Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks*

PubMed Central

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

Spreading of infection in a two species reaction-diffusion process in networks

NASA Astrophysics Data System (ADS)

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 tc 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) .

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

Hammond, Glenn Edward; Yang, Xiaofan; Song, Xuehang

The groundwater-surface water interaction zone (GSIZ) plays an important role in riverine and watershed ecosystems as the exchange of waters of variable composition and temperature (hydrologic exchange flows) stimulate microbial activity and associated biogeochemical reactions. Variable temporal and spatial scales of hydrologic exchange flows, heterogeneity of the subsurface environment, and complexity of biogeochemical reaction networks in the GSIZ present challenges to incorporation of fundamental process representations and model parameterization across a range of spatial scales (e.g. from pore-scale to field scale). This paper presents a novel hybrid multiscale simulation approach that couples hydrologic-biogeochemical (HBGC) processes between two distinct length scalesmore » of interest.« less

On understanding nuclear reaction network flows with branchings on directed graphs

NASA Astrophysics Data System (ADS)

Meyer, Bradley S.

2018-04-01

Nuclear reaction network flow diagrams are useful for understanding which reactions are governing the abundance changes at a particular time during nucleosynthesis. This is especially true when the flows are largely unidirectional, such as during the s-process of nucleosynthesis. In explosive nucleosynthesis, when reaction flows are large, and when forward reactions are nearly balanced by their reverses, reaction flows no longer give a clear picture of the abundance evolution in the network. This paper presents a way of understanding network evolution in terms of sums of branchings on a directed graph, which extends the concept of reaction flows to allow for multiple reaction pathways.

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

PubMed

Smith, Robert W; van Sluijs, Bob; Fleck, Christian

2017-12-02

Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

Thermodynamics of Computational Copying in Biochemical Systems

NASA Astrophysics Data System (ADS)

Ouldridge, Thomas E.; Govern, Christopher C.; ten Wolde, Pieter Rein

2017-04-01

Living cells use readout molecules to record the state of receptor proteins, similar to measurements or copies in typical computational devices. But is this analogy rigorous? Can cells be optimally efficient, and if not, why? We show that, as in computation, a canonical biochemical readout network generates correlations; extracting no work from these correlations sets a lower bound on dissipation. For general input, the biochemical network cannot reach this bound, even with arbitrarily slow reactions or weak thermodynamic driving. It faces an accuracy-dissipation trade-off that is qualitatively distinct from and worse than implied by the bound, and more complex steady-state copy processes cannot perform better. Nonetheless, the cost remains close to the thermodynamic bound unless accuracy is extremely high. Additionally, we show that biomolecular reactions could be used in thermodynamically optimal devices under exogenous manipulation of chemical fuels, suggesting an experimental system for testing computational thermodynamics.

Correlation between elastic and plastic deformations of partially cured epoxy networks

NASA Astrophysics Data System (ADS)

Müller, Michael; Böhm, Robert; Geller, Sirko; Kupfer, Robert; Jäger, Hubert; Gude, Maik

2018-05-01

The thermo-mechanical behavior of polymer matrix materials is strongly dependent on the curing reaction as well as temperature and time. To date, investigations of epoxy resins and their composites mainly focused on the elastic domain because plastic deformation of cross-linked polymer networks was considered as irrelevant or not feasible. This paper presents a novel approach which combines both elastic and plastic domain. Based on an analytical framework describing the storage modulus, analogous parameter combinations are defined in order to reduce complexity when variations in temperature, strain rate and degree of cure are encountered.

New types of experimental data shape the use of enzyme kinetics for dynamic network modeling.

PubMed

Tummler, Katja; Lubitz, Timo; Schelker, Max; Klipp, Edda

2014-01-01

Since the publication of Leonor Michaelis and Maude Menten's paper on the reaction kinetics of the enzyme invertase in 1913, molecular biology has evolved tremendously. New measurement techniques allow in vivo characterization of the whole genome, proteome or transcriptome of cells, whereas the classical enzyme essay only allows determination of the two Michaelis-Menten parameters V and K(m). Nevertheless, Michaelis-Menten kinetics are still commonly used, not only in the in vitro context of enzyme characterization but also as a rate law for enzymatic reactions in larger biochemical reaction networks. In this review, we give an overview of the historical development of kinetic rate laws originating from Michaelis-Menten kinetics over the past 100 years. Furthermore, we briefly summarize the experimental techniques used for the characterization of enzymes, and discuss web resources that systematically store kinetic parameters and related information. Finally, describe the novel opportunities that arise from using these data in dynamic mathematical modeling. In this framework, traditional in vitro approaches may be combined with modern genome-scale measurements to foster thorough understanding of the underlying complex mechanisms. © 2013 FEBS.

Analytical Solution of Steady State Equations for Chemical Reaction Networks with Bilinear Rate Laws

PubMed Central

Halász, Ádám M.; Lai, Hong-Jian; McCabe, Meghan M.; Radhakrishnan, Krishnan; Edwards, Jeremy S.

2014-01-01

True steady states are a rare occurrence in living organisms, yet their knowledge is essential for quasi-steady state approximations, multistability analysis, and other important tools in the investigation of chemical reaction networks (CRN) used to describe molecular processes on the cellular level. Here we present an approach that can provide closed form steady-state solutions to complex systems, resulting from CRN with binary reactions and mass-action rate laws. We map the nonlinear algebraic problem of finding steady states onto a linear problem in a higher dimensional space. We show that the linearized version of the steady state equations obeys the linear conservation laws of the original CRN. We identify two classes of problems for which complete, minimally parameterized solutions may be obtained using only the machinery of linear systems and a judicious choice of the variables used as free parameters. We exemplify our method, providing explicit formulae, on CRN describing signal initiation of two important types of RTK receptor-ligand systems, VEGF and EGF-ErbB1. PMID:24334389

Theorems and application of local activity of CNN with five state variables and one port.

PubMed

Xiong, Gang; Dong, Xisong; Xie, Li; Yang, Thomas

2012-01-01

Coupled nonlinear dynamical systems have been widely studied recently. However, the dynamical properties of these systems are difficult to deal with. The local activity of cellular neural network (CNN) has provided a powerful tool for studying the emergence of complex patterns in a homogeneous lattice, which is composed of coupled cells. In this paper, the analytical criteria for the local activity in reaction-diffusion CNN with five state variables and one port are presented, which consists of four theorems, including a serial of inequalities involving CNN parameters. These theorems can be used for calculating the bifurcation diagram to determine or analyze the emergence of complex dynamic patterns, such as chaos. As a case study, a reaction-diffusion CNN of hepatitis B Virus (HBV) mutation-selection model is analyzed and simulated, the bifurcation diagram is calculated. Using the diagram, numerical simulations of this CNN model provide reasonable explanations of complex mutant phenomena during therapy. Therefore, it is demonstrated that the local activity of CNN provides a practical tool for the complex dynamics study of some coupled nonlinear systems.

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.

FERN - a Java framework for stochastic simulation and evaluation of reaction networks.

PubMed

Erhard, Florian; Friedel, Caroline C; Zimmer, Ralf

2008-08-29

Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new systems biology applications. Finally, complex scenarios requiring intervention during the simulation progress can be modelled easily with FERN.

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.

PubMed

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. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Strategies and Rubrics for Teaching Complex Systems Theory to Novices (Invited)

NASA Astrophysics Data System (ADS)

Fichter, L. S.

2010-12-01

Bifurcation. Self-similarity. Fractal. Sensitive dependent. Agents. Self-organized criticality. Avalanche behavior. Power laws. Strange attractors. Emergence. The language of complexity is fundamentally different from the language of equilibrium. If students do not know these phenomena, and what they tell us about the pulse of dynamic systems, complex systems will be opaque. A complex system is a group of agents. (individual interacting units, like birds in a flock, sand grains in a ripple, or individual friction units along a fault zone), existing far from equilibrium, interacting through positive and negative feedbacks, following simple rules, forming interdependent, dynamic, evolutionary networks. Complex systems produce behaviors that cannot be predicted deductively from knowledge of the behaviors of the individual components themselves; they must be experienced. What complexity theory demonstrates is that, by following simple rules, all the agents end up coordinating their behavior—self organizing—so that what emerges is not chaos, but meaningful patterns. How can we introduce Freshman, non-science, general education students to complex systems theories, in 3 to 5 classes; in a way they really get it, and can use the principles to understand real systems? Complex systems theories are not a series of unconnected or disconnected equations or models; they are developed as narratives that makes sense of how all the pieces and properties are interrelated. The principles of complex systems must be taught as deliberately and systematically as the equilibrium principles normally taught; as, say, the systematic training from pre-algebra and geometry to algebra. We have developed a sequence of logically connected narratives (strategies and rubrics) that introduce complex systems principles using models that can be simulated in a computer, in class, in real time. The learning progression has a series of 12 models (e.g. logistic system, bifurcation diagrams, genetic algorithms, etc.) leading to 19 learning outcomes that encompass most of the universality properties that characterize complex systems. They are developed in a specific order to achieve specific ends of understanding. We use these models in various depths and formats in courses ranging from gened courses, to evolutionary systems and environmental systems, to upper level geology courses. Depending on the goals of a course, the learning outcomes can be applied to understanding many other complex systems; e.g. oscillating chemical reactions (reaction-diffusion and activator-inhibitor systems), autocatalytic networks, hysteresis (bistable) systems, networks, and the rise/collapse of complex societies. We use these and other complex systems concepts in various classes to talk about the origin of life, ecosystem organization, game theory, extinction events, and environmental system behaviors. The applications are almost endless. The complete learning progression with models, computer programs, experiments, and learning outcomes is available at: www.jmu.edu/geology/ComplexEvolutionarySystems/

Exhaustive Analysis of a Genotype Space Comprising 1015 Central Carbon Metabolisms Reveals an Organization Conducive to Metabolic Innovation

PubMed Central

Hosseini, Sayed-Rzgar; Barve, Aditya; Wagner, Andreas

2015-01-01

All biological evolution takes place in a space of possible genotypes and their phenotypes. The structure of this space defines the evolutionary potential and limitations of an evolving system. Metabolism is one of the most ancient and fundamental evolving systems, sustaining life by extracting energy from extracellular nutrients. Here we study metabolism’s potential for innovation by analyzing an exhaustive genotype-phenotype map for a space of 1015 metabolisms that encodes all possible subsets of 51 reactions in central carbon metabolism. Using flux balance analysis, we predict the viability of these metabolisms on 10 different carbon sources which give rise to 1024 potential metabolic phenotypes. Although viable metabolisms with any one phenotype comprise a tiny fraction of genotype space, their absolute numbers exceed 109 for some phenotypes. Metabolisms with any one phenotype typically form a single network of genotypes that extends far or all the way through metabolic genotype space, where any two genotypes can be reached from each other through a series of single reaction changes. The minimal distance of genotype networks associated with different phenotypes is small, such that one can reach metabolisms with novel phenotypes – viable on new carbon sources – through one or few genotypic changes. Exceptions to these principles exist for those metabolisms whose complexity (number of reactions) is close to the minimum needed for viability. Increasing metabolic complexity enhances the potential for both evolutionary conservation and evolutionary innovation. PMID:26252881

Attractor Metabolic Networks

PubMed Central

De la Fuente, Ildefonso M.; Cortes, Jesus M.; Pelta, David A.; Veguillas, Juan

2013-01-01

Background The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. Methodology/Principal Findings In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. Conclusions/Significance We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed. PMID:23554883

Modular and Orthogonal Synthesis of Hybrid Polymers and Networks

PubMed Central

Liu, Shuang; Dicker, Kevin T.; Jia, Xinqiao

2015-01-01

Biomaterials scientists strive to develop polymeric materials with distinct chemical make-up, complex molecular architectures, robust mechanical properties and defined biological functions by drawing inspirations from biological systems. Salient features of biological designs include (1) repetitive presentation of basic motifs; and (2) efficient integration of diverse building blocks. Thus, an appealing approach to biomaterials synthesis is to combine synthetic and natural building blocks in a modular fashion employing novel chemical methods. Over the past decade, orthogonal chemistries have become powerful enabling tools for the modular synthesis of advanced biomaterials. These reactions require building blocks with complementary functionalities, occur under mild conditions in the presence of biological molecules and living cells and proceed with high yield and exceptional selectivity. These chemistries have facilitated the construction of complex polymers and networks in a step-growth fashion, allowing facile modulation of materials properties by simple variations of the building blocks. In this review, we first summarize features of several types of orthogonal chemistries. We then discuss recent progress in the synthesis of step growth linear polymers, dendrimers and networks that find application in drug delivery, 3D cell culture and tissue engineering. Overall, orthogonal reactions and modulular synthesis have not only minimized the steps needed for the desired chemical transformations but also maximized the diversity and functionality of the final products. The modular nature of the design, combined with the potential synergistic effect of the hybrid system, will likely result in novel hydrogel matrices with robust structures and defined functions. PMID:25572255

Fast and Precise Emulation of Stochastic Biochemical Reaction Networks With Amplified Thermal Noise in Silicon Chips.

PubMed

Kim, Jaewook; Woo, Sung Sik; Sarpeshkar, Rahul

2018-04-01

The analysis and simulation of complex interacting biochemical reaction pathways in cells is important in all of systems biology and medicine. Yet, the dynamics of even a modest number of noisy or stochastic coupled biochemical reactions is extremely time consuming to simulate. In large part, this is because of the expensive cost of random number and Poisson process generation and the presence of stiff, coupled, nonlinear differential equations. Here, we demonstrate that we can amplify inherent thermal noise in chips to emulate randomness physically, thus alleviating these costs significantly. Concurrently, molecular flux in thermodynamic biochemical reactions maps to thermodynamic electronic current in a transistor such that stiff nonlinear biochemical differential equations are emulated exactly in compact, digitally programmable, highly parallel analog "cytomorphic" transistor circuits. For even small-scale systems involving just 80 stochastic reactions, our 0.35-μm BiCMOS chips yield a 311× speedup in the simulation time of Gillespie's stochastic algorithm over COPASI, a fast biochemical-reaction software simulator that is widely used in computational biology; they yield a 15 500× speedup over equivalent MATLAB stochastic simulations. The chip emulation results are consistent with these software simulations over a large range of signal-to-noise ratios. Most importantly, our physical emulation of Poisson chemical dynamics does not involve any inherently sequential processes and updates such that, unlike prior exact simulation approaches, they are parallelizable, asynchronous, and enable even more speedup for larger-size networks.

Interconnectivity of human cellular metabolism and disease prevalence

NASA Astrophysics Data System (ADS)

Lee, Deok-Sun

2010-12-01

Fluctuations of metabolic reaction fluxes may cause abnormal concentrations of toxic or essential metabolites, possibly leading to metabolic diseases. The mutual binding of enzymatic proteins and ones involving common metabolites enforces distinct coupled reactions, by which local perturbations may spread through the cellular network. Such network effects at the molecular interaction level in human cellular metabolism can reappear in the patterns of disease occurrence. Here we construct the enzyme-reaction network and the metabolite-reaction network, capturing the flux coupling of metabolic reactions caused by the interacting enzymes and the shared metabolites, respectively. Diseases potentially caused by the failure of individual metabolic reactions can be identified by using the known disease-gene association, which allows us to derive the probability of an inactivated reaction causing diseases from the disease records at the population level. We find that the greater the number of proteins that catalyze a reaction, the higher the mean prevalence of its associated diseases. Moreover, the number of connected reactions and the mean size of the avalanches in the networks constructed are also shown to be positively correlated with the disease prevalence. These findings illuminate the impact of the cellular network topology on disease development, suggesting that the global organization of the molecular interaction network should be understood to assist in disease diagnosis, treatment, and drug discovery.

Model validation of simple-graph representations of metabolism

PubMed Central

Holme, Petter

2009-01-01

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules. PMID:19158012

Learning Universal Computations with Spikes

PubMed Central

Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

2016-01-01

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

Antecedent acute cycling exercise affects attention control: an ERP study using attention network test

PubMed Central

Chang, Yu-Kai; Pesce, Caterina; Chiang, Yi-Te; Kuo, Cheng-Yuh; Fong, Dong-Yang

2015-01-01

The purpose of this study was to investigate the after-effects of an acute bout of moderate intensity aerobic cycling exercise on neuroelectric and behavioral indices of efficiency of three attentional networks: alerting, orienting, and executive (conflict) control. Thirty young, highly fit amateur basketball players performed a multifunctional attentional reaction time task, the attention network test (ANT), with a two-group randomized experimental design after an acute bout of moderate intensity spinning wheel exercise or without antecedent exercise. The ANT combined warning signals prior to targets, spatial cueing of potential target locations and target stimuli surrounded by congruent or incongruent flankers, which were provided to assess three attentional networks. Event-related brain potentials and task performance were measured during the ANT. Exercise resulted in a larger P3 amplitude in the alerting and executive control subtasks across frontal, central and parietal midline sites that was paralleled by an enhanced reaction speed only on trials with incongruent flankers of the executive control network. The P3 latency and response accuracy were not affected by exercise. These findings suggest that after spinning, more resources are allocated to task-relevant stimuli in tasks that rely on the alerting and executive control networks. However, the improvement in performance was observed in only the executively challenging conflict condition, suggesting that whether the brain resources that are rendered available immediately after acute exercise translate into better attention performance depends on the cognitive task complexity. PMID:25914634

Knowledge representation in metabolic pathway databases.

