
2013-10-01
This Report characterizes the defects in the defect reaction network in silicon-doped, n-type InP deduced from first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si-doped InP until culminating in immobile reaction products. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon-related defects in the reaction network. This Report serves to extend the results for intrinsic defects in SAND 2012-3313: %E2%80%9CSimple intrinsic defects in InP: Numerical predictions%E2%80%9D to include Si-containing simple defects likely to be present in a radiation-induced defect reaction sequence.
Identify Dynamic Network Modules with Temporal and Spatial Constraints
Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J
2007-09-24
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.
Stochastic Analysis of Chemical Reaction Networks Using Linear Noise Approximation.
Cardelli, Luca; Kwiatkowska, Marta; Laurenti, Luca
2016-10-28
Stochastic evolution of Chemical Reactions Networks (CRNs) over time is usually analysed through solving the Chemical Master Equation (CME) or performing extensive simulations. Analysing stochasticity is often needed, particularly when some molecules occur in low numbers. Unfortunately, both approaches become infeasible if the system is complex and/or it cannot be ensured that initial populations are small. We develop a probabilistic logic for CRNs that enables stochastic analysis of the evolution of populations of molecular species. We present an approximate model checking algorithm based on the Linear Noise Approximation (LNA) of the CME, whose computational complexity is independent of the population size of each species and polynomial in the number of different species. The algorithm requires the solution of first order polynomial differential equations. We prove that our approach is valid for any CRN close enough to the thermodynamical limit. However, we show on four case studies that it can still provide good approximation even for low molecule counts. Our approach enables rigorous analysis of CRNs that are not analyzable by solving the CME, but are far from the deterministic limit. Moreover, it can be used for a fast approximate stochastic characterization of a CRN.
Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation.
Cardelli, Luca; Kwiatkowska, Marta; Laurenti, Luca
2016-11-01
Stochastic evolution of Chemical Reactions Networks (CRNs) over time is usually analyzed through solving the Chemical Master Equation (CME) or performing extensive simulations. Analysing stochasticity is often needed, particularly when some molecules occur in low numbers. Unfortunately, both approaches become infeasible if the system is complex and/or it cannot be ensured that initial populations are small. We develop a probabilistic logic for CRNs that enables stochastic analysis of the evolution of populations of molecular species. We present an approximate model checking algorithm based on the Linear Noise Approximation (LNA) of the CME, whose computational complexity is independent of the population size of each species and polynomial in the number of different species. The algorithm requires the solution of first order polynomial differential equations. We prove that our approach is valid for any CRN close enough to the thermodynamical limit. However, we show on four case studies that it can still provide good approximation even for low molecule counts. Our approach enables rigorous analysis of CRNs that are not analyzable by solving the CME, but are far from the deterministic limit. Moreover, it can be used for a fast approximate stochastic characterization of a CRN.
Determining equilibrium constants for dimerization reactions from molecular dynamics simulations.
De Jong, Djurre H; Schäfer, Lars V; De Vries, Alex H; Marrink, Siewert J; Berendsen, Herman J C; Grubmüller, Helmut
2011-07-15
With today's available computer power, free energy calculations from equilibrium molecular dynamics simulations "via counting" become feasible for an increasing number of reactions. An example is the dimerization reaction of transmembrane alpha-helices. If an extended simulation of the two helices covers sufficiently many dimerization and dissociation events, their binding free energy is readily derived from the fraction of time during which the two helices are observed in dimeric form. Exactly how the correct value for the free energy is to be calculated, however, is unclear, and indeed several different and contradictory approaches have been used. In particular, results obtained via Boltzmann statistics differ from those determined via the law of mass action. Here, we develop a theory that resolves this discrepancy. We show that for simulation systems containing two molecules, the dimerization free energy is given by a formula of the form ΔG ∝ ln(P(1) /P(0) ). Our theory is also applicable to high concentrations that typically have to be used in molecular dynamics simulations to keep the simulation system small, where the textbook dilute approximations fail. It also covers simulations with an arbitrary number of monomers and dimers and provides rigorous error estimates. Comparison with test simulations of a simple Lennard Jones system with various particle numbers as well as with reference free energy values obtained from radial distribution functions show full agreement for both binding free energies and dimerization statistics.
Structure and Dynamics of ER: Minimal Networks and Biophysical Constraints
Lin, Congping; Zhang, Yiwei; Sparkes, Imogen; Ashwin, Peter
2014-01-01
The endoplasmic reticulum (ER) in live cells is a highly mobile network whose structure dynamically changes on a number of timescales. The role of such drastic changes in any system is unclear, although there are correlations with ER function. A better understanding of the fundamental biophysical constraints on the system will allow biologists to determine the effects of molecular factors on ER dynamics. Previous studies have identified potential static elements that the ER may remodel around. Here, we use these structural elements to assess biophysical principles behind the network dynamics. By analyzing imaging data of tobacco leaf epidermal cells under two different conditions, i.e., native state (control) and latrunculin B (treated), we show that the geometric structure and dynamics of ER networks can be understood in terms of minimal networks. Our results show that the ER network is well modeled as a locally minimal-length network between the static elements that potentially anchor the ER to the cell cortex over longer timescales; this network is perturbed by a mixture of random and deterministic forces. The network need not have globally minimum length; we observe cases where the local topology may change dynamically between different Euclidean Steiner network topologies. The networks in the treated cells are easier to quantify, because they are less dynamic (the treatment suppresses actin dynamics), but the same general features are found in control cells. Using a Langevin approach, we model the dynamics of the nonpersistent nodes and use this to show that the images can be used to estimate both local viscoelastic behavior of the cytoplasm and filament tension in the ER network. This means we can explain several aspects of the ER geometry in terms of biophysical principles. PMID:25099815
Reducing congestion on complex networks by dynamic relaxation processes
NASA Astrophysics Data System (ADS)
Macri, Pablo A.; Pastore y Piontti, Ana L.; Braunstein, Lidia A.
2007-12-01
We study the effects of relaxational dynamics on the congestion pressure in general transport networks. We show that the congestion pressure is reduced in scale-free networks if a relaxation mechanism is utilized, while this is in general not the case for non-scale-free graphs such as random graphs. We also present evidence supporting the idea that the emergence of scale-free networks arise from optimization mechanisms to balance the load of the networks nodes.
Bimolecular reaction dynamics from photoelectron spectroscopy of negative ions
Bradforth, S.E.
1992-11-01
The transition state region of a neutral bimolecular reaction may be experimentally investigated by photoelectron spectroscopy of an appropriate negative ion. The photoelectron spectrum provides information on the spectroscopy and dynamics of the short lived transition state and may be used to develop model potential energy surfaces that are semi-quantitative in this important region. The principles of bound {yields} bound negative ion photoelectron spectroscopy are illustrated by way of an example: a full analysis of the photoelectron bands of CN{sup {minus}}, NCO{sup {minus}} and NCS{sup {minus}}. Transition state photoelectron spectra are presented for the following systems Br + HI, Cl + HI, F + HI, F + CH{sub 3}0H,F + C{sub 2}H{sub 5}OH,F + OH and F + H{sub 2}. A time dependent framework for the simulation and interpretation of the bound {yields} free transition state photoelectron spectra is subsequently developed and applied to the hydrogen transfer reactions Br + HI, F + OH {yields} O({sup 3}P, {sup 1}D) + HF and F + H{sub 2}. The theoretical approach for the simulations is a fully quantum-mechanical wave packet propagation on a collinear model reaction potential surface. The connection between the wavepacket time evolution and the photoelectron spectrum is given by the time autocorrelation function. For the benchmark F + H{sub 2} system, comparisons with three-dimensional quantum calculations are made.
Systematic development of reduced reaction mechanisms for dynamic modeling
NASA Technical Reports Server (NTRS)
Frenklach, M.; Kailasanath, K.; Oran, E. S.
1986-01-01
A method for systematically developing a reduced chemical reaction mechanism for dynamic modeling of chemically reactive flows is presented. The method is based on the postulate that if a reduced reaction mechanism faithfully describes the time evolution of both thermal and chain reaction processes characteristic of a more complete mechanism, then the reduced mechanism will describe the chemical processes in a chemically reacting flow with approximately the same degree of accuracy. Here this postulate is tested by producing a series of mechanisms of reduced accuracy, which are derived from a full detailed mechanism for methane-oxygen combustion. These mechanisms were then tested in a series of reactive flow calculations in which a large-amplitude sinusoidal perturbation is applied to a system that is initially quiescent and whose temperature is high enough to start ignition processes. Comparison of the results for systems with and without convective flow show that this approach produces reduced mechanisms that are useful for calculations of explosions and detonations. Extensions and applicability to flames are discussed.
RPMDRATE: Bimolecular chemical reaction rates from ring polymer molecular dynamics
NASA Astrophysics Data System (ADS)
Suleimanov, Yu. V.; Allen, J. W.; Green, W. H.
2013-03-01
We present RPMDRATE, a computer program for the calculation of gas phase bimolecular reaction rate coefficients using the ring polymer molecular dynamics (RPMD) method. The RPMD rate coefficient is calculated using the Bennett-Chandler method as a product of a static (centroid density quantum transition state theory (QTST) rate) and a dynamic (ring polymer transmission coefficient) factor. The computational procedure is general and can be used to treat bimolecular polyatomic reactions of any complexity in their full dimensionality. The program has been tested for the H+H2, H+CH4, OH+CH4 and H+C2H6 reactions. Catalogue identifier: AENW_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AENW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: MIT license No. of lines in distributed program, including test data, etc.: 94512 No. of bytes in distributed program, including test data, etc.: 1395674 Distribution format: tar.gz Programming language: Fortran 90/95, Python (version 2.6.x or later, including any version of Python 3, is recommended). Computer: Not computer specific. Operating system: Any for which Python, Fortran 90/95 compiler and the required external routines are available. Has the code been vectorized or parallelized?: The program can efficiently utilize 4096+ processors, depending on problem and available computer. At low temperatures, 110 processors are reasonable for a typical umbrella integration run with an analytic potential energy function and gradients on the latest x86-64 machines.
H + CD4 abstraction reaction dynamics: product energy partitioning.
Hu, Wenfang; Lendvay, György; Troya, Diego; Schatz, George C; Camden, Jon P; Bechtel, Hans A; Brown, Davida J A; Martin, Marion R; Zare, Richard N
2006-03-09
This paper presents experimental and theoretical studies of the product energy partitioning associated with the H + CD4 (nu = 0) --> HD + CD3 reaction for the collision energy range 0.5-3.0 eV. The theoretical results are based on quasiclassical trajectories from (1) first principles direct dynamics calculations (B3LYP/6-31G), (2) an empirical surface developed by Espinosa-García [J. Chem. Phys. 2002, 116, 10664] (EG), and (3) two semiempirical surfaces (MSINDO and reparametrized MSINDO). We find that most of the energy appears in product translation at energies just above the reactive threshold; however, HD vibration and rotation become quite important at energies above 1 eV, each accounting for over 20% of the available energy above 1.5 eV, according to the B3LYP calculations. The barrier on the B3LYP surface, though being later than that on EG, predicts significantly higher HD vibrational excitation than EG. This deviation is contradictory to what would be expected on the basis of the Polanyi rules and derives from modest differences in the potential energy surfaces. The CD3 internal energy is generally quite low, and we present detailed rotational state distributions which show that the CD3 rotational distribution is largely independent of collision energy in the 0.75-1.95 eV range. The most populated rotational levels are N = 5 and 6 on B3LYP, with most of that excitation being associated with motion about the C2 axes, rather than C3 axis, of the CD3 product, in good agreement with the experimental results. Through our extensive studies in this and previous work concerning the scattering dynamics, we conclude that B3LYP/6-31G provides the best available description of the overall dynamics for the title reaction at relatively high collision energies.
Information dynamics in small-world Boolean networks.
