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
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.
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.
Pietak, Alexis; Levin, Michael
2017-09-01
Gene regulatory networks (GRNs) describe interactions between gene products and transcription factors that control gene expression. In combination with reaction-diffusion models, GRNs have enhanced comprehension of biological pattern formation. However, although it is well known that biological systems exploit an interplay of genetic and physical mechanisms, instructive factors such as transmembrane potential (Vmem) have not been integrated into full GRN models. Here we extend regulatory networks to include bioelectric signalling, developing a novel synthesis: the bioelectricity-integrated gene and reaction (BIGR) network. Using in silico simulations, we highlight the capacity for Vmem to alter steady-state concentrations of key signalling molecules inside and out of cells. We characterize fundamental feedbacks where Vmem both controls, and is in turn regulated by, biochemical signals and thereby demonstrate Vmem homeostatic control, Vmem memory and Vmem controlled state switching. BIGR networks demonstrating hysteresis are identified as a mechanisms through which more complex patterns of stable Vmem spots and stripes, along with correlated concentration patterns, can spontaneously emerge. As further proof of principle, we present and analyse a BIGR network model that mechanistically explains key aspects of the remarkable regenerative powers of creatures such as planarian flatworms. The functional properties of BIGR networks generate the first testable, quantitative hypotheses for biophysical mechanisms underlying the stability and adaptive regulation of anatomical bioelectric pattern. © 2017 The Author(s).
Li, Fangting
2017-01-01
The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat. The theory is equally applicable not only to intracellular dynamics of biochemical regulatory networks within an individual cell but also to tissue dynamics of heterogeneous interacting cell populations. The landscape for an individual cell, applicable to a population of isogenic non-interacting cells under the same environmental conditions, is defined on the counting space of intracellular chemical compositions x = (x1,x2, … ,xN) in a cell, where xℓ is the concentration of the ℓth biochemical species. Equivalently, for heterogeneous cell population dynamics xℓ is the number density of cells of the ℓth cell type. One of the insights derived from the landscape perspective is that the life history of an individual organism, which occurs on the hillsides of a landscape, is nearly deterministic and ‘programmed’, while population-wise an asynchronous non-equilibrium steady state resides mostly in the lowlands of the landscape. We argue that a dynamic ‘blue-sky’ bifurcation, as a representation of Waddington's landscape, is a more robust mechanism for a cell fate decision and subsequent differentiation than the widely pictured pitch-fork bifurcation. We revisit, in terms of the chemostatic driving forces upon active, living matter, the notions of near-equilibrium thermodynamic branches versus far-from-equilibrium states. The emergent
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.
NASA Astrophysics Data System (ADS)
Agrawal, Paras M.; Samadh, Abdul N. A.; Raff, Lionel M.; Hagan, Martin T.; Bukkapatnam, Satish T.; Komanduri, Ranga
2005-12-01
A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5.
A compositional framework for reaction networks
NASA Astrophysics Data System (ADS)
Baez, John C.; Pollard, Blake S.
Reaction networks, or equivalently Petri nets, are a general framework for describing processes in which entities of various kinds interact and turn into other entities. In chemistry, where the reactions are assigned ‘rate constants’, any reaction network gives rise to a nonlinear dynamical system called its ‘rate equation’. Here we generalize these ideas to ‘open’ reaction networks, which allow entities to flow in and out at certain designated inputs and outputs. We treat open reaction networks as morphisms in a category. Composing two such morphisms connects the outputs of the first to the inputs of the second. We construct a functor sending any open reaction network to its corresponding ‘open dynamical system’. This provides a compositional framework for studying the dynamics of reaction networks. We then turn to statics: that is, steady state solutions of open dynamical systems. We construct a ‘black-boxing’ functor that sends any open dynamical system to the relation that it imposes between input and output variables in steady states. This extends our earlier work on black-boxing for Markov processes.
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
1989-10-31
static approach to constrained networks has been used to develop a dynamic theory of constraint networks for problem solving. The scientific results of this development resulted in six scientific publications . (KT)
Universality in network dynamics
Barzel, Baruch; Barabási, Albert-László
2013-01-01
Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system's response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system's dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system's dynamics. PMID:24319492
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.
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. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Reaction networks and evolutionary game theory.
Veloz, Tomas; Razeto-Barry, Pablo; Dittrich, Peter; Fajardo, Alejandro
2014-01-01
The powerful mathematical tools developed for the study of large scale reaction networks have given rise to applications of this framework beyond the scope of biochemistry. Recently, reaction networks have been suggested as an alternative way to model social phenomena. In this "socio-chemical metaphor" molecular species play the role of agents' decisions and their outcomes, and chemical reactions play the role of interactions among these decisions. From here, it is possible to study the dynamical properties of social systems using standard tools of biochemical modelling. In this work we show how to use reaction networks to model systems that are usually studied via evolutionary game theory. We first illustrate our framework by modeling the repeated prisoners' dilemma. The model is built from the payoff matrix together with assumptions of the agents' memory and recognizability capacities. The model provides consistent results concerning the performance of the agents, and allows for the examination of the steady states of the system in a simple manner. We further develop a model considering the interaction among Tit for Tat and Defector agents. We produce analytical results concerning the performance of the strategies in different situations of agents' memory and recognizability. This approach unites two important theories and may produce new insights in classical problems such as the evolution of cooperation in large scale systems.
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.
Deblurring Signal Network Dynamics.
Kamps, Dominic; Dehmelt, Leif
2017-09-15
To orchestrate the function and development of multicellular organisms, cells integrate intra- and extracellular information. This information is processed via signal networks in space and time, steering dynamic changes in cellular structure and function. Defects in those signal networks can lead to developmental disorders or cancer. However, experimental analysis of signal networks is challenging as their state changes dynamically and differs between individual cells. Thus, causal relationships between network components are blurred if lysates from large cell populations are analyzed. To directly study causal relationships, perturbations that target specific components have to be combined with measurements of cellular responses within individual cells. However, using standard single-cell techniques, the number of signal activities that can be monitored simultaneously is limited. Furthermore, diffusion of signal network components limits the spatial precision of perturbations, which blurs the analysis of spatiotemporal processing in signal networks. Hybrid strategies based on optogenetics, surface patterning, chemical tools, and protein design can overcome those limitations and thereby sharpen our view into the dynamic spatiotemporal state of signal networks and enable unique insights into the mechanisms that control cellular function in space and time.
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
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
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.
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.
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.
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.
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.
Modular verification of chemical reaction network encodings via serializability analysis
Lakin, Matthew R.; Stefanovic, Darko; Phillips, Andrew
2015-01-01
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. PMID:27325906
Dias, C S; Araújo, N A M; Telo da Gama, M M
2017-07-06
Network fluids are structured fluids consisting of chains and branches. They are characterized by unusual physical properties, such as, exotic bulk phase diagrams, interfacial roughening and wetting transitions, and equilibrium and nonequilibrium gels. Here, we provide an overview of a selection of their equilibrium and dynamical properties. Recent research efforts towards bridging equilibrium and non-equilibrium studies are discussed, as well as several open questions. Copyright © 2017 Elsevier B.V. All rights reserved.
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.
Combining molecular dynamics with mesoscopic Green's function reaction dynamics simulations
NASA Astrophysics Data System (ADS)
Vijaykumar, Adithya; Bolhuis, Peter G.; ten Wolde, Pieter Rein
2015-12-01
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.
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.
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
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.
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.
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.
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.
Stochastic flux analysis of chemical reaction networks.
Kahramanoğulları, Ozan; Lynch, James F
2013-12-07
Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.
Stochastic flux analysis of chemical reaction networks
2013-01-01
Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153
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. Copyright © 2014 Elsevier Ltd. All rights reserved.
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
Multilayer Network Analysis of Nuclear Reactions
NASA Astrophysics Data System (ADS)
Zhu, Liang; Ma, Yu-Gang; Chen, Qu; Han, Ding-Ding
2016-08-01
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.
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.
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.
Grip on complexity in chemical reaction networks
2017-01-01
A new discipline of “systems chemistry” is emerging, which aims to capture the complexity observed in natural systems within a synthetic chemical framework. Living systems rely on complex networks of chemical reactions to control the concentration of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. To truly understand how living systems function, we need a complete understanding of how chemical reaction networks (CRNs) create function. We propose the development of a bottom-up approach to design and construct CRNs where we can follow the influence of single chemical entities on the properties of the network as a whole. Ultimately, this approach should allow us to not only understand such complex networks but also to guide and control their behavior. PMID:28845192
Grip on complexity in chemical reaction networks.
Wong, Albert S Y; Huck, Wilhelm T S
2017-01-01
A new discipline of "systems chemistry" is emerging, which aims to capture the complexity observed in natural systems within a synthetic chemical framework. Living systems rely on complex networks of chemical reactions to control the concentration of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. To truly understand how living systems function, we need a complete understanding of how chemical reaction networks (CRNs) create function. We propose the development of a bottom-up approach to design and construct CRNs where we can follow the influence of single chemical entities on the properties of the network as a whole. Ultimately, this approach should allow us to not only understand such complex networks but also to guide and control their behavior.
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
Collective Dynamics in Sparse Networks
NASA Astrophysics Data System (ADS)
Luccioli, Stefano; Olmi, Simona; Politi, Antonio; Torcini, Alessandro
2012-09-01
The microscopic and macroscopic dynamics of random networks is investigated in the strong-dilution limit (i.e., for sparse networks). By simulating chaotic maps, Stuart-Landau oscillators, and leaky integrate-and-fire neurons, we show that a finite connectivity (of the order of a few tens) is able to sustain a nontrivial collective dynamics even in the thermodynamic limit. Although the network structure implies a nonadditive dynamics, the microscopic evolution is extensive (i.e., the number of active degrees of freedom is proportional to the number of network elements).
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
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.
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
Fractional dynamics of complex networks
NASA Astrophysics Data System (ADS)
Turalska, Malgorzata; West, Bruce J.
2014-03-01
The relation between the behavior of a single element and the global dynamics of its host network is an open problem in the science of complex networks. Typically one attempts to infer the global dynamics by combining the behavior of single elements within the system, following a bottom-up approach. Here we address an inverse problem. We show that for a generic model within the Ising universality class it is possible to construct a description of the dynamics of an individual element, given the information about the network's global behavior. We demonstrate that the individual trajectory response to the collective motion of the network is described by a linear fractional differential equation, whose analytic solution is the Mittag-Leffler function. This solution is obtained through a subordination procedure without the necessity of linearizing the underlying dynamics, that is, the solution retains the influence of the nonlinear network dynamics on the individual. Moreover the solutions to the fractional equation of motion suggest a new direction for designing control mechanisms for complex networks. The implications of this new perspective are explored by introducing a control signal into a small number of network elements and analyzing the subsequent change in the network dynamics.
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…
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…
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
2017-05-01
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. Copyright © 2017 Elsevier Ltd. All rights reserved.
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.
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.
Entropy of dynamical social networks
NASA Astrophysics Data System (ADS)
Zhao, Kun; Karsai, Marton; Bianconi, Ginestra
2012-02-01
Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.
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.
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.
Dynamic behaviors in directed networks
Park, Sung Min; Kim, Beom Jun
2006-08-15
Motivated by the abundance of directed synaptic couplings in a real biological neuronal network, we investigate the synchronization behavior of the Hodgkin-Huxley model in a directed network. We start from the standard model of the Watts-Strogatz undirected network and then change undirected edges to directed arcs with a given probability, still preserving the connectivity of the network. A generalized clustering coefficient for directed networks is defined and used to investigate the interplay between the synchronization behavior and underlying structural properties of directed networks. We observe that the directedness of complex networks plays an important role in emerging dynamical behaviors, which is also confirmed by a numerical study of the sociological game theoretic voter model on directed 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
Network dynamics and cell physiology.
Tyson, J J; Chen, K; Novak, B
2001-12-01
Complex assemblies of interacting proteins carry out most of the interesting jobs in a cell, such as metabolism, DNA synthesis, movement and information processing. These physiological properties play out as a subtle molecular dance, choreographed by underlying regulatory networks. To understand this dance, a new breed of theoretical molecular biologists reproduces these networks in computers and in the mathematical language of dynamical systems.
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.
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.
Visualization of chemical reaction dynamics: Toward understanding complex polyatomic reactions
SUZUKI, Toshinori
2013-01-01
Polyatomic molecules have several electronic states that have similar energies. Consequently, their chemical dynamics often involve nonadiabatic transitions between multiple potential energy surfaces. Elucidating the complex reactions of polyatomic molecules is one of the most important tasks of theoretical and experimental studies of chemical dynamics. This paper describes our recent experimental studies of the multidimensional multisurface dynamics of polyatomic molecules based on two-dimensional ion/electron imaging. It also discusses ultrafast photoelectron spectroscopy of liquids for elucidating nonadiabatic electronic dynamics in aqueous solutions. PMID:23318678
Visualization of chemical reaction dynamics: toward understanding complex polyatomic reactions.
Suzuki, Toshinori
2013-01-01
Polyatomic molecules have several electronic states that have similar energies. Consequently, their chemical dynamics often involve nonadiabatic transitions between multiple potential energy surfaces. Elucidating the complex reactions of polyatomic molecules is one of the most important tasks of theoretical and experimental studies of chemical dynamics. This paper describes our recent experimental studies of the multidimensional multisurface dynamics of polyatomic molecules based on two-dimensional ion/electron imaging. It also discusses ultrafast photoelectron spectroscopy of liquids for elucidating nonadiabatic electronic dynamics in aqueous solutions. (Communicated by Hiroo INOKUCHI, M.J.A.)
Stochastic Simulation and Analysis of Biomolecular Reaction Networks
2009-01-01
and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not recieved much attention. In order to...exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data . Results: Specific issues...affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time
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
Dynamics of bimolecular reactions in solution.