PubMed

Stobbe, Miranda D; Jansen, Gerbert A; Moerland, Perry D; van Kampen, Antoine H C

2014-05-01

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

Network Analysis of Postharvest Senescence Process in Citrus Fruits Revealed by Transcriptomic and Metabolomic Profiling1[OPEN

PubMed Central

Ding, Yuduan; Chang, Jiwei; Ma, Qiaoli; Chen, Lingling; Liu, Shuzhen; Jin, Shuai; Han, Jingwen; Xu, Rangwei; Zhu, Andan; Guo, Jing; Luo, Yi; Xu, Juan; Xu, Qiang; Zeng, YunLiu; Deng, Xiuxin

2015-01-01

Citrus (Citrus spp.), a nonclimacteric fruit, is one of the most important fruit crops in global fruit industry. However, the biological behavior of citrus fruit ripening and postharvest senescence remains unclear. To better understand the senescence process of citrus fruit, we analyzed data sets from commercial microarrays, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry and validated physiological quality detection of four main varieties in the genus Citrus. Network-based approaches of data mining and modeling were used to investigate complex molecular processes in citrus. The Citrus Metabolic Pathway Network and correlation networks were constructed to explore the modules and relationships of the functional genes/metabolites. We found that the different flesh-rind transport of nutrients and water due to the anatomic structural differences among citrus varieties might be an important factor that influences fruit senescence behavior. We then modeled and verified the citrus senescence process. As fruit rind is exposed directly to the environment, which results in energy expenditure in response to biotic and abiotic stresses, nutrients are exported from flesh to rind to maintain the activity of the whole fruit. The depletion of internal substances causes abiotic stresses, which further induces phytohormone reactions, transcription factor regulation, and a series of physiological and biochemical reactions. PMID:25802366

Switchable cucurbituril-bipyridine beacons.

PubMed

Sinha, Mantosh K; Reany, Ofer; Parvari, Galit; Karmakar, Ananta; Keinan, Ehud

2010-08-09

4-Aminobipyridine derivatives form strong inclusion complexes with cucurbit[6]uril, exhibiting remarkably large enhancements in fluorescence intensity and quantum yields. The remarkable complexation-induced pK(a) shift (DeltapK(a)=3.3) highlights the strong charge-dipole interaction upon binding. The reversible binding phenomenon can be used for the design of switchable beacons that can be incorporated into cascades of binding networks. This concept is demonstrated herein by three different applications: 1) a switchable fluorescent beacon for chemical sensing of transition metals and other ligands; 2) direct measurement of binding constants between cucurbit[6]uril and various nonfluorescent guest molecules; and 3) quantitative monitoring of biocatalytic reactions and determination of their kinetic parameters. The latter application is illustrated by the hydrolysis of an amide catalyzed by penicillin G acylase and by the elimination reaction of a beta-cabamoyloxy ketone catalyzed by aldolase antibody 38C2.

Complex trauma and mental health in children and adolescents placed in foster care: findings from the National Child Traumatic Stress Network.

PubMed

Greeson, Johanna K P; Briggs, Ernestine C; Kisiel, Cassandra L; Layne, Christopher M; Ake, George S; Ko, Susan J; Gerrity, Ellen T; Steinberg, Alan M; Howard, Michael L; Pynoos, Robert S; Fairbank, John A

2011-01-01

Many children in the child welfare system (CWS) have histories of recurrent interpersonal trauma perpetrated by caregivers early in life often referred to as complex trauma. Children in the CWS also experience a diverse range of reactions across multiple areas of functioning that are associated with such exposure. Nevertheless, few CWSs routinely screen for trauma exposure and associated symptoms beyond an initial assessment of the precipitating event. This study examines trauma histories, including complex trauma exposure (physical abuse, sexual abuse, emotional abuse, neglect, domestic violence), posttraumatic stress, and behavioral and emotional problems of 2,251 youth (age 0 to 21; M = 9.5, SD = 4.3) in foster care who were referred to a National Child Traumatic Stress Network site for treatment. High prevalence rates of complex trauma exposure were observed: 70.4% of the sample reported at least two of the traumas that constitute complex trauma; 11.7% of the sample reported all 5 types. Compared to youth with other types of trauma, those with complex trauma histories had significantly higher rates of internalizing problems, posttraumatic stress, and clinical diagnoses, and differed on some demographic variables. Implications for child welfare practice and future research are discussed.

Defect reaction network in Si-doped InAs. Numerical predictions.

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

Schultz, Peter A.

This Report characterizes the defects in the def ect reaction network in silicon - doped, n - type InAs predicted with first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si - doped InAs , until culminating in immobile reaction p roducts. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon - related defects in the reaction network. This Report serves to extend the results for the properties of intrinsic defects in bulkmore » InAs as colla ted in SAND 2013 - 2477 : Simple intrinsic defects in InAs : Numerical predictions to include Si - containing simple defects likely to be present in a radiation - induced defect reaction sequence . This page intentionally left blank« less

Capping protein regulatory cycle driven by CARMIL and V-1 may promote actin network assembly at protruding edges

PubMed Central

Fujiwara, Ikuko; Remmert, Kirsten; Piszczek, Grzegorz; Hammer, John A.

2014-01-01

Although capping protein (CP) terminates actin filament elongation, it promotes Arp2/3-dependent actin network assembly and accelerates actin-based motility both in vitro and in vivo. In vitro, capping protein Arp2/3 myosin I linker (CARMIL) antagonizes CP by reducing its affinity for the barbed end and by uncapping CP-capped filaments, whereas the protein V-1/myotrophin sequesters CP in an inactive complex. Previous work showed that CARMIL can readily retrieve CP from the CP:V-1 complex, thereby converting inactive CP into a version with moderate affinity for the barbed end. Here we further clarify the mechanism of this exchange reaction, and we demonstrate that the CP:CARMIL complex created by complex exchange slows the rate of barbed-end elongation by rapidly associating with, and dissociating from, the barbed end. Importantly, the cellular concentrations of V-1 and CP determined here argue that most CP is sequestered by V-1 at steady state in vivo. Finally, we show that CARMIL is recruited to the plasma membrane and only at cell edges undergoing active protrusion. Assuming that CARMIL is active only at this location, our data argue that a large pool of freely diffusing, inactive CP (CP:V-1) feeds, via CARMIL-driven complex exchange, the formation of weak-capping complexes (CP:CARMIL) at the plasma membrane of protruding edges. In vivo, therefore, CARMIL should promote Arp2/3-dependent actin network assembly at the leading edge by promoting barbed-end capping there. PMID:24778263

Envisioning, quantifying, and managing thermal regimes on river networks

USGS Publications Warehouse

Steel, E. Ashley; Beechie, Timothy J.; Torgersen, Christian E.; Fullerton, Aimee H.

2017-01-01

Water temperatures fluctuate in time and space, creating diverse thermal regimes on river networks. Temporal variability in these thermal landscapes has important biological and ecological consequences because of nonlinearities in physiological reactions; spatial diversity in thermal landscapes provides aquatic organisms with options to maximize growth and survival. However, human activities and climate change threaten to alter the dynamics of riverine thermal regimes. New data and tools can identify particular facets of the thermal landscape that describe ecological and management concerns and that are linked to human actions. The emerging complexity of thermal landscapes demands innovations in communication, opens the door to exciting research opportunities on the human impacts to and biological consequences of thermal variability, suggests improvements in monitoring programs to better capture empirical patterns, provides a framework for suites of actions to restore and protect the natural processes that drive thermal complexity, and indicates opportunities for better managing thermal landscapes.

New oxalate-bridged CrIII-MnII polymeric network incorporating a spin-crossover [Co(terpy)2]2+ cation.

PubMed

Kou, Hui-Zhong; Sato, Osamu

2007-11-12

The reaction of Mn2+ with [Cr(ox)3]3- in the presence of the spin-crossover [Co(terpy)2]2+ cation gives rise to a 1D [Co(terpy)2][Mn(H2O)ClCr(ox)3].H2O.0.5MeOH (1) or a 2D [Co(terpy)2][Mn(H2O)Cr(ox)3]2.5H2O.0.5MeOH (2). The trimetallic complexes display dominant ferromagnetic behavior, and spin-crossover of [Co(terpy)2]2+ is suppressed by the chemical pressure of the polymeric oxalate-bridged network.

Toward the automated generation of genome-scale metabolic networks in the SEED.

PubMed

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the stage for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED. With each genome that is processed using our tools, the database of common components grows to cover more of the diversity of metabolic pathways. This increases the likelihood that components of reaction networks for subsequently processed genomes can be retrieved from the database, rather than assembled and verified manually.

Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts.

PubMed

Mrabet, Yassine; Semmar, Nabil

2010-05-01

Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.

Cortical Specializations Underlying Fast Computations

PubMed Central

Volgushev, Maxim

2016-01-01

The time course of behaviorally relevant environmental events sets temporal constraints on neuronal processing. How does the mammalian brain make use of the increasingly complex networks of the neocortex, while making decisions and executing behavioral reactions within a reasonable time? The key parameter determining the speed of computations in neuronal networks is a time interval that neuronal ensembles need to process changes at their input and communicate results of this processing to downstream neurons. Theoretical analysis identified basic requirements for fast processing: use of neuronal populations for encoding, background activity, and fast onset dynamics of action potentials in neurons. Experimental evidence shows that populations of neocortical neurons fulfil these requirements. Indeed, they can change firing rate in response to input perturbations very quickly, within 1 to 3 ms, and encode high-frequency components of the input by phase-locking their spiking to frequencies up to 300 to 1000 Hz. This implies that time unit of computations by cortical ensembles is only few, 1 to 3 ms, which is considerably faster than the membrane time constant of individual neurons. The ability of cortical neuronal ensembles to communicate on a millisecond time scale allows for complex, multiple-step processing and precise coordination of neuronal activity in parallel processing streams, while keeping the speed of behavioral reactions within environmentally set temporal constraints. PMID:25689988

Kinetic products in coordination networks: ab initio X-ray powder diffraction analysis.

PubMed

Martí-Rujas, Javier; Kawano, Masaki

2013-02-19

Porous coordination networks are materials that maintain their crystal structure as molecular "guests" enter and exit their pores. They are of great research interest with applications in areas such as catalysis, gas adsorption, proton conductivity, and drug release. As with zeolite preparation, the kinetic states in coordination network preparation play a crucial role in determining the final products. Controlling the kinetic state during self-assembly of coordination networks is a fundamental aspect of developing further functionalization of this class of materials. However, unlike for zeolites, there are few structural studies reporting the kinetic products made during self-assembly of coordination networks. Synthetic routes that produce the necessary selectivity are complex. The structural knowledge obtained from X-ray crystallography has been crucial for developing rational strategies for design of organic-inorganic hybrid networks. However, despite the explosive progress in the solid-state study of coordination networks during the last 15 years, researchers still do not understand many chemical reaction processes because of the difficulties in growing single crystals suitable for X-ray diffraction: Fast precipitation can lead to kinetic (metastable) products, but in microcrystalline form, unsuitable for single crystal X-ray analysis. X-ray powder diffraction (XRPD) routinely is used to check phase purity, crystallinity, and to monitor the stability of frameworks upon guest removal/inclusion under various conditions, but rarely is used for structure elucidation. Recent advances in structure determination of microcrystalline solids from ab initio XRPD have allowed three-dimensional structure determination when single crystals are not available. Thus, ab initio XRPD structure determination is becoming a powerful method for structure determination of microcrystalline solids, including porous coordination networks. Because of the great interest across scientific disciplines in coordination networks, especially porous coordination networks, the ability to determine crystal structures when the crystals are not suitable for single crystal X-ray analysis is of paramount importance. In this Account, we report the potential of kinetic control to synthesize new coordination networks and we describe ab initio XRPD structure determination to characterize these networks' crystal structures. We describe our recent work on selective instant synthesis to yield kinetically controlled porous coordination networks. We demonstrate that instant synthesis can selectively produce metastable networks that are not possible to synthesize by conventional solution chemistry. Using kinetic products, we provide mechanistic insights into thermally induced (573-723 K) (i.e., annealing method) structural transformations in porous coordination networks as well as examples of guest exchange/inclusion reactions. Finally, we describe a memory effect that allows the transfer of structural information from kinetic precursor structures to thermally stable structures through amorphous intermediate phases. We believe that ab initio XRPD structure determination will soon be used to investigate chemical processes that lead intrinsically to microcrystalline solids, which up to now have not been fully understood due to the unavailability of single crystals. For example, only recently have researchers used single-crystal X-ray diffraction to elucidate crystal-to-crystal chemical reactions taking place in the crystalline scaffold of coordination networks. The potential of ab initio X-ray powder diffraction analysis goes beyond single-crystal-to-single-crystal processes, potentially allowing members of this field to study intriguing in situ reactions, such as reactions within pores.

On the asymmetric autocatalysis of aldol reactions: The case of 4-nitrobenzaldehyde and acetone. A critical appraisal with a focus on theory.

PubMed

Romero-Fernández, M Pilar; Babiano, Reyes; Cintas, Pedro

2018-04-01

Under neutral conditions, spontaneous mirror symmetry breaking has been occasionally reported for aldol reactions starting from achiral reagents and conditions. Chiral induction might be interpreted in terms of autocatalysis exerted by chiral mono-aldol or bis-aldol products as source of initial enantiomeric excesses, which may account for such experimental observations. We describe here a thorough Density Functional Theory (DFT) study on this complex and otherwise difficult problem, which provides some insights into this phenomenon. The picture adds further rationale to an in-depth analysis by Moyano et al, who showed the isolation and characterization of bis-aldol adducts and their participation in a complex network of reversible steps. However, the lack of enantiodiscrimination (ees vanish rapidly in solution) suggests, according to the present results, a weak association in complexes formed by the catalysts and substrates. The latter would also be consistent with almost flat transition states having similar heights for competitive catalyst-bound transition structures (actually, we were unable to locate them at the level explored). Overall, neither autocatalysis as once conjectured nor mutual inhibition of enantiomers appears to be operating mechanisms. Asymmetric amplification in early stages harnessing unavoidable enantiomeric imbalances in reaction mixtures of chiral products represents a plausible interpretation. © 2018 Wiley Periodicals, Inc.

Atomic Force Microscopy Nanomechanical Mapping Visualizes Interfacial Broadening between Networks Due to Chemical Exchange Reactions.

PubMed

He, Changfei; Shi, Shaowei; Wu, Xuefei; Russell, Thomas P; Wang, Dong

2018-06-06

The interfacial broadening between two different epoxy networks having different moduli was nanomechanically mapped. The interfacial broadening of the two networks produced an interfacial zone having a gradient in the concentration and, hence, properties of the original two networks. This interfacial broadening of the networks leads to the generation of a new network with a segmental composition corresponding to a mixture of the original two network segments. The intermixing of the two, by nature of the exchange reactions, was on the segmental level. By mapping the time dependence of the variation in the modulus at different temperatures, the kinetics of the exchange reaction was measured and, by varying the temperature, the activation energy of the exchange reaction was determined.

Robustness, efficiency, and optimality in the Fenna-Matthews-Olson photosynthetic pigment-protein complex

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

Baker, Lewis A.; Habershon, Scott, E-mail: S.Habershon@warwick.ac.uk

Pigment-protein complexes (PPCs) play a central role in facilitating excitation energy transfer (EET) from light-harvesting antenna complexes to reaction centres in photosynthetic systems; understanding molecular organisation in these biological networks is key to developing better artificial light-harvesting systems. In this article, we combine quantum-mechanical simulations and a network-based picture of transport to investigate how chromophore organization and protein environment in PPCs impacts on EET efficiency and robustness. In a prototypical PPC model, the Fenna-Matthews-Olson (FMO) complex, we consider the impact on EET efficiency of both disrupting the chromophore network and changing the influence of (local and global) environmental dephasing. Surprisingly,more » we find a large degree of resilience to changes in both chromophore network and protein environmental dephasing, the extent of which is greater than previously observed; for example, FMO maintains EET when 50% of the constituent chromophores are removed, or when environmental dephasing fluctuations vary over two orders-of-magnitude relative to the in vivo system. We also highlight the fact that the influence of local dephasing can be strongly dependent on the characteristics of the EET network and the initial excitation; for example, initial excitations resulting in rapid coherent decay are generally insensitive to the environment, whereas the incoherent population decay observed following excitation at weakly coupled chromophores demonstrates a more pronounced dependence on dephasing rate as a result of the greater possibility of local exciton trapping. Finally, we show that the FMO electronic Hamiltonian is not particularly optimised for EET; instead, it is just one of many possible chromophore organisations which demonstrate a good level of EET transport efficiency following excitation at different chromophores. Overall, these robustness and efficiency characteristics are attributed to the highly connected nature of the chromophore network and the presence of multiple EET pathways, features which might easily be built into artificial photosynthetic systems.« less

Modeling the chemistry of complex petroleum mixtures.