Lizier, Joseph T; Pritam, Siddharth; Prokopenko, Mikhail
2011-01-01
Small-world networks have been one of the most influential concepts in complex systems science, partly due to their prevalence in naturally occurring networks. It is often suggested that this prevalence is due to an inherent capability to store and transfer information efficiently. We perform an ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies. To generate dynamic behavior for this experiment, we imbue the nodes in these networks with random Boolean functions. We find that the ordered phase of the dynamics (low activity in dynamics) and topologies with low randomness are dominated by information storage, while the chaotic phase (high activity in dynamics) and topologies with high randomness are dominated by information transfer. Information storage and information transfer are somewhat balanced (crossed over) near the small-world regime, providing quantitative evidence that small-world networks do indeed have a propensity to combine comparably large information storage and transfer capacity.
Stability in flux: community structure in dynamic networks.
Bryden, John; Funk, Sebastian; Geard, Nicholas; Bullock, Seth; Jansen, Vincent A A
2011-07-06
The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.
Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network.
Rosenthal, Gideon; Sporns, Olaf; Avidan, Galia
2016-09-12
Using the "face inversion effect", a hallmark of face perception, we examined network mechanisms supporting face representation by tracking functional magnetic resonance imaging (fMRI) stimulus-dependent dynamic functional connectivity within and between brain networks associated with the processing of upright and inverted faces. We developed a novel approach adapting the general linear model (GLM) framework classically used for univariate fMRI analysis to capture stimulus-dependent fMRI dynamic connectivity of the face network. We show that under the face inversion manipulation, the face and non-face networks have complementary roles that are evident in their stimulus-dependent dynamic connectivity patterns as assessed by network decomposition into components or communities. Moreover, we show that connectivity patterns are associated with the behavioral face inversion effect. Thus, we establish "a network-level signature" of the face inversion effect and demonstrate how a simple physical transformation of the face stimulus induces a dramatic functional reorganization across related brain networks. Finally, we suggest that the dynamic GLM network analysis approach, developed here for the face network, provides a general framework for modeling the dynamics of blocked stimulus-dependent connectivity experimental designs and hence can be applied to a host of neuroimaging studies.
Predicting the evolution of complex networks via similarity dynamics
NASA Astrophysics Data System (ADS)
Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping
2017-01-01
Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial-temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links' likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.
Perspective: network-guided pattern formation of neural dynamics.
Hütt, Marc-Thorsten; Kaiser, Marcus; Hilgetag, Claus C
2014-10-05
The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.
Awazu, Akinori
2008-07-01
Dynamical aspects of the asymmetric cellular automata were investigated to consider the signaling processes in biological systems. As a meta-model of the cascade of feed-forward loop type network motifs in biological reaction networks, we consider the one dimensional asymmetric cellular automata where the state of each cell is controlled by a trio of cells, the cell itself, the nearest upstream cell and the next nearest upstream cell. Through the systematic simulations, some novel input-dependent wave propagations were found in certain asymmetric CA, which may be useful for the signaling processes like the distinction, the filtering and the memory of external stimuli.
Reaction dynamics and mechanism of the Cl + HD(v = 1) reaction: a quantum mechanical study.
González-Sánchez, L; Aldegunde, J; Jambrina, P G; Aoiz, F J
2013-08-15
Time-independent quantum mechanical calculations have been performed in order to characterize the dynamics and stereodynamics of Cl + HD reactive collisions. Calculations have been carried out at two different total energy values and for various initial states using the adiabatic potential energy surface by Bian and Werner [J. Chem. Phys. 2000, 112, 220]. Special attention has been paid to the reaction with HD(v = 1) for which integral and differential cross-sections have been calculated and the effect of vibrational vs translational energy on the reactivity has been examined. In addition, the reactant polarization parameters and polarization-dependent differential cross-sections have been determined. From these results, the spatial preferences of the reaction and the extent of the control of the cross sections achievable through a suitable preparation of the reactants have been also studied. The directional requirements are tighter for the HCl channel than for the DCl one. Formation of the products takes place preferentially when the rotational angular momentum of the HD molecule is perpendicular to the reactants approach direction. Cross-sections and polarization moments computed from the scattering calculations have been compared with experimental results by Kandel et al. [J. Chem. Phys. 2000, 112, 670] for the reaction with HD(v = 1) produced by stimulated Raman pumping. The agreement so obtained is good, and it improves the accordance found in previous calculations with other methodologies and potential energy surfaces.
Sriyudthsak, Kansuporn; Shiraishi, Fumihide; Hirai, Masami Yokota
2016-01-01
The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level. PMID:27200361
Bauß, Anna; Langenmaier, Michael; Strittmatter, Eric; Plattner, Dietmar A; Koslowski, Thorsten
2016-06-09
We present a computer simulation study of the thermodynamics and kinetics of charge transfer reactions within the fungal peroxidase AauDyPI from Auricularia auriculae-judae. Driving forces and reorganization energies are obtained from a thermodynamic integration scheme based upon molecular dynamics simulations. To enhance the numerical accuracy, the free energies are analyzed within a least-squares scheme of a closely knit thermodynamic network. We identify Tyr147, Tyr229, and Trp105 as oxidative agents, and find Trp377 to be a long-lived reaction intermediate. The results are compared to recent experimental findings.
Prediction of ground reaction forces during gait based on kinematics and a neural network model.
Oh, Seung Eel; Choi, Ahnryul; Mun, Joung Hwan
2013-09-27
Kinetic information during human gait can be estimated with inverse dynamics, which is based on anthropometric, kinematic, and ground reaction data. While collecting ground reaction data with a force plate is useful, it is costly and requires regulated space. The goal of this study was to propose a new, accurate methodology for predicting ground reaction forces (GRFs) during level walking without the help of a force plate. To predict GRFs without a force plate, the traditional method of Newtonian mechanics was used for the single support phase. In addition, an artificial neural network (ANN) model was applied for the double support phase to solve statically indeterminate structure problems. The input variables of the ANN model, which were selected to have both dependency and independency, were limited to the trajectory, velocity, and acceleration of the whole segment's mass centre to minimise errors. The predicted GRFs were validated with actual GRFs through a ten-fold cross-validation method, and the correlation coefficients (R) for the ground forces were 0.918 in the medial-lateral axis, 0.985 in the anterior-posterior axis, and 0.991 in the vertical axis during gait. The ground moments were 0.987 in the sagittal plane, 0.841 in the frontal plane, and 0.868 in the transverse plane during gait. The high correlation coefficients(R) are due to the improvement of the prediction rate in the double support phase. This study also proved the possibility of calculating joint forces and moments based on the GRFs predicted with the proposed new hybrid method. Data generated with the proposed method may thus be used instead of raw GRF data in gait analysis and in calculating joint dynamic data using inverse dynamics.
Dynamic social networks promote cooperation in experiments with humans.
Rand, David G; Arbesman, Samuel; Christakis, Nicholas A
2011-11-29
Human populations are both highly cooperative and highly organized. Human interactions are not random but rather are structured in social networks. Importantly, ties in these networks often are dynamic, changing in response to the behavior of one's social partners. This dynamic structure permits an important form of conditional action that has been explored theoretically but has received little empirical attention: People can respond to the cooperation and defection of those around them by making or breaking network links. Here, we present experimental evidence of the power of using strategic link formation and dissolution, and the network modification it entails, to stabilize cooperation in sizable groups. Our experiments explore large-scale cooperation, where subjects' cooperative actions are equally beneficial to all those with whom they interact. Consistent with previous research, we find that cooperation decays over time when social networks are shuffled randomly every round or are fixed across all rounds. We also find that, when networks are dynamic but are updated only infrequently, cooperation again fails. However, when subjects can update their network connections frequently, we see a qualitatively different outcome: Cooperation is maintained at a high level through network rewiring. Subjects preferentially break links with defectors and form new links with cooperators, creating an incentive to cooperate and leading to substantial changes in network structure. Our experiments confirm the predictions of a set of evolutionary game theoretic models and demonstrate the important role that dynamic social networks can play in supporting large-scale human cooperation.
Growth states of catalytic reaction networks exhibiting energy metabolism
NASA Astrophysics Data System (ADS)
Kondo, Yohei; Kaneko, Kunihiko
2011-07-01
All cells derive nutrition by absorbing some chemical and energy resources from the environment; these resources are used by the cells to reproduce the chemicals within them, which in turn leads to an increase in their volume. In this study we introduce a protocell model exhibiting catalytic reaction dynamics, energy metabolism, and cell growth. Results of extensive simulations of this model show the existence of four phases with regard to the rates of both the influx of resources and cell growth. These phases include an active phase with high influx and high growth rates, an inefficient phase with high influx but low growth rates, a quasistatic phase with low influx and low growth rates, and a death phase with negative growth rate. A mean field model well explains the transition among these phases as bifurcations. The statistical distribution of the active phase is characterized by a power law, and that of the inefficient phase is characterized by a nearly equilibrium distribution. We also discuss the relevance of the results of this study to distinct states in the existing cells.
Dynamics of epidemic diseases on a growing adaptive network
NASA Astrophysics Data System (ADS)
Demirel, Güven; Barter, Edmund; Gross, Thilo
2017-02-01
The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.
Dynamics of epidemic diseases on a growing adaptive network
Demirel, Güven; Barter, Edmund; Gross, Thilo
2017-01-01
The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists. PMID:28186146
Design and implementation of dynamic hybrid Honeypot network
NASA Astrophysics Data System (ADS)
Qiao, Peili; Hu, Shan-Shan; Zhai, Ji-Qiang
2013-05-01
The method of constructing a dynamic and self-adaptive virtual network is suggested to puzzle adversaries, delay and divert attacks, exhaust attacker resources and collect attacking information. The concepts of Honeypot and Honeyd, which is the frame of virtual Honeypot are introduced. The techniques of network scanning including active fingerprint recognition are analyzed. Dynamic virtual network system is designed and implemented. A virtual network similar to real network topology is built according to the collected messages from real environments in this system. By doing this, the system can perplex the attackers when Hackers attack and can further analyze and research the attacks. The tests to this system prove that this design can successfully simulate real network environment and can be used in network security analysis.
Dynamical weights and enhanced synchronization in adaptive complex networks.
Zhou, Changsong; Kurths, Jürgen
2006-04-28
Dynamical organization of connection weights is studied in scale-free networks of chaotic oscillators, where the coupling strength of a node from its neighbors develops adaptively according to the local synchronization property between the node and its neighbors. We find that when complete synchronization is achieved, the coupling strength becomes weighted and correlated with the topology due to a hierarchical transition to synchronization in heterogeneous networks. Importantly, such an adaptive process enhances significantly the synchronizability of the networks, which could have meaningful implications in the manipulation of dynamical networks.
Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.
Podobnik, Boris; Lipic, Tomislav; Horvatic, Davor; Majdandzic, Antonio; Bishop, Steven R; Eugene Stanley, H
2015-09-21
Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.
Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks
Podobnik, Boris; Lipic, Tomislav; Horvatic, Davor; Majdandzic, Antonio; Bishop, Steven R.; Eugene Stanley, H.
2015-01-01
Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science. PMID:26387609
A User Driven Dynamic Circuit Network Implementation
Guok, Chin; Robertson, David; Chaniotakis, Evangelos; Thompson, Mary; Johnston, William; Tierney, Brian
2008-10-01
The requirements for network predictability are becoming increasingly critical to the DoE science community where resources are widely distributed and collaborations are world-wide. To accommodate these emerging requirements, the Energy Sciences Network has established a Science Data Network to provide user driven guaranteed bandwidth allocations. In this paper we outline the design, implementation, and secure coordinated use of such a network, as well as some lessons learned.
Minimal model for dynamic bonding in colloidal transient networks
NASA Astrophysics Data System (ADS)
Krinninger, Philip; Fortini, Andrea; Schmidt, Matthias
2016-04-01
We investigate a model for colloidal network formation using Brownian dynamics computer simulations. Hysteretic springs establish transient bonds between particles with repulsive cores. If a bonded pair of particles is separated by a cutoff distance, the spring vanishes and reappears only if the two particles contact each other. We present results for the bond lifetime distribution and investigate the properties of the van Hove dynamical two-body correlation function. The model displays crossover from fluidlike dynamics, via transient network formation, to arrested quasistatic network behavior.