Orr-Ewing, Andrew J
2015-04-01
Mechanisms of bimolecular chemical reactions in solution are amenable to study on picosecond timescales, both by transient absorption spectroscopy and by computer simulation. The dynamics of exothermic reactions of CN radicals and of Cl and F atoms with organic solutes in commonly used solvents are contrasted with the corresponding dynamics in the gas phase. Many characteristics of the gas-phase reaction dynamics persist in solution, such as efficient energy release to specific vibrational modes of the products. However, additional complexities associated with the presence of the solvent are open to investigation. These features of liquid-phase reactions include the role of solvent-solute complexes, solvent caging, coupling of the product motions to the solvent bath, thermalization of internally excited reaction products, incipient hydrogen bond formation, and involvement of charge-separated states that arise from proton transfer.
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.
Stochastic simulation and analysis of biomolecular reaction networks
Frazier, John M; Chushak, Yaroslav; Foy, Brent
2009-01-01
Background In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not recieved much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene in vitro transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data. Results Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures. Conclusion The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior. PMID:19534796
Stochastic simulation and analysis of biomolecular reaction networks.
Frazier, John M; Chushak, Yaroslav; Foy, Brent
2009-06-17
In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not received much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene in vitro transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data. Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures. The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior.
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.
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.
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).
Dynamic behavior of nonlinear networks
NASA Astrophysics Data System (ADS)
Choi, M. Y.; Huberman, B. A.
1983-08-01
We study the global dynamics of nonlinear networks made up of synchronous threshold elements. By writing a master equation for the system, we obtain an expression for the time dependence of its activity as a function of parameter values. We show that with both excitatory and inhibitory couplings, a network can display collective behavior which can be either multiple periodic or deterministic chaotic, a result that appears to be quite general.
Dynamics of associating networks
NASA Astrophysics Data System (ADS)
Tang, Shengchang; Habicht, Axel; Wang, Muzhou; Li, Shuaili; Seiffert, Sebastian; Olsen, Bradley
Associating polymers offer important technological solutions to renewable and self-healing materials, conducting electrolytes for energy storage and transport, and vehicles for cell and protein deliveries. The interplay between polymer topologies and association chemistries warrants new interesting physics from associating networks, yet poses significant challenges to study these systems over a wide range of time and length scales. In a series of studies, we explored self-diffusion mechanisms of associating polymers above the percolation threshold, by combining experimental measurements using forced Rayleigh scattering and analytical insights from a two-state model. Despite the differences in molecular structures, a universal super-diffusion phenomenon is observed when diffusion of molecular species is hindered by dissociation kinetics. The molecular dissociation rate can be used to renormalize shear rheology data, which yields an unprecedented time-temperature-concentration superposition. The obtained shear rheology master curves provide experimental evidence of the relaxation hierarchy in associating networks.
Reaction network analysis for thin film deposition processes
NASA Astrophysics Data System (ADS)
Ramakrishnasubramanian, Krishnaprasath
Understanding the growth of thin films produced by Atomic Layer Deposition (ALD) and Chemical Vapor Deposition (CVD) has been one of the most important challenge for surface chemists over the last two to three decades. There has been a lack of complete understanding of the surface chemistry behind these systems due to the dearth of experimental reaction kinetics data available. The data that do exist are generally derived through quantum computations. Thus, it becomes ever so important to develop a deposition model which not only predicts the bulk film chemistry but also explains its self-limiting nature and growth surface stability without the use of reaction rate data. The reaction network analysis tools developed in this thesis are based on a reaction factorization approach that aims to decouple the reaction rates by accounting for the chemical species surface balance dynamic equations. This process eliminates the redundant dynamic modes and identifies conserved modes as reaction invariants. The analysis of these invariants is carried out using a Species-Reaction (S-R) graph approach which also serves to simplify the representation of the complex reaction network. The S-R graph is self explanatory and consistent for all systems. The invariants can be easily extracted from the S-R graph by following a set of straightforward rules and this is demonstrated for the CVD of gallium nitride and the ALD of gallium arsenide. We propose that understanding invariants through these S-R graphs not only provides us with the physical significance of conserved modes but also give us a better insight into the deposition mechanism.
2013-06-13
possible network configurations. It is important to address the impacts this modification has to the cryptanalysis of AES. In [19], the creators address...differential and linear cryptanalysis through the propagation of bit patterns over the algorithm steps. Daemen and Rijmen state the ShiftRow has the...linear cryptanalysis . The ShiftInterfaces step provides further diffusion of the bit pattern, since the step moves bits of one layer into another. The
Predictive structural dynamic network analysis.
Chen, Rong; Herskovits, Edward H
2015-04-30
Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.
Time-Dependent Molecular Reaction Dynamics
NASA Astrophysics Data System (ADS)
Öhrn, Yngve
2007-11-01
This paper is a brief review of a time-dependent, direct, nonadiabatic theory of molecular processes called Electron Nuclear Dynamics (END). This approach to the study of molecular reaction dynamics is a hierarchical theory that can be applied at various levels of approximation. The simplest level of END uses classical nuclei and represents all electrons by a single, complex, determinantal wave function. The wave function parameters such as average nuclear positions and momenta, and molecular orbital coefcients carry the time dependence and serve as dynamical variables. Examples of application are given of the simplest level of END to ion-atom and ion-molecule reactions.
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'. © 2017 The Author(s).
Solving moment hierarchies for chemical reaction networks
NASA Astrophysics Data System (ADS)
Krishnamurthy, Supriya; Smith, Eric
2017-10-01
The study of chemical reaction networks (CRN’s) is a very active field. Earlier well-known results (Feinberg 1987 Chem. Enc. Sci. 42 2229, Anderson et al 2010 Bull. Math. Biol. 72 1947) identify a topological quantity called deficiency, for any CRN, which, when exactly equal to zero, leads to a unique factorized steady-state for these networks. No results exist however for the steady states of non-zero-deficiency networks. In this paper, we show how to write the full moment-hierarchy for any non-zero-deficiency CRN obeying mass-action kinetics, in terms of equations for the factorial moments. Using these, we can recursively predict values for lower moments from higher moments, reversing the procedure usually used to solve moment hierarchies. We show, for non-trivial examples, that in this manner we can predict any moment of interest, for CRN’s with non-zero deficiency and non-factorizable steady states.
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.
Automated Discovery of Complex Reaction Networks: Reaction Topology, Thermochemistry and Kinetics
2015-07-08
AFRL-OSR-VA-TR-2015-0165 Automated Discovery of Complex Reaction Networks Reaction Walter Pfaendtner UNIVERSITY OF WASHINGTON Final Report 07/08/2015...01-04-2012 to 31-03-2015 4. TITLE AND SUBTITLE Automated Discovery of Complex Reaction Networks Reaction 5a. CONTRACT NUMBER 5b. GRANT NUMBER...incoming airflow, short lifetime of intermediates, the complexity of combustion reaction networks, and the difficulty to identify important reaction
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.
Adaptive hybrid simulations for multiscale stochastic reaction networks
Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa
2015-01-21
The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.
Adaptive hybrid simulations for multiscale stochastic reaction networks.
Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa
2015-01-21
The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.
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
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.
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
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.
Viologen-Based Conjugated Covalent Organic Networks via Zincke Reaction.
Das, Gobinda; Skorjanc, Tina; Sharma, Sudhir Kumar; Gándara, Felipe; Lusi, Matteo; Shankar Rao, D S; Vimala, Sridurai; Krishna Prasad, Subbarao; Raya, Jesus; Han, Dong Suk; Jagannathan, Ramesh; Olsen, John-Carl; Trabolsi, Ali
2017-07-19
Morphology influences the functionality of covalent organic networks and determines potential applications. Here, we report for the first time the use of Zincke reaction to fabricate, under either solvothermal or microwave conditions, a viologen-linked covalent organic network in the form of hollow particles or nanosheets. The synthesized materials are stable in acidic, neutral, and basic aqueous solutions. Under basic conditions, the neutral network assumes radical cationic character without decomposing or changing structure. Solvent polarity and heating method determine product morphology. Depending upon solvent polarity, the resulting polymeric network forms either uniform self-templated hollow spheres (HS) or hollow tubes (HT). The spheres develop via an inside-out Ostwald ripening mechanism. Interestingly, microwave conditions and certain solvent polarities result in the formation of a robust covalent organic gel framework (COGF) that is organized in nanosheets stacked several layers thick. In the gel phase, the nanosheets are crystalline and form honeycomb lattices. The use of the Zincke reaction has previously been limited to the synthesis of small viologen molecules and conjugated viologen oligomers. Its application here expands the repertoire of tools for the fabrication of covalent organic networks (which are usually prepared by dynamic covalent chemistry) and for the synthesis of viologen-based materials. All three materials-HT, HS, and COGF-serve as efficient adsorbents of iodine due to the presence of the cationic viologen linker and, in the cases of HT and HS, permanent porosity.
Wealth dynamics on complex networks
NASA Astrophysics Data System (ADS)
Garlaschelli, Diego; Loffredo, Maria I.
2004-07-01
We study a model of wealth dynamics (Physica A 282 (2000) 536) which mimics transactions among economic agents. The outcomes of the model are shown to depend strongly on the topological properties of the underlying transaction network. The extreme cases of a fully connected and a fully disconnected network yield power-law and log-normal forms of the wealth distribution, respectively. We perform numerical simulations in order to test the model on more complex network topologies. We show that the mixed form of most empirical distributions (displaying a non-smooth transition from a log-normal to a power-law form) can be traced back to a heterogeneous topology with varying link density, which on the other hand is a recently observed property of real networks.
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
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.
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. Copyright © 2016 Elsevier Inc. All rights reserved.
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.
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.
Performance improvements for nuclear reaction network integration
NASA Astrophysics Data System (ADS)
Longland, R.; Martin, D.; José, J.
2014-03-01
Aims: The aim of this work is to compare the performance of three reaction network integration methods used in stellar nucleosynthesis calculations. These are the Gear's backward differentiation method, Wagoner's method (a 2nd-order Runge-Kutta method), and the Bader-Deuflehard semi-implicit multi-step method. Methods: To investigate the efficiency of each of the integration methods considered here, a test suite of temperature and density versus time profiles is used. This suite provides a range of situations ranging from constant temperature and density to the dramatically varying conditions present in white dwarf mergers, novae, and X-ray bursts. Some of these profiles are obtained separately from full hydrodynamic calculations. The integration efficiencies are investigated with respect to input parameters that constrain the desired accuracy and precision. Results: Gear's backward differentiation method is found to improve accuracy, performance, and stability in integrating nuclear reaction networks. For temperature-density profiles that vary strongly with time, it is found to outperform the Bader-Deuflehard method (although that method is very powerful for more smoothly varying profiles). Wagoner's method, while relatively fast for many scenarios, exhibits hard-to-predict inaccuracies for some choices of integration parameters owing to its lack of error estimations.
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.
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.
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.
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.
Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks
Xu, Jianfeng; Lan, Yueheng
2015-01-01
Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347
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
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
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.
Deconvolution of dynamic mechanical networks
Hinczewski, Michael; von Hansen, Yann; Netz, Roland R.
2010-01-01
Time-resolved single-molecule biophysical experiments yield data that contain a wealth of dynamic information, in addition to the equilibrium distributions derived from histograms of the time series. In typical force spectroscopic setups the molecule is connected via linkers to a readout device, forming a mechanically coupled dynamic network. Deconvolution of equilibrium distributions, filtering out the influence of the linkers, is a straightforward and common practice. We have developed an analogous dynamic deconvolution theory for the more challenging task of extracting kinetic properties of individual components in networks of arbitrary complexity and topology. Our method determines the intrinsic linear response functions of a given object in the network, describing the power spectrum of conformational fluctuations. The practicality of our approach is demonstrated for the particular case of a protein linked via DNA handles to two optically trapped beads at constant stretching force, which we mimic through Brownian dynamics simulations. Each well in the protein free energy landscape (corresponding to folded, unfolded, or possibly intermediate states) will have its own characteristic equilibrium fluctuations. The associated linear response function is rich in physical content, because it depends both on the shape of the well and its diffusivity—a measure of the internal friction arising from such processes as the transient breaking and reformation of bonds in the protein structure. Starting from the autocorrelation functions of the equilibrium bead fluctuations measured in this force clamp setup, we show how an experimentalist can accurately extract the state-dependent protein diffusivity using a straightforward two-step procedure. PMID:21118989
Wang, Shufen; Yuan, Jiuchuang; Li, Huixing; Chen, Maodu
2017-08-02
In order to study the dynamics of the reaction H((2)S) + NaH(X(1)Σ(+)) → Na((2)S) + H2(X(1)Σg(+)), a new potential energy surface (PES) for the ground state of the NaH2 system is constructed based on 35 730 ab initio energy points. Using basis sets of quadruple zeta quality, multireference configuration interaction calculations with Davidson correction were carried out to obtain the ab initio energy points. The neural network method is used to fit the PES, and the root mean square error is very small (0.00639 eV). The bond lengths, dissociation energies, zero-point energies and spectroscopic constants of H2(X(1)Σg(+)) and NaH(X(1)Σ(+)) obtained on the new NaH2 PES are in good agreement with the experiment data. On the new PES, the reactant coordinate-based time-dependent wave packet method is applied to study the reaction dynamics of H((2)S) + NaH(X(1)Σ(+)) → Na((2)S) + H2(X(1)Σg(+)), and the reaction probabilities, integral cross-sections (ICSs) and differential cross-sections (DCSs) are obtained. There is no threshold in the reaction due to the absence of an energy barrier on the minimum energy path. When the collision energy increases, the ICSs decrease from a high value at low collision energy. The DCS results show that the angular distribution of the product molecules tends to the forward direction. Compared with the LiH2 system, the NaH2 system has a larger mass and the PES has a larger well at the H-NaH configuration, which leads to a higher ICS value in the H((2)S) + NaH(X(1)Σ(+)) → Na((2)S) + H2(X(1)Σg(+)) reaction. Because the H((2)S) + NaH(X(1)Σ(+)) → Na((2)S) + H2(X(1)Σg(+)) reaction releases more energy, the product molecules can be excited to a higher vibrational state.