PubMed Central

Quann, R J

1998-01-01

Determining the complete molecular composition of petroleum and its refined products is not feasible with current analytical techniques because of the astronomical number of molecular components. Modeling the composition and behavior of such complex mixtures in refinery processes has accordingly evolved along a simplifying concept called lumping. Lumping reduces the complexity of the problem to a manageable form by grouping the entire set of molecular components into a handful of lumps. This traditional approach does not have a molecular basis and therefore excludes important aspects of process chemistry and molecular property fundamentals from the model's formulation. A new approach called structure-oriented lumping has been developed to model the composition and chemistry of complex mixtures at a molecular level. The central concept is to represent an individual molecular or a set of closely related isomers as a mathematical construct of certain specific and repeating structural groups. A complex mixture such as petroleum can then be represented as thousands of distinct molecular components, each having a mathematical identity. This enables the automated construction of large complex reaction networks with tens of thousands of specific reactions for simulating the chemistry of complex mixtures. Further, the method provides a convenient framework for incorporating molecular physical property correlations, existing group contribution methods, molecular thermodynamic properties, and the structure--activity relationships of chemical kinetics in the development of models. PMID:9860903

Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

PubMed

Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

2013-01-01

The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more detailed mathematical models.

Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

PubMed Central

Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

2013-01-01

The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more detailed mathematical models. PMID:24244124

Single-Site Zeolite-Anchored Organoiridium Carbonyl Complexes: Characterization of Structure and Reactivity by Spectroscopy and Computational Chemistry

DOE PAGES

Martinez-Macias, Claudia; Chen, Mingyang; Dixon, David A.; ...

2015-07-03

We formed a family of HY zeolite-supported cationic organoiridium carbonyl complexes by reaction of Ir(CO) 2(acac) (acac=acetylacetonate) to form supported Ir(CO) 2 complexes, which were treated at 298K and 1atm with flowing gas-phase reactants, including C 2H 4, H 2, (CO)-C-12, (CO)-C-13, and D 2O. Mass spectrometry was used to identify effluent gases, and infrared and X-ray absorption spectroscopies were used to characterize the supported species, with the results bolstered by DFT calculations. The support is crystalline and presents a nearly uniform array of bonding sites for the iridium species, so these were characterized by a high degree of uniformity,more » which allowed a precise determination of the species involved in the replacement, for example, of one CO ligand of each Ir(CO) 2 complex with ethylene. The supported species include the following: Ir(CO) 2, Ir(CO)(C 2H 4) 2, Ir(CO)(C 2H 4), Ir(CO)(C 2H 5), and (tentatively) Ir(CO)(H). The data determine a reaction network involving all of these species.« less

Rule-based modeling with Virtual Cell

PubMed Central

Schaff, James C.; Vasilescu, Dan; Moraru, Ion I.; Loew, Leslie M.; Blinov, Michael L.

2016-01-01

Summary: Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. Availability and implementation: Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user’s computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. Contact: vcell_support@uchc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27497444

Stochastic flux analysis of chemical reaction networks

PubMed Central

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

Stochastic flux analysis of chemical reaction networks.

PubMed

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.

Modular verification of chemical reaction network encodings via serializability analysis

PubMed Central

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

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.

PubMed

Klimovskaia, Anna; Ganscha, Stefan; Claassen, Manfred

2016-12-01

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.

Expanding the informational chemistries of life: peptide/RNA networks

NASA Astrophysics Data System (ADS)

Taran, Olga; Chen, Chenrui; Omosun, Tolulope O.; Hsieh, Ming-Chien; Rha, Allisandra; Goodwin, Jay T.; Mehta, Anil K.; Grover, Martha A.; Lynn, David G.

2017-11-01

The RNA world hypothesis simplifies the complex biopolymer networks underlining the informational and metabolic needs of living systems to a single biopolymer scaffold. This simplification requires abiotic reaction cascades for the construction of RNA, and this chemistry remains the subject of active research. Here, we explore a complementary approach involving the design of dynamic peptide networks capable of amplifying encoded chemical information and setting the stage for mutualistic associations with RNA. Peptide conformational networks are known to be capable of evolution in disease states and of co-opting metal ions, aromatic heterocycles and lipids to extend their emergent behaviours. The coexistence and association of dynamic peptide and RNA networks appear to have driven the emergence of higher-order informational systems in biology that are not available to either scaffold independently, and such mutualistic interdependence poses critical questions regarding the search for life across our Solar System and beyond. This article is part of the themed issue 'Reconceptualizing the origins of life'.

An integrated cell-free metabolic platform for protein production and synthetic biology

PubMed Central

Jewett, Michael C; Calhoun, Kara A; Voloshin, Alexei; Wuu, Jessica J; Swartz, James R

2008-01-01

Cell-free systems offer a unique platform for expanding the capabilities of natural biological systems for useful purposes, i.e. synthetic biology. They reduce complexity, remove structural barriers, and do not require the maintenance of cell viability. Cell-free systems, however, have been limited by their inability to co-activate multiple biochemical networks in a single integrated platform. Here, we report the assessment of biochemical reactions in an Escherichia coli cell-free platform designed to activate natural metabolism, the Cytomim system. We reveal that central catabolism, oxidative phosphorylation, and protein synthesis can be co-activated in a single reaction system. Never before have these complex systems been shown to be simultaneously activated without living cells. The Cytomim system therefore promises to provide the metabolic foundation for diverse ab initio cell-free synthetic biology projects. In addition, we describe an improved Cytomim system with enhanced protein synthesis yields (up to 1200 mg/l in 2 h) and lower costs to facilitate production of protein therapeutics and biochemicals that are difficult to make in vivo because of their toxicity, complexity, or unusual cofactor requirements. PMID:18854819

Stoichiometric network analysis and associated dimensionless kinetic equations. Application to a model of the Bray-Liebhafsky reaction.

PubMed

Schmitz, Guy; Kolar-Anić, Ljiljana Z; Anić, Slobodan R; Cupić, Zeljko D

2008-12-25

The stoichiometric network analysis (SNA) introduced by B. L. Clarke is applied to a simplified model of the complex oscillating Bray-Liebhafsky reaction under batch conditions, which was not examined by this method earlier. This powerful method for the analysis of steady-states stability is also used to transform the classical differential equations into dimensionless equations. This transformation is easy and leads to a form of the equations combining the advantages of classical dimensionless equations with the advantages of the SNA. The used dimensionless parameters have orders of magnitude given by the experimental information about concentrations and currents. This simplifies greatly the study of the slow manifold and shows which parameters are essential for controlling its shape and consequently have an important influence on the trajectories. The effectiveness of these equations is illustrated on two examples: the study of the bifurcations points and a simple sensitivity analysis, different from the classical one, more based on the chemistry of the studied system.

The protein folding network

NASA Astrophysics Data System (ADS)

Rao, Francesco; Caflisch, Amedeo

2004-03-01

Networks are everywhere. The conformation space of a 20-residue antiparallel beta-sheet peptide [1], sampled by molecular dynamics simulations, is mapped to a network. Conformations are nodes of the network, and the transitions between them are links. As previously found for the World-Wide Web as well as for social and biological networks , the conformation space contains highly connected hubs like the native state which is the most populated free energy basin. Furthermore, the network shows a hierarchical modularity [2] which is consistent with the funnel mechanism of folding [3] and is not observed for a random heteropolymer lacking a native state. Here we show that the conformation space network describes the free energy landscape without requiring projections into arbitrarily chosen reaction coordinates. The network analysis provides a basis for understanding the heterogeneity of the folding transition state and the existence of multiple pathways. [1] P. Ferrara and A. Caflisch, Folding simulations of a three-stranded antiparallel beta-sheet peptide, PNAS 97, 10780-10785 (2000). [2] Ravasz, E. and Barabási, A. L. Hierarchical organization in complex networks. Phys. Rev. E 67, 026112 (2003). [3] Dill, K. and Chan, H From Levinthal to pathways to funnels. Nature Struct. Biol. 4, 10-19 (1997)

2-(hydroxymethyl)-1H-benzo[d]imidazole-5-carboxylic acid as linker for Co(II)/Ni(II)/Cu(II) coordination polymers: Synthesis, structures and properties

NASA Astrophysics Data System (ADS)

Wang, Duo-Zhi; Wang, Xin-Fang; Du, Jia-Qiang; Dong, Jun-Liang; Xie, Fei

2018-02-01

We report the synthesis and characterization of five transition metal coordination polymers (CPs) based on M(II) (M: Co, Ni and Cu), 2-(hydroxymethyl)-1H-benzo[d]imidazole-5-carboxylic acid (H2L) ligand. They are formulated as {[Co2(HL)2(H2O)3(SO4)]·H2O}n (1), {[Co2(HL)2(H2O)2]·SiF6}n (2), {[Ni2(HL)2(H2O)3(SO4)]·2H2O}n (3), {[Ni2(HL)2(H2O)4]·H2O·SiF6}n (4), {[Cu2(HL)2(H2O)2]·SiF6}n (5). The complexes 1-5 structure were characterized by single-crystal X-ray diffraction, elemental analyses, infrared spectroscopy (IR), powder X-ray diffraction (PXRD), and thermogravimetric analyses (TGA). Complexes 1-5 are two-dimensional (2D) network type coordination polymers that 1-3, 5 crystallize in monoclinic system within the centrosymmetric space group P2(1)/c, and 4 in triclinic system P-1 space group, they show the same coordination modes (κ1-κ1)-(κ1)-(κ1)-μ3 in coordination polymers. Complexes 1 and 3 expand to three-dimensional framework by means of hydrogen bond interactions, and can be rationalized to be three-connected {63} topological network, while 2, 4, 5 exhibit the topological network with a four-connected {44·62} topological sql network. The luminescent properties (for complexes 1, 2) and UV diffuse reflectance (for complexes 1-5) in the solid state at room temperature were also investigated and discussed. Complexes 1-5 act as effective heterogeneous catalysts, under mild conditions, for the homocoupling reaction of 4-substituted aryl iodides bearing electron-donating groups (-CH3, -OCH3).

A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)

PubMed Central

Mourão, Márcio A.; Srividhya, Jeyaraman; McSharry, Patrick E.; Crampin, Edmund J.; Schnell, Santiago

2011-01-01

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1). PMID:22096591

A graphical user interface for a method to infer kinetics and network architecture (MIKANA).

PubMed

Mourão, Márcio A; Srividhya, Jeyaraman; McSharry, Patrick E; Crampin, Edmund J; Schnell, Santiago

2011-01-01

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).

H-atom addition and abstraction reactions in mixed CO, H2CO and CH3OH ices - an extended view on complex organic molecule formation

NASA Astrophysics Data System (ADS)

Chuang, K.-J.; Fedoseev, G.; Ioppolo, S.; van Dishoeck, E. F.; Linnartz, H.

2016-01-01

Complex organic molecules (COMs) have been observed not only in the hot cores surrounding low- and high-mass protostars, but also in cold dark clouds. Therefore, it is interesting to understand how such species can be formed without the presence of embedded energy sources. We present new laboratory experiments on the low-temperature solid state formation of three complex molecules - methyl formate (HC(O)OCH3), glycolaldehyde (HC(O)CH2OH) and ethylene glycol (H2C(OH)CH2OH) - through recombination of free radicals formed via H-atom addition and abstraction reactions at different stages in the CO→H2CO→CH3OH hydrogenation network at 15 K. The experiments extend previous CO hydrogenation studies and aim at resembling the physical-chemical conditions typical of the CO freeze-out stage in dark molecular clouds, when H2CO and CH3OH form by recombination of accreting CO molecules and H-atoms on ice grains. We confirm that H2CO, once formed through CO hydrogenation, not only yields CH3OH through ongoing H-atom addition reactions, but is also subject to H-atom-induced abstraction reactions, yielding CO again. In a similar way, H2CO is also formed in abstraction reactions involving CH3OH. The dominant methanol H-atom abstraction product is expected to be CH2OH, while H-atom additions to H2CO should at least partially proceed through CH3O intermediate radicals. The occurrence of H-atom abstraction reactions in ice mantles leads to more reactive intermediates (HCO, CH3O and CH2OH) than previously thought, when assuming sequential H-atom addition reactions only. This enhances the probability to form COMs through radical-radical recombination without the need of UV photolysis or cosmic rays as external triggers.

A biological approach to assembling tissue modules through endothelial capillary network formation.

PubMed

Riesberg, Jeremiah J; Shen, Wei

2015-09-01

To create functional tissues having complex structures, bottom-up approaches to assembling small tissue modules into larger constructs have been emerging. Most of these approaches are based on chemical reactions or physical interactions at the interface between tissue modules. Here we report a biological assembly approach to integrate small tissue modules through endothelial capillary network formation. When adjacent tissue modules contain appropriate extracellular matrix materials and cell types that support robust endothelial capillary network formation, capillary tubules form and grow across the interface, resulting in assembly of the modules into a single, larger construct. It was shown that capillary networks formed in modules of dense fibrin gels seeded with human umbilical vein endothelial cells (HUVECs) and mesenchymal stem cells (MSCs); adjacent modules were firmly assembled into an integrated construct having a strain to failure of 117 ± 26%, a tensile strength of 2208 ± 83 Pa and a Young's modulus of 2548 ± 574 Pa. Under the same culture conditions, capillary networks were absent in modules of dense fibrin gels seeded with either HUVECs or MSCs alone; adjacent modules disconnected even when handled gently. This biological assembly approach eliminates the need for chemical reactions or physical interactions and their associated limitations. In addition, the integrated constructs are prevascularized, and therefore this bottom-up assembly approach may also help address the issue of vascularization, another key challenge in tissue engineering. Copyright © 2015 John Wiley & Sons, Ltd.

The compositional and evolutionary logic of metabolism

NASA Astrophysics Data System (ADS)

Braakman, Rogier; Smith, Eric

2013-02-01

Metabolism is built on a foundation of organic chemistry, and employs structures and interactions at many scales. Despite these sources of complexity, metabolism also displays striking and robust regularities in the forms of modularity and hierarchy, which may be described compactly in terms of relatively few principles of composition. These regularities render metabolic architecture comprehensible as a system, and also suggests the order in which layers of that system came into existence. In addition metabolism also serves as a foundational layer in other hierarchies, up to at least the levels of cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, motivates us to interpret metabolism as a source of causation or constraint on many forms of organization in the biosphere. Many of the forms of modularity and hierarchy exhibited by metabolism are readily interpreted as stages in the emergence of catalytic control by living systems over organic chemistry, sometimes recapitulating or incorporating geochemical mechanisms. We identify as modules, either subsets of chemicals and reactions, or subsets of functions, that are re-used in many contexts with a conserved internal structure. At the small molecule substrate level, module boundaries are often associated with the most complex reaction mechanisms, catalyzed by highly conserved enzymes. Cofactors form a biosynthetically and functionally distinctive control layer over the small-molecule substrate. The most complex members among the cofactors are often associated with the reactions at module boundaries in the substrate networks, while simpler cofactors participate in widely generalized reactions. The highly tuned chemical structures of cofactors (sometimes exploiting distinctive properties of the elements of the periodic table) thereby act as ‘keys’ that incorporate classes of organic reactions within biochemistry. Module boundaries provide the interfaces where change is concentrated, when we catalogue extant diversity of metabolic phenotypes. The same modules that organize the compositional diversity of metabolism are argued, with many explicit examples, to have governed long-term evolution. Early evolution of core metabolism, and especially of carbon-fixation, appears to have required very few innovations, and to have used few rules of composition of conserved modules, to produce adaptations to simple chemical or energetic differences of environment without diverse solutions and without historical contingency. We demonstrate these features of metabolism at each of several levels of hierarchy, beginning with the small-molecule metabolic substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.

Mathematical approach to nonlocal interactions using a reaction-diffusion system.

PubMed

Tanaka, Yoshitaro; Yamamoto, Hiroko; Ninomiya, Hirokazu

2017-06-01

In recent years, spatial long range interactions during developmental processes have been introduced as a result of the integration of microscopic information, such as molecular events and signaling networks. They are often called nonlocal interactions. If the profile of a nonlocal interaction is determined by experiments, we can easily investigate how patterns generate by numerical simulations without detailed microscopic events. Thus, nonlocal interactions are useful tools to understand complex biosystems. However, nonlocal interactions are often inconvenient for observing specific mechanisms because of the integration of information. Accordingly, we proposed a new method that could convert nonlocal interactions into a reaction-diffusion system with auxiliary unknown variables. In this review, by introducing biological and mathematical studies related to nonlocal interactions, we will present the heuristic understanding of nonlocal interactions using a reaction-diffusion system. © 2017 Japanese Society of Developmental Biologists.