Characterization of CD46 and β1 integrin dynamics during sperm acrosome reaction
Frolikova, Michaela; Sebkova, Natasa; Ded, Lukas; Dvorakova-Hortova, Katerina
2016-01-01
The acrosome reaction (AR) is a process of membrane fusion and lytic enzyme release, which enables sperm to penetrate the egg surroundings. It is widely recognized that specific sperm proteins form an active network prior to fertilization, and their dynamic relocation is crucial for the sperm-egg fusion. The unique presence of the membrane cofactor protein CD46 in the sperm acrosomal membrane was shown, however, its behaviour and connection with other sperm proteins has not been explored further. Using super resolution microscopy, we demonstrated a dynamic CD46 reorganisation over the sperm head during the AR, and its interaction with transmembrane protein integrins, which was confirmed by proximity ligation assay. Furthermore, we propose their joint involvement in actin network rearrangement. Moreover, CD46 and β1 integrins with subunit α3, but not α6, are localized into the apical acrosome and are expected to be involved in signal transduction pathways directing the acrosome stability and essential protein network rearrangements prior to gamete fusion. PMID:27666019
A unique transformation from ordinary differential equations to reaction networks.
Soliman, Sylvain; Heiner, Monika
2010-12-22
Many models in Systems Biology are described as a system of Ordinary Differential Equations, which allows for transient, steady-state or bifurcation analysis when kinetic information is available. Complementary structure-related qualitative analysis techniques have become increasingly popular in recent years, like qualitative model checking or pathway analysis (elementary modes, invariants, flux balance analysis, graph-based analyses, chemical organization theory, etc.). They do not rely on kinetic information but require a well-defined structure as stochastic analysis techniques equally do. In this article, we look into the structure inference problem for a model described by a system of Ordinary Differential Equations and provide conditions for the uniqueness of its solution. We describe a method to extract a structured reaction network model, represented as a bipartite multigraph, for example, a continuous Petri net (CPN), from a system of Ordinary Differential Equations (ODEs). A CPN uniquely defines an ODE, and each ODE can be transformed into a CPN. However, it is not obvious under which conditions the transformation of an ODE into a CPN is unique, that is, when a given ODE defines exactly one CPN. We provide biochemically relevant sufficient conditions under which the derived structure is unique and counterexamples showing the necessity of each condition. Our method is implemented and available; we illustrate it on some signal transduction models from the BioModels database. A prototype implementation of the method is made available to modellers at http://contraintes.inria.fr/~soliman/ode2pn.html, and the data mentioned in the "Results" section at http://contraintes.inria.fr/~soliman/ode2pn_data/. Our results yield a new recommendation for the import/export feature of tools supporting the SBML exchange format.
NASA Astrophysics Data System (ADS)
Lai, Pik-Yin
2017-02-01
Reconstructing network connection topology and interaction strengths solely from measurement of the dynamics of the nodes is a challenging inverse problem of broad applicability in various areas of science and engineering. For a discrete-time step network under noises whose noise-free dynamics is stationary, we derive general analytic results relating the weighted connection matrix of the network to the correlation functions obtained from time-series measurements of the nodes for networks with one-dimensional intrinsic node dynamics. Information about the intrinsic node dynamics and the noise strengths acting on the nodes can also be obtained. Based on these results, we develop a scheme that can reconstruct the above information of the network using only the time-series measurements of node dynamics as input. Reconstruction formulas for higher-dimensional node dynamics are also derived and illustrated with a two-dimensional node dynamics network system. Furthermore, we extend our results and obtain a reconstruction scheme even for the cases when the noise-free dynamics is periodic. We demonstrate that our method can give accurate reconstruction results for weighted directed networks with linear or nonlinear node dynamics of various connection topologies, and with linear or nonlinear couplings.
Enabling direct nanoscale observations of biological reactions with dynamic TEM
Evans, James E.; Browning, Nigel D.
2013-01-01
Biological processes occur on a wide range of spatial and temporal scales: from femtoseconds to hours and from angstroms to meters. Many new biological insights can be expected from a better understanding of the processes that occur on these very fast and very small scales. In this regard, new instruments that use fast X-ray or electron pulses are expected to reveal novel mechanistic details for macromolecular protein dynamics. To ensure that any observed conformational change is physiologically relevant and not constrained by 3D crystal packing, it would be preferable for experiments to utilize small protein samples such as single particles or 2D crystals that mimic the target protein's native environment. These samples are not typically amenable to X-ray analysis, but transmission electron microscopy has imaged such sample geometries for over 40 years using both direct imaging and diffraction modes. While conventional transmission electron microscopes (TEM) have visualized biological samples with atomic resolution in an arrested or frozen state, the recent development of the dynamic TEM (DTEM) extends electron microscopy into a dynamic regime using pump-probe imaging. A new second-generation DTEM, which is currently being constructed, has the potential to observe live biological processes with unprecedented spatiotemporal resolution by using pulsed electron packets to probe the sample on micro- and nanosecond timescales. This article reviews the experimental parameters necessary for coupling DTEM with in situ liquid microscopy to enable direct imaging of protein conformational dynamics in a fully hydrated environment and visualize reactions propagating in real time. PMID:23315566
2D pattern evolution constrained by complex network dynamics
NASA Astrophysics Data System (ADS)
da Rocha, L. E. C.; Costa, L. da F.
2007-03-01
Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling several complex natural and artificial systems. In the same time in which the structural intricacies of such networks are being revealed and understood, efforts have also been directed at investigating how such connectivity properties define and constrain the dynamics of systems unfolding on such structures. However, less attention has been focused on hybrid systems, i.e. involving more than one type of network and/or dynamics. Several real systems present such an organization, e.g. the dynamics of a disease coexisting with the dynamics of the immune system. The current paper investigates a specific system involving diffusive (linear and nonlinear) dynamics taking place in a regular network while interacting with a complex network of defensive agents following Erdös Rényi (ER) and Barabási Albert (BA) graph models with moveable nodes. More specifically, the complex network is expected to control, and if possible, to extinguish the diffusion of some given unwanted process (e.g. fire, oil spilling, pest dissemination, and virus or bacteria reproduction during an infection). Two types of pattern evolution are considered: Fick and Gray Scott. The nodes of the defensive network then interact with the diffusing patterns and communicate between themselves in order to control the diffusion. The main findings include the identification of higher efficiency for the BA control networks and the presence of relapses in the case of the ER model.
Dynamic-Sensitive centrality of nodes in temporal networks
Huang, Da-Wen; Yu, Zu-Guo
2017-01-01
Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold bc, especially for the temporal scale-free networks. PMID:28150735
Dynamic-Sensitive centrality of nodes in temporal networks
NASA Astrophysics Data System (ADS)
Huang, Da-Wen; Yu, Zu-Guo
2017-02-01
Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold bc, especially for the temporal scale-free networks.
Role of Substrate Dynamics in Protein Prenylation Reactions
2015-01-01
Conspectus The role dynamics plays in proteins is of intense contemporary interest. Fundamental insights into how dynamics affects reactivity and product distributions will facilitate the design of novel catalysts that can produce high quality compounds that can be employed, for example, as fuels and life saving drugs. We have used molecular dynamics (MD) methods and combined quantum mechanical/molecular mechanical (QM/MM) methods to study a series of proteins either whose substrates are too far away from the catalytic center or whose experimentally resolved substrate binding modes cannot explain the observed product distribution. In particular, we describe studies of farnesyl transferase (FTase) where the farnesyl pyrophosphate (FPP) substrate is ∼8 Å from the zinc-bound peptide in the active site of FTase. Using MD and QM/MM studies, we explain how the FPP substrate spans the gulf between it and the active site, and we have elucidated the nature of the transition state (TS) and offered an alternate explanation of experimentally observed kinetic isotope effects (KIEs). Our second story focuses on the nature of substrate dynamics in the aromatic prenyltransferase (APTase) protein NphB and how substrate dynamics affects the observed product distribution. Through the examples chosen we show the power of MD and QM/MM methods to provide unique insights into how protein substrate dynamics affects catalytic efficiency. We also illustrate how complex these reactions are and highlight the challenges faced when attempting to design de novo catalysts. While the methods used in our previous studies provided useful insights, several clear challenges still remain. In particular, we have utilized a semiempirical QM model (self-consistent charge density functional tight binding, SCC-DFTB) in our QM/MM studies since the problems we were addressing required extensive sampling. For the problems illustrated, this approach performed admirably (we estimate for these systems an
Highly dynamic animal contact network and implications on disease transmission
Chen, Shi; White, Brad J.; Sanderson, Michael W.; Amrine, David E.; Ilany, Amiyaal; Lanzas, Cristina
2014-01-01
Contact patterns among hosts are considered as one of the most critical factors contributing to unequal pathogen transmission. Consequently, networks have been widely applied in infectious disease modeling. However most studies assume static network structure due to lack of accurate observation and appropriate analytic tools. In this study we used high temporal and spatial resolution animal position data to construct a high-resolution contact network relevant to infectious disease transmission. The animal contact network aggregated at hourly level was highly variable and dynamic within and between days, for both network structure (network degree distribution) and individual rank of degree distribution in the network (degree order). We integrated network degree distribution and degree order heterogeneities with a commonly used contact-based, directly transmitted disease model to quantify the effect of these two sources of heterogeneity on the infectious disease dynamics. Four conditions were simulated based on the combination of these two heterogeneities. Simulation results indicated that disease dynamics and individual contribution to new infections varied substantially among these four conditions under both parameter settings. Changes in the contact network had a greater effect on disease dynamics for pathogens with smaller basic reproduction number (i.e. R0 < 2). PMID:24667241
Perspective: Insight into reaction coordinates and dynamics from the potential energy landscape
Wales, D. J.
2015-04-07
This perspective focuses on conceptual and computational aspects of the potential energy landscape framework. It has two objectives: first to summarise some key developments of the approach and second to illustrate how such techniques can be applied using a specific example that exploits knowledge of pathways. Recent developments in theory and simulation within the landscape framework are first outlined, including methods for structure prediction, analysis of global thermodynamic properties, and treatment of rare event dynamics. We then develop a connection between the kinetic transition network treatment of dynamics and a potential of mean force defined by a reaction coordinate. The effect of projection from the full configuration space to low dimensionality is illustrated for an atomic cluster. In this example, where a relatively successful structural order parameter is available, the principal change in cluster morphology is reproduced, but some details are not faithfully represented. In contrast, a profile based on configurations that correspond to the discrete path defined geometrically retains all the barriers and minima. This comparison provides insight into the physical origins of “friction” effects in low-dimensionality descriptions of dynamics based upon a reaction coordinate.
Network cloning unfolds the effect of clustering on dynamical processes
NASA Astrophysics Data System (ADS)
Faqeeh, Ali; Melnik, Sergey; Gleeson, James P.
2015-05-01
We introduce network L -cloning, a technique for creating ensembles of random networks from any given real-world or artificial network. Each member of the ensemble is an L -cloned network constructed from L copies of the original network. The degree distribution of an L -cloned network and, more importantly, the degree-degree correlation between and beyond nearest neighbors are identical to those of the original network. The density of triangles in an L -cloned network, and hence its clustering coefficient, is reduced by a factor of L compared to those of the original network. Furthermore, the density of loops of any fixed length approaches zero for sufficiently large values of L . Other variants of L -cloning allow us to keep intact the short loops of certain lengths. As an application, we employ these network cloning methods to investigate the effect of short loops on dynamical processes running on networks and to inspect the accuracy of corresponding tree-based theories. We demonstrate that dynamics on L -cloned networks (with sufficiently large L ) are accurately described by the so-called adjacency tree-based theories, examples of which include the message passing technique, some pair approximation methods, and the belief propagation algorithm used respectively to study bond percolation, SI epidemics, and the Ising model.
Analysis of weblike network structures of directed graphs for chemical reactions in methane plasmas
Sakai, Osamu Nobuto, Kyosuke; Miyagi, Shigeyuki; Tachibana, Kunihide
2015-10-15
Chemical reactions of molecular gases like methane are so complicated that a chart of decomposed and/or synthesized species originating from molecules in plasma resembles a weblike network in which we write down species and reactions among them. Here we consider properties of the network structures of chemical reactions in methane plasmas. In the network, atoms/molecules/radical species are assumed to form nodes and chemical reactions correspond to directed edges in the terminology of graph theory. Investigation of the centrality index reveals importance of CH{sub 3} in the global chemical reaction, and difference of an index for each radical species between cases with and without electrons clarifies that the electrons are at an influential position to tighten the network structure.