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 spatio-temporal 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. PMID:22161349
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
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.
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
Dynamic network management and service integration for airborne network
NASA Astrophysics Data System (ADS)
Pan, Wei; Li, Weihua
2009-12-01
The development of airborne network is conducive to resource sharing, flight management and interoperability in civilian and military aviation fields. To enhance the integrated ability of airborne network, the paper focuses on dynamic network management and service integration architecture for airborne network (DNMSIAN). Adaptive routing based on the mapping mechanism between connection identification and routing identification can provide diversified network access, and ensure the credibility and mobility of the aviation information exchange. Dynamic network management based on trustworthy cluster can ensure dynamic airborne network controllable and safe. Service integration based on semantic web and ontology can meet the customized and diversified needs for aviation information services. The DNMSIAN simulation platform demonstrates that our proposed methods can effectively perform dynamic network management and service integration.
Nuclear Reaction Data Center Network; International and Asia
Kato, Kiyoshi; Otuka, Naohiko
2009-03-31
The activities of the Nuclear Reaction Data Centre at Hokkaido University and International Network of Nuclear Reaction Data Centre (NRDC) are explained. Finally, collaboration in the nuclear data activities among Asian countries are proposed.
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, Y.T.
1990-03-01
The major thrust of this research project is to elucidate detailed dynamics of simple 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. 34 refs.
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.
Sensitivity and network topology in chemical reaction systems
NASA Astrophysics Data System (ADS)
Okada, Takashi; Mochizuki, Atsushi
2017-08-01
In living cells, biochemical reactions are catalyzed by specific enzymes and connect to one another by sharing substrates and products, forming complex networks. In our previous studies, we established a framework determining the responses to enzyme perturbations only from network topology, and then proved a theorem, called the law of localization, explaining response patterns in terms of network topology. In this paper, we generalize these results to reaction networks with conserved concentrations, which allows us to study any reaction system. We also propose network characteristics quantifying robustness. We compare E. coli metabolic network with randomly rewired networks, and find that the robustness of the E. coli network is significantly higher than that of the random networks.
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
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.
Discerning connectivity from dynamics in climate networks
NASA Astrophysics Data System (ADS)
Paluš, M.; Hartman, D.; Hlinka, J.; Vejmelka, M.
2011-10-01
The bias due to dynamical memory (serial correlations) in an association/dependence measure (absolute cross-correlation) is demonstrated in model data and identified in time series of meteorological variables used for construction of climate networks. Accounting for such bias in inferring links of the climate network markedly changes the network topology and allows to observe previously hidden phenomena in climate network evolution.
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.
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
On electronic representations in molecular reaction dynamics
NASA Astrophysics Data System (ADS)
Killian, Benjamin J.
For many decades, the field of chemical reaction dynamics has utilized computational methods that rely on potential energy surfaces that are constructed using stationary-state calculations. These methods are typically devoid of dynamical couplings between the electronic and nuclear degrees of freedom, a fact that can result in incorrect descriptions of dynamical processes. Often, non-adiabatic coupling expressions are included in these methodologies. The Electron-Nuclear Dynamics (END) formalism, in contrast, circumvents these deficiencies by calculating all intermolecular forces directly at each time step in the dynamics and by explicitly maintaining all electronic-nuclear couplings. The purpose of this work is to offer two new frameworks for implementing electronic representations in dynamical calculations. Firstly, a new schema is proposed for developing atomic basis sets that are consistent with dynamical calculations. Traditionally, basis sets have been designed for use in stationary-state calculations of the structures and properties of molecules in their ground states. As a consequence of common construction techniques that utilize energy optimization methods, the unoccupied orbitals bear little resemblance to physical virtual atomic orbitals. We develop and implement a method for basis set construction that relies upon physical properties of atomic orbitals and that results in meaningful virtual orbitals. These basis sets are shown to provide a significant improvement in the accuracy of calculated dynamical properties such as charge transfer probabilities. Secondly, the theoretical framework of END is expanded to incorporate a multi-configurational representation for electrons. This formalism, named Vector Hartree-Fock, is based in the theory of vector coherent states and utilizes a complete active space electronic representation. The Vector Hartree-Fock method is fully disclosed, with derivation of the equations of motion. The expressions for the equation
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.
Cluster Dynamics: Fast Reactions and Coulomb Explosion
NASA Astrophysics Data System (ADS)
Poth, Lutz; Wisniewski, Eric S.; Welford Castleman, A., Jr.
2002-07-01
One of the enduring mysteries of chemistry is exactly what happens during the few femtoseconds it takes for a chemical reaction to transpire. We see what goes in and what comes out, but the in between happens so fast it has proved devilishly hard to study. One promising area of investigation involves the use of clusters—tiny atomic or molecular assemblages with weak bonding. Clusters can be rapidly ionized using laser pulses of very short duration, producing like charges of sufficient intensity and closeness to cause explosive repulsion, called Coulomb explosion. By carefully adjusting the delivery interval between femtosecond-duration laser pulses to clusters, the authors have begun to reveal the secrets of reaction dynamics.
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.
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.
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.
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
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.
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
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
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.
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.
Networked Dynamic Systems: Identification, Controllability, and Randomness
NASA Astrophysics Data System (ADS)
Nabi-Abdolyousefi, Marzieh
The presented dissertation aims to develop a graph-centric framework for the analysis and synthesis of networked dynamic systems (NDS) consisting of multiple dynamic units that interact via an interconnection topology. We examined three categories of network problems, namely, identification, controllability, and randomness. In network identification, as a subclass of inverse problems, we made an explicit relation between the input-output behavior of an NDS and the underlying interacting network. In network controllability, we provided structural and algebraic insights into features of the network that enable external signal(s) to control the state of the nodes in the network for certain classes of interconnections, namely, path, circulant, and Cartesian networks. We also examined the relation between network controllability and the symmetry structure of the graph. Motivated by the analysis results for the controllability and observability of deterministic networks, a natural question is whether randomness in the network layer or in the layer of inputs and outputs generically leads to favorable system theoretic properties. In this direction, we examined system theoretic properties of random networks including controllability, observability, and performance of optimal feedback controllers and estimators. We explored some of the ramifications of such an analysis framework in opinion dynamics over social networks and sensor networks in estimating the real-time position of a Seaglider from experimental data.
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.
Local Dynamics in Trained Recurrent Neural Networks
NASA Astrophysics Data System (ADS)
Rivkind, Alexander; Barak, Omri
2017-06-01
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.
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.
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.
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.
Hierarchical decomposition of dynamically evolving regulatory networks.
Ay, Ahmet; Gong, Dihong; Kahveci, Tamer
2015-05-15
Gene regulatory networks describe the interplay between genes and their products. These networks control almost every biological activity in the cell through interactions. The hierarchy of genes in these networks as defined by their interactions gives important insights into how these functions are governed. Accurately determining the hierarchy of genes is however a computationally difficult problem. This problem is further complicated by the fact that an intrinsic characteristic of regulatory networks is that the wiring of interactions can change over time. Determining how the hierarchy in the gene regulatory networks changes with dynamically evolving network topology remains to be an unsolved challenge. In this study, we develop a new method, named D-HIDEN (Dynamic-HIerarchical DEcomposition of Networks) to find the hierarchy of the genes in dynamically evolving gene regulatory network topologies. Unlike earlier methods, which recompute the hierarchy from scratch when the network topology changes, our method adapts the hierarchy based on the wiring of the interactions only for the nodes which have the potential to move in the hierarchy. We compare D-HIDEN to five currently available hierarchical decomposition methods on synthetic and real gene regulatory networks. Our experiments demonstrate that D-HIDEN significantly outperforms existing methods in running time, accuracy, or both. Furthermore, our method is robust against dynamic changes in hierarchy. Our experiments on human gene regulatory networks suggest that our method may be used to reconstruct hierarchy in gene regulatory 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.
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.
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.
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
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.
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.
Dynamical advantages of scale-free networks.
Willeboordse, Frederick H
2006-01-13
A dynamical analysis of common network topologies is given and it is reported that a scale-free structure has two vital and distinctive features. First, complex but nevertheless reproducible states exist and, second, single-site induced state switching reminiscent of gene-expression control exists also. This indicates that scale-free networks have key dynamical advantages over other network topologies that could have contributed to their evolutionary success and thus may provide another reason for their prevalence in nature.
Dynamic Neural Networks Supporting Memory Retrieval
St. Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.
2011-01-01
How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) Medial Prefrontal Cortex (PFC) Network, associated with self-referential processes, 2) Medial Temporal Lobe (MTL) Network, associated with memory, 3) Frontoparietal Network, associated with strategic search, and 4) Cingulooperculum Network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior. PMID:21550407
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
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
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.
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.
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.
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.
Temporal fidelity in dynamic social networks
NASA Astrophysics Data System (ADS)
Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex `Sandy'; Lehmann, Sune
2015-10-01
It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis.
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.
Reaction dynamics and photochemistry of divalent systems
Davis, H. Floyd
1992-05-01
Results are presented of molecular beam studies of bimolecular and unimolecular reactions of Ba. Chapter 1 discusses the reaction Ba + NO_{2}. Formation of the dominant BaO(^{1}Σ) + NO products resulted primarily from decay of long-lived Ba^{+}NO_{2}^{-} collision complexes. Secondary mechanisms led to formation of forward scattered, internally excited BaO, and BaNO + O. D_{o}(Ba-NO) = 65±20 kcal/mol. Reactions of ground state and electronically excited Ba with water and alcohols are examined in Chapter 2. Reaction of Ba(^{1}S) + H^{2}O led to BaO + H_{2}, whereas excited state Ba(^{1}D) + H_{2}O reacted to form BaOH + H. Collisions between Ba and CH_{3}OH led to BaOCH_{3} + H. Radical channels involve H-atom migration and are promoted by excitation of the incident Ba atom. In Chapter 3, reactions of Ba(^{1}S) with ClO_{2}2 and O_{3} are discussed. Again, direct and complex mechanisms were observed. Formation of BaCl + O_{2} from decomposition of Ba^{+}ClO_{2}^{-} accounted for 10% of total reaction crass section. Although Ba + O_{3} → BaO + 0_{2} occurs primarily by direct reaction mechanisms, the secondary channel Ba + 0_{3} → BaO_{2} + 0 involved decay of long lived Ba^{1+}O_{3}^{-} intermediates. D_{o}(Ba-O_{2}) = 120 ±20 kcal/mol. Photodissociation dynamics of NO_{3} is explored in chapter 4. Visible excitation leads to formation of NO + 0_{2} and NO_{2} + O. Wavelength dependence of branching ratios is investigated. D_{o}(O-NO_{2}) = 48.55 kcal/mole ;and calculate ΔH_{f}(NO_{3}) = 17.75 kcal/mole (298K). Chapter 5 discusses the photodissociation of OClO in a molecular beam. Although ClO(^{2}II) + O(^{3}P) is dominant, Cl(^{2}P) + O_{2 }
Dynamic network analysis of protein interactions
NASA Astrophysics Data System (ADS)
Almaas, Eivind; Deri, Joya
2007-03-01
Network approaches have recently become a popular tool to study complex systems such as cellular metabolism and protein interactions. A substantial number of analyses of the protein interaction network (PIN) of the yeast Saccharomyces cerevisiae have considered this network as a static entity, not taking the network's dynamic nature into account. Here, we examine the time-variation of gene regulation superimposed on the PIN by defining mRNA expression profiles throughout the cell cycle as node weights. To characterize these network dynamics, we have both developed a set of novel network measures as well as studied previously published measures for weighted networks. We expect that our approach will provide a deeper understanding of protein regulation during the cell cycle.
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.
Exploring Collective Dynamics in Communication Networks
Yuan, Jian; Mills, Kevin
2002-01-01
A communication network, such as the Internet, comprises a complex system where cooperative phenomena may emerge from interactions among various traffic flows generated and forwarded by individual nodes. To identify and understand such phenomena, we model a network as a two-dimensional cellular automaton. We suspect such models can promote better understanding of the spatial-temporal evolution of network congestion, and other emergent phenomena in communication networks. To search the behavior space of the model, we study dynamic patterns arising from interactions among traffic flows routed across shared network nodes, as we employ various configurations of parameters and two different congestion-control algorithms. In this paper, we characterize correlation in congestion behavior within the model at different system sizes and time granularities. As expected, we find that long-range dependence (LRD) appears at some time granularities, and that for a given network size LRD decays as time granularity increases. As network size increases, we find that long-range dependence exists at larger time scales. To distinguish effects due to network size from effects due to collective phenomena, we compare congestion behavior within networks of selected sizes to congestion behavior within comparably sized sub-areas in a larger network. We find stronger long-range dependence for sub-areas within the larger network. This suggests the importance of modeling networks of sufficiently large size when studying the effects of collective dynamics. PMID:27446726
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.
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.
Complex networks repair strategies: Dynamic models
NASA Astrophysics Data System (ADS)
Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang
2017-09-01
Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree < k > and enhances network invulnerability.
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
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.
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.