Memory functions reveal structural properties of gene regulatory networks

PubMed Central

Perez-Carrasco, Ruben

2018-01-01

Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. PMID:29470492

On the precision of quasi steady state assumptions in stochastic dynamics

NASA Astrophysics Data System (ADS)

Agarwal, Animesh; Adams, Rhys; Castellani, Gastone C.; Shouval, Harel Z.

2012-07-01

Many biochemical networks have complex multidimensional dynamics and there is a long history of methods that have been used for dimensionality reduction for such reaction networks. Usually a deterministic mass action approach is used; however, in small volumes, there are significant fluctuations from the mean which the mass action approach cannot capture. In such cases stochastic simulation methods should be used. In this paper, we evaluate the applicability of one such dimensionality reduction method, the quasi-steady state approximation (QSSA) [L. Menten and M. Michaelis, "Die kinetik der invertinwirkung," Biochem. Z 49, 333369 (1913)] for dimensionality reduction in case of stochastic dynamics. First, the applicability of QSSA approach is evaluated for a canonical system of enzyme reactions. Application of QSSA to such a reaction system in a deterministic setting leads to Michaelis-Menten reduced kinetics which can be used to derive the equilibrium concentrations of the reaction species. In the case of stochastic simulations, however, the steady state is characterized by fluctuations around the mean equilibrium concentration. Our analysis shows that a QSSA based approach for dimensionality reduction captures well the mean of the distribution as obtained from a full dimensional simulation but fails to accurately capture the distribution around that mean. Moreover, the QSSA approximation is not unique. We have then extended the analysis to a simple bistable biochemical network model proposed to account for the stability of synaptic efficacies; the substrate of learning and memory [J. E. Lisman, "A mechanism of memory storage insensitive to molecular turnover: A bistable autophosphorylating kinase," Proc. Natl. Acad. Sci. U.S.A. 82, 3055-3057 (1985)], 10.1073/pnas.82.9.3055. Our analysis shows that a QSSA based dimensionality reduction method results in errors as big as two orders of magnitude in predicting the residence times in the two stable states.

Regiospecificity determinants of human heme oxygenase: differential NADPH- and ascorbate-dependent heme cleavage by the R183E mutant.

PubMed

Wang, Jinling; Lad, Latesh; Poulos, Thomas L; Ortiz de Montellano, Paul R

2005-01-28

The ability of the human heme oxygenase-1 (hHO-1) R183E mutant to oxidize heme in reactions supported by either NADPH-cytochrome P450 reductase or ascorbic acid has been compared. The NADPH-dependent reaction, like that of wild-type hHO-1, yields exclusively biliverdin IXalpha. In contrast, the R183E mutant with ascorbic acid as the reductant produces biliverdin IXalpha (79 +/- 4%), IXdelta (19 +/- 3%), and a trace of IXbeta. In the presence of superoxide dismutase and catalase, the yield of biliverdin IXdelta is decreased to 8 +/- 1% with a corresponding increase in biliverdin IXalpha. Spectroscopic analysis of the NADPH-dependent reaction shows that the R183E ferric biliverdin complex accumulates, because reduction of the iron, which is required for sequential iron and biliverdin release, is impaired. Reversal of the charge at position 183 makes reduction of the iron more difficult. The crystal structure of the R183E mutant, determined in the ferric and ferrous-NO bound forms, shows that the heme primarily adopts the same orientation as in wild-type hHO-1. The structure of the Fe(II).NO complex suggests that an altered active site hydrogen bonding network supports catalysis in the R183E mutant. Furthermore, Arg-183 contributes to the regiospecificity of the wild-type enzyme, but its contribution is not critical. The results indicate that the ascorbate-dependent reaction is subject to a lower degree of regiochemical control than the NADPH-dependent reaction. Ascorbate may be able to reduce the R183E ferric and ferrous dioxygen complexes in active site conformations that cannot be reduced by NADPH-cytochrome P450 reductase.

Multi-equilibrium property of metabolic networks: SSI module.

PubMed

Lei, Hong-Bo; Zhang, Ji-Feng; Chen, Luonan

2011-06-20

Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.

Multi-equilibrium property of metabolic networks: SSI module

PubMed Central

2011-01-01

Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module. PMID:21689474

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

Sakai, Osamu, E-mail: sakai.o@e.usp.ac.jp; Nobuto, Kyosuke; Miyagi, Shigeyuki

Chemical reactions of molecular gases like methane are so complicated that a chart of decomposed and/or synthesized species originating from molecules in plasma resembles a weblike network in which we write down species and reactions among them. Here we consider properties of the network structures of chemical reactions in methane plasmas. In the network, atoms/molecules/radical species are assumed to form nodes and chemical reactions correspond to directed edges in the terminology of graph theory. Investigation of the centrality index reveals importance of CH{sub 3} in the global chemical reaction, and difference of an index for each radical species between casesmore » with and without electrons clarifies that the electrons are at an influential position to tighten the network structure.« less

An efficient graph theory based method to identify every minimal reaction set in a metabolic network

PubMed Central

2014-01-01

Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. PMID:24594118

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.

Complex Autocatalysis in Simple Chemistries.

PubMed

Virgo, Nathaniel; Ikegami, Takashi; McGregor, Simon

2016-01-01

Life on Earth must originally have arisen from abiotic chemistry. Since the details of this chemistry are unknown, we wish to understand, in general, which types of chemistry can lead to complex, lifelike behavior. Here we show that even very simple chemistries in the thermodynamically reversible regime can self-organize to form complex autocatalytic cycles, with the catalytic effects emerging from the network structure. We demonstrate this with a very simple but thermodynamically reasonable artificial chemistry model. By suppressing the direct reaction from reactants to products, we obtain the simplest kind of autocatalytic cycle, resulting in exponential growth. When these simple first-order cycles are prevented from forming, the system achieves superexponential growth through more complex, higher-order autocatalytic cycles. This leads to nonlinear phenomena such as oscillations and bistability, the latter of which is of particular interest regarding the origins of life.

Copper-catalyzed azide alkyne cycloaddition polymer networks

NASA Astrophysics Data System (ADS)

Alzahrani, Abeer Ahmed

The click reaction concept, introduced in 2001, has since spurred the rapid development and reexamination of efficient, high yield reactions which proceed rapidly under mild conditions. Prior to the discovery of facile copper catalysis in 2002, the thermally activated azide-alkyne or Huisgen cycloaddition reaction was largely ignored following its discovery in large part due to its slow kinetics, requirement for elevated temperature and limited selectivity. Now, arguably, the most prolific and capable of the click reactions, the copper-catalyzed azide alkyne cycloaddition (CuAAC) reaction is extremely efficient and affords exquisite control of the reaction. The orthogonally and chemoselectivity of this reaction enable its wide utility across varied scientific fields. Despite numerous inherent advantages and widespread use for small molecule synthesis and solution-based polymer chemistry, it has only recently and rarely been utilized to form polymer networks. This work focuses on the synthesis, mechanisms, and unique attributes of the CuAAC reaction for the fabrication of functional polymer networks. The photo-reduction of a series of copper(II)/amine complexes via ligand metal charge transfer was examined to determine their relative efficiency and selectivity in catalyzing the CuAAC reaction. The aliphatic amine ligands were used as an electron transfer species to reduce Cu(II) upon irradiation with 365 nm light while also functioning as an accelerating agent and as protecting ligands for the Cu(I) that was formed. Among the aliphatic amines studied, tertiary amines such as triethylamine (TEA), tetramethyldiamine (TMDA), N,N,N',N",N"-pentamethyldiethylenetriamine (PMDTA), and hexamethylenetetramine (HMTETA) were found to be the most effective. The reaction kinetics were accelerated by increasing the PMDETA : Cu(II) ratio with a ratio of ligand to Cu(II) of 4:1 yielding the maximum conversion in the shortest time. The sequential and orthogonal nature of the photo-CuAAC reaction and a chain-growth acrylate homopolymerization were demonstrated and used to form branched polymer structures. A bulk, organic soluble initiation system consisting of a Cu(II) salt and a primary amine was also examined in both model reactions and in bulk polymerizations. The system was shown to be highly efficient, leading to nearly complete CuAAC polymerization at ambient temperature. Increasing the ratio of amine to copper from 1 to 4 increases the CuAAC reaction rate significantly from 4 mM/min for 1:1 ratio of Cu(II):hexyalmine to 14mM/min for 1:4 ratio. The concentration dependence of the amine on the reaction rate enables the polymerization rate to be controlled simply by manipulating the hexylamine concentration. Sequential thiol--acrylate and photo-CuAAC click reactions were utilized to form two-stage reactive polymer networks capable of generating wrinkles in a facile manner. The click thiol-Michael addition reaction was utilized to form a cross-linked polymer with residual, reactive alkyne sites that remained tethered throughout the network. The latent, unreacted alkyne sites are subsequently reacted with diazide monomers via a photoinduced Cu(I)-catalyzed alkyne-azide cycloaddition (CuAAC) reaction to increase the cross-link density. Increased cross-linking raised the modulus and glass transition temperature from 1.6 MPa and 2 °C after the thiol-acrylate reaction to 4.4 MPa and 22 °C after the CuAAC reaction, respectively. The double click reaction approach led to micro-wrinkles with well-controlled wavelength and amplitude of 8.50 +/- 1.6 and 1.4 μm, respectively, for a polymer with a 1280 μm total film thickness. Additionally, this approach further enables spatial selectivity of wrinkle formation by photo-patterning. The CuAAC-based polymerization was also used to design smart, responsive porous materials from well-defined CuAAC networks, which possesses a high glass transition temperature (Tg= 115°C) due to the formation of the triazole linkages. The toughness, recovery, fixity, and shape memory attributes of this material were examined. The unique recovery behavior of the porous CuAAC material is characterized by its ability to recover plastic deformation upon heating. The tough and stiff nature of the glassy CuAAC polymer networks translates into desirable high compressive strain shape memory foams. The CuAAC foam exhibited excellent shape-memory behavior and was able to recover through each of five successive cycles of 80% compression at ambient temperature, presenting a significant volume change and resistance to fracture. In addition, the glassy CuAAC foam was able to withstand more than 10 cycles of compression to 50% strain and subsequent recovery at ambient temperature, indicative of ductile behavior in the glassy state.

Computer Assisted Design, Prediction, and Execution of Economical Organic Syntheses

NASA Astrophysics Data System (ADS)

Gothard, Nosheen Akber

The synthesis of useful organic molecules via simple and cost-effective routes is a core challenge in organic chemistry. In industry or academia, organic chemists use their chemical intuition, technical expertise and published procedures to determine an optimal pathway. This approach, not only takes time and effort, but also is cost prohibitive. Many potential optimal routes scratched on paper fail to get experimentally tested. In addition, with new methods being discovered daily are often overlooked by established techniques. This thesis reports a computational technique that assist the discovery of economical synthetic routes to useful organic targets. Organic chemistry exists as a network where chemicals are connected by reactions, analogous to citied connected by roads in a geographic map. This network topology of organic reactions in the network of organic chemistry (NOC) allows the application of graph-theory to devise algorithms for synthetic optimization of organic targets. A computational approach comprised of customizable algorithms, pre-screening filters, and existing chemoinformatic techniques is capable of answering complex questions and perform mechanistic tasks desired by chemists such as optimization of organic syntheses. One-pot reactions are central to modern synthesis since they save resources and time by avoiding isolation, purification, characterization, and production of chemical waste after each synthetic step. Sometimes, such reactions are identified by chance or, more often, by careful inspection of individual steps that are to be wired together. Algorithms are used to discover one-pot reactions and validated experimentally. Which demonstrate that the computationally predicted sequences can indeed by carried out experimentally in good overall yields. The experimental examples are chosen to from small networks of reactions around useful chemicals such as quinoline scaffolds, quinoline-based inhibitors of phosphoinositide 3-kinase delta (PI3Kdelta) enzyme, and thiophene derivatives. In this way, we replace individual synthetic connections with two-, three-, or even four-step one-pot sequences. Lastly, the computational method is utilized to devise hypothetical synthetic route to popular pharmaceutical drugs like NaproxenRTM and TaxolRTM. The algorithmically generated optimal pathways are evaluated with chemistry logic. By applying labor/cost factor It was revealed that not all shorter synthesis routes are economical, sometimes "Longest way round is the shortest way home" lengthier routes are found to be more economical and environmentally friendly.

Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway.

PubMed

Keller, Markus A; Zylstra, Andre; Castro, Cecilia; Turchyn, Alexandra V; Griffin, Julian L; Ralser, Markus

2016-01-01

Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks.

Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway

PubMed Central

Keller, Markus A.; Zylstra, Andre; Castro, Cecilia; Turchyn, Alexandra V.; Griffin, Julian L.; Ralser, Markus

2016-01-01

Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks. PMID:26824074

Nonequilibrium thermodynamics and a fluctuation theorem for individual reaction steps in a chemical reaction network

NASA Astrophysics Data System (ADS)

Pal, Krishnendu; Das, Biswajit; Banerjee, Kinshuk; Gangopadhyay, Gautam

2015-09-01

We have introduced an approach to nonequilibrium thermodynamics of an open chemical reaction network in terms of the propensities of the individual elementary reactions and the corresponding reverse reactions. The method is a microscopic formulation of the dissipation function in terms of the relative entropy or Kullback-Leibler distance which is based on the analogy of phase space trajectory with the path of elementary reactions in a network of chemical process. We have introduced here a fluctuation theorem valid for each opposite pair of elementary reactions which is useful in determining the contribution of each sub-reaction on the nonequilibrium thermodynamics of overall reaction. The methodology is applied to an oligomeric enzyme kinetics at a chemiostatic condition that leads the reaction to a nonequilibrium steady state for which we have estimated how each step of the reaction is energy driven or entropy driven to contribute to the overall reaction.

The US nuclear reaction data network. Summary of the first meeting, March 13 & 14 1996

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

NONE

1996-03-01

The first meeting of the US Nuclear Reaction Data Network (USNRDN) was held at the Colorado School of Mines, March 13-14, 1996 chaired by F. Edward Cecil. The Agenda of the meeting is attached. The Network, its mission, products and services; related nuclear data and data networks, members, and organization are described in Attachment 1. The following progress reports from the members of the USNRDN were distributed prior to the meeting and are given as Attachment 2. (1) Measurements and Development of Analytic Techniques for Basic Nuclear Physics and Nuclear Applications; (2) Nuclear Reaction Data Activities at the National Nuclearmore » Data Center; (3) Studies of nuclear reactions at very low energies; (4) Nuclear Reaction Data Activities, Nuclear Data Group; (5) Progress in Neutron Physics at Los Alamos - Experiments; (6) Nuclear Reaction Data Activities in Group T2; (7) Progress Report for the US Nuclear Reaction Data Network Meeting; (8) Nuclear Astrophysics Research Group (ORNL); (9) Progress Report from Ohio University; (10) Exciton Model Phenomenology; and (11) Progress Report for Coordination Meeting USNRDN.« less

A new network representation of the metabolism to detect chemical transformation modules.

PubMed

Sorokina, Maria; Medigue, Claudine; Vallenet, David

2015-11-14

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

Efficient surface formation route of interstellar hydroxylamine through NO hydrogenation. II. The multilayer regime in interstellar relevant ices

NASA Astrophysics Data System (ADS)

Fedoseev, G.; Ioppolo, S.; Lamberts, T.; Zhen, J. F.; Cuppen, H. M.; Linnartz, H.

2012-08-01

Hydroxylamine (NH2OH) is one of the potential precursors of complex pre-biotic species in space. Here, we present a detailed experimental study of hydroxylamine formation through nitric oxide (NO) surface hydrogenation for astronomically relevant conditions. The aim of this work is to investigate hydroxylamine formation efficiencies in polar (water-rich) and non-polar (carbon monoxide-rich) interstellar ice analogues. A complex reaction network involving both final (N2O, NH2OH) and intermediate (HNO, NH2O., etc.) products is discussed. The main conclusion is that hydroxyl-amine formation takes place via a fast and barrierless mechanism and it is found to be even more abundantly formed in a water-rich environment at lower temperatures. In parallel, we experimentally verify the non-formation of hydroxylamine upon UV photolysis of NO ice at cryogenic temperatures as well as the non-detection of NC- and NCO-bond bearing species after UV processing of NO in carbon monoxide-rich ices. Our results are implemented into an astrochemical reaction model, which shows that NH2OH is abundant in the solid phase under dark molecular cloud conditions. Once NH2OH desorbs from the ice grains, it becomes available to form more complex species (e.g., glycine and β-alanine) in gas phase reaction schemes.

Mechanisms of stochastic focusing and defocusing in biological reaction networks: insight from accurate chemical master equation (ACME) solutions.

PubMed

Gursoy, Gamze; Terebus, Anna; Youfang Cao; Jie Liang

2016-08-01

Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small. Studies based on Stochastic Simulation Algorithm (SSA) has shown that a basic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the signal noise. Although SSA has been widely used to study stochastic networks, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF. Here we use the ACME method to solve the exact solution of the discrete Chemical Master Equations and to study a network where SF was reported. We showed that the level of SF depends on the degree of the fluctuations of signal molecule. We discovered that signaling noise under certain conditions in the same reaction network can lead to a decrease in the system sensitivities, thus the network can experience stochastic defocusing. These results highlight the fundamental role of stochasticity in biological reaction networks and the need for exact computation of probability landscape of the molecules in the system.