The Graph Laplacian and the Dynamics of Complex Networks
Thulasidasan, Sunil
2012-06-11
In this talk, we explore the structure of networks from a spectral graph-theoretic perspective by analyzing the properties of the Laplacian matrix associated with the graph induced by a network. We will see how the eigenvalues of the graph Laplacian relate to the underlying network structure and dynamics and provides insight into a phenomenon frequently observed in real world networks - the emergence of collective behavior from purely local interactions seen in the coordinated motion of animals and phase transitions in biological networks, to name a few.
Non-transcriptional regulatory processes shape transcriptional network dynamics.
Ray, J Christian J; Tabor, Jeffrey J; Igoshin, Oleg A
2011-10-11
Information about the extra- or intracellular environment is often captured as biochemical signals that propagate through regulatory networks. These signals eventually drive phenotypic changes, typically by altering gene expression programmes in the cell. Reconstruction of transcriptional regulatory networks has given a compelling picture of bacterial physiology, but transcriptional network maps alone often fail to describe phenotypes. Cellular response dynamics are ultimately determined by interactions between transcriptional and non-transcriptional networks, with dramatic implications for physiology and evolution. Here, we provide an overview of non-transcriptional interactions that can affect the performance of natural and synthetic bacterial regulatory networks.
Dynamic reorganization of brain functional networks during cognition.
Bola, Michał; Sabel, Bernhard A
2015-07-01
How does cognition emerge from neural dynamics? The dominant hypothesis states that interactions among distributed brain regions through phase synchronization give basis for cognitive processing. Such phase-synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to perform specific cognitive operations. But unlike resting-state networks, the complex organization of transient cognitive networks is typically not characterized within the graph theory framework. Thus, it is not known whether cognitive processing merely changes the strength of functional connections or, conversely, requires qualitatively new topological arrangements of functional networks. To address this question, we recorded high-density EEG while subjects performed a visual discrimination task. We conducted an event-related network analysis (ERNA) where source-space weighted functional networks were characterized with graph measures. ERNA revealed rapid, transient, and frequency-specific reorganization of the network's topology during cognition. Specifically, cognitive networks were characterized by strong clustering, low modularity, and strong interactions between hub-nodes. Our findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the "network fingerprint" of cognition. Such reorganization patterns might facilitate global integration of information and provide a substrate for a "global workspace" necessary for cognition and consciousness to occur. Thus, characterizing topology of the event-related networks opens new vistas to interpret cognitive dynamics in the broader conceptual framework of graph theory.
Dynamic three-dimensional pore-scale imaging of reaction in a carbonate at reservoir conditions.
Menke, Hannah P; Bijeljic, Branko; Andrew, Matthew G; Blunt, Martin J
2015-04-07
Quantifying CO2 transport and average effective reaction rates in the subsurface is essential to assess the risks associated with underground carbon capture and storage. We use X-ray microtomography to investigate dynamic pore structure evolution in situ at temperatures and pressures representative of underground reservoirs and aquifers. A 4 mm diameter Ketton carbonate core is injected with CO2-saturated brine at 50 °C and 10 MPa while tomographic images are taken at 15 min intervals with a 3.8 μm spatial resolution over a period of 2(1/2) h. An approximate doubling of porosity with only a 3.6% increase in surface area to volume ratio is measured from the images. Pore-scale direct simulation and network modeling on the images quantify an order of magnitude increase in permeability and an appreciable alteration of the velocity field. We study the uniform reaction regime, with dissolution throughout the core. However, at the pore scale, we see variations in the degree of dissolution with an overall reaction rate which is approximately 14 times lower than estimated from batch measurements. This work implies that in heterogeneous rocks, pore-scale transport of reactants limits dissolution and can reduce the average effective reaction rate by an order of magnitude.
Mathematical Frameworks for Oscillatory Network Dynamics in Neuroscience.
Ashwin, Peter; Coombes, Stephen; Nicks, Rachel
2016-12-01
The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances where this theory is expected to break down, say in the presence of strong coupling, or must be carefully interpreted, as in the presence of stochastic forcing. There are also surprises in the dynamical complexity of the attractors that can robustly appear-for example, heteroclinic network attractors. In this review we present a set of mathematical tools that are suitable for addressing the dynamics of oscillatory neural networks, broadening from a standard phase oscillator perspective to provide a practical framework for further successful applications of mathematics to understanding network dynamics in neuroscience.
Optimal forwarding ratio on dynamical networks with heterogeneous mobility
NASA Astrophysics Data System (ADS)
Gan, Yu; Tang, Ming; Yang, Hanxin
2013-05-01
Since the discovery of non-Poisson statistics of human mobility trajectories, more attention has been paid to understand the role of these patterns in different dynamics. In this study, we first introduce the heterogeneous mobility of mobile agents into dynamical networks, and then investigate packet forwarding strategy on the heterogeneous dynamical networks. We find that the faster speed and the higher proportion of high-speed agents can enhance the network throughput and reduce the mean traveling time in random forwarding. A hierarchical structure in the dependence of high-speed is observed: the network throughput remains unchanged at small and large high-speed value. It is also interesting to find that a slightly preferential forwarding to high-speed agents can maximize the network capacity. Through theoretical analysis and numerical simulations, we show that the optimal forwarding ratio stems from the local structural heterogeneity of low-speed agents.
Evolving enhanced topologies for the synchronization of dynamical complex networks.
Gorochowski, Thomas E; di Bernardo, Mario; Grierson, Claire S
2010-05-01
Enhancing the synchronization of dynamical networks is of great interest to those designing and analyzing many man-made and natural systems. In this work, we investigate how network topology can be evolved to improve this property through the rewiring of edges. A computational tool called NETEVO performs this task using a simulated annealing metaheuristic. In contrast to other work which considers topological attributes when assessing current performance, we instead take a dynamical approach using simulated output from the system to direct the evolution of the network. Resultant topologies are analyzed using standard network measures, B matrices, and motif distributions. These uncover the convergence of many similar features for all our networks, highlighting also significant differences between those evolved using topological rather than dynamical performance measures.
Evolutionary link community structure discovery in dynamic weighted networks
NASA Astrophysics Data System (ADS)
Liu, Qiang; Liu, Caihong; Wang, Jiajia; Wang, Xiang; Zhou, Bin; Zou, Peng
2017-01-01
Traditional community detection methods are often restricted in static network analysis. In fact, most of networks in real world obviously show dynamic characteristics with time passing. In this paper, we design a link community structure discovery algorithm in dynamic weighted networks, which can not only reveal the evolutionary link community structure, but also detect overlapping communities by mapping link communities to node communities. Meanwhile, our algorithm can also get the hierarchical structure of link communities by tuning a parameter. The proposed algorithm is based on weighted edge fitness and weighted partition density so as to determine whether to add a link to a community and whether to merge two communities to form a new link community. Experiments on both synthetic and real world networks demonstrate the proposed algorithm can detect evolutionary link community structure in dynamic weighted networks effectively.
Dynamic Default Mode Network across Different Brain States
Lin, Pan; Yang, Yong; Gao, Junfeng; De Pisapia, Nicola; Ge, Sheng; Wang, Xiang; Zuo, Chun S.; Jonathan Levitt, James; Niu, Chen
2017-01-01
The default mode network (DMN) is a complex dynamic network that is critical for understanding cognitive function. However, whether dynamic topological reconfiguration of the DMN occurs across different brain states, and whether this potential reorganization is associated with prior learning or experience is unclear. To better understand the temporally changing topology of the DMN, we investigated both nodal and global dynamic DMN-topology metrics across different brain states. We found that DMN topology changes over time and those different patterns are associated with different brain states. Further, the nodal and global topological organization can be rebuilt by different brain states. These results indicate that the post-task, resting-state topology of the brain network is dynamically altered as a function of immediately prior cognitive experience, and that these modulated networks are assembled in the subsequent state. Together, these findings suggest that the changing topology of the DMN may play an important role in characterizing brain states. PMID:28382944
Generating functionals for autonomous latching dynamics in attractor relict networks
Linkerhand, Mathias; Gros, Claudius
2013-01-01
Coupling local, slowly adapting variables to an attractor network allows to destabilize all attractors, turning them into attractor ruins. The resulting attractor relict network may show ongoing autonomous latching dynamics. We propose to use two generating functionals for the construction of attractor relict networks, a Hopfield energy functional generating a neural attractor network and a functional based on information-theoretical principles, encoding the information content of the neural firing statistics, which induces latching transition from one transiently stable attractor ruin to the next. We investigate the influence of stress, in terms of conflicting optimization targets, on the resulting dynamics. Objective function stress is absent when the target level for the mean of neural activities is identical for the two generating functionals and the resulting latching dynamics is then found to be regular. Objective function stress is present when the respective target activity levels differ, inducing intermittent bursting latching dynamics. PMID:23784373
Ulissi, Zachary W; Medford, Andrew J; Bligaard, Thomas; Nørskov, Jens K
2017-03-06
Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.
Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.
2017-01-01
Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations. PMID:28262694
NASA Astrophysics Data System (ADS)
Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.
2017-03-01
Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.
Physics, stability, and dynamics of supply networks
NASA Astrophysics Data System (ADS)
Helbing, Dirk; Lämmer, Stefan; Seidel, Thomas; Šeba, Pétr; Płatkowski, Tadeusz
2004-12-01
We show how to treat supply networks as physical transport problems governed by balance equations and equations for the adaptation of production speeds. Although the nonlinear behavior is different, the linearized set of coupled differential equations is formally related to those of mechanical or electrical oscillator networks. Supply networks possess interesting features due to their complex topology and directed links. We derive analytical conditions for absolute and convective instabilities. The empirically observed “bullwhip effect” in supply chains is explained as a form of convective instability based on resonance effects. Moreover, it is generalized to arbitrary supply networks. Their related eigenvalues are usually complex, depending on the network structure (even without loops). Therefore, their generic behavior is characterized by damped or growing oscillations. We also show that regular distribution networks possess two negative eigenvalues only, but perturbations generate a spectrum of complex eigenvalues.
Complex networks: when random walk dynamics equals synchronization
NASA Astrophysics Data System (ADS)
Kriener, Birgit; Anand, Lishma; Timme, Marc
2012-09-01
Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits.
Evolving complex dynamics in electronic models of genetic networks
NASA Astrophysics Data System (ADS)
Mason, Jonathan; Linsay, Paul S.; Collins, J. J.; Glass, Leon
2004-09-01
Ordinary differential equations are often used to model the dynamics and interactions in genetic networks. In one particularly simple class of models, the model genes control the production rates of products of other genes by a logical function, resulting in piecewise linear differential equations. In this article, we construct and analyze an electronic circuit that models this class of piecewise linear equations. This circuit combines CMOS logic and RC circuits to model the logical control of the increase and decay of protein concentrations in genetic networks. We use these electronic networks to study the evolution of limit cycle dynamics. By mutating the truth tables giving the logical functions for these networks, we evolve the networks to obtain limit cycle oscillations of desired period. We also investigate the fitness landscapes of our networks to determine the optimal mutation rate for evolution.
Cell Fate Reprogramming by Control of Intracellular Network Dynamics
Zañudo, Jorge G. T.; Albert, Réka
2015-01-01
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell’s fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. PMID:25849586
Dynamic, discontinuous stream networks and their sensitivity to climate change
NASA Astrophysics Data System (ADS)
Godsey, S.; Kirchner, J. W.
2011-12-01
The temporal persistence and spatial continuity of surface flow in a stream channel is fundamentally important for the organisms that live in it. However, we lack a conceptual framework for understanding where flows are perennial versus ephemeral in channel networks, and for understanding where stream channels will be continuous versus intermittent. Here we show that stream networks are dynamic and discontinuous across a wide range of climatic regimes. We define the stream network as the extent of surface flow within the geomorphic expression of the channel network. We present a conceptual model linking extension, contraction, and intermittency of flow within channel networks to their hydroclimatic regimes and local subsurface characteristics. We discuss the sensitivity of these dynamic, discontinuous networks to climate and land use change, and discuss the ecological implications of potential changes at selected sites.