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
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
The Origin of Large Molecules in Primordial Autocatalytic Reaction Networks
Giri, Varun; Jain, Sanjay
2012-01-01
Large molecules such as proteins and nucleic acids are crucial for life, yet their primordial origin remains a major puzzle. The production of large molecules, as we know it today, requires good catalysts, and the only good catalysts we know that can accomplish this task consist of large molecules. Thus the origin of large molecules is a chicken and egg problem in chemistry. Here we present a mechanism, based on autocatalytic sets (ACSs), that is a possible solution to this problem. We discuss a mathematical model describing the population dynamics of molecules in a stylized but prebiotically plausible chemistry. Large molecules can be produced in this chemistry by the coalescing of smaller ones, with the smallest molecules, the ‘food set’, being buffered. Some of the reactions can be catalyzed by molecules within the chemistry with varying catalytic strengths. Normally the concentrations of large molecules in such a scenario are very small, diminishing exponentially with their size. ACSs, if present in the catalytic network, can focus the resources of the system into a sparse set of molecules. ACSs can produce a bistability in the population dynamics and, in particular, steady states wherein the ACS molecules dominate the population. However to reach these steady states from initial conditions that contain only the food set typically requires very large catalytic strengths, growing exponentially with the size of the catalyst molecule. We present a solution to this problem by studying ‘nested ACSs’, a structure in which a small ACS is connected to a larger one and reinforces it. We show that when the network contains a cascade of nested ACSs with the catalytic strengths of molecules increasing gradually with their size (e.g., as a power law), a sparse subset of molecules including some very large molecules can come to dominate the system. PMID:22238620
Functional Roles of Slow Enzyme Conformational Changes in Network Dynamics
Wu, Zhanghan; Xing, Jianhua
2012-01-01
Extensive studies from different fields reveal that many macromolecules, especially enzymes, show slow transitions among different conformations. This phenomenon is named such things as dynamic disorder, heterogeneity, hysteretic or mnemonic enzymes across these different fields, and has been directly demonstrated by single molecule enzymology and NMR studies recently. We analyzed enzyme slow conformational changes in the context of regulatory networks. A single enzymatic reaction with slow conformational changes can filter upstream network noises, and can either resonantly respond to the system stimulus at certain frequencies or respond adaptively for sustained input signals of the network fluctuations. It thus can serve as a basic functional motif with properties that are normally for larger intermolecular networks in the field of systems biology. We further analyzed examples including enzymes functioning against pH fluctuations, metabolic state change of Artemia embryos, and kinetic insulation of fluctuations in metabolic networks. The study also suggests that hysteretic enzymes may be building blocks of synthetic networks with various properties such as narrow-banded filtering. The work fills the missing gap between studies on enzyme biophysics and network level dynamics, and reveals that the coupling between the two is functionally important; it also suggests that the conformational dynamics of some enzymes may be evolutionally selected. PMID:23009855
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.
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
2015-01-01
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. PMID:26173555
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.
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.
A probability generating function method for stochastic reaction networks.
Kim, Pilwon; Lee, Chang Hyeong
2012-06-21
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 G(2)/M transition model.
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.
A Dynamic Navigation Algorithm Considering Network Disruptions
NASA Astrophysics Data System (ADS)
Jiang, J.; Wu, L.
2014-04-01
In traffic network, link disruptions or recoveries caused by sudden accidents, bad weather and traffic congestion, lead to significant increase or decrease in travel times on some network links. Similar situation also occurs in real-time emergency evacuation plan in indoor areas. As the dynamic nature of real-time network information generates better navigation solutions than the static one, a real-time dynamic navigation algorithm for emergency evacuation with stochastic disruptions or recoveries in the network is presented in this paper. Compared with traditional existing algorithms, this new algorithm adjusts pre-existing path to a new optimal one according to the changing link travel time. With real-time network information, it can provide the optional path quickly to adapt to the rapid changing network properties. Theoretical analysis and experimental results demonstrate that this proposed algorithm performs a high time efficiency to get exact solution and indirect information can be calculated in spare time.
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.
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.
Information Dynamics in Networks: Models and Algorithms
2016-09-13
the economics and computer science communities . Such a model of externality is motivated by several factors: • The physical effect of the number of...Information Dynamics in Networks: Models and Algorithms In this project, we investigated how network structure interplays with higher level processes in...online social networks. We investigated the appropriateness of existing mathematical models for explaining the structure of retweet cascades on
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.
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.
Krumholz, Elias W.; Libourel, Igor G. L.
2015-01-01
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. PMID:26041773
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.
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.
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
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.
The molecular dynamics of atmospheric reaction
NASA Technical Reports Server (NTRS)
Polanyi, J. C.
1971-01-01
Detailed information about the chemistry of the upper atmosphere took the form of quantitative data concerning the rate of reaction into specified states of product vibration, rotation and translation for exothermic reaction, as well as concerning the rate of reaction from specified states of reagent vibration, rotation and translation for endothermic reaction. The techniques used were variants on the infrared chemiluminescence method. Emphasis was placed on reactions that formed, and that removed, vibrationally-excited hydroxyl radicals. Fundamental studies were also performed on exothermic reactions involving hydrogen halides.
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
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.
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. PMID:26555073
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
Cellular Metabolic Network Analysis: Discovering Important Reactions in Treponema pallidum
Chen, Xueying; Zhao, Min; Qu, Hong
2015-01-01
T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pallidum, critical reactions are identified. As a comparison, we also apply the analytical approaches to the metabolic network of H. pylori to find coregulated drug targets and unique drug targets for different microorganisms. Based on the clustering results, all reactions are further classified into various roles. Therefore, the general picture of their metabolic network is obtained and two types of reactions, both of which are involved in nucleic acid metabolism, are found to be essential for T. pallidum. It is also discovered that both hubs of reactions and the isolated reactions in purine and pyrimidine metabolisms play important roles in T. pallidum. These reactions could be potential drug targets for treating syphilis. PMID:26495292
Gaussian estimates on networks with dynamic stochastic boundary conditions
NASA Astrophysics Data System (ADS)
Cordoni, Francesco; di Persio, Luca
In this paper we prove the existence and uniqueness for the solution to a stochastic reaction-diffusion equation, defined on a network, and subjected to nonlocal dynamic stochastic boundary conditions. The result is obtained by deriving a Gaussian-type estimate for the related leading semigroup, under rather mild regularity assumptions on the coefficients. An application of the latter to a stochastic optimal control problem on graphs, is also provided.
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.
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.
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
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.
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.
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
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.
Synchronization of fractional order complex dynamical networks
NASA Astrophysics Data System (ADS)
Wang, Yu; Li, Tianzeng
2015-06-01
In this letter the synchronization of complex dynamical networks with fractional order chaotic nodes is studied. A fractional order controller for synchronization of complex network is presented. Some new sufficient synchronization criteria are proposed based on the Lyapunov stability theory and the LaSalle invariance principle. These synchronization criteria can apply to an arbitrary fractional order complex network in which the coupling-configuration matrix and the inner-coupling matrix are not assumed to be symmetric or irreducible. It means that this method is more general and effective. Numerical simulations of two fractional order complex networks demonstrate the universality and the effectiveness of the proposed method.
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
Stochastic Simulation of Biomolecular Networks in Dynamic Environments
Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G.
2016-01-01
Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits. PMID:27248512
Stochastic Simulation of Biomolecular Networks in Dynamic Environments.
Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G
2016-06-01
Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.
Evolutionary dynamics of prokaryotic transcriptional regulatory networks.
Madan Babu, M; Teichmann, Sarah A; Aravind, L
2006-04-28
The structure of complex transcriptional regulatory networks has been studied extensively in certain model organisms. However, the evolutionary dynamics of these networks across organisms, which would reveal important principles of adaptive regulatory changes, are poorly understood. We use the known transcriptional regulatory network of Escherichia coli to analyse the conservation patterns of this network across 175 prokaryotic genomes, and predict components of the regulatory networks for these organisms. We observe that transcription factors are typically less conserved than their target genes and evolve independently of them, with different organisms evolving distinct repertoires of transcription factors responding to specific signals. We show that prokaryotic transcriptional regulatory networks have evolved principally through widespread tinkering of transcriptional interactions at the local level by embedding orthologous genes in different types of regulatory motifs. Different transcription factors have emerged independently as dominant regulatory hubs in various organisms, suggesting that they have convergently acquired similar network structures approximating a scale-free topology. We note that organisms with similar lifestyles across a wide phylogenetic range tend to conserve equivalent interactions and network motifs. Thus, organism-specific optimal network designs appear to have evolved due to selection for specific transcription factors and transcriptional interactions, allowing responses to prevalent environmental stimuli. The methods for biological network analysis introduced here can be applied generally to study other networks, and these predictions can be used to guide specific experiments.
Design of multi-phase dynamic chemical networks
NASA Astrophysics Data System (ADS)
Chen, Chenrui; Tan, Junjun; Hsieh, Ming-Chien; Pan, Ting; Goodwin, Jay T.; Mehta, Anil K.; Grover, Martha A.; Lynn, David G.
2017-08-01
Template-directed polymerization reactions enable the accurate storage and processing of nature's biopolymer information. This mutualistic relationship of nucleic acids and proteins, a network known as life's central dogma, is now marvellously complex, and the progressive steps necessary for creating the initial sequence and chain-length-specific polymer templates are lost to time. Here we design and construct dynamic polymerization networks that exploit metastable prion cross-β phases. Mixed-phase environments have been used for constructing synthetic polymers, but these dynamic phases emerge naturally from the growing peptide oligomers and create environments suitable both to nucleate assembly and select for ordered templates. The resulting templates direct the amplification of a phase containing only chain-length-specific peptide-like oligomers. Such multi-phase biopolymer dynamics reveal pathways for the emergence, self-selection and amplification of chain-length- and possibly sequence-specific biopolymers.
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
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
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.
Efficient inference for hybrid dynamic Bayesian networks
NASA Astrophysics Data System (ADS)
Chang, Kuo Chu; Chen, Hongda
2005-07-01
This paper is a revision of a paper presented at the SPIE conference on Signal Processing, Senior Fusion, and Target Recognition XII, Aug. 2004, Orlando, Florida. The paper presented there appears (unrefereed) in SPIE Proceedings Vol. 5429. Bayesian networks for static as well as for dynamic cases have been the subject of a great deal of theoretical analysis and practical inference-algorithm development in the research community of artificial intelligence, machine learning, and pattern recognition. After summarizing the well-known theory of discrete and continuous Bayesian networks, we introduce an efficient reasoning scheme into hybrid Bayesian networks. In addition to illustrating the similarities between the dynamic Bayesian networks and the Kalman filter, we present a computationally efficient approach for the inference problem of hybrid dynamic Bayesian networks (HDBNs). The proposed method is based on the separation of the dynamic and static nodes, and subsequent hypercubic partitions via the decision tree algorithm. Experiments show that with high statistical confidence the novel algorithm used in the HDBN performs favorably in the trade-offs of computational complexity and accuracy performance, compared to other exact and approximate methods for applications with uncertainty in a dynamic system.
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.
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
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).
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
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
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
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.
Critical dynamics in associative memory networks
Uhlig, Maximilian; Levina, Anna; Geisel, Theo; Herrmann, J. Michael
2013-01-01
Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network only endowed with Hebbian learning does not allow for simultaneous information storage and criticality. However, the critical regime can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity. PMID:23898261
Perceived personal networks as mediators of stress reactions.
Steinglass, P; Weisstub, E; De-Nour, A K
1988-10-01
The evacuation of an Israeli community in the Sinai peninsula afforded an unusual opportunity to study the longitudinal relationships between personal network characteristics and psychosocial adjustment. Friendship network characteristics proved highly stable longitudinally but were poor predictors of long-term adjustment. The kinship network characteristics before relocation were better predictors, despite the fact that kin were not actually present in the community before relocation. These data support a hypothesis that the perception of social embeddedness rather than the actual availability of social supports mediates reactions to stressful life events.
Incremental Centrality Algorithms for Dynamic Network Analysis
2013-08-01
dynamically. Betweenness is also commonly discussed within the concept of vulnerability of networks to different attacks since it is useful for...perform coordinated attacks . In other words, we issue shrinking network updates and introduce progressive failures to the central nodes. Simulated...protocol ( OLSR ) as the routing protocol. It is a proactive link state routing protocol where each node stores next-hop destinations for all nodes in
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.
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
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.
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. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
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.
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.
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.
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.
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.
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.
Modeling brain electrical activity in epilepsy by reaction-diffusion cellular neural networks
NASA Astrophysics Data System (ADS)
Gollas, F.; Tetzlaff, R.
2005-06-01
Reaction-Diffusion systems can be applied to describe a broad class of nonlinear phenomena, in particular in biological systems and in the propagation of nonlinear waves in excitable media. Especially, pattern formation and chaotic behavior are observed in Reaction-Diffusion systems and can be analyzed. Due to their structure multi-layer Cellular Neural Networks (CNN) are capable of representing Reaction-Diffusion systems effectively. In this contribution Reaction-Diffusion CNN are considered for modeling dynamics of brain activity in epilepsy. Thereby the parameters of Reaction-Diffusion systems are determined in a supervised optimization process, and brain electrical activity using invasive multi-electrode EEG recordings is analyzed with the aim to detect of precursors of impending epileptic seizures. A detailed discussion of first results and potentiality of the proposed approach will be given.
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.
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.
Cytoskeletal Network Morphology Regulates Intracellular Transport Dynamics
Ando, David; Korabel, Nickolay; Huang, Kerwyn Casey; Gopinathan, Ajay
2015-01-01
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. PMID:26488648
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
Automatic network coupling analysis for dynamical systems based on detailed kinetic models.
Lebiedz, Dirk; Kammerer, Julia; Brandt-Pollmann, Ulrich
2005-10-01
We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.
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
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.
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.
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.