Kinetic Evidence of an Apparent Negative Activation Enthalpy in an Organocatalytic Process

PubMed Central

Han, Xiao; Lee, Richmond; Chen, Tao; Luo, Jie; Lu, Yixin; Huang, Kuo-Wei

2013-01-01

A combined kinetic and computational study on our tryptophan-based bifunctional thiourea catalyzed asymmetric Mannich reactions reveals an apparent negative activation enthalpy. The formation of the pre-transition state complex has been unambiguously confirmed and these observations provide an experimental support for the formation of multiple hydrogen bonding network between the substrates and the catalyst. Such interactions allow the creation of a binding cavity, a key factor to install high enantioselectivity. PMID:23990028

Extending atomistic scale chemistry to mesoscale model of condensed-phase deflagration

NASA Astrophysics Data System (ADS)

Joshi, Kaushik; Chaudhuri, Santanu

2017-01-01

Predictive simulations connecting chemistry that follow the shock or thermal initiation of energetic materials to subsequent deflagration or detonation events is currently outside the realm of possibilities. Molecular dynamics and first-principles based dynamics have made progress in understanding reactions in picosecond to nanosecond time scale. Results from thermal ignition of different phases of RDX show a complex reaction network and emergence of a deterministic behavior for critical temperature before ignition and hot spot growth rates. The kinetics observed is dependent on the hot spot temperature, system size and thermal conductivity. For cases where ignition is observed, the incubation period is dominated by intermolecular and intramolecular hydrogen transfer reactions. The gradual temperature and pressure increase in the incubation period is accompanied by accumulation of heavier polyradicals. The challenge of connecting such chemistry in mesoscale simulations remain in reducing the complexity of chemistry. The hot spot growth kinetics in RDX grains and interfaces is an important challenge for reactive simulations aiming to fill in the gaps in our knowledge in the nanoseconds to microseconds time scale. The results discussed indicate that the mesoscale chemistry may include large polyradical molecules in dense reactive mix reaching an instability point at certain temperatures and pressures.

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

PubMed

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

2017-08-01

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

Multidimensional MnO2 nanohair-decorated hybrid multichannel carbon nanofiber as an electrode material for high-performance supercapacitors

NASA Astrophysics Data System (ADS)

Jun, Jaemoon; Lee, Jun Seop; Shin, Dong Hoon; Kim, Sung Gun; Jang, Jyongsik

2015-09-01

One-dimensional (1D)-structured nanomaterials represent one of the most attractive candidates for energy-storage systems due to their contribution to design simplicity, fast charge-transportation network, and their allowance for more accessible ion diffusion. In particular, 1D-structured nanomaterials with a highly complex inner-pore configuration enhance functionality by taking advantage of both the hollow and 1D structures. In this study, we report a MnO2 nanohair-decorated, hybrid multichannel carbon nanofiber (Mn_MCNF) fabricated via single-nozzle co-electrospinning of two immiscible polymer solutions, followed by carbonization and redox reactions. With improved ion accessibility, the optimized Mn_MCNF sample (Mn_MCNF_60 corresponding to a reaction duration time of 60 min for optimal MnO2 nanohair growth) exhibited a high specific capacitance of 855 F g-1 and excellent cycling performance with ~87.3% capacitance retention over 5000 cycles.One-dimensional (1D)-structured nanomaterials represent one of the most attractive candidates for energy-storage systems due to their contribution to design simplicity, fast charge-transportation network, and their allowance for more accessible ion diffusion. In particular, 1D-structured nanomaterials with a highly complex inner-pore configuration enhance functionality by taking advantage of both the hollow and 1D structures. In this study, we report a MnO2 nanohair-decorated, hybrid multichannel carbon nanofiber (Mn_MCNF) fabricated via single-nozzle co-electrospinning of two immiscible polymer solutions, followed by carbonization and redox reactions. With improved ion accessibility, the optimized Mn_MCNF sample (Mn_MCNF_60 corresponding to a reaction duration time of 60 min for optimal MnO2 nanohair growth) exhibited a high specific capacitance of 855 F g-1 and excellent cycling performance with ~87.3% capacitance retention over 5000 cycles. Electronic supplementary information (ESI) available: Experimental data includes optical images, TGA, magnified pore distribution curves and supercapacitor device of the MCNF and Mn_MCNF. See DOI: 10.1039/C5NR03616J

Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

PubMed

Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

2016-04-01

In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

A strategy for the preparation of thioantimonates based on the concept of weak acids and corresponding strong bases.

PubMed

Anderer, Carolin; Delwa de Alarcón, Natalie; Näther, Christian; Bensch, Wolfgang

2014-12-15

By following a new synthetic approach, which is based on the in situ formation of a basic medium by the reaction between the strong base Sb(V)S4 (3-) and the weak acid H2 O, it was possible to prepare three layered thioantimonate(III) compounds of composition [TM(2,2'-bipyridine)3 ][Sb6 S10 ] (TM=Ni, Fe) and [Ni(4,4'-dimethyl-2,2'-bipyridine)3 ][Sb6 S10 ] under hydrothermal conditions featuring two different thioantimonate(III) network topologies. The antimony source, Na3 SbS4 ⋅ 9 H2 O, undergoes several decomposition reactions and produces the Sb(III) S3 species, which condenses to generate the layered anion. The application of transition-metal complexes avoids crystallization of dense phases. The reactions are very fast compared to conventional hydrothermal/solvothermal syntheses and are much less sensitive to changes of the reaction parameters. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cellular metabolic network analysis: discovering important reactions in Treponema pallidum.

PubMed

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.

Copper(II) ion catalytic oxidation of o-phenylenediamine and characterization, X-ray crystal structure and solution studies of the final product [DAPH][H3O][Cu(dipic)2]·3H2O

NASA Astrophysics Data System (ADS)

Ghasemi, Khaled; Rezvani, Ali Reza; Shokrollahi, Ardeshir; Abdul Razak, Ibrahim; Refahi, Masoud; Moghimi, Abolghasem; Rosli, Mohd Mustaqim

2015-09-01

The complex [DAPH][H3O][Cu(dipic)2]·3H2O, (1) (dipicH2 = 2,6-pyridinedicarboxylic acid and DAP = 2,3-diaminophenazine) was prepared from the reaction of Cu(NO3)2·2H2O with mixture of o-phenylenediamine (OPD) and 2,6-pyridinedicarboxylic acid in water. The complex was characterized by FTIR, elemental analysis, UV-Vis and the single-crystal X-ray diffraction. The crystal system is monoclinic with the space group P21/c. This complex is stabilized in the solid state by an extensive network of hydrogen bonds between crystallized water, anionic and cationic fragments, which form a three-dimensional network. Furthermore, hydrogen bonds, π⋯π and Csbnd O⋯π stacking interactions seem to be effective in stabilizing the crystal structures. The protonation constants of dipic (L) and DAP (Q), the equilibrium constants for the dipic-DAP proton transfer system and the stoichiometry and stability constants of binary complexes including each of ligands (dipic, DAP) in presence Cu2+ ion, ternary complexes including, both of ligands (dipic-DAP) in presence of metal ion were calculated in aqueous solutions by potentiometric pH titration method using the Hyperquad2008 program. The stoichiometry of the most complexes species in solution was found to be very similar to the solid-state of cited metal ion complex.

Environmental versatility promotes modularity in genome-scale metabolic networks.

PubMed

Samal, Areejit; Wagner, Andreas; Martin, Olivier C

2011-08-24

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple environments. This organizational principle is insensitive to the environments we consider and to the number of reactions in a metabolic network. Because we observe this principle not just in one or few biological networks, but in large random samples of networks, we propose that it may be a generic principle of metabolic network organization.

Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

PubMed Central

Murabito, Ettore; Verma, Malkhey; Bekker, Martijn; Bellomo, Domenico; Westerhoff, Hans V.; Teusink, Bas; Steuer, Ralf

2014-01-01

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models. PMID:25268481

Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

PubMed Central

Ibrahim, Bashar; Henze, Richard; Gruenert, Gerd; Egbert, Matthew; Huwald, Jan; Dittrich, Peter

2013-01-01

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models. PMID:24709796

The simple neuroendocrine-immune regulatory network in oyster Crassostrea gigas mediates complex functions.

PubMed

Liu, Zhaoqun; Wang, Lingling; Zhou, Zhi; Sun, Ying; Wang, Mengqiang; Wang, Hao; Hou, Zhanhui; Gao, Dahai; Gao, Qiang; Song, Linsheng

2016-05-19

The neuroendocrine-immune (NEI) regulatory network is a complex system, which plays an indispensable role in the immunity of the host. In the present study, the bioinformatical analysis of the transcriptomic data from oyster Crassostrea gigas and further biological validation revealed that oyster TNF (CgTNF-1 CGI_10018786) could activate the transcription factors NF-κB and HSF (heat shock transcription factor) through MAPK signaling pathway, and then regulate apoptosis, redox reaction, neuro-regulation and protein folding in oyster haemocytes. The activated immune cells then released neurotransmitters including acetylcholine, norepinephrine and [Met(5)]-enkephalin to regulate the immune response by arising the expression of three TNF (CGI_10005109, CGI_10005110 and CGI_10006440) and translocating two NF-κB (Cgp65, CGI_10018142 and CgRel, CGI_10021567) between the cytoplasm and nuclei of haemocytes. Neurotransmitters exhibited the immunomodulation effects by influencing apoptosis and phagocytosis of oyster haemocytes. Acetylcholine and norepinephrine could down-regulate the immune response, while [Met(5)]-enkephalin up-regulate the immune response. These results suggested that the simple neuroendocrine-immune regulatory network in oyster might be activated by oyster TNF and then regulate the immune response by virtue of neurotransmitters, cytokines and transcription factors.

The simple neuroendocrine-immune regulatory network in oyster Crassostrea gigas mediates complex functions

NASA Astrophysics Data System (ADS)

Liu, Zhaoqun; Wang, Lingling; Zhou, Zhi; Sun, Ying; Wang, Mengqiang; Wang, Hao; Hou, Zhanhui; Gao, Dahai; Gao, Qiang; Song, Linsheng

2016-05-01

The neuroendocrine-immune (NEI) regulatory network is a complex system, which plays an indispensable role in the immunity of the host. In the present study, the bioinformatical analysis of the transcriptomic data from oyster Crassostrea gigas and further biological validation revealed that oyster TNF (CgTNF-1 CGI_10018786) could activate the transcription factors NF-κB and HSF (heat shock transcription factor) through MAPK signaling pathway, and then regulate apoptosis, redox reaction, neuro-regulation and protein folding in oyster haemocytes. The activated immune cells then released neurotransmitters including acetylcholine, norepinephrine and [Met5]-enkephalin to regulate the immune response by arising the expression of three TNF (CGI_10005109, CGI_10005110 and CGI_10006440) and translocating two NF-κB (Cgp65, CGI_10018142 and CgRel, CGI_10021567) between the cytoplasm and nuclei of haemocytes. Neurotransmitters exhibited the immunomodulation effects by influencing apoptosis and phagocytosis of oyster haemocytes. Acetylcholine and norepinephrine could down-regulate the immune response, while [Met5]-enkephalin up-regulate the immune response. These results suggested that the simple neuroendocrine-immune regulatory network in oyster might be activated by oyster TNF and then regulate the immune response by virtue of neurotransmitters, cytokines and transcription factors.

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

PubMed Central

Schultz, André; Qutub, Amina A.

2016-01-01

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. PMID:26942765

Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

PubMed

Sheng, Yin; Zhang, Hao; Zeng, Zhigang

2017-10-01

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

Selected-node stochastic simulation algorithm

NASA Astrophysics Data System (ADS)

Duso, Lorenzo; Zechner, Christoph

2018-04-01

Stochastic simulations of biochemical networks are of vital importance for understanding complex dynamics in cells and tissues. However, existing methods to perform such simulations are associated with computational difficulties and addressing those remains a daunting challenge to the present. Here we introduce the selected-node stochastic simulation algorithm (snSSA), which allows us to exclusively simulate an arbitrary, selected subset of molecular species of a possibly large and complex reaction network. The algorithm is based on an analytical elimination of chemical species, thereby avoiding explicit simulation of the associated chemical events. These species are instead described continuously in terms of statistical moments derived from a stochastic filtering equation, resulting in a substantial speedup when compared to Gillespie's stochastic simulation algorithm (SSA). Moreover, we show that statistics obtained via snSSA profit from a variance reduction, which can significantly lower the number of Monte Carlo samples needed to achieve a certain performance. We demonstrate the algorithm using several biological case studies for which the simulation time could be reduced by orders of magnitude.

Computer-assisted design for scaling up systems based on DNA reaction networks.

PubMed

Aubert, Nathanaël; Mosca, Clément; Fujii, Teruo; Hagiya, Masami; Rondelez, Yannick

2014-04-06

In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the possibility to use CMA-ES, a state-of-the-art optimization algorithm that will automatically evolve parameters chosen by the user to try to match a specified behaviour. Finally, because all possible functionality cannot be captured by a single software, DACCAD includes the possibility to export a system in the synthetic biology markup language, a widely used language for describing biological reaction systems. DACCAD can be downloaded online at http://www.yannick-rondelez.com/downloads/.

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.

Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

PubMed

Yang, Yi; Hu, Xiao-Pan; Ma, Bin-Guang

2017-02-28

Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

A moment-convergence method for stochastic analysis of biochemical reaction networks.

PubMed

Zhang, Jiajun; Nie, Qing; Zhou, Tianshou

2016-05-21

Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.

Concordant Chemical Reaction Networks and the Species-Reaction Graph

PubMed Central

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

Cyclometalated platinum(ii) complexes of 2,2'-bipyridine N-oxide containing a 1,1'-bis(diphenylphosphino)ferrocene ligand: structural, computational and electrochemical studies.

PubMed

Shahsavari, Hamid R; Fereidoonnezhad, Masood; Niazi, Maryam; Mosavi, S Talaat; Habib Kazemi, Sayed; Kia, Reza; Shirkhan, Shima; Abdollahi Aghdam, Siamak; Raithby, Paul R

2017-02-14

The preparation and characterization of new heteronuclear-platinum(ii) complexes containing a 1,1'-bis(diphenylphosphino)ferrocene (dppf) ligand are described. The reaction of the known starting complex [PtMe(κ 2 N,C-bipyO-H)(SMe 2 )], A, in which bipyO-H is a cyclometalated rollover 2,2'-bipyridine N-oxide, with the dppf ligand in a 2 : 1 ratio or an equimolar ratio led to the formation of the corresponding binuclear complex [Pt 2 Me 2 (κ 2 N,C-bipyO-H) 2 (μ-dppf)], 1, or the mononuclear complex [PtMe(κ 1 C-bipyO-H)(dppf)], 2, respectively. According to the reaction conditions, the dppf ligand in 1 and 2 behaves as either a bridging or chelating ligand. All complexes were characterized by NMR spectroscopy. The solid-state structure of 2 was determined by the single-crystal X-ray diffraction method and it was shown that the chelating dppf ligand in this complex was arranged in a "synclinal-staggered" conformation. Also, the occurrence of intermolecular C-H Cp O bipyO-H interactions in the solid-state gave rise to an extended 1-D network. The electronic absorption spectra and the electrochemical behavior of these complexes are discussed. Density functional theory (DFT) was used for geometry optimization of the singlet states in solution and for electronic structure calculations. The analysis of the molecular orbital (MO) compositions in terms of occupied and unoccupied fragment orbitals in 2 was performed.

A Compartmentalized Out-of-Equilibrium Enzymatic Reaction Network for Sustained Autonomous Movement

PubMed Central

2016-01-01

Every living cell is a compartmentalized out-of-equilibrium system exquisitely able to convert chemical energy into function. In order to maintain homeostasis, the flux of metabolites is tightly controlled by regulatory enzymatic networks. A crucial prerequisite for the development of lifelike materials is the construction of synthetic systems with compartmentalized reaction networks that maintain out-of-equilibrium function. Here, we aim for autonomous movement as an example of the conversion of feedstock molecules into function. The flux of the conversion is regulated by a rationally designed enzymatic reaction network with multiple feedforward loops. By compartmentalizing the network into bowl-shaped nanocapsules the output of the network is harvested as kinetic energy. The entire system shows sustained and tunable microscopic motion resulting from the conversion of multiple external substrates. The successful compartmentalization of an out-of-equilibrium reaction network is a major first step in harnessing the design principles of life for construction of adaptive and internally regulated lifelike systems. PMID:27924313

Aerobic metabolism underlies complexity and capacity

PubMed Central

Koch, Lauren G; Britton, Steven L

2008-01-01

The evolution of biological complexity beyond single-celled organisms was linked temporally with the development of an oxygen atmosphere. Functionally, this linkage can be attributed to oxygen ranking high in both abundance and electronegativity amongst the stable elements of the universe. That is, reduction of oxygen provides for close to the largest possible transfer of energy for each electron transfer reaction. This suggests the general hypothesis that the steep thermodynamic gradient of an oxygen environment was permissive for the development of multicellular complexity. A corollary of this hypothesis is that aerobic metabolism underwrites complex biological function mechanistically at all levels of organization. The strong contemporary functional association of aerobic metabolism with both physical capacity and health is presumably a product of the integral role of oxygen in our evolutionary history. Here we provide arguments from thermodynamics, evolution, metabolic network analysis, clinical observations and animal models that are in accord with the centrality of oxygen in biology. PMID:17947307

Adaptive Chemical Networks under Non-Equilibrium Conditions: The Evaporating Droplet.