Practical synchronization on complex dynamical networks via optimal pinning control
NASA Astrophysics Data System (ADS)
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.
Practical synchronization on complex dynamical networks via optimal pinning control.
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.
Insider Threat Mitigation Project: A Dynamic Network Approach (Poster)
2014-10-23
OCT 2014 2. REPORT TYPE N/A 3. DATES COVERED 4. TITLE AND SUBTITLE Insider Threat Mitigation Project: A Dynamic Network Approach 5a...Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Insider Threat Mitigation Project A Dynamic Network Approach Approach: • Semi-automated coding...to- external communication • Remove suspected distribution lists • Identify “normal behavior” using Enron • Develop pattern for “ insiders ” in
Dynamic Network Formation Using Ant Colony Optimization
2009-03-01
global solver into a distributed solver where each network node independently assigned their network links. 1.3 Methodology Previous research into...the solution to the network topology is generated at the node level 10 and then consolidated and evaluated at a global level to properly evaluate...application. The specific MILP tool was XPRESS -MP which employs the dual simplex, primal simplex, or the Newton Barrier method to solve the relaxed linear
Neural network based dynamic controllers for industrial robots.
Oh, S Y; Shin, W C; Kim, H G
1995-09-01
The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.
Quantifying dynamical spillover in co-evolving multiplex networks
Vijayaraghavan, Vikram S.; Noël, Pierre-André; Maoz, Zeev; D’Souza, Raissa M.
2015-01-01
Multiplex networks (a system of multiple networks that have different types of links but share a common set of nodes) arise naturally in a wide spectrum of fields. Theoretical studies show that in such multiplex networks, correlated edge dynamics between the layers can have a profound effect on dynamical processes. However, how to extract the correlations from real-world systems is an outstanding challenge. Here we introduce the Multiplex Markov chain to quantify correlations in edge dynamics found in longitudinal data of multiplex networks. By comparing the results obtained from the multiplex perspective to a null model which assumes layers in a network are independent, we can identify real correlations as distinct from simultaneous changes that occur due to random chance. We use this approach on two different data sets: the network of trade and alliances between nation states, and the email and co-commit networks between developers of open source software. We establish the existence of “dynamical spillover” showing the correlated formation (or deletion) of edges of different types as the system evolves. The details of the dynamics over time provide insight into potential causal pathways. PMID:26459949
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.
Popularity and Adolescent Friendship Networks: Selection and Influence Dynamics
ERIC Educational Resources Information Center
Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey
2013-01-01
This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to enhance their status through befriending higher…
Converting PSO dynamics into complex network - Initial study
NASA Astrophysics Data System (ADS)
Pluhacek, Michal; Janostik, Jakub; Senkerik, Roman; Zelinka, Ivan
2016-06-01
In this paper it is presented the initial study on the possibility of capturing the inner dynamic of Particle Swarm Optimization algorithm into a complex network structure. Inspired in previous works there are two different approaches for creating the complex network presented in this paper. Visualizations of the networks are presented and commented. The possibilities for future applications of the proposed design are given in detail.
Functional holography analysis: Simplifying the complexity of dynamical networks
NASA Astrophysics Data System (ADS)
Baruchi, Itay; Grossman, Danny; Volman, Vladislav; Shein, Mark; Hunter, John; Towle, Vernon L.; Ben-Jacob, Eshel
2006-03-01
We present a novel functional holography (FH) analysis devised to study the dynamics of task-performing dynamical networks. The latter term refers to networks composed of dynamical systems or elements, like gene networks or neural networks. The new approach is based on the realization that task-performing networks follow some underlying principles that are reflected in their activity. Therefore, the analysis is designed to decipher the existence of simple causal motives that are expected to be embedded in the observed complex activity of the networks under study. First we evaluate the matrix of similarities (correlations) between the activities of the network's components. We then perform collective normalization of the similarities (or affinity transformation) to construct a matrix of functional correlations. Using dimension reduction algorithms on the affinity matrix, the matrix is projected onto a principal three-dimensional space of the leading eigenvectors computed by the algorithm. To retrieve back information that is lost in the dimension reduction, we connect the nodes by colored lines that represent the level of the similarities to construct a holographic network in the principal space. Next we calculate the activity propagation in the network (temporal ordering) using different methods like temporal center of mass and cross correlations. The causal information is superimposed on the holographic network by coloring the nodes locations according to the temporal ordering of their activities. First, we illustrate the analysis for simple, artificially constructed examples. Then we demonstrate that by applying the FH analysis to modeled and real neural networks as well as recorded brain activity, hidden causal manifolds with simple yet characteristic geometrical and topological features are deciphered in the complex activity. The term "functional holography" is used to indicate that the goal of the analysis is to extract the maximum amount of functional
Feedback mechanism in network dynamics with preferential flow
NASA Astrophysics Data System (ADS)
Fan, H.; Wang, Z.; Chen, L.; Aihara, K.
2009-02-01
We study complex systems or networks in which each node holds an internal dynamics and interacts with other nodes through some kinds of topologies. Collective behavior with dynamical fluctuations is analyzed in complex systems. The dynamical fluctuations of a node can be divided into two parts: one is the internal dynamical fluctuation of the node and the other is the external dynamical fluctuation caused by other nodes. Based on a theoretical analysis, a hidden feedback mechanism is identified in complex systems, which is illustrated in a macroeconomic network and in a city-population network. Furthermore, we study the effect of the topology of the networks on the feedback mechanism. The feedback mechanism is preserved for hub nodes in the networks with a scale-free topology as well as in the networks with an evolving topology. By the hidden feedback mechanism, the observation data can be utilized to judge directly whether the system of each node is with positive feedback or with negative feedback even without knowing its dynamical model.
Simulating market dynamics: interactions between consumer psychology and social networks.
Janssen, Marco A; Jager, Wander
2003-01-01
Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).
Network Dynamic Connectivity for Identifying Hotspots of Fluvial Geomorphic Change
NASA Astrophysics Data System (ADS)
Czuba, J. A.; Foufoula-Georgiou, E.
2014-12-01
The hierarchical branching structure of a river network serves as a template upon which environmental fluxes of water, sediment, nutrients, etc. are conveyed and organized both spatially and temporally within a basin. Dynamical processes occurring on a river network tend to heterogeneously distribute fluxes on the network, often concentrating them into "clusters," i.e., places of excess flux accumulation. Here, we put forward the hypothesis that places in the network predisposed (due to process dynamics and network topology) to accumulate excess bed-material sediment over a considerable river reach and over a considerable period of time reflect locations where a local imbalance in sediment flux may occur thereby highlighting a susceptibility to potential fluvial geomorphic change. We have developed a framework where we are able to track fluxes on a "static" river network using a simplified Lagrangian transport model and use the spatial-temporal distribution of that flux to form a new "dynamic" network of the flux that evolves over time. From this dynamic network we can quantify the dynamic connectivity of the flux and integrate emergent "clusters" over time through a cluster persistence index (CPI) to assess the persistence of mass throughout the network. The framework was applied to sand transport on the Greater Blue Earth River Network in Minnesota where three hotspots of fluvial geomorphic change have been defined based on high rates of channel migration observed from aerial photographic analysis. Locations within the network with high CPI coincided with two of these hotspots, possibly suggesting that channel migration here is driven by sediment deposition "pushing" the stream into and thus eroding the opposite bank. The third hotspot was not identified by high CPI, but instead is believed to be a hotspot of streamflow-driven change based on additional information and the fact that high bed shear stress coincided with this hotspot. The proposed network
Optimizing controllability of edge dynamics in complex networks by perturbing network structure
NASA Astrophysics Data System (ADS)
Pang, Shaopeng; Hao, Fei
2017-03-01
Using the minimum input signals to drive the dynamics in complex networks toward some desired state is a fundamental issue in the field of network controllability. For a complex network with the dynamical process defined on its edges, the controllability of this network is optimal if it can be fully controlled by applying one input signal to an arbitrary non-isolated vertex of it. In this paper, the adding-edge strategy and turning-edge strategy are proposed to optimize the controllability by minimum structural perturbations. Simulations and analyses indicate that the minimum number of adding-edges required for the optimal controllability is equal to the minimum number of turning-edges, and networks with positively correlated in- and out-degrees are easier to achieve optimal controllability. Furthermore, both the strategies have the capacity to reveal the relationship between certain structural properties of a complex network and its controllability of edge dynamics.
Discriminating lysosomal membrane protein types using dynamic neural network.
Tripathi, Vijay; Gupta, Dwijendra Kumar
2014-01-01
This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.
The role of dimensionality in neuronal network dynamics
Ulloa Severino, Francesco Paolo; Ban, Jelena; Song, Qin; Tang, Mingliang; Bianconi, Ginestra; Cheng, Guosheng; Torre, Vincent
2016-01-01
Recent results from network theory show that complexity affects several dynamical properties of networks that favor synchronization. Here we show that synchronization in 2D and 3D neuronal networks is significantly different. Using dissociated hippocampal neurons we compared properties of cultures grown on a flat 2D substrates with those formed on 3D graphene foam scaffolds. Both 2D and 3D cultures had comparable glia to neuron ratio and the percentage of GABAergic inhibitory neurons. 3D cultures because of their dimension have many connections among distant neurons leading to small-world networks and their characteristic dynamics. After one week, calcium imaging revealed moderately synchronous activity in 2D networks, but the degree of synchrony of 3D networks was higher and had two regimes: a highly synchronized (HS) and a moderately synchronized (MS) regime. The HS regime was never observed in 2D networks. During the MS regime, neuronal assemblies in synchrony changed with time as observed in mammalian brains. After two weeks, the degree of synchrony in 3D networks decreased, as observed in vivo. These results show that dimensionality determines properties of neuronal networks and that several features of brain dynamics are a consequence of its 3D topology. PMID:27404281
Recovery processes and dynamics in single and interdependent networks
NASA Astrophysics Data System (ADS)
Majdandzic, Antonio
Systems composed of dynamical networks --- such as the human body with its biological networks or the global economic network consisting of regional clusters --- often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread, and recovery. Here we develop a model for such systems and find phase diagrams for single and interacting networks. By investigating networks with a small number of nodes, where finite-size effects are pronounced, we describe the spontaneous recovery phenomenon present in these systems. In the case of interacting networks the phase diagram is very rich and becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions, and two forbidden transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyze an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model.
DAWN: Dynamic Ad-hoc Wireless Network
2016-06-19
none) (c) Presentations 03/15/2007 03/15/2007 27.00 28.00 Received Paper Ning Li, Jennifer C. Hou. A Scalable, Power-Efficient Broadcast Algorithm for...Wireless Networks, , ( ): . doi: Ning Li, Jennifer C. Hou. Localized Topology Control Algorithms for Heterogeneous Wireless Networks, IEEE
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.
Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu
2016-07-14
This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.
Binary dynamics on star networks under external perturbations
NASA Astrophysics Data System (ADS)
Moreira, Carolina A.; Schneider, David M.; de Aguiar, Marcus A. M.
2015-10-01
We study a binary dynamical process that is a representation of the voter model with two candidates and opinion makers. The voters are represented by nodes of a network of social contacts with internal states labeled 0 or 1 and nodes that are connected can influence each other. The network is also perturbed by opinion makers, a set of external nodes whose states are frozen in 0 or 1 and that can influence all nodes of the network. The quantity of interest is the probability of finding m nodes in state 1 at time t . Here we study this process on star networks, which are simple representations of hubs found in complex systems, and compare the results with those obtained for networks that are fully connected. In both cases a transition from disordered to ordered equilibrium states is observed as the number of external nodes becomes small. For fully connected networks the probability distribution becomes uniform at the critical point. For star networks, on the other hand, we show that the equilibrium distribution splits in two peaks, reflecting the two possible states of the central node. We obtain approximate analytical solutions for the equilibrium distribution that clarify the role of the central node in the process. We show that the network topology also affects the time scale of oscillations in single realizations of the dynamics, which are much faster for the star network. Finally, extending the analysis to two stars we compare our results with simulations in simple scale-free networks.