Robust Stochastic Chemical Reaction Networks and Bounded Tau-Leaping
2009-01-01
Abstract The behavior of some stochastic chemical reaction networks is largely unaffected by slight inaccuracies in reaction rates. We formalize the robustness of state probabilities to reaction rate deviations, and describe a formal connection between robustness and efficiency of simulation. Without robustness guarantees, stochastic simulation seems to require computational time proportional to the total number of reaction events. Even if the concentration (molecular count per volume) stays bounded, the number of reaction events can be linear in the duration of simulated time and total molecular count. We show that the behavior of robust systems can be predicted such that the computational work scales linearly with the duration of simulated time and concentration, and only polylogarithmically in the total molecular count. Thus our asymptotic analysis captures the dramatic speedup when molecular counts are large, and shows that for bounded concentrations the computation time is essentially invariant with molecular count. Finally, by noticing that even robust stochastic chemical reaction networks are capable of embedding complex computational problems, we argue that the linear dependence on simulated time and concentration is likely optimal. PMID:19254187
Dynamical Dipole mode in heavy-ion fusion reactions
NASA Astrophysics Data System (ADS)
Parascandolo, C.; Pierroutsakou, D.; Alba, R.; Del Zoppo, A.; Maiolino, C.; Santonocito, D.; Agodi, C.; Baran, V.; Boiano, A.; Colonna, M.; Coniglione, R.; De Filippo, E.; Di Toro, M.; Emanuele, U.; Farinon, F.; Guglielmetti, A.; La Commara, M.; Martin, B.; Mazzocchi, C.; Mazzocco, M.; Rizzo, C.; Romoli, M.; Signorini, C.; Silvestri, R.; Soramel, F.; Strano, E.; Torresi, D.; Trifirò, A.; Trimarchi, M.
2017-06-01
In this work we give evidence for the first time of the Dynamical Dipole mode in a heavy system in the A˜190 mass region, in both fusion-evaporation and fission events. The 40,48Ca + 152,144Sm reactions at E lab =11(10.1) MeV/nucleon were employed. Our results for evaporation and fission events (preliminary) show that the dynamical dipole mode survives in reactions involving heavier nuclei than those studied previously.
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
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.
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.
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.
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.
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.
Dynamics of moment neuronal networks
Feng Jianfeng; Deng Yingchun; Rossoni, Enrico
2006-04-15
A theoretical framework is developed for moment neuronal networks (MNNs). Within this framework, the behavior of the system of spiking neurons is specified in terms of the first- and second-order statistics of their interspike intervals, i.e., the mean, the variance, and the cross correlations of spike activity. Since neurons emit and receive spike trains which can be described by renewal--but generally non-Poisson--processes, we first derive a suitable diffusion-type approximation of such processes. Two approximation schemes are introduced: the usual approximation scheme (UAS) and the Ornstein-Uhlenbeck scheme. It is found that both schemes approximate well the input-output characteristics of spiking models such as the IF and the Hodgkin-Huxley models. The MNN framework is then developed according to the UAS scheme, and its predictions are tested on a few examples.
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
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. Copyright © 2015 Elsevier Inc. All rights reserved.
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.
Water dynamics in rigid ionomer networks.
Osti, N C; Etampawala, T N; Shrestha, U M; Aryal, D; Tyagi, M; Diallo, S O; Mamontov, E; Cornelius, C J; Perahia, D
2016-12-14
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.
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.
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.
Dynamics on modular networks with heterogeneous correlations
NASA Astrophysics Data System (ADS)
Melnik, Sergey; Porter, Mason A.; Mucha, Peter J.; Gleeson, James P.
2014-06-01
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.
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.
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.
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
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
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.
Volunteerism: Social Network Dynamics and Education
Ajrouch, Kristine J.; Antonucci, Toni C.; Webster, Noah J.
2016-01-01
Objectives . We examine how changes in social networks influence volunteerism through bridging (diversity) and bonding (spending time) mechanisms. We further investigate whether social network change substitutes or amplifies the effects of education on volunteerism. Methods . Data (n = 543) are drawn from a two-wave survey of Social Relations and Health over the Life Course (SRHLC). Zero-inflated negative binomial regressions were conducted to test competing hypotheses about how changes in social network characteristics alone and in conjunction with education level predict likelihood and frequency of volunteering. Results . Changes in social networks were associated with volunteerism: as the proportion of family members decreased and the average number of network members living within a one-hour drive increased over time, participants reported higher odds of volunteering. The substitution hypothesis was supported: social networks that exhibited more geographic proximity and greater contact frequency over-time compensated for lower levels of education to predict volunteering more hours. Discussion . The dynamic role of social networks and the ways in which they may work through bridging and bonding to influence both likelihood and frequency of volunteering are discussed. The potential benefits of volunteerism in light of longer life expectancies and smaller families are also considered. PMID:25512570
Volunteerism: Social Network Dynamics and Education.
Ajrouch, Kristine J; Antonucci, Toni C; Webster, Noah J
2016-03-01
. We examine how changes in social networks influence volunteerism through bridging (diversity) and bonding (spending time) mechanisms. We further investigate whether social network change substitutes or amplifies the effects of education on volunteerism. . Data (n = 543) are drawn from a two-wave survey of Social Relations and Health over the Life Course (SRHLC). Zero-inflated negative binomial regressions were conducted to test competing hypotheses about how changes in social network characteristics alone and in conjunction with education level predict likelihood and frequency of volunteering. . Changes in social networks were associated with volunteerism: as the proportion of family members decreased and the average number of network members living within a one-hour drive increased over time, participants reported higher odds of volunteering. The substitution hypothesis was supported: social networks that exhibited more geographic proximity and greater contact frequency over-time compensated for lower levels of education to predict volunteering more hours. . The dynamic role of social networks and the ways in which they may work through bridging and bonding to influence both likelihood and frequency of volunteering are discussed. The potential benefits of volunteerism in light of longer life expectancies and smaller families are also considered. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Symbolic representation of recurrent neural network dynamics.
Huynh, Thuan Q; Reggia, James A
2012-10-01
Simple recurrent error backpropagation networks have been widely used to learn temporal sequence data, including regular and context-free languages. However, the production of relatively large and opaque weight matrices during learning has inspired substantial research on how to extract symbolic human-readable interpretations from trained networks. Unlike feedforward networks, where research has focused mainly on rule extraction, most past work with recurrent networks has viewed them as dynamical systems that can be approximated symbolically by finite-state machine (FSMs). With this approach, the network's hidden layer activation space is typically divided into a finite number of regions. Past research has mainly focused on better techniques for dividing up this activation space. In contrast, very little work has tried to influence the network training process to produce a better representation in hidden layer activation space, and that which has been done has had only limited success. Here we propose a powerful general technique to bias the error backpropagation training process so that it learns an activation space representation from which it is easier to extract FSMs. Using four publicly available data sets that are based on regular and context-free languages, we show via computational experiments that the modified learning method helps to extract FSMs with substantially fewer states and less variance than unmodified backpropagation learning, without decreasing the neural networks' accuracy. We conclude that modifying error backpropagation so that it more effectively separates learned pattern encodings in the hidden layer is an effective way to improve contemporary FSM extraction methods.
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
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.
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. © 2015 The Author(s).
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
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.
Dynamics of deceptive interactions in social networks
Barrio, Rafael A.; Govezensky, Tzipe; Dunbar, Robin; Kaski, Kimmo
2015-01-01
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. PMID:26510829
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.
Dynamic Network Mechanisms of Relational Integration
Parkin, Beth L.; Hellyer, Peter J.; Leech, Robert
2015-01-01
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. PMID:25995457
Friction Networks: Network-Configurations of Dynamic Friction Patterns
NASA Astrophysics Data System (ADS)
Ghaffari, H.; Young, R. P.
2012-04-01
The complex configurations of dynamic friction patterns-regarding real time contact areas- are transformed into appropriate networks. With this transformation of a system to network space, many properties can be inferred about the structure and dynamics of the system. Here, we analyze the dynamics of static friction, i.e. nucleation processes, with respect to "friction networks". We show that networks can successfully capture the crack-like shear ruptures and possible corresponding acoustic features. We found that the fraction of triangles remarkably scales with the detachment fronts. There is a universal power law between nodes' degree and motifs frequency . We confirmed the obtained universality in aperture-based friction networks. Based on the achieved results, we extracted a possible friction law in terms of network parameters and compared it with the rate and state friction laws. In particular, the evolutions of loops are scaled with power law, indicating the aggregation of cycles around hub nodes. Also, the transition to slow rupture is scaled with the fast variation of local heterogeneity. Furthermore, the motif distributions and modularity space of networks -in terms of within-module degree and participation coefficient-show non-uniform general trends, indicating a universal aspect of energy flow in shear ruptures. As a conclusion to our study, we introduced friction networks over dynamics of different real time contact areas. Based on our solid observations, we formulated a probabilistic frame for the evolution of the state variable in terms of friction networks. Moreover, we confirmed that slow ruptures generally hold small localization, while regular ruptures carry a high level of energy localization. We also introduced two new universalities with respect to the evolution of dry frictional interfaces: the scaling of local and global characteristics and the occupation of certain regions of modularity parameter space. Our results showed how the relatively
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.
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.
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.
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.
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.
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…
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…
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
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
Photoreverse Reaction Dynamics of Octopus Rhodopsin
Inoue, Keiichi; Tsuda, Motoyuki; Terazima, Masahide
2007-01-01
Photoreverse reactions of octopus rhodopsin (Rh) from acid-metarhodopsin (Acid-Meta), which is the final product of the photoreaction of Rh, to Rh were studied by the time-resolved transient absorption and transient grating methods. The time course of the absorption signal showed a rapid change within 500 ns followed by one phase with a time constant of ∼470 μs, whereas the transient grating signal indicates three phases with time constants of <500 ns, ∼490 μs, and 2.6 ms. The faster two phases indicate the conformational change in the vicinity of the chromophore, and the slowest one represents conformational change far from the chromophore. The absorption spectrum of the first intermediate created just after the laser excitation (<500 ns) is already very similar to the final product, Rh. This behavior is quite different from that of the forward reaction from Rh to Acid-Meta, in which several intermediates with different absorption spectra are involved within 50 ns–500 μs. This result indicates that the conformation around the chromophore is easily adjusted from all-trans to 11-cis forms compared with that from 11-cis to all-trans forms. Furthermore, it was found that the protein energy is quickly relaxed after the excitation. One of the significantly different properties between Rh and Acid-Meta is the diffusion coefficient (D). D is reduced by about half the transformation from Rh to Acid-Meta. This large reduction was interpreted in terms of the helix opening of the Rh structure. PMID:17325000
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.
Predicting perfect adaptation motifs in reaction kinetic networks.
Drengstig, Tormod; Ueda, Hiroki R; Ruoff, Peter
2008-12-25
Adaptation and compensation mechanisms are important to keep organisms fit in a changing environment. "Perfect adaptation" describes an organism's response to an external stepwise perturbation by resetting some of its variables precisely to their original preperturbation values. Examples of perfect adaptation are found in bacterial chemotaxis, photoreceptor responses, or MAP kinase activities. Two concepts have evolved for how perfect adaptation may be understood. In one approach, so-called "robust perfect adaptation", the adaptation is a network property (due to integral feedback control), which is independent of rate constant values. In the other approach, which we have termed "nonrobust perfect adaptation", a fine-tuning of rate constant values is needed to show perfect adaptation. Although integral feedback describes robust perfect adaptation in general terms, it does not directly show where in a network perfect adaptation may be observed. Using control theoretic methods, we are able to predict robust perfect adaptation sites within reaction kinetic networks and show that a prerequisite for robust perfect adaptation is that the network is open and irreversible. We applied the method on various reaction schemes and found that new (robust) perfect adaptation motifs emerge when considering suggested models of bacterial and eukaryotic chemotaxis.
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.
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.
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.
System crash as dynamics of complex networks.
Yu, Yi; Xiao, Gaoxi; Zhou, Jie; Wang, Yubo; Wang, Zhen; Kurths, Jürgen; Schellnhuber, Hans Joachim
2016-10-18
Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the "peculiar" dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed.
Jambrina, P G; Menéndez, M; Aoiz, F J
2017-06-28
In spite of its importance in the Hg atmospheric chemistry, the dynamics of the Hg + Br2 → HgBr + Br reaction is poorly understood. In this article, we have carried out a comprehensive study of the reaction mechanism of this reaction by means of quasiclassical trajectories (QCTs) on an existing ab initio potential energy surface (PES). The reaction has a non trivial dynamics, as a consequence of its large endothermicity, the presence of a deep potential well, and the competition between the Br exchange and the collision induced dissociation processes. Our calculations demonstrate that insertion is only relevant at energies just above the reaction threshold and that, at energies above 2.3 eV, HgBr formation typically takes place via a sort of frustrated dissociation. In order to compare directly with the results obtained in extensive cross molecular beam experiments for the homologous reaction with I2, angular distributions in the laboratory frame for Hg + Br2 have been simulated under similar experimental conditions. The lack of agreement at the highest energies considered suggests that either the two reactions have substantially different mechanisms or that calculations on a single PES cannot account for the dynamics at those energies.
Understanding migraine using dynamic network biomarkers.
Dahlem, Markus A; Kurths, Jürgen; Ferrari, Michel D; Aihara, Kazuyuki; Scheffer, Marten; May, Arne
2015-06-01
Mathematical modeling approaches are becoming ever more established in clinical neuroscience. They provide insight that is key to understanding complex interactions of network phenomena, in general, and interactions within the migraine-generator network, in particular. In this study, two recent modeling studies on migraine are set in the context of premonitory symptoms that are easy to confuse for trigger factors. This causality confusion is explained, if migraine attacks are initiated by a transition caused by a tipping point. We need to characterize the involved neuronal and autonomic subnetworks and their connections during all parts of the migraine cycle if we are ever to understand migraine. We predict that mathematical models have the potential to dismantle large and correlated fluctuations in such subnetworks as a dynamic network biomarker of migraine. © International Headache Society 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Connectivity, topology and dynamics in climate networks
NASA Astrophysics Data System (ADS)
Palus, M.; Hartman, D.; Hlinka, J.; Vejmelka, M.