PubMed

Armao, Joseph J; Lehn, Jean-Marie

2016-10-17

Non-volatile solutes in an evaporating drop experience an out-of-equilibrium state due to non-linear concentration effects and complex flow patterns. Here, we demonstrate a small molecule chemical reaction network that undergoes a rapid adaptation response to the out-of-equilibrium conditions inside the droplet leading to control over the molecular constitution and spatial arrangement of the deposition pattern. Adaptation results in a pronounced coffee stain effect and coupling to chemical concentration gradients within the drop is demonstrated. Amplification and suppression of network species are readily identifiable with confocal fluorescence microscopy. We anticipate that these observations will contribute to the design and exploration of out-of-equilibrium chemical systems, as well as be useful towards the development of point-of-care medical diagnostics and controlled deposition of small molecules through inkjet printing. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions

PubMed Central

2012-01-01

Background It is well known that the deterministic dynamics of biochemical reaction networks can be more easily studied if timescale separation conditions are invoked (the quasi-steady-state assumption). In this case the deterministic dynamics of a large network of elementary reactions are well described by the dynamics of a smaller network of effective reactions. Each of the latter represents a group of elementary reactions in the large network and has associated with it an effective macroscopic rate law. A popular method to achieve model reduction in the presence of intrinsic noise consists of using the effective macroscopic rate laws to heuristically deduce effective probabilities for the effective reactions which then enables simulation via the stochastic simulation algorithm (SSA). The validity of this heuristic SSA method is a priori doubtful because the reaction probabilities for the SSA have only been rigorously derived from microscopic physics arguments for elementary reactions. Results We here obtain, by rigorous means and in closed-form, a reduced linear Langevin equation description of the stochastic dynamics of monostable biochemical networks in conditions characterized by small intrinsic noise and timescale separation. The slow-scale linear noise approximation (ssLNA), as the new method is called, is used to calculate the intrinsic noise statistics of enzyme and gene networks. The results agree very well with SSA simulations of the non-reduced network of elementary reactions. In contrast the conventional heuristic SSA is shown to overestimate the size of noise for Michaelis-Menten kinetics, considerably under-estimate the size of noise for Hill-type kinetics and in some cases even miss the prediction of noise-induced oscillations. Conclusions A new general method, the ssLNA, is derived and shown to correctly describe the statistics of intrinsic noise about the macroscopic concentrations under timescale separation conditions. The ssLNA provides a simple and accurate means of performing stochastic model reduction and hence it is expected to be of widespread utility in studying the dynamics of large noisy reaction networks, as is common in computational and systems biology. PMID:22583770

A modified preparation procedure for carbon nanotube-confined Nd/Na heterobimetallic catalyst for anti-selective catalytic asymmetric nitroaldol reactions.

PubMed

Sureshkumar, Devarajulu; Hashimoto, Kazuki; Kumagai, Naoya; Shibasaki, Masakatsu

2013-11-15

A recyclable asymmetric metal-based catalyst is a rare entity among the vast collection of asymmetric catalysts developed so far. Recently we found that the combination of a self-assembling metal-based asymmetric catalyst and multiwalled carbon nanotubes (MWNTs) produced a highly active and recyclable catalyst in which the catalytically active metal complex was dispersed in the MWNT network. Herein we describe an improved preparation procedure and full details of a Nd/Na heterobimetallic complex confined in MWNTs. Facilitated self-assembly of the catalyst with MWNTs avoided the sacrificial use of excess chiral ligand for the formation of the heterobimetallic complex, improving the loading ratio of the catalyst components. Eighty-five percent of the catalyst components were incorporated onto MWNTs to produce the confined catalyst, which was a highly efficient and recyclable catalyst for the anti-selective asymmetric nitroaldol reaction. The requisite precautions for the catalyst preparation to elicit reproducible catalytic performance are summarized. Superior catalytic profiles over the prototype catalyst without MWNTs were revealed in the synthesis of optically active 1,2-nitroalkanols, which are key intermediates for the synthesis of therapeutics.

Systematic assignment of thermodynamic constraints in metabolic network models

PubMed Central

Kümmel, Anne; Panke, Sven; Heinemann, Matthias

2006-01-01

Background The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models. Results We present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to disable thermodynamically infeasible energy production. Conclusion Although not being fully comprehensive, our algorithm for systematic reaction direction assignment could define a significant number of irreversible reactions automatically with low computational effort. We envision that the presented algorithm is a valuable part of a computational framework that assists the automated reconstruction of genome-scale metabolic models. PMID:17123434

Computational models of airway branching morphogenesis.

PubMed

Varner, Victor D; Nelson, Celeste M

2017-07-01

The bronchial network of the mammalian lung consists of millions of dichotomous branches arranged in a highly complex, space-filling tree. Recent computational models of branching morphogenesis in the lung have helped uncover the biological mechanisms that construct this ramified architecture. In this review, we focus on three different theoretical approaches - geometric modeling, reaction-diffusion modeling, and continuum mechanical modeling - and discuss how, taken together, these models have identified the geometric principles necessary to build an efficient bronchial network, as well as the patterning mechanisms that specify airway geometry in the developing embryo. We emphasize models that are integrated with biological experiments and suggest how recent progress in computational modeling has advanced our understanding of airway branching morphogenesis. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Genealogical Tree of Ethanol: Gas-phase Formation of Glycolaldehyde, Acetic Acid, and Formic Acid

NASA Astrophysics Data System (ADS)

Skouteris, Dimitrios; Balucani, Nadia; Ceccarelli, Cecilia; Vazart, Fanny; Puzzarini, Cristina; Barone, Vincenzo; Codella, Claudio; Lefloch, Bertrand

2018-02-01

Despite the harsh conditions of the interstellar medium, chemistry thrives in it, especially in star-forming regions where several interstellar complex organic molecules (iCOMs) have been detected. Yet, how these species are synthesized is a mystery. The majority of current models claim that this happens on interstellar grain surfaces. Nevertheless, evidence is mounting that neutral gas-phase chemistry plays an important role. In this paper, we propose a new scheme for the gas-phase synthesis of glycolaldehyde, a species with a prebiotic potential and for which no gas-phase formation route was previously known. In the proposed scheme, the ancestor is ethanol and the glycolaldehyde sister species are acetic acid (another iCOM with unknown gas-phase formation routes) and formic acid. For the reactions of the new scheme with no available data, we have performed electronic structure and kinetics calculations deriving rate coefficients and branching ratios. Furthermore, after a careful review of the chemistry literature, we revised the available chemical networks, adding and correcting several reactions related to glycolaldehyde, acetic acid, and formic acid. The new chemical network has been used in an astrochemical model to predict the abundance of glycolaldehyde, acetic acid, and formic acid. The predicted abundance of glycolaldehyde depends on the ethanol abundance in the gas phase and is in excellent agreement with the measured one in hot corinos and shock sites. Our new model overpredicts the abundance of acetic acid and formic acid by about a factor of 10, which might imply a yet incomplete reaction network.

Reaction schemes visualized in network form: the syntheses of strychnine as an example.

PubMed

Proudfoot, John R

2013-05-24

Representation of synthesis sequences in a network form provides an effective method for the comparison of multiple reaction schemes and an opportunity to emphasize features such as reaction scale that are often relegated to experimental sections. An example of data formatting that allows construction of network maps in Cytoscape is presented, along with maps that illustrate the comparison of multiple reaction sequences, comparison of scaffold changes within sequences, and consolidation to highlight common key intermediates used across sequences. The 17 different synthetic routes reported for strychnine are used as an example basis set. The reaction maps presented required a significant data extraction and curation, and a standardized tabular format for reporting reaction information, if applied in a consistent way, could allow the automated combination of reaction information across different sources.

A computational approach to extinction events in chemical reaction networks with discrete state spaces.

PubMed

Johnston, Matthew D

2017-12-01

Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results. Copyright © 2017 Elsevier Inc. All rights reserved.

Mechanism of epoxide hydrolysis in microsolvated nucleotide bases adenine, guanine and cytosine: a DFT study.

PubMed

Vijayalakshmi, Kunduchi P; Mohan, Neetha; Ajitha, Manjaly J; Suresh, Cherumuttathu H

2011-07-21

Six water molecules have been used for microsolvation to outline a hydrogen bonded network around complexes of ethylene epoxide with nucleotide bases adenine (EAw), guanine (EGw) and cytosine (ECw). These models have been developed with the MPWB1K-PCM/6-311++G(3df,2p)//MPWB1K/6-31+G(d,p) level of DFT method and calculated S(N)2 type ring opening of the epoxide due to amino group of the nucleotide bases, viz. the N6 position of adenine, N2 position of guanine and N4 position of cytosine. Activation energy (E(act)) for the ring opening was found to be 28.06, 28.64, and 28.37 kcal mol(-1) respectively for EAw, EGw and ECw. If water molecules were not used, the reactions occurred at considerably high value of E(act), viz. 53.51 kcal mol(-1) for EA, 55.76 kcal mol(-1) for EG and 56.93 kcal mol(-1) for EC. The ring opening led to accumulation of negative charge on the developing alkoxide moiety and the water molecules around the charge localized regions showed strong hydrogen bond interactions to provide stability to the intermediate systems EAw-1, EGw-1 and ECw-1. This led to an easy migration of a proton from an activated water molecule to the alkoxide moiety to generate a hydroxide. Almost simultaneously, a proton transfer chain reaction occurred through the hydrogen bonded network of water molecules and resulted in the rupture of one of the N-H bonds of the quaternized amino group. The highest value of E(act) for the proton transfer step of the reaction was 2.17 kcal mol(-1) for EAw, 2.93 kcal mol(-1) for EGw and 0.02 kcal mol(-1) for ECw. Further, the overall reaction was exothermic by 17.99, 22.49 and 13.18 kcal mol(-1) for EAw, EGw and ECw, respectively, suggesting that the reaction is irreversible. Based on geometric features of the epoxide-nucleotide base complexes and the energetics, the highest reactivity is assigned for adenine followed by cytosine and guanine. Epoxide-mediated damage of DNA is reported in the literature and the present results suggest that hydrated DNA bases become highly S(N)2 active on epoxide systems and the occurrence of such reactions can inflict permanent damage to the DNA.

Consistent dust and gas models for protoplanetary disks. II. Chemical networks and rates

NASA Astrophysics Data System (ADS)

Kamp, I.; Thi, W.-F.; Woitke, P.; Rab, C.; Bouma, S.; Ménard, F.

2017-11-01

Aims: We aim to define a small and large chemical network which can be used for the quantitative simultaneous analysis of molecular emission from the near-IR to the submm. We also aim to revise reactions of excited molecular hydrogen, which are not included in UMIST, to provide a homogeneous database for future applications. Methods: We have used the thermo-chemical disk modeling code ProDiMo and a standard T Tauri disk model to evaluate the impact of various chemical networks, reaction rate databases and sets of adsorption energies on a large sample of chemical species and emerging line fluxes from the near-IR to the submm wavelength range. Results: We find large differences in the masses and radial distribution of ice reservoirs when considering freeze-out on bare or polar ice coated grains. Most strongly the ammonia ice mass and the location of the snow line (water) change. As a consequence molecules associated to the ice lines such as N2H+ change their emitting region; none of the line fluxes in the sample considered here changes by more than 25% except CO isotopologues, CN and N2H+ lines. The three-body reaction N+H2+M plays a key role in the formation of water in the outer disk. Besides that, differences between the UMIST 2006 and 2012 database change line fluxes in the sample considered here by less than a factor of two (a subset of low excitation CO and fine structure lines stays even within 25%); exceptions are OH, CN, HCN, HCO+ and N2H+ lines. However, different networks such as OSU and KIDA 2011 lead to pronounced differences in the chemistry inside 100 au and thus affect emission lines from high excitation CO, OH and CN lines. H2 is easily excited at the disk surface and state-to-state reactions enhance the abundance of CH+ and to a lesser extent HCO+. For sub-mm lines of HCN, N2H+ and HCO+, a more complex larger network is recommended. Conclusions: More work is required to consolidate data on key reactions leading to the formation of water, molecular ions such as HCO+ and N2H+ as well as the nitrogen chemistry. This affects many of the key lines used in the interpretation of disk observations. Differential analysis of various disk models using the same chemical input data will be more robust than the interpretation of absolute fluxes.

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection.

PubMed

Yoon, Jeongah; Si, Yaguang; Nolan, Ryan; Lee, Kyongbum

2007-09-15

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism. Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism. Supplementary data are available at Bioinformatics online.

Rapid neutral-neutral reactions at low temperatures: a new network and first results for TMC-1

NASA Astrophysics Data System (ADS)

Smith, Ian W. M.; Herbst, Eric; Chang, Qiang

2004-05-01

There is now ample evidence from an assortment of experiments, especially those involving the CRESU (Cinétique de Réaction en Ecoulement Supersonique Uniforme) technique, that a variety of neutral-neutral reactions possess no activation energy barrier and are quite rapid at very low temperatures. These reactions include both radical-radical systems and, more surprisingly, systems involving an atom or a radical and one `stable' species. Generalizing from the small but growing number of systems studied in the laboratory, we estimate reaction rate coefficients for a larger number of such reactions and include these estimates in a new network of gas-phase reactions for use in low-temperature interstellar chemistry. Designated osu.2003, the new network is available on the World Wide Web and will be continually updated. A table of new results for molecular abundances in the dark cloud TMC-1 (CP) is provided and compared with results from an older (new standard model; nsm) network.

Synchronization in node of complex networks consist of complex chaotic system

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

Wei, Qiang, E-mail: qiangweibeihua@163.com; Digital Images Processing Institute of Beihua University, BeiHua University, Jilin, 132011, Jilin; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024

2014-07-15

A new synchronization method is investigated for node of complex networks consists of complex chaotic system. When complex networks realize synchronization, different component of complex state variable synchronize up to different scaling complex function by a designed complex feedback controller. This paper change synchronization scaling function from real field to complex field for synchronization in node of complex networks with complex chaotic system. Synchronization in constant delay and time-varying coupling delay complex networks are investigated, respectively. Numerical simulations are provided to show the effectiveness of the proposed method.

Methanol synthesis on ZnO(0001{sup ¯}). IV. Reaction mechanisms and electronic structure

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

Frenzel, Johannes, E-mail: johannes.frenzel@theochem.rub.de; Marx, Dominik

2014-09-28

Methanol synthesis from CO and H{sub 2} over ZnO, which requires high temperatures and high pressures giving rise to a complex interplay of physical and chemical processes over this heterogeneous catalyst surface, is investigated using ab initio simulations. The redox properties of the surrounding gas phase are known to directly impact on the catalyst properties and thus, set the overall catalytic reactivity of this easily reducible oxide material. In Paper III of our series [J. Kiss, J. Frenzel, N. N. Nair, B. Meyer, and D. Marx, J. Chem. Phys. 134, 064710 (2011)] we have qualitatively shown that for the partiallymore » hydroxylated and defective ZnO(0001{sup ¯}) surface there exists an intricate network of surface chemical reactions. In the present study, we employ advanced molecular dynamics techniques to resolve in detail this reaction network in terms of elementary steps on the defective surface, which is in stepwise equilibrium with the gas phase. The two individual reduction steps were investigated by ab initio metadynamics sampling of free energy landscapes in three-dimensional reaction subspaces. By also sampling adsorption and desorption processes and thus molecular species that are in the gas phase but close to the surface, our approach successfully generated several alternative pathways of methanol synthesis. The obtained results suggest an Eley-Rideal mechanism for both reduction steps, thus involving “near-surface” molecules from the gas phase, to give methanol preferentially over a strongly reduced catalyst surface, while important side reactions are of Langmuir-Hinshelwood type. Catalyst re-reduction by H{sub 2} stemming from the gas phase is a crucial process after each reduction step in order to maintain the catalyst's activity toward methanol formation and to close the catalytic cycle in some reaction channels. Furthermore, the role of oxygen vacancies, side reactions, and spectator species is investigated and mechanistic details are discussed based on extensive electronic structure analysis.« less

MetNetMaker: a free and open-source tool for the creation of novel metabolic networks in SBML format.