Johnson, Todd; Bartol, Tom; Sejnowski, Terrence; Mjolsness, Eric
2015-06-18
A stochastic reaction network model of Ca(2+) dynamics in synapses (Pepke et al PLoS Comput. Biol. 6 e1000675) is expressed and simulated using rule-based reaction modeling notation in dynamical grammars and in MCell. The model tracks the response of calmodulin and CaMKII to calcium influx in synapses. Data from numerically intensive simulations is used to train a reduced model that, out of sample, correctly predicts the evolution of interaction parameters characterizing the instantaneous probability distribution over molecular states in the much larger fine-scale models. The novel model reduction method, 'graph-constrained correlation dynamics', requires a graph of plausible state variables and interactions as input. It parametrically optimizes a set of constant coefficients appearing in differential equations governing the time-varying interaction parameters that determine all correlations between variables in the reduced model at any time slice.
Gan, Qintao; Lv, Tianshi; Fu, Zhenhua
2016-04-01
In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.
Scalable Approaches to Control Network Dynamics: Prospects for City Networks
NASA Astrophysics Data System (ADS)
Motter, Adilson E.; Gray, Kimberly A.
2014-07-01
A city is a complex, emergent system and as such can be conveniently represented as a network of interacting components. A fundamental aspect of networks is that the systemic properties can depend as much on the interactions as they depend on the properties of the individual components themselves. Another fundamental aspect is that changes to one component can affect other components, in a process that may cause the entire or a substantial part of the system to change behavior. Over the past 2 decades, much research has been done on the modeling of large and complex networks involved in communication and transportation, disease propagation, and supply chains, as well as emergent phenomena, robustness and optimization in such systems...
Collective relaxation dynamics of small-world networks.
Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc
2015-05-01
Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k, and topological randomness q. We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.
Out of control: Fluctuation of cascading dynamics in networks
NASA Astrophysics Data System (ADS)
Wang, Jianwei; Cai, Lin; Xu, Bo; Li, Peng; Sun, Enhui; Zhu, Zhiguo
2016-11-01
Applying two preferential selection mechanisms of flow destination, we develop two new methods to quantify the initial load of a node, where the flow is transported along the shortest path between two nodes. We propose a simple cascading model and study cascading dynamics induced by attacking the node with the highest load in some synthetic and actual networks. Surprisingly, we observe the abnormal fluctuation of cascading dynamics, i.e., more damage can be triggered if we spend significantly higher cost to protect a network. In particular, this phenomenon is independent of the initial flow distribution and the preferential selection mechanisms of flow destination. However, it remains unclear which specific structural patterns may affect the fluctuation of cascading dynamics. In this paper, we examine the local evolution of the cascading propagation by constructing some special networks. We show that revivals of some nodes in the double ring structure facilitate the transportation of the flow between two unconnected sub-networks, cause more damage and subsequently lead to the abnormal fluctuation of cascading dynamics. Compared with the traditional definition of the betweenness, we adopt two new proposed methods to further evaluate the resilience of several actual networks. We find that some real world networks reach the strongest resilience level against cascading failures in our preferential selection mechanisms of flow destination. Moreover, we explore how to use the minimum cost to maximize the resilience of the studied networks.
Exact results for noise power spectra in linear biochemical reaction networks
NASA Astrophysics Data System (ADS)
Warren, Patrick B.; Tǎnase-Nicola, Sorin; ten Wolde, Pieter Rein
2006-10-01
We present a simple method for determining the exact noise power spectra and related statistical properties for linear chemical reaction networks. The method is applied to reaction networks which are representative of biochemical processes such as gene expression. We find, for example, that a post-translational modification reaction can reduce the noise associated with gene expression. Our results also indicate how to coarse grain networks by the elimination of fast reactions. In this context we have discovered a breakdown of the sum rule which relates the noise power spectrum to the total noise. The breakdown can be quantified by a sum rule deficit, which is found to be universal, and can be attributed to the high-frequency noise in the fast reactions.
Intrinsic dynamics induce global symmetry in network controllability
NASA Astrophysics Data System (ADS)
Zhao, Chen; Wang, Wen-Xu; Liu, Yang-Yu; Slotine, Jean-Jacques
2015-02-01
Controlling complex networked systems to desired states is a key research goal in contemporary science. Despite recent advances in studying the impact of network topology on controllability, a comprehensive understanding of the synergistic effect of network topology and individual dynamics on controllability is still lacking. Here we offer a theoretical study with particular interest in the diversity of dynamic units characterized by different types of individual dynamics. Interestingly, we find a global symmetry accounting for the invariance of controllability with respect to exchanging the densities of any two different types of dynamic units, irrespective of the network topology. The highest controllability arises at the global symmetry point, at which different types of dynamic units are of the same density. The lowest controllability occurs when all self-loops are either completely absent or present with identical weights. These findings further improve our understanding of network controllability and have implications for devising the optimal control of complex networked systems in a wide range of fields.
Nonlinear network dynamics under perturbations of the underlying graph
NASA Astrophysics Data System (ADS)
Radulescu, Anca; Verduzco-Flores, Sergio
2015-01-01
Many natural systems are organized as networks, in which the nodes (be they cells, individuals or populations) interact in a time-dependent fashion. The dynamic behavior of these networks depends on how these nodes are connected, which can be understood in terms of an adjacency matrix and connection strengths. The object of our study is to relate connectivity to temporal behavior in networks of coupled nonlinear oscillators. We investigate the relationship between classes of system architectures and classes of their possible dynamics, when the nodes are coupled according to a connectivity scheme that obeys certain constrains, but also incorporates random aspects. We illustrate how the phase space dynamics and bifurcations of the system change when perturbing the underlying adjacency graph. We differentiate between the effects on dynamics of the following operations that directly modulate network connectivity: (1) increasing/decreasing edge weights, (2) increasing/decreasing edge density, (3) altering edge configuration by adding, deleting, or moving edges. We discuss the significance of our results in the context of real life networks. Some interpretations lead us to draw conclusions that may apply to brain networks, synaptic restructuring, and neural dynamics.
Temporal dynamics of connectivity and epidemic properties of growing networks
NASA Astrophysics Data System (ADS)
Fotouhi, Babak; Shirkoohi, Mehrdad Khani
2016-01-01
Traditional mathematical models of epidemic disease had for decades conventionally considered static structure for contacts. Recently, an upsurge of theoretical inquiry has strived towards rendering the models more realistic by incorporating the temporal aspects of networks of contacts, societal and online, that are of interest in the study of epidemics (and other similar diffusion processes). However, temporal dynamics have predominantly focused on link fluctuations and nodal activities, and less attention has been paid to the growth of the underlying network. Many real networks grow: Online networks are evidently in constant growth, and societal networks can grow due to migration flux and reproduction. The effect of network growth on the epidemic properties of networks is hitherto unknown, mainly due to the predominant focus of the network growth literature on the so-called steady state. This paper takes a step towards alleviating this gap. We analytically study the degree dynamics of a given arbitrary network that is subject to growth. We use the theoretical findings to predict the epidemic properties of the network as a function of time. We observe that the introduction of new individuals into the network can enhance or diminish its resilience against endemic outbreaks and investigate how this regime shift depends upon the connectivity of newcomers and on how they establish connections to existing nodes. Throughout, theoretical findings are corroborated with Monte Carlo simulations over synthetic and real networks. The results shed light on the effects of network growth on the future epidemic properties of networks and offers insights for devising a priori immunization strategies.
Dynamical complexity in the perception-based network formation model
NASA Astrophysics Data System (ADS)
Jo, Hang-Hyun; Moon, Eunyoung
2016-12-01
Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.
Dynamics of Opinion Forming in Structurally Balanced Social Networks
Altafini, Claudio
2012-01-01
A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends. PMID:22761667
Dynamic route choice model of large-scale traffic network
Boyce, D.W.; Lee, D.H.; Janson, B.N.; Berka, S.
1997-08-01
Application and extensions of a dynamic network equilibrium model to the Advanced Driver and Vehicle Advisory Navigation Concept (ADVANCE) Network are described in this paper. ADVANCE is a dynamic route guidance field test designed for 800 km{sup 2} in the northwestern suburbs of Chicago. The dynamic route choice model employed in this paper is solved efficiently by a modified version of Janson`s DYMOD algorithm. Realistic traffic engineering-based link delay functions, instead of the simplistic Bureau of Public Roads (BPR) function, are used to estimate link travel times and intersection delays for most types of links and intersections. Further, an expanded intersection representation is utilized, resulting in a network of nearly 23,000 links and 10,000 nodes. Time-dependent link flows, travel times, speeds and queue spillbacks are generated for the ADVANCE Network. The model was solved on a CONVEX-C3880. Convergence and computational results are presented and analyzed.
Modeling Scalable Pattern Generation in DNA Reaction Networks
Allen, Peter B.; Chen, Xi; Simpson, Zack B.; Ellington, Andrew D.
2013-01-01
We have developed a theoretical framework for developing patterns in multiple dimensions using controllable diffusion and designed reactions implemented in DNA. This includes so-called strand displacement reactions in which one single-stranded DNA hybridizes to a hemi-duplex DNA and displaces another single-stranded DNA, reversibly or irreversibly. These reactions can be designed to proceed with designed rate and molecular specificity. By also controlling diffusion by partial complementarity to a stationary, cross-linked DNA, we can generate predictable patterns. We demonstrate this with several simulations showing deterministic, predictable shapes in space. PMID:25506295
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution
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
Robust global synchronization of two complex dynamical networks.
Asheghan, Mohammad Mostafa; Míguez, Joaquín
2013-06-01
We investigate the synchronization of two coupled complex dynamical networks, a problem that has been termed outer synchronization in the literature. Our approach relies on (a) a basic lemma on the eigendecomposition of matrices resulting from Kronecker products and (b) a suitable choice of Lyapunov function related to the synchronization error dynamics. Starting from these two ingredients, a theorem that provides a sufficient condition for outer synchronization of the networks is proved. The condition in the theorem is expressed as a linear matrix inequality. When satisfied, synchronization is guaranteed to occur globally, i.e., independently of the initial conditions of the networks. The argument of the proof includes the design of the gain of the synchronizer, which is a constant square matrix with dimension dependent on the number of dynamic variables in a single network node, but independent of the size of the overall network, which can be much larger. This basic result is subsequently elaborated to simplify the design of the synchronizer, to avoid unnecessarily restrictive assumptions (e.g., diffusivity) on the coupling matrix that defines the topology of the networks and, finally, to obtain synchronizers that are robust to model errors in the parameters of the coupled networks. An illustrative numerical example for the outer synchronization of two networks of classical Lorenz nodes with perturbed parameters is presented.
Generalized synchronization of complex dynamical networks via impulsive control.
Chen, Juan; Lu, Jun-An; Wu, Xiaoqun; Zheng, Wei Xing
2009-12-01
This paper investigates the generalized synchronization (GS) of two typical complex dynamical networks, small-world networks and scale-free networks, in terms of impulsive control strategy. By applying the auxiliary-system approach to networks, we demonstrate theoretically that for any given coupling strength, GS can take place in complex dynamical networks consisting of nonidentical systems. Particularly, for Barabasi-Albert scale-free networks, we look into the relations between GS error and topological parameter m, which denotes the number of edges linking to a new node at each time step, and find out that GS speeds up with increasing m. And for Newman-Watts small-world networks, the time needed to achieve GS decreases as the probability of adding random edges increases. We further reveal how node dynamics affects GS speed on both small-world and scale-free networks. Finally, we analyze how the development of GS depends on impulsive control gains. Some abnormal but interesting phenomena regarding the GS process are also found in simulations.
Dynamic structural network evolution in compressed granular systems
NASA Astrophysics Data System (ADS)
Papadopoulos, Lia; Puckett, James; Daniels, Karen; Bassett, Danielle
The heterogeneous dynamic behavior of granular packings under shear or compression is not well-understood. In this study, we use novel techniques from network science to investigate the structural evolution that occurs in compressed granular systems. Specifically, we treat particles as network nodes, and pressure-dependent forces between particles as layer-specific network edges. Then, we use a generalization of community detection methods to multilayer networks, and develop quantitative measures that characterize changes in the architecture of the force network as a function of pressure. We observe that branchlike domains reminiscent of force chains evolve differentially as pressure is applied: topological characteristics of these domains at rest predict their coalescence or dispersion under pressure. Our methods allow us to study the dynamics of mesoscale structure in granular systems, and provide a direct way to compare data from systems under different external conditions or with different physical makeup.