2012-04-01
Complex network theory allows for novel analyses of multivariate and spatio-temporal data [1]. Mutual dependencies between corresponding subsystems can be represented as a discrete structure - a weighted graph - where each subsystem is represented by a single vertex and each dependence by a connection (a weighted edge) between two such vertices. Then the graph theory is used to identify important features of the studied systems such as scale-free or small-world topology, highly connected hubs and modularity, and helps to understand information or mass transfers among the subsystems. Constructing the complex networks from multivariate time series, however, the choice of a measure of dependence (the "connectivity measure" ) is critical and can influence the topology of the resulted network representation. Interplay of intrinsic properties of a connectivity measure and used time series can generate interesting, but spurious phenomena resembling highly-connected hubs or small-world topology. For instance, the bias due to dynamical memory (serial correlations) [2] in an association/dependence measure (absolute crosscorrelation) can be demonstrated in model data and identified in time series of meteorological variables used for construction of climate networks. Accounting for such bias in inferring links of the climate network markedly changes the network topology and allows to observe previously hidden phenomena in climate network evolution. In particular, the role of the North Atlantic Oscillation in connectivity of the global climate networks is sharply increased at the cost of the role of the El Nino Southern Oscillation [3]. Acknowledgement This study is supported by the Czech Science Foundation, Project No. P103/11/J068.
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.
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
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.
Spatial aggregation query in dynamic geosensor networks
NASA Astrophysics Data System (ADS)
Yi, Baolin; Feng, Dayang; Xiao, Shisong; Zhao, Erdun
2007-11-01
Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. In many of these applications, the researches mainly aim at building sensor network based systems to leverage the sensed data to applications. However, the existing works seldom exploited spatial aggregation query considering the dynamic characteristics of sensor networks. In this paper, we investigate how to process spatial aggregation query over dynamic geosensor networks where both the sink node and sensor nodes are mobile and propose several novel improvements on enabling techniques. The mobility of sensors makes the existing routing protocol based on information of fixed framework or the neighborhood infeasible. We present an improved location-based stateless implicit geographic forwarding (IGF) protocol for routing a query toward the area specified by query window, a diameter-based window aggregation query (DWAQ) algorithm for query propagation and data aggregation in the query window, finally considering the location changing of the sink node, we present two schemes to forward the result to the sink node. Simulation results show that the proposed algorithms can improve query latency and query accuracy.
Network configurations of dynamic friction patterns
NASA Astrophysics Data System (ADS)
Ghaffari, H. O.; Young, R. P.
2012-05-01
The complex configurations of dynamic friction patterns, regarding real-time contact areas, are transformed into appropriate networks. In this letter, we analyze the dynamics of static friction, i.e. nucleation processes, with respect to friction networks. We show that networks can successfully capture the crack-like shear ruptures and possible corresponding acoustic features. We found that the fraction of triangles scales remarkably with the detachment fronts. There is a universal power law between nodes' degrees and motifs' frequencies (for triangles, it reads T(k)k~kβ (β≈ 2 ± 0.4)). In particular, the evolutions of loops are scaled with power law, indicating the aggregation of cycles around hub nodes. Furthermore, the motif distributions and modularity space of networks —in terms of within-module degrees and participation coefficients— show universal trends, indicating a common aspect of energy flow in shear ruptures. Moreover, we confirmed that slow ruptures generally hold small localization, while regular ruptures carry a high level of energy localization. We proposed that assortativity, as an index to correlation of a node's degree, can uncover acoustic features of the interfaces.
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.
Detecting Allosteric Networks Using Molecular Dynamics Simulation.
Bowerman, S; Wereszczynski, J
2016-01-01
Allosteric networks allow enzymes to transmit information and regulate their catalytic activities over vast distances. In principle, molecular dynamics (MD) simulations can be used to reveal the mechanisms that underlie this phenomenon; in practice, it can be difficult to discern allosteric signals from MD trajectories. Here, we describe how MD simulations can be analyzed to reveal correlated motions and allosteric networks, and provide an example of their use on the coagulation enzyme thrombin. Methods are discussed for calculating residue-pair correlations from atomic fluctuations and mutual information, which can be combined with contact information to identify allosteric networks and to dynamically cluster a system into highly correlated communities. In the case of thrombin, these methods show that binding of the antagonist hirugen significantly alters the enzyme's correlation landscape through a series of pathways between Exosite I and the catalytic core. Results suggest that hirugen binding curtails dynamic diversity and enforces stricter venues of influence, thus reducing the accessibility of thrombin to other molecules. © 2016 Elsevier Inc. All rights reserved.
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
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
Dynamics of competitive reactions: endothermic proton transfer and exothermic substitution.
Ren, Jianhua; Brauman, John I
2004-03-03
Dynamics of an endothermic proton-transfer reaction, F(-) with dimethyl sulfoxide, and an endothermic proton-transfer reaction with a competing exothermic substitution (S(N)2) channel, F(-) with borane-methyl sulfide complex, were investigated using a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR) and kinetic modeling. The two proton-transfer reactions have slightly positive and a small negative overall free energy changes, respectively. Energy-dependent rate constants were measured as a function of F(-) ion translational energy, and the resulting kinetics were modeled with the RRKM (Rice-Ramsperger-Kassel-Marcus) theory. The observed rate constants for the proton-transfer reactions of F(-) with dimethyl sulfoxide and with borane-methyl sulfide complex are identical, with a value of 0.17 x 10(-9) cm(3) molecule(-1) s(-1); for the S(N)2 reaction, k = 0.90 x 10(-9) cm(3) molecule(-1) s(-1) at 350 K. Both proton-transfer reactions have positive entropy changes in the forward direction and show positive energy dependences. The competing S(N)2 reaction exhibits negative energy dependence and becomes less important at higher energies. The changes of the observed rate constants agree with RRKM theory predictions for a few kcal/mol of additional kinetic energy. The dynamic change of the branching ratio for the competing proton transfer and the substitution reactions results from the competition between the microscopic rate constants associated with each channel.
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.
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.
Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks
Nelson Butuk
2004-12-01
This is an annual technical report for the work done over the last year (period ending 9/30/2004) 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 procedure to speed up the training of NPCA. The developed procedure is based on the non-parametric statistical technique of kernel smoothing. When this smoothing technique is implemented as a Neural Network, It is know as Generalized Regression Neural Network (GRNN). We present results of implementing GRNN on a test problem. In addition, we present results of an in house developed 2-D CFD code that will be used through out the project period.
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.
Dynamic network structure of interhemispheric coordination
Doron, Karl W.; Bassett, Danielle S.; Gazzaniga, Michael S.
2012-01-01
Fifty years ago Gazzaniga and coworkers published a seminal article that discussed the separate roles of the cerebral hemispheres in humans. Today, the study of interhemispheric communication is facilitated by a battery of novel data analysis techniques drawn from across disciplinary boundaries, including dynamic systems theory and network theory. These techniques enable the characterization of dynamic changes in the brain’s functional connectivity, thereby providing an unprecedented means of decoding interhemispheric communication. Here, we illustrate the use of these techniques to examine interhemispheric coordination in healthy human participants performing a split visual field experiment in which they process lexical stimuli. We find that interhemispheric coordination is greater when lexical information is introduced to the right hemisphere and must subsequently be transferred to the left hemisphere for language processing than when it is directly introduced to the language-dominant (left) hemisphere. Further, we find that putative functional modules defined by coherent interhemispheric coordination come online in a transient manner, highlighting the underlying dynamic nature of brain communication. Our work illustrates that recently developed dynamic, network-based analysis techniques can provide novel and previously unapproachable insights into the role of interhemispheric coordination in cognition. PMID:23112199
The Kinetics and Dynamics of Elementary Gas-Phase Reactions
2002-09-01
Hawthorne Jati Salh Paul Sharkey Rowland Spencer-Smith Lee Herbert Meez Islam Chris Moore David Stewart Gary Ward Michael Osborne Sarah Henton...energy. The Kinetics and Dynamics of Elementary Gas Phase Reactions, 2002 23 REMPI-LIF Studies of Ion-Molecule Reactions Michael J. Frost...for the Study of Collisional Energy Transfer Hilary Crichton School of Engineering and Physical Sciences, Heriot-Watt University. Previous
NASA Astrophysics Data System (ADS)
Jablonski, Piotr; Poe, Gina; Zochowski, Michal
2007-03-01
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)
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.
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
Taking the plunge: chemical reaction dynamics in liquids.
Orr-Ewing, Andrew J
2017-07-06
The dynamics of chemical reactions in liquid solutions are now amenable to direct study using ultrafast laser spectroscopy techniques and advances in computer simulation methods. The surrounding solvent affects the chemical reaction dynamics in numerous ways, which include: (i) formation of complexes between reactants and solvent molecules; (ii) modifications to transition state energies and structures relative to the reactants and products; (iii) coupling between the motions of the reacting molecules and the solvent modes, and exchange of energy; (iv) solvent caging of reactants and products; and (v) structural changes to the solvation shells in response to the changing chemical identity of the solutes, on timescales which may be slower than the reactive events. This article reviews progress in the study of bimolecular chemical reaction dynamics in solution, concentrating on reactions which occur on ground electronic states. It illustrates this progress with reference to recent experimental and computational studies, and considers how the various ways in which a solvent affects the chemical reaction dynamics can be unravelled. Implications are considered for research in fields such as mechanistic synthetic chemistry.
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.
Molecular Dynamics Analysis of a Liquid Explosive Reaction Zone
NASA Astrophysics Data System (ADS)
Soulard, L.; Crouzet, B.
2006-07-01
We present an analysis of the reaction zone of a stationary planar detonation by a equilibrium molecular dynamics method (EMD). We particularly focus on the influence of chemical characteristics such as the reactions reversibility and endothermicity. First, equilibrium and unreacted Hugoniot of the reactive system are calculated by EMD. These results are then used to predict the detonation characteristics such as the thermodynamic properties of ZND spike and the sonic point. We observe in particular the influence of the preliminary endothermic phase on the detonation velocity and its stability. The comparison between these predictions and non equilibrium molecular dynamics calculations validate the EMD method.
Indirect dynamics in a highly exoergic substitution reaction
Mikosch, Jochen; Zhang, Jiaxu; Trippel, Sebastian; Eichhorn, Christoph; Otto, Rico; Sun, Rui; De Jong, Wibe A.; Weidemuller, Matthias; Hase, William L.; Wester, Roland
2013-01-16
The highly exoergic nucleophilic substitution reaction F^{-} + CH_{3}I shows strikingly different reaction dynamics than substitution reactions of larger halogen ions. Over a wide range of collision energies, a large fraction of indirect scattering via a long-lived hydrogen-bonded complex is found both in crossed-beam imaging experiments and in direct chemical dynamics simulations. Our measured differential scattering cross sections show dominant large-angle scattering and low product velocities for all collision energies, resulting from efficient transfer of the collision energy to internal energy of the CH_{3}F reaction product. Both findings are in strong contrast to the previously studied substitution reaction of Cl^{-} + CH_{3}I [Science 2008, 319, 183] at all but the lowest collision energies, a discrepancy that was not captured in a subsequent study at only a low collision energy [J. Phys. Chem. Lett. 2010, 1, 2747]. Our direct chemical dynamics simulations at the DFT/B97-1 level of theory show that the reaction is dominated by three atomic-level mechanisms, an indirect reaction proceeding via an F^{-}–HCH2I hydrogen-bonded complex, a direct rebound and a direct stripping reaction. The indirect mechanism is found to contribute ~ 60% of the overall substitution reaction at both low and high collision energies. This large fraction of indirect scattering at high collision energy is particularly surprising, since the barrier for the F^{-}–HCH_{2}I complex to form products is only 0.10 eV. Overall, experiment and simulation agree very favorably in both the scattering angle and the product internal energy distributions.
Reconciliation of metabolites and biochemical reactions for metabolic networks
Bernard, Thomas; Bridge, Alan; Morgat, Anne; Moretti, Sébastien; Xenarios, Ioannis
2014-01-01
Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models. PMID:23172809
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.
Dunning, G T; Glowacki, D R; Preston, T J; Greaves, S J; Greetham, G M; Clark, I P; Towrie, M; Harvey, J N; Orr-Ewing, A J
2015-01-30
Solvent-solute interactions influence the mechanisms of chemical reactions in solution, but the response of the solvent is often slower than the reactive event. Here, we report that exothermic reactions of fluorine (F) atoms in d3-acetonitrile and d2-dichloromethane involve efficient energy flow to vibrational motion of the deuterium fluoride (DF) product that competes with dissipation of the energy to the solvent bath, despite strong solvent coupling. Transient infrared absorption spectroscopy and molecular dynamics simulations show that after DF forms its first hydrogen bond on a subpicosecond time scale, DF vibrational relaxation and further solvent restructuring occur over more than 10 picoseconds. Characteristic dynamics of gas-phase F-atom reactions with hydrogen-containing molecules persist in polar organic solvents, and the spectral evolution of the DF products serves as a probe of solvent reorganization induced by a chemical reaction. Copyright © 2015, American Association for the Advancement of Science.
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
Stationary patterns in star networks of bistable units: Theory and application to chemical reactions
NASA Astrophysics Data System (ADS)
Kouvaris, Nikos E.; Sebek, Michael; Iribarne, Albert; Díaz-Guilera, Albert; Kiss, István Z.