PubMed

Forth, Thomas; McConkey, Glenn A; Westhead, David R

2010-09-15

An application has been developed to help with the creation and editing of Systems Biology Markup Language (SBML) format metabolic networks up to the organism scale. Networks are defined as a collection of Kyoto Encyclopedia of Genes and Genomes (KEGG) LIGAND reactions with an optional associated Enzyme Classification (EC) number for each reaction. Additional custom reactions can be defined by the user. Reactions within the network can be assigned flux constraints and compartmentalization is supported for each reaction in addition to the support for reactions that occur across compartment boundaries. Exported networks are fully SBML L2V4 compatible with an optional L2V1 export for compatibility with old versions of the COBRA toolbox. The software runs in the free Microsoft Access 2007 Runtime (Microsoft Inc.), which is included with the installer and works on Windows XP SP2 or better. Full source code is viewable in the full version of Access 2007 or 2010. Users must have a license to use the KEGG LIGAND database (free academic licensing is available). Please go to www.bioinformatics.leeds.ac.uk/~pytf/metnetmaker for software download, help and tutorials.

Cycloaddition Reactions of Cobalt-Complexed Macrocyclic Alkynes: The Transannular Pauson-Khand Reaction.

PubMed

Karabiyikoglu, Sedef; Boon, Byron A; Merlic, Craig A

2017-08-04

The Pauson-Khand reaction is a powerful tool for the synthesis of cyclopentenones through the efficient [2 + 2 + 1] cycloaddition of dicobalt alkyne complexes with alkenes. While intermolecular and intramolecular variants are widely known, transannular versions of this reaction are unknown and the basis of this study. Macrocyclic enyne and dienyne complexes were readily synthesized by palladium(II)-catalyzed oxidative macrocyclizations of bis(vinyl boronate esters) or ring-closing metathesis reactions followed by complexation with dicobalt octacarbonyl. Several reaction modalities of these macrocyclic complexes were uncovered. In addition to the first successful transannular Pauson-Khand reactions, other intermolecular and transannular cycloaddition reactions included intermolecular Pauson-Khand reactions, transannular [4 + 2] cycloaddition reactions, intermolecular [2 + 2 + 2] cycloaddition reactions, and intermolecular [2 + 2 + 1 + 1] cycloaddition reactions. The structural and reaction requirements for each process are presented.

What does systems biology mean for drug development?

PubMed

Schrattenholz, André; Soskić, Vukić

2008-01-01

The complexity and flexibility of cellular architectures is increasingly recognized by impressive progress on the side of molecular analytics, i.e. proteomics, genomics and metabolomics. One of the messages from systems biology is that the number of molecular species in cellular networks is orders of magnitude bigger than anticipated by genomic analysis, in particular by fast posttranslational modifications of proteins. The requirements to manage external signals, integrate spatiotemporal signal transduction inside an organism and at the same time optimizing networks of biochemical and chemical reactions result in chemically extremely fine tuned molecular entities. Chemical side reactions of enzymatic activity, like e.g. random oxidative damage of proteins by free radicals during aging constantly introduce epigenetic alterations of protein targets. These events gradually and on an individual stochastic scale, keep modifying activities of these targets, and their affinities and selectivities towards biological and pharmacological ligands. One further message is that many of the key reactions in living systems are essentially based on interactions of low affinities and even low selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. So, in complex disorders like cancer or neurodegenerative diseases, which are rooted in relatively subtle and multimodal dysfunction of important physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which still dominate the pharmaceutical industry increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade and the treatment of "complex diseases" remains a most pressing medical need. Currently a change of paradigm can be observed with regard to a new focus on agents that modulate multiple targets simultaneously. Targeting cellular function as a system rather than on the level of the single protein molecule significantly increases the size of the drugable proteome and is expected to introduce novel classes of multi-target drugs with fewer adverse effects and toxicity. Multiple target approaches have recently been used to design medications against atherosclerosis, cancer, depression, psychosis and neurodegenerative diseases. A focussed approach towards "systemic" drugs will certainly require the development of novel computational and mathematical concepts for appropriate modelling of complex data and extraction of "screenable" information from biological systems essentially ruled by deterministic chaotic processes on a background of individual stochasticity.

Eliminating fast reactions in stochastic simulations of biochemical networks: A bistable genetic switch

NASA Astrophysics Data System (ADS)

Morelli, Marco J.; Allen, Rosalind J.; Tǎnase-Nicola, Sorin; ten Wolde, Pieter Rein

2008-01-01

In many stochastic simulations of biochemical reaction networks, it is desirable to "coarse grain" the reaction set, removing fast reactions while retaining the correct system dynamics. Various coarse-graining methods have been proposed, but it remains unclear which methods are reliable and which reactions can safely be eliminated. We address these issues for a model gene regulatory network that is particularly sensitive to dynamical fluctuations: a bistable genetic switch. We remove protein-DNA and/or protein-protein association-dissociation reactions from the reaction set using various coarse-graining strategies. We determine the effects on the steady-state probability distribution function and on the rate of fluctuation-driven switch flipping transitions. We find that protein-protein interactions may be safely eliminated from the reaction set, but protein-DNA interactions may not. We also find that it is important to use the chemical master equation rather than macroscopic rate equations to compute effective propensity functions for the coarse-grained reactions.

A Facile CD Protocol for Rapid Determination of Enantiomeric Excess and Concentration of Chiral Primary Amines

PubMed Central

Nieto, Sonia; Dragna, Justin M.; Anslyn, Eric V.

2010-01-01

A protocol for the rapid determination of the absolute configuration and enantiomeric excess of α-chiral primary amines with potential applications in asymmetric reaction discovery has been developed. The protocol requires derivatization of α-chiral primary amines via condensation with pyridine carboxaldehyde to quantitatively yield the corresponding imine. The Cu(I) complex with 2,2'-bis (diphenylphosphino)-1,1'-dinaphthyl (BINAP -CuI) with the imine yields a metal-to-ligand-charge-transfer band (MLCT) in the visible region of the circular dichroism spectrum upon binding. Diastereomeric host-guest complexes give CD signals of the same signs, but different amplitudes, allowing for differentiation of enantiomers. Processing the primary optical data from the CD spectrum with linear discriminant analysis (LDA) allows for the determination of absolute configuration and identification of the amines, and processing with a supervised multi-layer perceptron artifical neural network (MLP-ANN) allows for the simultaneous determination of ee and concentration. The primary optical data necessary to determine the ee of unknown samples is obtained in 2 minutes per sample. To demonstrate the utility of the protocol in asymmetric reaction discovery, the ee's and concentrations for an asymmetric metal catalyzed reaction are determined. The potential of the protocol's application in high-throughput screening (HTS) of ee is discussed. PMID:19946914

Concordant Chemical Reaction Networks

PubMed Central

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

RuleMonkey: software for stochastic simulation of rule-based models

PubMed Central

2010-01-01

Background The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. Results Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods. Conclusions RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application http://public.tgen.org/rulemonkey. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models. PMID:20673321

Lattice Boltzmann simulation of CO2 reactive transport in network fractured media

NASA Astrophysics Data System (ADS)

Tian, Zhiwei; Wang, Junye

2017-08-01

Carbon dioxide (CO2) geological sequestration plays an important role in mitigating CO2 emissions for climate change. Understanding interactions of the injected CO2 with network fractures and hydrocarbons is key for optimizing and controlling CO2 geological sequestration and evaluating its risks to ground water. However, there is a well-known, difficult process in simulating the dynamic interaction of fracture-matrix, such as dynamic change of matrix porosity, unsaturated processes in rock matrix, and effect of rock mineral properties. In this paper, we develop an explicit model of the fracture-matrix interactions using multilayer bounce-back treatment as a first attempt to simulate CO2 reactive transport in network fractured media through coupling the Dardis's LBM porous model for a new interface treatment. Two kinds of typical fracture networks in porous media are simulated: straight cross network fractures and interleaving network fractures. The reaction rate and porosity distribution are illustrated and well-matched patterns are found. The species concentration distribution and evolution with time steps are also analyzed and compared with different transport properties. The results demonstrate the capability of this model to investigate the complex processes of CO2 geological injection and reactive transport in network fractured media, such as dynamic change of matrix porosity.

The Variance Reaction Time Model

ERIC Educational Resources Information Center

Sikstrom, Sverker

2004-01-01

The variance reaction time model (VRTM) is proposed to account for various recognition data on reaction time, the mirror effect, receiver-operating-characteristic (ROC) curves, etc. The model is based on simple and plausible assumptions within a neural network: VRTM is a two layer neural network where one layer represents items and one layer…

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: The Fractal Dimensions of Complex Networks

NASA Astrophysics Data System (ADS)

Guo, Long; Cai, XU

2009-08-01

It is shown that many real complex networks share distinctive features, such as the small-world effect and the heterogeneous property of connectivity of vertices, which are different from random networks and regular lattices. Although these features capture the important characteristics of complex networks, their applicability depends on the style of networks. To unravel the universal characteristics many complex networks have in common, we study the fractal dimensions of complex networks using the method introduced by Shanker. We find that the average 'density' (ρ(r)) of complex networks follows a better power-law function as a function of distance r with the exponent df, which is defined as the fractal dimension, in some real complex networks. Furthermore, we study the relation between df and the shortcuts Nadd in small-world networks and the size N in regular lattices. Our present work provides a new perspective to understand the dependence of the fractal dimension df on the complex network structure.

SkyNet: Modular nuclear reaction network library

NASA Astrophysics Data System (ADS)

Lippuner, Jonas; Roberts, Luke F.

2017-10-01

The general-purpose nuclear reaction network SkyNet evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. Any list of isotopes can be evolved and SkyNet supports various different types of nuclear reactions. SkyNet is modular, permitting new or existing physics, such as nuclear reactions or equations of state, to be easily added or modified.

Systems approach to excitation-energy and electron transfer reaction networks in photosystem II complex: model studies for chlorophyll a fluorescence induction kinetics.

PubMed

Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi

2015-09-07

Photosystem II (PS II) is a protein complex which evolves oxygen and drives charge separation for photosynthesis employing electron and excitation-energy transfer processes over a wide timescale range from picoseconds to milliseconds. While the fluorescence emitted by the antenna pigments of this complex is known as an important indicator of the activity of photosynthesis, its interpretation was difficult because of the complexity of PS II. In this study, an extensive kinetic model which describes the complex and multi-timescale characteristics of PS II is analyzed through the use of the hierarchical coarse-graining method proposed in the authors׳ earlier work. In this coarse-grained analysis, the reaction center (RC) is described by two states, open and closed RCs, both of which consist of oxidized and neutral special pairs being in quasi-equilibrium states. Besides, the PS II model at millisecond scale with three-state RC, which was studied previously, could be derived by suitably adjusting the kinetic parameters of electron transfer between tyrosine and RC. Our novel coarse-grained model of PS II can appropriately explain the light-intensity dependent change of the characteristic patterns of fluorescence induction kinetics from O-J-I-P, which shows two inflection points, J and I, between initial point O and peak point P, to O-J-D-I-P, which shows a dip D between J and I inflection points. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks

NASA Astrophysics Data System (ADS)

Raff, L. M.; Malshe, M.; Hagan, M.; Doughan, D. I.; Rockley, M. G.; Komanduri, R.

2005-02-01

A neural network/trajectory approach is presented for the development of accurate potential-energy hypersurfaces that can be utilized to conduct ab initio molecular dynamics (AIMD) and Monte Carlo studies of gas-phase chemical reactions, nanometric cutting, and nanotribology, and of a variety of mechanical properties of importance in potential microelectromechanical systems applications. The method is sufficiently robust that it can be applied to a wide range of polyatomic systems. The overall method integrates ab initio electronic structure calculations with importance sampling techniques that permit the critical regions of configuration space to be determined. The computed ab initio energies and gradients are then accurately interpolated using neural networks (NN) rather than arbitrary parametrized analytical functional forms, moving interpolation or least-squares methods. The sampling method involves a tight integration of molecular dynamics calculations with neural networks that employ early stopping and regularization procedures to improve network performance and test for convergence. The procedure can be initiated using an empirical potential surface or direct dynamics. The accuracy and interpolation power of the method has been tested for two cases, the global potential surface for vinyl bromide undergoing unimolecular decomposition via four different reaction channels and nanometric cutting of silicon. The results show that the sampling methods permit the important regions of configuration space to be easily and rapidly identified, that convergence of the NN fit to the ab initio electronic structure database can be easily monitored, and that the interpolation accuracy of the NN fits is excellent, even for systems involving five atoms or more. The method permits a substantial computational speed and accuracy advantage over existing methods, is robust, and relatively easy to implement.

Combining SVM and flame radiation to forecast BOF end-point

NASA Astrophysics Data System (ADS)

Wen, Hongyuan; Zhao, Qi; Xu, Lingfei; Zhou, Munchun; Chen, Yanru

2009-05-01

Because of complex reactions in Basic Oxygen Furnace (BOF) for steelmaking, the main end-point control methods of steelmaking have insurmountable difficulties. Aiming at these problems, a support vector machine (SVM) method for forecasting the BOF steelmaking end-point is presented based on flame radiation information. The basis is that the furnace flame is the performance of the carbon oxygen reaction, because the carbon oxygen reaction is the major reaction in the steelmaking furnace. The system can acquire spectrum and image data quickly in the steelmaking adverse environment. The structure of SVM and the multilayer feed-ward neural network are similar, but SVM model could overcome the inherent defects of the latter. The model is trained and forecasted by using SVM and some appropriate variables of light and image characteristic information. The model training process follows the structure risk minimum (SRM) criterion and the design parameter can be adjusted automatically according to the sampled data in the training process. Experimental results indicate that the prediction precision of the SVM model and the executive time both meet the requirements of end-point judgment online.

Mapping and discrimination of networks in the complexity-entropy plane

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2017-10-01

Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or social networks, on such a basis. Here we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged per-node entropic measure characterizing the network's information content and the associated Jenson-Shannon divergence as a measure of disequilibrium. We study 29 real-world networks and show that networks of the same category tend to cluster in distinct areas of the resulting complexity-entropy plane. We demonstrate that within our framework, connectome networks exhibit among the highest complexity while, e.g., transportation and infrastructure networks display significantly lower values. Furthermore, we demonstrate the utility of our framework by applying it to families of random scale-free and Watts-Strogatz model networks. We then show in a second application that the proposed framework is useful to objectively construct threshold-based networks, such as functional climate networks or recurrence networks, by choosing the threshold such that the statistical network complexity is maximized.

Taming Prebiotic Chemistry: The Role of Heterogeneous and Interfacial Catalysis in the Emergence of a Prebiotic Catalytic/Information Polymer System

PubMed Central

Monnard, Pierre-Alain

2016-01-01

Cellular life is based on interacting polymer networks that serve as catalysts, genetic information and structural molecules. The complexity of the DNA, RNA and protein biochemistry suggests that it must have been preceded by simpler systems. The RNA world hypothesis proposes RNA as the prime candidate for such a primal system. Even though this proposition has gained currency, its investigations have highlighted several challenges with respect to bulk aqueous media: (1) the synthesis of RNA monomers is difficult; (2) efficient pathways for monomer polymerization into functional RNAs and their subsequent, sequence-specific replication remain elusive; and (3) the evolution of the RNA function towards cellular metabolism in isolation is questionable in view of the chemical mixtures expected on the early Earth. This review will address the question of the possible roles of heterogeneous media and catalysis as drivers for the emergence of RNA-based polymer networks. We will show that this approach to non-enzymatic polymerizations of RNA from monomers and RNA evolution cannot only solve some issues encountered during reactions in bulk aqueous solutions, but may also explain the co-emergence of the various polymers indispensable for life in complex mixtures and their organization into primitive networks. PMID:27827919

A two-dimensional hydrogen-bonded water layer in the structure of a cobalt(III) cubane complex.

PubMed

Qi, Ji; Zhai, Xiang-Sheng; Zhu, Hong-Lin; Lin, Jian-Li

2014-02-01

A tetranuclear Co(III) oxide complex with cubane topology, tetrakis(2,2'-bipyridine-κ(2)N,N')di-μ2-carbonato-κ(4)O:O'-tetra-μ3-oxido-tetracobalt(III) pentadecahydrate, [Co4(CO3)2O4(C10H8N2)4]·15H2O, with an unbounded hydrogen-bonded water layer, has been synthesized by reaction of CoCO3 and 2,2'-bipyridine. The solvent water molecules form a hydrogen-bonded net with tetrameric and pentameric water clusters as subunits. The Co4O4 cubane-like cores are sandwiched between the water layers, which are further stacked into a three-dimensional metallo-supramolecular network.