Latino social network dynamics and the Hurricane Katrina disaster.
Hilfinger Messias, DeAnne K; Barrington, Clare; Lacy, Elaine
2012-01-01
The aim of this qualitative research was to examine the dynamics of existing and emerging social networks among Latino survivors of Hurricane Katrina. Data were generated through individual, in-depth interviews conducted with 65 Latinos within six months of the storm striking the Gulf Coast of the United States in August 2005. The findings illustrated both the role of social networks in gathering information, making decisions and accessing resources, and how these existing social networks were disrupted and strained by overwhelming needs. Broader structural issues, including poverty and a lack of transportation, combined with marginalised status as immigrants, further constrained access to essential information and resources. In response, new, if temporary, social networks emerged, based primarily on shared nationality, language, and a sense of collective commitment. Practice implications include the need to consider the social network dynamics of marginalised groups in developing innovative strategies to overcome structural barriers to accessing resources essential for disaster preparedness and survival.
Measuring Predictability of Autonomous Network Transitions into Bursting Dynamics
Mofakham, Sima; Zochowski, Michal
2015-01-01
Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-temporal features of network activity, predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. These metrics quantify spike-timing distributions within a narrow time window as a function of the relative location of the active neurons. We applied these metrics to investigate the properties of these transitions in excitatory-only and excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular heterogeneity affect both the reliability and the timeliness of the predictions. The developed measures can be calculated in real time and therefore potentially applied in clinical situations. PMID:25855975
Measuring predictability of autonomous network transitions into bursting dynamics.
Mofakham, Sima; Zochowski, Michal
2015-01-01
Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-temporal features of network activity, predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. These metrics quantify spike-timing distributions within a narrow time window as a function of the relative location of the active neurons. We applied these metrics to investigate the properties of these transitions in excitatory-only and excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular heterogeneity affect both the reliability and the timeliness of the predictions. The developed measures can be calculated in real time and therefore potentially applied in clinical situations.
Identifying and tracking dynamic processes in social networks
NASA Astrophysics Data System (ADS)
Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George
2006-05-01
The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.
Dynamics of TCP traffic over ATM networks
Floyd, S.; Romanow, A.
1994-08-01
The authors investigate the performance of TCP (Transport Control Protocol) connections over ATM (Asynchronous Transfer Mode) networks without ATM-level congestion control, and compare it to the performance of TCP over packet-based networks. For simulations of congested networks, the effective throughput of TCP over ATM can be quite low when cells are dropped at the congested ATM switch. The low throughput is due to wasted bandwidth as the congested link transmits cells from ``corrupted`` packets, i.e., packets in which at least one cell is dropped by the switch. This fragmentation effect can be corrected and high throughput can be achieved if the switch drops whole packets prior to buffer overflow; they call this strategy Early Packet Discard. They also discuss general issues of congestion avoidance for best-effort traffic in ATM networks.
NASA Astrophysics Data System (ADS)
Nobuto, Kyosuke; Mizui, Yasutaka; Miyagi, Shigeyuki; Sakai, Osamu; Murakami, Tomoyuki
2016-09-01
We visualize complicated chemical reaction systems in weakly-ionized plasmas by analysing network structure for chemical processes, and calculate some indexes by assuming interspecies relationships to be a network to clarify them. With the current social evolution, the mean size of general data which we can use in computers grows huge, and significance of the data analysis increases. The methods of the network analysis which we focus on in this study do not depend on a specific analysis target, but the field where it has been already applied is still limited. In this study, we analyse chemical reaction systems in plasmas for configuring the network structure. We visualize them by expressing a reaction system in a specific plasma by a directed graph and examine the indexes and the relations with the characteristic of the species in the reaction system. For example, in the methane plasma network, the centrality index reveals importance of CH3 in an influential position of species in the reaction. In addition, silane and atmospheric pressure plasmas can be also visualized in reaction networks, suggesting other characteristics in the centrality indexes.
Dynamic Network-Based Epistasis Analysis: Boolean Examples
Azpeitia, Eugenio; Benítez, Mariana; Padilla-Longoria, Pablo; Espinosa-Soto, Carlos; Alvarez-Buylla, Elena R.
2011-01-01
In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and
Dynamics of Boolean networks controlled by biologically meaningful functions.
Raeymaekers, L
2002-10-07
The remarkably stable dynamics displayed by randomly constructed Boolean networks is one of the most striking examples of the spontaneous emergence of self-organization in model systems composed of many interacting elements (Kauffman, S., J. theor. Biol.22, 437-467, 1969; The Origins of Order, Oxford University Press, Oxford, 1993). The dynamics of such networks is most stable for a connectivity of two inputs per element, and decreases dramatically with increasing number of connections. Whereas the simplicity of this model system allows the tracing of the dynamical trajectories, it leaves out many features of real biological connections. For instance, the dynamics has been studied in detail only for networks constructed by allowing all theoretically possible Boolean rules, whereas only a subset of them make sense in the material world. This paper analyses the effect on the dynamics of using only Boolean functions which are meaningful in a biological sense. This analysis is particularly relevant for nets with more than two inputs per element because biological networks generally appear to be more extensively interconnected. Sets of the meaningful functions were assembled for up to four inputs per element. The use of these rules results in a smaller number of distinct attractors which have a shorter length, with relatively little sensitivity to the size of the network and to the number of inputs per element. Forcing away the activator/inhibitor ratio from the expected value of 50% further enhances the stability. This effect is more pronounced for networks consisting of a majority of activators than for networks with a corresponding majority of inhibitors, indicating that the former allow the evolution of larger genetic networks. The data further support the idea of the usefulness of logical networks as a conceptual framework for the understanding of real-world phenomena.
Using neural networks for dynamic light scattering time series processing
NASA Astrophysics Data System (ADS)
Chicea, Dan
2017-04-01
A basic experiment to record dynamic light scattering (DLS) time series was assembled using basic components. The DLS time series processing using the Lorentzian function fit was considered as reference. A Neural Network was designed and trained using simulated frequency spectra for spherical particles in the range 0–350 nm, assumed to be scattering centers, and the neural network design and training procedure are described in detail. The neural network output accuracy was tested both on simulated and on experimental time series. The match with the DLS results, considered as reference, was good serving as a proof of concept for using neural networks in fast DLS time series processing.
Information Dynamics as Foundation for Network Management
2014-12-04
networks. Dozens of research breakthroughs have been made, ranging from geometry and topology to signal processing and communication protocol , from...breakthroughs have been made, ranging from geometry and topology to signal processing and communication protocol , from network optimization to systems...software simulator running higher-layer protocols on a PC host, configurable RF front- ends (RouterStation Pro from Ubiquiti), and digital switch. We
A moment-convergence method for stochastic analysis of biochemical reaction networks
NASA Astrophysics Data System (ADS)
Zhang, Jiajun; Nie, Qing; Zhou, Tianshou
2016-05-01
Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.
A moment-convergence method for stochastic analysis of biochemical reaction networks.
Zhang, Jiajun; Nie, Qing; Zhou, Tianshou
2016-05-21
Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.
Critical dynamics of randomly assembled and diluted threshold networks
NASA Astrophysics Data System (ADS)
Kürten, Karl E.; Clark, John W.
2008-04-01
The dynamical behavior of a class of randomly assembled networks of binary threshold units subject to random deletion of connections is studied based on the annealed approximation suitable in the thermodynamic limit. The dynamical phase diagram is constructed for several forms of the probability density distribution of nonvanishing connection strengths. The family of power-law distribution functions ρ0(x)=(1-α)/(2|x|α) is found to play a special role in expanding the domain of stable, ordered dynamics at the expense of the disordered, “chaotic” phase. Relationships with other recent studies of the dynamics of complex networks allowing for variable in-degree of the units are explored. The relevance of the pruning of network connections to neural modeling and developmental neurobiology is discussed.
Revealing physical interaction networks from statistics of collective dynamics.
Nitzan, Mor; Casadiego, Jose; Timme, Marc
2017-02-01
Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system's model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Dynamical observations following driving suffice to infer physical connectivity even if they are temporally disordered, are acquired at large sampling intervals, and stem from different experiments. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems.
A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics
Yuan, Kai; Liu, Jian; Liu, Kaipei; Tan, Tianyuan
2015-01-01
Background Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk. Methods This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors – device, structure, load and special operation – a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method. Conclusion Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic. PMID:25789859
Revealing physical interaction networks from statistics of collective dynamics
Nitzan, Mor; Casadiego, Jose; Timme, Marc
2017-01-01
Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system’s model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Dynamical observations following driving suffice to infer physical connectivity even if they are temporally disordered, are acquired at large sampling intervals, and stem from different experiments. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems. PMID:28246630
The relevance of network micro-structure for neural dynamics
Pernice, Volker; Deger, Moritz; Cardanobile, Stefano; Rotter, Stefan
2013-01-01
The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits. PMID:23761758
Efficient rewirings for enhancing synchronizability of dynamical networks.
Rad, Ali Ajdari; Jalili, Mahdi; Hasler, Martin
2008-09-01
In this paper, we present an algorithm for optimizing synchronizability of complex dynamical networks. Starting with an undirected and unweighted network, we end up with an undirected and unweighted network with the same number of nodes and edges having enhanced synchronizability. To this end, based on some network properties, rewirings, i.e., eliminating an edge and creating a new edge elsewhere, are performed iteratively avoiding always self-loops and multiple edges between the same nodes. We show that the method is able to enhance the synchronizability of networks of any size and topological properties in a small number of steps that scales with the network size. For numerical simulations, an optimization algorithm based on simulated annealing is used. Also, the evolution of different topological properties of the network such as distribution of node degree, node and edge betweenness centrality is tracked with the iteration steps. We use networks such as scale-free, Strogatz-Watts and random to start with and we show that regardless of the initial network, the final optimized network becomes homogeneous. In other words, in the network with high synchronizability, parameters, such as, degree, shortest distance, node, and edge betweenness centralities are almost homogeneously distributed. Also, parameters, such as, maximum node and edge betweenness centralities are small for the rewired network. Although we take the eigenratio of the Laplacian as the target function for optimization, we show that it is also possible to choose other appropriate target functions exhibiting almost the same performance. Furthermore, we show that even if the network is optimized taking into account another interpretation of synchronizability, i.e., synchronization cost, the optimal network has the same synchronization properties. Indeed, in networks with optimized synchronizability, different interpretations of synchronizability coincide. The optimized networks are Ramanujan graphs
Distal gap junctions and active dendrites can tune network dynamics.
Saraga, Fernanda; Ng, Leo; Skinner, Frances K
2006-03-01
Gap junctions allow direct electrical communication between CNS neurons. From theoretical and modeling studies, it is well known that although gap junctions can act to synchronize network output, they can also give rise to many other dynamic patterns including antiphase and other phase-locked states. The particular network pattern that arises depends on cellular, intrinsic properties that affect firing frequencies as well as the strength and location of the gap junctions. Interneurons or GABAergic neurons in hippocampus are diverse in their cellular characteristics and have been shown to have active dendrites. Furthermore, parvalbumin-positive GABAergic neurons, also known as basket cells, can contact one another via gap junctions on their distal dendrites. Using two-cell network models, we explore how distal electrical connections affect network output. We build multi-compartment models of hippocampal basket cells using NEURON and endow them with varying amounts of active dendrites. Two-cell networks of these model cells as well as reduced versions are explored. The relationship between intrinsic frequency and the level of active dendrites allows us to define three regions based on what sort of network dynamics occur with distal gap junction coupling. Weak coupling theory is used to predict the delineation of these regions as well as examination of phase response curves and distal dendritic polarization levels. We find that a nonmonotonic dependence of network dynamic characteristics (phase lags) on gap junction conductance occurs. This suggests that distal electrical coupling and active dendrite levels can control how sensitive network dynamics are to gap junction modulation. With the extended geometry, gap junctions located at more distal locations must have larger conductances for pure synchrony to occur. Furthermore, based on simulations with heterogeneous networks, it may be that one requires active dendrites if phase-locking is to occur in networks formed
Lin, Pan; Yang, Yong; Jovicich, Jorge; De Pisapia, Nicola; Wang, Xiang; Zuo, Chun S; Levitt, James Jonathan
2016-03-01
Characterization of the default mode network (DMN) as a complex network of functionally interacting dynamic systems has received great interest for the study of DMN neural mechanisms. In particular, understanding the relationship of intrinsic resting-state DMN brain network with cognitive behaviors is an important issue in healthy cognition and mental disorders. However, it is still unclear how DMN functional connectivity links to cognitive behaviors during resting-state. In this study, we hypothesize that static and dynamic DMN nodal topology is associated with upcoming cognitive task performance. We used graph theory analysis in order to understand better the relationship between the DMN functional connectivity and cognitive behavior during resting-state and task performance. Nodal degree of the DMN was calculated as a metric of network topology. We found that the static and dynamic posterior cingulate cortex (PCC) nodal degree within the DMN was associated with task performance (Reaction Time). Our results show that the core node PCC nodal degree within the DMN was significantly correlated with reaction time, which suggests that the PCC plays a key role in supporting cognitive function.