2017-04-01
We present theoretical and experimental studies on pattern formation with bistable dynamical units coupled in a star network configuration. By applying a localized perturbation to the central or the peripheral elements, we demonstrate the subsequent spreading, pinning, or retraction of the activations; such analysis enables the characterization of the formation of stationary patterns of localized activity. The results are interpreted with a theoretical analysis of a simplified bistable reaction-diffusion model. Weak coupling results in trivial pinned states where the activation cannot propagate. At strong coupling, a uniform state is expected with active or inactive elements at small or large degree networks, respectively. A nontrivial stationary spatial pattern, corresponding to an activation pinning, is predicted to occur at an intermediate number of peripheral elements and at intermediate coupling strengths, where the central activation of the network is pinned, but the peripheral activation propagates toward the center. The results are confirmed in experiments with star networks of bistable electrochemical reactions. The experiments confirm the existence of the stationary spatial patterns and the dependence of coupling strength on the number of peripheral elements for transitions between pinned and retreating or spreading fronts in forced network configurations (where the central or periphery elements are forced to maintain their states).
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.
Pattern dynamics and optimization by reaction diffusion systems
Ebeling, W.
1986-12-01
Reaction-diffusion systems show a fast and rather complex response on patterns produced by external space- and/or time-dependent perturbations. For example, one-component autocatalytic reactions rapidly find the loci where the given space-dependent reaction rates have relatively high values by following a kind of Darwinian strategy (combining self-reproduction and diffusion). It is shown that a simulation of this strategy in combination with annealing (decreasing the diffusion rates in time) may be used as an alternative to thermodynamic annealing strategies. Many-component reactions, such as the light-sensitive Belousov-Zhabotinsky reaction, show a more complex response to patterns impressed by illumination, for example. The response behavior and possible applications to dynamic information processing are discussed.
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.
Dynamic mechanical response of polymer networks
NASA Astrophysics Data System (ADS)
Edwards, S. F.; Takano, H.; Terentjev, E. M.
2000-10-01
The dynamic-mechanical response of flexible polymer networks is studied in the framework of the tube model, in the limit of small affine deformations, using the approach based on Rayleighian dissipation function. The dynamic complex modulus G*(ω) is calculated from the analysis of a network strand relaxation to the new equilibrium conformation around the distorted primitive path. Chain equilibration is achieved via a sliding motion of polymer segments along the tube, eliminating the inhomogeneity of the polymer density caused by the deformation. The characteristic relaxation time of this motion τe separates the low-frequency limit of the complex modulus from the high-frequency one, where the main role is played by chain entanglements, analogous to the rubber plateau in melts. The dependence of storage and loss moduli, G'(ω) and G″(ω), on crosslink and entanglement densities gives an interpolation between polymer melts and crosslinked networks. We discuss the experimental implications of the rather short relaxation time and the slow square-root variation of the moduli and the loss factor tan δ(ω) at higher frequencies.
Conditions for extinction events in chemical reaction networks with discrete state spaces.
Johnston, Matthew D; Anderson, David F; Craciun, Gheorghe; Brijder, Robert
2017-09-26
We study chemical reaction networks with discrete state spaces and present sufficient conditions on the structure of the network that guarantee the system exhibits an extinction event. The conditions we derive involve creating a modified chemical reaction network called a domination-expanded reaction network and then checking properties of this network. Unlike previous results, our analysis allows algorithmic implementation via systems of equalities and inequalities and suggests sequences of reactions which may lead to extinction events. We apply the results to several networks including an EnvZ-OmpR signaling pathway in Escherichia coli.
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.
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.
Fast imaging for mapping dynamic networks.
LeVan, Pierre; Akin, Burak; Hennig, Jürgen
2017-08-10
The development of highly accelerated fMRI acquisition techniques has led to novel possibilities to monitor cerebral activity non-invasively and with unprecedented temporal resolutions. With the emergence of dynamic connectivity and its ability to provide a much richer characterization of brain function compared to static measures, fast fMRI may yet play a crucial role in tracking dynamically varying networks. In spite of the dominance of slow hemodynamic contributions to the BOLD signal, high temporal sampling rates nevertheless improve the measurement of physiological noise, yielding an exceptional sensitivity for the detection of periods of transient connectivity at time scales of a few tens of seconds. There is also evidence that relevant BOLD fluctuations are detectable at high frequencies, implying that the benefits of fast fMRI extend beyond the ability to sample nuisance confounds. Here we review the latest technological advancements that have established fast fMRI as an effective acquisition technique, as well as its current and future implications on the analysis of dynamic networks. Copyright © 2017 Elsevier Inc. All rights reserved.
Lumping evolutionary game dynamics on networks.
Iacobelli, G; Madeo, D; Mocenni, C
2016-10-21
We study evolutionary game dynamics on networks (EGN), where players reside in the vertices of a graph, and games are played between neighboring vertices. The model is described by a system of ordinary differential equations which depends on players payoff functions, as well as on the adjacency matrix of the underlying graph. Since the number of differential equations increases with the number of vertices in the graph, the analysis of EGN becomes hard for large graphs. Building on the notion of lumpability for Markov chains, we identify conditions on the network structure allowing to reduce the original graph. In particular, we identify a partition of the vertex set of the graph and show that players in the same block of a lumpable partition have equivalent dynamical behaviors, whenever their payoff functions and initial conditions are equivalent. Therefore, vertices belonging to the same partition block can be merged into a single vertex, giving rise to a reduced graph and consequently to a simplified system of equations. We also introduce a tighter condition, called strong lumpability, which can be used to identify dynamical symmetries in EGN which are related to the interchangeability of players in the system. Copyright © 2016 Elsevier Ltd. All rights reserved.
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
Can shoulder joint reaction forces be estimated by neural networks?
de Vries, W H K; Veeger, H E J; Baten, C T M; van der Helm, F C T
2016-01-04
To facilitate the development of future shoulder endoprostheses, a long term load profile of the shoulder joint is desired. A musculoskeletal model using 3D kinematics and external forces as input can estimate the mechanical load on the glenohumeral joint, in terms of joint reaction forces. For long term ambulatory measurements, these 3D kinematics can be measured by means of Inertial Magnetic Measurement Systems. Recording of external forces under daily conditions is not feasible; estimations of joint loading should preferably be independent of this input. EMG signals reflect the musculoskeletal response and can easily be measured under daily conditions. This study presents the use of a neural network for the prediction of glenohumeral joint reaction forces based upon arm kinematics and shoulder muscle EMG. Several setups were examined for NN training, with varying combinations of type of input, type of motion, and handled weights. When joint reaction forces are predicted by a trained NN, for motion data independent of the training data, results show a high intraclass correlation (ICC up to 0.98) and relative SEM as low as 3%, compared to similar output of a musculoskeletal model. A convenient setup in which kinematics and only one channel of EMG were used as input for the NN׳s showed comparable predictive power as more complex setups. These results are promising and enable long term estimation of shoulder joint reaction forces outside the motion lab, independent of external forces. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
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.
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
Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo
2016-01-01
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. PMID:27067020
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.
Optimizing Dynamical Network Structure for Pinning Control.
Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo
2016-04-12
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.
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
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.
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
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.
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.
When to choose dynamic versus static social network analysis.
Farine, Damien R
2017-10-09
1.There is increasing interest in using dynamic social networks in the study of animal sociality and its consequences. However, there is a general lack of guidance on the when and how such an approach will be valuable. 2.The aim of this paper is to provide a guide on when to choose dynamic versus static social network analysis, and how to choose the appropriate temporal scale for the dynamic network. 3.I first discuss the motivations for using dynamic animal social networks. I then provide guidance on how to choose between dynamic networks and the 'standard' approach of using static networks. I discuss this in the context of the temporal scale of changes observed, of their predictability, and of the data availability. 4.Dynamic networks are important in a number of scenarios. First, if the network data are being compared to independent processes, such as the spread of information or disease or environmental changes, then dynamics networks will provide more accurate estimates of spreading rates. Second, if the network has predictable patterns of change, for example diel cycles or seasonal changes, then dynamic networks should be used to capture these changes. Third, dynamic networks are important for studies of spread through networks when the relationship between edge weight and transmission probability is non-linear. Finally, dynamic social networks are also useful in situations where interactions among individuals are dense, such as in studies of captive groups. 5.The use of static versus dynamic network requires careful consideration, both from a research question perspective and from a data perspective, and this paper provides a guide on how to evaluate the relative importance of these. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Potential energy surfaces and reaction dynamics of polyatomic molecules
Chang, Yan-Tyng
1991-11-01
A simple empirical valence bond (EVB) model approach is suggested for constructing global potential energy surfaces for reactions of polyatomic molecular systems. This approach produces smooth and continuous potential surfaces which can be directly utilized in a dynamical study. Two types of reactions are of special interest, the unimolecular dissociation and the unimolecular isomerization. For the first type, the molecular dissociation dynamics of formaldehyde on the ground electronic surface is investigated through classical trajectory calculations on EVB surfaces. The product state distributions and vector correlations obtained from this study suggest very similar behaviors seen in the experiments. The intramolecular hydrogen atom transfer in the formic acid dimer is an example of the isomerization reaction. High level ab initio quantum chemistry calculations are performed to obtain optimized equilibrium and transition state dimer geometries and also the harmonic frequencies.
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.
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.
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
Regulatory dynamics of synthetic gene networks with positive feedback.
Maeda, Yusuke T; Sano, Masaki
2006-06-16
Biological processes are governed by complex networks ranging from gene regulation to signal transduction. Positive feedback is a key element in such networks. The regulation enables cells to adopt multiple internal expression states in response to a single external input signal. However, past works lacked a dynamical aspect of this system. To address the dynamical property of the positive feedback system, we employ synthetic gene circuits in Escherichia coli to measure the rise-time of both the no-feedback system and the positive feedback system. We show that the kinetics of gene expression is slowed down if the gene regulatory system includes positive feedback. We also report that the transition of gene switching behaviors from the hysteretic one to the graded one occurs. A mathematical model based on the chemical reactions shows that the response delay is an inherited property of the positive feedback system. Furthermore, with the aid of the phase diagram, we demonstrate the decline of the feedback activation causes the transition of switching behaviors. Our findings provide a further understanding of a positive feedback system in a living cell from a dynamical point of view.
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.
Dynamics of nephron-vascular network.
Postnov, D D; Postnov, D E; Marsh, D J; Holstein-Rathlou, N-H; Sosnovtseva, O V
2012-12-01
The paper presents a modeling study of the spatial dynamics of a nephro-vascular network consisting of individual nephrons connected via a tree-like vascular branching structure. We focus on the effects of nonlinear mechanisms that are responsible for the formation of synchronous patterns in order to learn about processes not directly amenable to experimentation. We demonstrate that: (i) the nearest nephrons are synchronized in-phase due to a vascular propagated electrical coupling, (ii) the next few branching levels display a formation of phase-shifted patterns due to hemodynamic coupling and mode elimination, and (iii) distantly located areas show asynchronous behavior or, if all nephrons and branches are perfectly identical, an infinitely long transient behavior. These results contribute to the understanding of mechanisms responsible for the highly dynamic and limited synchronization observed among groups of nephrons despite of the fairly strong interaction between the individual units.
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
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…
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…
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.
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.
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
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
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.
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 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
Network Dynamics Mediate Circadian Clock Plasticity.
Azzi, Abdelhalim; Evans, Jennifer A; Leise, Tanya; Myung, Jihwan; Takumi, Toru; Davidson, Alec J; Brown, Steven A
2017-01-18
A circadian clock governs most aspects of mammalian behavior. Although its properties are in part genetically determined, altered light-dark environment can change circadian period length through a mechanism requiring de novo DNA methylation. We show here that this mechanism is mediated not via cell-autonomous clock properties, but rather through altered networking within the suprachiasmatic nuclei (SCN), the circadian "master clock," which is DNA methylated in region-specific manner. DNA methylation is necessary to temporally reorganize circadian phasing among SCN neurons, which in turn changes the period length of the network as a whole. Interruption of neural communication by inhibiting neuronal firing or by physical cutting suppresses both SCN reorganization and period changes. Mathematical modeling suggests, and experiments confirm, that this SCN reorganization depends upon GABAergic signaling. Our results therefore show that basic circadian clock properties are governed by dynamic interactions among SCN neurons, with neuroadaptations in network function driven by the environment. Copyright © 2017 Elsevier Inc. All rights reserved.
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.
Molecular dynamics analysis of a liquid explosive reaction zone
NASA Astrophysics Data System (ADS)
Soulard, Laurent
2005-07-01
We present in this work a detailed analysis by molecular dynamics of the reaction zone of a stationary planar detonation. In particular, we look at the influence of chemical characteristics such as the reactions reversibility and endothermicity. So, equilibrium and frozen Hugoniot of the reactive system are calculated by a specific molecular dynamics method. These results can be used to a predict the detonation characteristics such as the thermodynamic properties of ZND spike and the CJ point. We observe in particular the influence of the preliminary endothermic phase on the detonation velocity and its stability. The comparisons between these predictions and non equilibrium molecular dynamics results confirm the results of this first theoretical part. In a second step, the main hypotheses of a ZND model are extracted from the MD simulations (mainly the formalism of the reactive EOS in the reaction zone). The parameters of the corresponding model are then fitted on MD results. The final step is the implementation of the model in an hydrodynamic code. Direct comparisons between molecular dynamics simulations and hydrodynamics calculations for various 1D and 2D (in the hydrodynamics sens) configurations are presented.