Ab initio dynamics of the cytochrome P450 hydroxylation reaction
Elenewski, Justin E.; Hackett, John C
2015-01-01
The iron(IV)-oxo porphyrin π-cation radical known as Compound I is the primary oxidant within the cytochromes P450, allowing these enzymes to affect the substrate hydroxylation. In the course of this reaction, a hydrogen atom is abstracted from the substrate to generate hydroxyiron(IV) porphyrin and a substrate-centered radical. The hydroxy radical then rebounds from the iron to the substrate, yielding the hydroxylated product. While Compound I has succumbed to theoretical and spectroscopic characterization, the associated hydroxyiron species is elusive as a consequence of its very short lifetime, for which there are no quantitative estimates. To ascertain the physical mechanism underlying substrate hydroxylation and probe this timescale, ab initio molecular dynamics simulations and free energy calculations are performed for a model of Compound I catalysis. Semiclassical estimates based on these calculations reveal the hydrogen atom abstraction step to be extremely fast, kinetically comparable to enzymes such as carbonic anhydrase. Using an ensemble of ab initio simulations, the resultant hydroxyiron species is found to have a similarly short lifetime, ranging between 300 fs and 3600 fs, putatively depending on the enzyme active site architecture. The addition of tunneling corrections to these rates suggests a strong contribution from nuclear quantum effects, which should accelerate every step of substrate hydroxylation by an order of magnitude. These observations have strong implications for the detection of individual hydroxylation intermediates during P450 catalysis. PMID:25681906
Reaction Diffusion Modeling of Calcium Dynamics with Realistic ER Geometry
Means, Shawn; Smith, Alexander J.; Shepherd, Jason; Shadid, John; Fowler, John; Wojcikiewicz, Richard J. H.; Mazel, Tomas; Smith, Gregory D.; Wilson, Bridget S.
2006-01-01
We describe a finite-element model of mast cell calcium dynamics that incorporates the endoplasmic reticulum's complex geometry. The model is built upon a three-dimensional reconstruction of the endoplasmic reticulum (ER) from an electron tomographic tilt series. Tetrahedral meshes provide volumetric representations of the ER lumen, ER membrane, cytoplasm, and plasma membrane. The reaction-diffusion model simultaneously tracks changes in cytoplasmic and ER intraluminal calcium concentrations and includes luminal and cytoplasmic protein buffers. Transport fluxes via PMCA, SERCA, ER leakage, and Type II IP3 receptors are also represented. Unique features of the model include stochastic behavior of IP3 receptor calcium channels and comparisons of channel open times when diffusely distributed or aggregated in clusters on the ER surface. Simulations show that IP3R channels in close proximity modulate activity of their neighbors through local Ca2+ feedback effects. Cytoplasmic calcium levels rise higher, and ER luminal calcium concentrations drop lower, after IP3-mediated release from receptors in the diffuse configuration. Simulation results also suggest that the buffering capacity of the ER, and not restricted diffusion, is the predominant factor influencing average luminal calcium concentrations. PMID:16617072
Ab initio dynamics of the cytochrome P450 hydroxylation reaction
Elenewski, Justin E.; Hackett, John C
2015-02-14
The iron(IV)-oxo porphyrin π-cation radical known as Compound I is the primary oxidant within the cytochromes P450, allowing these enzymes to affect the substrate hydroxylation. In the course of this reaction, a hydrogen atom is abstracted from the substrate to generate hydroxyiron(IV) porphyrin and a substrate-centered radical. The hydroxy radical then rebounds from the iron to the substrate, yielding the hydroxylated product. While Compound I has succumbed to theoretical and spectroscopic characterization, the associated hydroxyiron species is elusive as a consequence of its very short lifetime, for which there are no quantitative estimates. To ascertain the physical mechanism underlying substrate hydroxylation and probe this timescale, ab initio molecular dynamics simulations and free energy calculations are performed for a model of Compound I catalysis. Semiclassical estimates based on these calculations reveal the hydrogen atom abstraction step to be extremely fast, kinetically comparable to enzymes such as carbonic anhydrase. Using an ensemble of ab initio simulations, the resultant hydroxyiron species is found to have a similarly short lifetime, ranging between 300 fs and 3600 fs, putatively depending on the enzyme active site architecture. The addition of tunneling corrections to these rates suggests a strong contribution from nuclear quantum effects, which should accelerate every step of substrate hydroxylation by an order of magnitude. These observations have strong implications for the detection of individual hydroxylation intermediates during P450 catalysis.
Min, Wei; Xie, X Sunney; Bagchi, Biman
2008-01-17
We introduce a two-dimensional (2D) multisurface reaction free energy description of the catalytic cycle that explicitly connects the recently observed multi-time-scale conformational dynamics as well as dispersed enzymatic kinetics to the classical Michaelis-Menten equation. A slow conformational motion on a collective enzyme coordinate Q facilitates the catalytic reaction along the intrinsic reaction coordinate X, providing a dynamic realization of Pauling's well-known idea of transition-state stabilization. The catalytic cycle is modeled as transitions between multiple displaced harmonic wells in the XQ space representing different states of the cycle, which is constructed according to the free energy driving force of the cycle. Subsequent to substrate association with the enzyme, the enzyme-substrate complex under strain exhibits a nonequilibrium relaxation toward a new conformation that lowers the activation energy of the reaction, as first proposed by Haldane. The chemical reaction in X is thus enslaved to the down hill slow motion on the Q surface. One consequence of the present theory is that, in spite of the existence of dispersive kinetics, the Michaelis-Menten expression of the catalysis rate remains valid under certain conditions, as observed in recent single-molecule experiments. This dynamic theory builds the relationship between the protein conformational dynamics and the enzymatic reaction kinetics and offers a unified description of enzyme fluctuation-assisted catalysis.
Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
Doni Jayavelu, Naresh; Bar, Nadav
2014-01-01
Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.
Social Insects: A Model System for Network Dynamics
NASA Astrophysics Data System (ADS)
Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna
Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.
Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks
Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena
2014-01-01
A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic–phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits. PMID:24723897
Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks.
Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena
2014-01-01
A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic-phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits.
Inferring slowly-changing dynamic gene-regulatory networks
2015-01-01
Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying network. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slow changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course microarray data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with ℓ1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset. PMID:25917062
Cytoskeleton dynamics: Fluctuations within the network
Bursac, Predrag; Fabry, Ben; Trepat, Xavier; Lenormand, Guillaume; Butler, James P.; Wang, Ning; Fredberg, Jeffrey J.; An, Steven S.; E-mail: san@jhsph.edu
2007-04-06
Out-of-equilibrium systems, such as the dynamics of a living cytoskeleton (CSK), are inherently noisy with fluctuations arising from the stochastic nature of the underlying biochemical and molecular events. Recently, such fluctuations within the cell were characterized by observing spontaneous nano-scale motions of an RGD-coated microbead bound to the cell surface [Bursac et al., Nat. Mater. 4 (2005) 557-561]. While these reported anomalous bead motions represent a molecular level reorganization (remodeling) of microstructures in contact with the bead, a precise nature of these cytoskeletal constituents and forces that drive their remodeling dynamics are largely unclear. Here, we focused upon spontaneous motions of an RGD-coated bead and, in particular, assessed to what extent these motions are attributable to (i) bulk cell movement (cell crawling), (ii) dynamics of focal adhesions, (iii) dynamics of lipid membrane, and/or (iv) dynamics of the underlying actin CSK driven by myosin motors.
Deciphering modular and dynamic behaviors of transcriptional networks.
Zhan, Ming
2007-01-01
The coordinated and dynamic modulation or interaction of genes or proteins acts as an important mechanism used by a cell in functional regulation. Recent studies have shown that many transcriptional networks exhibit a scale-free topology and hierarchical modular architecture. It has also been shown that transcriptional networks or pathways are dynamic and behave only in certain ways and controlled manners in response to disease development, changing cellular conditions, and different environmental factors. Moreover, evolutionarily conserved and divergent transcriptional modules underline fundamental and species-specific molecular mechanisms controlling disease development or cellular phenotypes. Various computational algorithms have been developed to explore transcriptional networks and modules from gene expression data. In silico studies have also been made to mimic the dynamic behavior of regulatory networks, analyzing how disease or cellular phenotypes arise from the connectivity or networks of genes and their products. Here, we review the recent development in computational biology research on deciphering modular and dynamic behaviors of transcriptional networks, highlighting important findings. We also demonstrate how these computational algorithms can be applied in systems biology studies as on disease, stem cells, and drug discovery.
[The psychoimmunological network og panic disorders, agoraphobia and allergic reactions].
Schmidt-Traub, S
1995-02-01
While treating panic and agoraphobia patients with behaviour therapy, a high frequency of allergic reaction of the IgE-mediated type I was observed. Panic disorder, agoraphobia, allergic disorder, and vasomotor reactions are briefly discussed in the framework of psycho-endocrino-immunological research. A pilot study had shown a high correlation between panic disorder with and without agoraphobia and allergic reaction. A controlled study was then planned to test the hypothesized psychoimmunological relationship. 100 allergic patients, 79 panic/agoraphobic patients, and 66 controls underwent psychodiagnostic and allergic screening. 70% of the anxiety patients responded to test allergens with IgE-mediated type-I immediate reactions in comparison to 28% of the control persons. Another 15% of the panic patients reacted to nickle compound with type-IV delayed skin reactions (7% of the controls). Conversely, 10% of the allergic patients suffered from panic disorder (45% had experienced panic attacks) in contrast to 2% of the controls (24% of these reported panic attacks). The relative risk for allergic patients to develop panic disorder with and without agoraphobia is obviously five times as high as for controls. With this assumption of a psychoimmunological preparedness in mind, a behavioural medical diagnostic and therapeutic concept seems more adequate in coping both with panic/agoraphobia and allergic disorder.
Molecular Dynamics Simulations of Chemical Reactions for Use in Education
ERIC Educational Resources Information Center
Qian Xie; Tinker, Robert
2006-01-01
One of the simulation engines of an open-source program called the Molecular Workbench, which can simulate thermodynamics of chemical reactions, is described. This type of real-time, interactive simulation and visualization of chemical reactions at the atomic scale could help students understand the connections between chemical reaction equations…
Dynamical Crossover in Complex Networks near the Percolation Transition
NASA Astrophysics Data System (ADS)
Kawasaki, Fumiya; Yakubo, Kousuke
2011-10-01
The return probability P0(t) of random walkers is investigated numerically for several scale-free fractal networks. Our results show that P0(t) is proportional to t-ds/2 with the non-integer spectral dimension ds as in the case of non-scale free fractal networks. We also study how the diffusion process is affected by the structural crossover from a fractal to a small-world architecture in a network near the percolation transition. It is elucidated that the corresponding dynamical crossover is scaled only by the unique characteristic time tξ regardless of whether the network is scale free or not. In addition, the scaling relation ds= 2Df/dw is found to be valid even for scale-free fractal networks, where Df and dw are the fractal and the walk dimensions. These results suggest that qualitative properties of P0(t) are irrelevant to the scale-free nature of networks.