Modeling the Dynamics of Chemical Reactions Involving Multidimensional Tunneling
NASA Astrophysics Data System (ADS)
Liu, Yi-Ping
The direct dynamics approach is employed to study prototype reactions including hydrogen and hydride transfer. The dynamics are treated with variational transition state theory including multidimensional semiclassical tunneling corrections, and the force field is modeled with semiempirical molecular orbital theory. The primary kinetic isotope effect for the (1,5) sigmatropic rearrangement reaction of cis-1,3-pentadiene is predicted and compared to experiment. The force field is obtained by molecular orbital theory with the AM1, PM3, and MINDO/3 parameterizations. The kinetic isotope effects calculated with the MINDO/3 and PM3 Hamiltonians agree with those calculated by AM1 within 13%, and the latter agree with experiment within 13%. The tunneling contributions to the kinetic isotope effects are analyzed, and the nature of the vibrationally assisted tunneling process is elucidated. The kinetic isotope effects of the reactions of CF_3 with CD_3H are studied including all internal degrees of freedom. The force field necessary for the dynamics calculations is evaluated using the neglect of diatomic differential overlap (NDDO) molecular orbital theory with semiempirical specific -reaction parameters (SRP), which are based on the standard AM1 parameterization adjusted to improve the agreement between experiment and the calculated quantities such as the vibrational frequencies of reactants and products and the classical barrier. The kinetic isotope effects are calculated using two different SRP force fields, and they are in good agreement with the experimental measurements. The picture of the corner cutting tunneling process that emerges is discussed graphically. The two NDDO-SRP models are further used to study the hydrogen abstraction reactions of CF_3 with CH_4, CD_4, and C_2 H_6, and very good agreement with experiment is obtained. Finally, a simple model hydride transfer reaction of formic acid is investigated usine the AM1 and PM3 Hamiltonians, and the results are
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
Stochastic Online Learning in Dynamic Networks under Unknown Models
2016-08-02
Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning, multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for
Identifying the topology of networks with discrete-time dynamics
NASA Astrophysics Data System (ADS)
Guo, Shu-Juan; Fu, Xin-Chu
2010-07-01
We suggest a method for identifying the topology of networks with discrete-time dynamics based on the dynamical evolution supported by the networks. The Frobenius matrix norm and Lasalle's invariance principle are used in this work. The network concerned can be directed or undirected, weighted or unweighted, and the local dynamics of each node can be nonidentical. The connections among the nodes can be all unknown or partially known. Finally, several examples are illustrated to verify the theoretical results.
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
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.
Backpropagation algorithms for a broad class of dynamic networks.
De Jesús, Orlando; Hagan, Martin T
2007-01-01
This paper introduces a general framework for describing dynamic neural networks--the layered digital dynamic network (LDDN). This framework allows the development of two general algorithms for computing the gradients and Jacobians for these dynamic networks: backpropagation-through-time (BPTT) and real-time recurrent learning (RTRL). The structure of the LDDN framework enables an efficient implementation of both algorithms for arbitrary dynamic networks. This paper demonstrates that the BPTT algorithm is more efficient for gradient calculations, but the RTRL algorithm is more efficient for Jacobian calculations.
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 }
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.
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.
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.
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
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.
Untangling Knots Via Reaction-Diffusion Dynamics of Vortex Strings
NASA Astrophysics Data System (ADS)
Maucher, Fabian; Sutcliffe, Paul
2016-04-01
We introduce and illustrate a new approach to the unknotting problem via the dynamics of vortex strings in a nonlinear partial differential equation of reaction-diffusion type. To untangle a given knot, a Biot-Savart construction is used to initialize the knot as a vortex string in the FitzHugh-Nagumo equation. Remarkably, we find that the subsequent evolution preserves the topology of the knot and can untangle an unknot into a circle. Illustrative test case examples are presented, including the untangling of a hard unknot known as the culprit. Our approach to the unknotting problem has two novel features, in that it applies field theory rather than particle mechanics and uses reaction-diffusion dynamics in place of energy minimization.
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.
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).
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.
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.
Self-organization of complex networks as a dynamical system
NASA Astrophysics Data System (ADS)
Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio
2015-01-01
To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.
Fluid dynamic modeling of nano-thermite reactions
Martirosyan, Karen S.; Zyskin, Maxim; Jenkins, Charles M.; Horie, Yasuyuki
2014-03-14
This paper presents a direct numerical method based on gas dynamic equations to predict pressure evolution during the discharge of nanoenergetic materials. The direct numerical method provides for modeling reflections of the shock waves from the reactor walls that generates pressure-time fluctuations. The results of gas pressure prediction are consistent with the experimental evidence and estimates based on the self-similar solution. Artificial viscosity provides sufficient smoothing of shock wave discontinuity for the numerical procedure. The direct numerical method is more computationally demanding and flexible than self-similar solution, in particular it allows study of a shock wave in its early stage of reaction and allows the investigation of “slower” reactions, which may produce weaker shock waves. Moreover, numerical results indicate that peak pressure is not very sensitive to initial density and reaction time, providing that all the material reacts well before the shock wave arrives at the end of the reactor.
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
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].
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.
Dynamic signaling cascades: reversible covalent reaction-coupled molecular switches.
Ren, Yulong; You, Lei
2015-11-11
The research of systems chemistry exploring complex mixtures of interacting synthetic molecules has been burgeoning recently. Herein we demonstrate for the first time the coupling of molecular switches with a dynamic covalent reaction (DCR) and the modulation of created chemical cascades with a variety of inputs, thus closely mimicking a biological signaling system. A novel Michael type DCR of 10-methylacridinium perchlorate and monothiols exhibiting excellent regioselectivity and tunable affinity was discovered. A delicate balance between the unique reactivity of the reactant and the stability of the adduct leads to the generation of a strong acid in a thermodynamically controlled system. The dynamic cascade was next created via coupling of the DCR and a protonation-induced configurational switch (E/Z isomerization) through a proton relay. Detailed examination of the interdependence of the equilibrium enabled us to rationally optimize the cascade and also shed light on the possible intermediate of the switching process. Furthermore, relative independence of the coupled reactions was verified by the identification of stimuli that are able to facilitate one reaction but suppress the other. To further enhance systematic complexity, a second DCR of electrophilic aldehydes and thiols was employed for the reversible inhibition of the binary system, thus achieving the interplay of multiple equilibria. Finally, a fluorescence switch was turned on through coupling with the DCR, showcasing the versatility of our strategy. The results described herein should pave the way for the exploitation of multifunctional dynamic covalent cascades.
Smooth landscape solvent dynamics in electron transfer reactions
NASA Astrophysics Data System (ADS)
Leite, Vitor B. P.
1999-05-01
Solvent effects play a major role in controlling electron-transfer reactions. The solvent dynamics happens on a very high-dimensional surface, and this complex landscape is populated by a large number of minima. A critical problem is to understand the conditions under which the solvent dynamics can be represented by a single collective reaction coordinate. When this unidimensional representation is valid, one recovers the successful Marcus theory. In this study the approach used in a previous work [V. B. P. Leite and J. N. Onuchic; J. Phys. Chem. 100, 7680 (1996)] is extended to treat a more realistic solvent model, which includes energy correlation. The dynamics takes place in a smooth and well behaved landscape. The single shell of solvent molecules around a cavity is described by a two-dimensional system with periodic boundary conditions with nearest neighbor interaction. It is shown how the polarization-dependent effects can be inferred. The existence of phase transitions depends on a factor γ proportional to the contribution from the two parameters of the model. For the present model, γ suggests the existence of "weak kinetic phase transitions," which are used in the analysis of solvent effects in charge-transfer reactions.
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.
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.
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.
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.
Lowering the Healing Temperature of Photoswitchable Dynamic Covalent Polymer Networks.
Fuhrmann, Anne; Broi, Kevin; Hecht, Stefan
2017-08-10
To reduce the environmental footprint of the modern society, it is desirable to elongate the lifetime of consumer products, for example by implementing healable coatings and protective layers. However, since most healing processes carried out by heat or light suffer from material degradation, improving the robustness and integrity of healable materials is of tremendous importance to prolong their lifetime. In recent work, a prototype is created of a dynamic covalent polymer network, whose thermal healing ability can be switched "on" and "off" by light to provide a means to locally control repair of a damaged coating. Based on the initial concept, herein a new set of difunctional crosslinkers and linear polymers of various compositions is presented to form dynamic covalent polymer networks, in which the barrier for the retro Diels-Alder decrosslinking reaction is decreased. The approach results in lower healing temperatures and thus a longer lifetime of the material. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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
Spatial Dynamics of Multilayer Cellular Neural Networks
NASA Astrophysics Data System (ADS)
Wu, Shi-Liang; Hsu, Cheng-Hsiung
2017-06-01
The purpose of this work is to study the spatial dynamics of one-dimensional multilayer cellular neural networks. We first establish the existence of rightward and leftward spreading speeds of the model. Then we show that the spreading speeds coincide with the minimum wave speeds of the traveling wave fronts in the right and left directions. Moreover, we obtain the asymptotic behavior of the traveling wave fronts when the wave speeds are positive and greater than the spreading speeds. According to the asymptotic behavior and using various kinds of comparison theorems, some front-like entire solutions are constructed by combining the rightward and leftward traveling wave fronts with different speeds and a spatially homogeneous solution of the model. Finally, various qualitative features of such entire solutions are investigated.
Gene network dynamics controlling keratinocyte migration
Busch, Hauke; Camacho-Trullio, David; Rogon, Zbigniew; Breuhahn, Kai; Angel, Peter; Eils, Roland; Szabowski, Axel
2008-01-01
Translation of large-scale data into a coherent model that allows one to simulate, predict and control cellular behavior is far from being resolved. Assuming that long-term cellular behavior is reflected in the gene expression kinetics, we infer a dynamic gene regulatory network from time-series measurements of DNA microarray data of hepatocyte growth factor-induced migration of primary human keratinocytes. Transferring the obtained interactions to the level of signaling pathways, we predict in silico and verify in vitro the necessary and sufficient time-ordered events that control migration. We show that pulse-like activation of the proto-oncogene receptor Met triggers a responsive state, whereas time sequential activation of EGF-R is required to initiate and maintain migration. Context information for enhancing, delaying or stopping migration is provided by the activity of the protein kinase A signaling pathway. Our study reveals the complex orchestration of multiple pathways controlling cell migration. PMID:18594517
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).
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
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.
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.
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
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.
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.
Avalanches dynamics in reaction fronts in disordered flows.
Chevalier, T; Dubey, A K; Atis, S; Rosso, A; Salin, D; Talon, L
2017-04-01
We report on numerical studies of avalanches of an autocatalytic reaction front in a porous medium. The front propagation is controlled by an adverse flow resulting in upstream, static, or downstream regimes. In an earlier study focusing on front shape, we identified three different universality classes associated with this system by following the front dynamics experimentally and numerically. Here, using numerical simulations in the vicinity of the second-order transition, we identify an avalanche dynamics characterized by power-law distributions of avalanche sizes, durations, and lateral extensions. The related exponents agree well with the quenched-Kardar-Parisi-Zhang theory, which describes the front dynamics. However, the geometry of the propagating front differs slightly from that of the theoretical one. We show that this discrepancy can be understood in terms of the nonquasistatic correction induced by the finite front velocity.
Quantum dynamics of tunneling dominated reactions at low temperatures
NASA Astrophysics Data System (ADS)
Hazra, Jisha; Balakrishnan, N.
2015-05-01
We report a quantum dynamics study of the Li + HF → LiF + H reaction at low temperatures of interest to cooling and trapping experiments. Contributions from non-zero partial waves are analyzed and results show narrow resonances in the energy dependence of the cross section that survive partial wave summation. The computations are performed using the ABC code and a simple modification of the ABC code that enables separate energy cutoffs for the reactant and product rovibrational energy levels is found to dramatically reduce the basis set size and computational expense. Results obtained using two ab initio electronic potential energy surfaces for the LiHF system show strong sensitivity to the choice of the potential. In particular, small differences in the barrier heights of the two potential surfaces are found to dramatically influence the reaction cross sections at low energies. Comparison with recent measurements of the reaction cross section (Bobbenkamp et al 2011 J. Chem. Phys. 135 204306) shows similar energy dependence in the threshold regime and an overall good agreement with experimental data compared to previous theoretical results. Also, usefulness of a recently introduced method for ultracold reactions that employ the quantum close-coupling method at short-range and the multichannel quantum defect theory at long-range, is demonstrated in accurately evaluating product state-resolved cross sections for D + H2 and H + D2 reactions.
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.
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.
Correlated Dynamics in Egocentric Communication Networks
Karsai, Márton; Kaski, Kimmo; Kertész, János
2012-01-01
We investigate the communication sequences of millions of people through two different channels and analyse the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbours, thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constraints (for short messages) and partly to the human behavioural features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics. PMID:22866176
Conedy: a scientific tool to investigate complex network dynamics.
Rothkegel, Alexander; Lehnertz, Klaus
2012-03-01
We present Conedy, a performant scientific tool to numerically investigate dynamics on complex networks. Conedy allows to create networks and provides automatic code generation and compilation to ensure performant treatment of arbitrary node dynamics. Conedy can be interfaced via an internal script interpreter or via a Python module.
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.
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.
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.
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.
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
2015-01-01
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. PMID:26206315
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.
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
Dynamic response of a plane-symmetrical exothermic reaction center.
NASA Technical Reports Server (NTRS)
Meyer, J. W.; Oppenheim, A. K.
1972-01-01
?sger,An analysis of the dynamic behavior of an idealized, plane-symmetrical exothermic reaction center is presented. The conservation equations for the reaction center are combined and yield a single integral equation expressing a nonlinear transfer function of the system for which the input is provided by a given time profile of the heat released per unit mass while the output gives the pressure pulse it generates under the restriction of plane-symmetrical motion. The solution is governed by a Daumk]hler number. For a given form of the exothermic power pulse profile, the dynamic behavior of the system is completely specified in terms of only this Daumk]hler number and the heat of reaction per unit mass of the combustible medium. Specific solutions are worked out for a set of typical elementary power pulse profiles, and the practical significance of the results is illustrated by their application to the problem of transition to detonation in an explosive gas.
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.
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.
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.
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.
Major component analysis of dynamic networks of physiologic organ interactions