Insensitive dependence of delay-induced oscillation death on complex networks

NASA Astrophysics Data System (ADS)

Zou, Wei; Zheng, Xing; Zhan, Meng

2011-06-01

Oscillation death (also called amplitude death), a phenomenon of coupling induced stabilization of an unstable equilibrium, is studied for an arbitrary symmetric complex network with delay-coupled oscillators, and the critical conditions for its linear stability are explicitly obtained. All cases including one oscillator, a pair of oscillators, regular oscillator networks, and complex oscillator networks with delay feedback coupling, can be treated in a unified form. For an arbitrary symmetric network, we find that the corresponding smallest eigenvalue of the Laplacian λN (0 >λN ≥ -1) completely determines the death island, and as λN is located within the insensitive parameter region for nearly all complex networks, the death island keeps nearly the largest and does not sensitively depend on the complex network structures. This insensitivity effect has been tested for many typical complex networks including Watts-Strogatz (WS) and Newman-Watts (NW) small world networks, general scale-free (SF) networks, Erdos-Renyi (ER) random networks, geographical networks, and networks with community structures and is expected to be helpful for our understanding of dynamics on complex networks.

Modeling Biogeochemical Cycling of Heavy Metals in Lake Coeur d'Alene Sediments

NASA Astrophysics Data System (ADS)

Sengor, S. S.; Spycher, N.; Belding, E.; Curthoys, K.; Ginn, T. R.

2005-12-01

Mining of precious metals since the late 1800's have left Lake Coeur d'Alene (LCdA) sediments heavily enriched with toxic metals, including Cd, Cu, Pb, and Zn. Indigenous microbes however are capable of catalyzing reactions that detoxify the benthic and aqueous lake environments, and thus constitute an important driving component in the biogeochemical cycles of these metals. Here we report on the development of a quantitative model of transport, fate, exposure and effects of toxic compounds on benthic microbial communities at LCdA. First, chemical data from the LCdA area have been compiled from multiple sources to investigate trends in chemical occurrence, as well as to define model boundary conditions. The model is structured as 1-D diffusive reactive transport model to simulate spatial and temporal distribution of metals through the benthic sediments. Inorganic reaction processes included in the model are aqueous speciation, surface complexation, mineral precipitation/dissolution and abiotic redox reactions. Simulations with and without surface complexation are carried out to evaluate the effect of sorption and the conservative behaviour of metals within the benthic sediments under abiotic and purely diffusive transport. The 1-D inorganic diffusive transport model is then coupled to a biotic reaction network including consortium biodegradation kinetics with multiple electron acceptors, product toxicity, and energy partitioning. Multiyear simulations are performed, with water column chemistry established as a boundary condition from extant data, to explore the role of biogeochemical dynamics on benthic fluxes of metals in the long term.

SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs

PubMed Central

Weeding, Emma; Houle, Jason

2010-01-01

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications. PMID:20639523

A moment-convergence method for stochastic analysis of biochemical reaction networks

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

Zhang, Jiajun; Nie, Qing; Zhou, Tianshou, E-mail: mcszhtsh@mail.sysu.edu.cn

Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in termsmore » of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.« less

Physical understanding of complex multiscale biochemical models via algorithmic simplification: Glycolysis in Saccharomyces cerevisiae

NASA Astrophysics Data System (ADS)

Kourdis, Panayotis D.; Steuer, Ralf; Goussis, Dimitris A.

2010-09-01

Large-scale models of cellular reaction networks are usually highly complex and characterized by a wide spectrum of time scales, making a direct interpretation and understanding of the relevant mechanisms almost impossible. We address this issue by demonstrating the benefits provided by model reduction techniques. We employ the Computational Singular Perturbation (CSP) algorithm to analyze the glycolytic pathway of intact yeast cells in the oscillatory regime. As a primary object of research for many decades, glycolytic oscillations represent a paradigmatic candidate for studying biochemical function and mechanisms. Using a previously published full-scale model of glycolysis, we show that, due to fast dissipative time scales, the solution is asymptotically attracted on a low dimensional manifold. Without any further input from the investigator, CSP clarifies several long-standing questions in the analysis of glycolytic oscillations, such as the origin of the oscillations in the upper part of glycolysis, the importance of energy and redox status, as well as the fact that neither the oscillations nor cell-cell synchronization can be understood in terms of glycolysis as a simple linear chain of sequentially coupled reactions.

Pt monolayer coating on complex network substrate with high catalytic activity for the hydrogen evolution reaction

PubMed Central

Li, Man; Ma, Qiang; Zi, Wei; Liu, Xiaojing; Zhu, Xuejie; Liu, Shengzhong (Frank)

2015-01-01

A deposition process has been developed to fabricate a complete-monolayer Pt coating on a large-surface-area three-dimensional (3D) Ni foam substrate using a buffer layer (Ag or Au) strategy. The quartz crystal microbalance, current density analysis, cyclic voltammetry integration, and X-ray photoelectron spectroscopy results show that the monolayer deposition process accomplishes full coverage on the substrate and the deposition can be controlled to a single atomic layer thickness. To our knowledge, this is the first report on a complete-monolayer Pt coating on a 3D bulk substrate with complex fine structures; all prior literature reported on submonolayer or incomplete-monolayer coating. A thin underlayer of Ag or Au is found to be necessary to cover a very reactive Ni substrate to ensure complete-monolayer Pt coverage; otherwise, only an incomplete monolayer is formed. Moreover, the Pt monolayer is found to work as well as a thick Pt film for catalytic reactions. This development may pave a way to fabricating a high-activity Pt catalyst with minimal Pt usage. PMID:26601247

Solid-Phase Enrichment and Analysis of Azide-Labeled Natural Products: Fishing Downstream of Biochemical Pathways.

PubMed

Pérez, Alexander J; Wesche, Frank; Adihou, Hélène; Bode, Helge B

2016-01-11

Many methods have been devised over the decades to trace precursors of specific molecules in cellular environments as, for example, in biosynthesis studies. The advent of click chemistry has facilitated the powerful combination of tracing and at the same time sieving the highly complex metabolome for compounds derived from simple or complex starting materials, especially when the click reaction takes place on a solid support. While the principle of solid-phase click reactions has already been successfully applied for selective protein and peptide enrichment, the successful enrichment of much smaller primary and secondary metabolites, showing great structural diversity and undergoing many different biosynthetic steps, has seen only little development. For bacterial secondary metabolism, a far broader tolerance for "clickable" precursors was observed than in ribosomal proteinogenesis, thus making this method a surprisingly valuable tool for the tracking and discovery of compounds within the cellular biochemical network. The implementation of this method has led to the identification of several new compounds from the bacterial genera Photorhabdus and Xenorhabdus, clearly proving its power. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A chemometric method to identify enzymatic reactions leading to the transition from glycolytic oscillations to waves

NASA Astrophysics Data System (ADS)

Zimányi, László; Khoroshyy, Petro; Mair, Thomas

2010-06-01

In the present work we demonstrate that FTIR-spectroscopy is a powerful tool for the time resolved and noninvasive measurement of multi-substrate/product interactions in complex metabolic networks as exemplified by the oscillating glycolysis in a yeast extract. Based on a spectral library constructed from the pure glycolytic intermediates, chemometric analysis of the complex spectra allowed us the identification of many of these intermediates. Singular value decomposition and multiple level wavelet decomposition were used to separate drifting substances from oscillating ones. This enabled us to identify slow and fast variables of glycolytic oscillations. Most importantly, we can attribute a qualitative change in the positive feedback regulation of the autocatalytic reaction to the transition from homogeneous oscillations to travelling waves. During the oscillatory phase the enzyme phosphofructokinase is mainly activated by its own product ADP, whereas the transition to waves is accompanied with a shift of the positive feedback from ADP to AMP. This indicates that the overall energetic state of the yeast extract determines the transition between spatially homogeneous oscillations and travelling waves.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

PubMed

Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk

2016-03-18

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

The Molybdenum Cofactor Biosynthesis Network: In vivo Protein-Protein Interactions of an Actin Associated Multi-Protein Complex.

PubMed

Kaufholdt, David; Baillie, Christin-Kirsty; Meinen, Rieke; Mendel, Ralf R; Hänsch, Robert

2017-01-01

Survival of plants and nearly all organisms depends on the pterin based molybdenum cofactor (Moco) as well as its effective biosynthesis and insertion into apo-enzymes. To this end, both the central Moco biosynthesis enzymes are characterized and the conserved four-step reaction pathway for Moco biosynthesis is well-understood. However, protection mechanisms to prevent degradation during biosynthesis as well as transfer of the highly oxygen sensitive Moco and its intermediates are not fully enlightened. The formation of protein complexes involving transient protein-protein interactions is an efficient strategy for protected metabolic channelling of sensitive molecules. In this review, Moco biosynthesis and allocation network is presented and discussed. This network was intensively studied based on two in vivo interaction methods: bimolecular fluorescence complementation (BiFC) and split-luciferase. Whereas BiFC allows localisation of interacting partners, split-luciferase assay determines interaction strengths in vivo . Results demonstrate (i) interaction of Cnx2 and Cnx3 within the mitochondria and (ii) assembly of a biosynthesis complex including the cytosolic enzymes Cnx5, Cnx6, Cnx7, and Cnx1, which enables a protected transfer of intermediates. The whole complex is associated with actin filaments via Cnx1 as anchor protein. After biosynthesis, Moco needs to be handed over to the specific apo-enzymes. A potential pathway was discovered. Molybdenum-containing enzymes of the sulphite oxidase family interact directly with Cnx1. In contrast, the xanthine oxidoreductase family acquires Moco indirectly via a Moco binding protein (MoBP2) and Moco sulphurase ABA3. In summary, the uncovered interaction matrix enables an efficient transfer for intermediate and product protection via micro-compartmentation.

Catalytic asymmetric Michael reactions promoted by a lithium-free lanthanum-BINOL complex

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

Sasai, Hiroaki; Arai, Takayoshi; Shibasaki, Masakatsu

1994-02-23

In this communication, we report about a new lithium-free BINOL-lanthanum complex, which is quite effective in catalytic asymmetric Michael reaction. We have succeeded in developing effective asymmetric base catalysts, in particular, asymmetric ester enolate catalysts for asymmetric Michael reactions. Two asymmetric lanthanum complexes are now available, namely, BINOL-lanthanum-lithium complex, which is quite effective in catalytic asymmetric nitrosaldol reactions, and a new lithium-free BINOL-lanthanum ester enolate complex, that is very effective in catalytic asymmetric Michael reactions. The two complexes complement each other in their ability to catalyze asymmetric nitroaldol and asymmetric Michael reactions. 14 refs., 1 fig., 2 tabs.

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Thermoreversible Folding as a Route to the Unique Shape-Memory Character in Ductile Polymer Networks.

PubMed

McBride, Matthew K; Podgorski, Maciej; Chatani, Shunsuke; Worrell, Brady T; Bowman, Christopher N

2018-06-21

Ductile, cross-linked films were folded as a means to program temporary shapes without the need for complex heating cycles or specialized equipment. Certain cross-linked polymer networks, formed here with the thiol-isocyanate reaction, possessed the ability to be pseudoplastically deformed below the glass transition, and the original shape was recovered during heating through the glass transition. To circumvent the large forces required to plastically deform a glassy polymer network, we have utilized folding, which localizes the deformation in small creases, and achieved large dimensional changes with simple programming procedures. In addition to dimension changes, three-dimensional objects such as swans and airplanes were developed to demonstrate applying origami principles to shape memory. We explored the fundamental mechanical properties that are required to fold polymer sheets and observed that a yield point that does not correspond to catastrophic failure is required. Unfolding occurred during heating through the glass transition, indicating the vitrification of the network that maintained the temporary, folded shape. Folding was demonstrated as a powerful tool to simply and effectively program ductile shape-memory polymers without the need for thermal cycling.

In situ gelable interpenetrating double network hydrogel formulated from binary components: thiolated chitosan and oxidized dextran.

PubMed

Zhang, Hanwei; Qadeer, Aisha; Chen, Weiliam

2011-05-09

In situ gelable interpenetrating double-network hydrogels composed of thiolated chitosan (Chitosan-NAC) and oxidized dextran (Odex), completely devoid of potentially cytotoxic small molecule cross-linkers and that do not require complex maneuvers or catalysis, have been formulated. The interpenetrating network structure is created by Schiff base formations and disulfide bond inter-cross-linkings through exploiting the disparity of their reaction times. Compared with the autogelable thiolated chitosan hydrogels that typically require a relatively long time span for gelation to occur, the Odex/Chitosan-NAC composition solidifies rapidly and forms a well-developed 3D network in a short time span. Compared with typical hydrogels derived from natural materials, the Odex/Chitosan-NAC hydrogels are mechanically strong and resist degradation. The cytotoxicity potential of the hydrogels was determined by an in vitro viability assay using fibroblast as a model cell, and the results reveal that the hydrogels are noncytotoxic. In parallel, in vivo results from subdermal implantation in mice models demonstrate that this hydrogel is not only highly resistant to degradation but also induces very mild tissue response.

In situ Gelable Interpenetrating Double Network Hydrogel Formulated from Binary Components: Thiolated Chitosan and Oxidized Dextran

PubMed Central

Zhang, Hanwei; Qadeer, Aisha; Chen, Weiliam

2011-01-01

In situ gelable interpenetrating double network hydrogels composed of thiolated chitosan (Chitosan-NAC) and oxidized dextran (Odex), completely devoid of potentially cytotoxic small molecule crosslinkers and do not require complex maneuvers or catalysis, have been formulated. The interpenetrating network structure is created by Schiff base formations and disulfide bond inter-crosslinkings through exploiting the disparity of their reaction times. Compare to the auto-gelable thiolated chitosan hydrogels that typically require a relatively long time span for gelation to occur, the Odex/Chitosan-NAC composition solidifies rapidly and forms a well-developed three-dimensional network in a short time span. Compare to typical hydrogels derived from natural materials, the Odex/Chitosan-NAC hydrogels are mechanically strong and resist degradation. The cytotoxicity potential of the hydrogels was determined by an in vitro viability assay using fibroblast as a model cell and the results reveal that the hydrogels are non-cytotoxic. In parallel, in vivo results from subdermal implantation in mice models demonstrate that this hydrogel is not only highly resistant to degradation but also induces very mild tissue response. PMID:21410248

SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks.

PubMed

Katsuragi, Tetsuo; Ono, Naoaki; Yasumoto, Keiichi; Altaf-Ul-Amin, Md; Hirai, Masami Y; Sriyudthsak, Kansuporn; Sawada, Yuji; Yamashita, Yui; Chiba, Yukako; Onouchi, Hitoshi; Fujiwara, Toru; Naito, Satoshi; Shiraishi, Fumihide; Kanaya, Shigehiko

2013-05-01

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

Consideration of reactivity to acute fish toxicity of α,β-unsaturated carbonyl ketones and aldehydes.

PubMed

Furuhama, A; Aoki, Y; Shiraishi, H

2012-01-01

To understand the key factor for fish toxicity of 11 α,β-unsaturated carbonyl aldehydes and ketones, we used quantum chemical calculations to investigate their Michael reactions with methanethiol or glutathione. We used two reaction schemes, with and without an explicit water molecule (Scheme-1wat and Scheme-0wat, respectively), to account for the effects of a catalytic water molecule on the reaction pathway. We determined the energies of the reactants, transition states (TS), and products, as well as the activation energies of the reactions. The acute fish toxicities of nine of the carbonyl compounds were evaluated to correlate with their hydrophobicities; no correlation was observed for acrolein and crotonaldehyde. The most toxic compound, acrolein, had the lowest activation energy. The activation energy of the reaction could be estimated with Scheme-1wat but not with Scheme-0wat. The complexity of the reaction pathways of the compounds was reflected in the difficulty of the TS structure searches when Scheme-1wat was used with the polarizable continuum model. The theoretical estimations of activation energies of α,β-unsaturated carbonyl compounds with catalytic molecules or groups including hydrogen-bond networks may complement traditional tools for predicting the acute aquatic toxicities of compounds that cannot be easily obtained experimentally.

Cumulative Significance of Hyporheic Exchange and Biogeochemical Processing in River Networks

NASA Astrophysics Data System (ADS)

Harvey, J. W.; Gomez-Velez, J. D.

2014-12-01

Biogeochemical reactions in rivers that decrease excessive loads of nutrients, metals, organic compounds, etc. are enhanced by hydrologic interactions with microbially and geochemically active sediments of the hyporheic zone. The significance of reactions in individual hyporheic flow paths has been shown to be controlled by the contact time between river water and sediment and the intrinsic reaction rate in the sediment. However, little is known about how the cumulative effects of hyporheic processing in large river basins. We used the river network model NEXSS (Gomez-Velez and Harvey, submitted) to simulate hyporheic exchange through synthetic river networks based on the best available models of network topology, hydraulic geometry and scaling of geomorphic features, grain size, hydraulic conductivity, and intrinsic reaction rates of nutrients and metals in river sediment. The dimensionless reaction significance factor, RSF (Harvey et al., 2013) was used to quantify the cumulative removal fraction of a reactive solute by hyporheic processing. SF scales reaction progress in a single pass through the hyporheic zone with the proportion of stream discharge passing through the hyporheic zone for a specified distance. Reaction progress is optimal where the intrinsic reaction timescale in sediment matches the residence time of hyporheic flow and is less efficient in longer residence time hyporheic flow as a result of the decreasing proportion of river flow that is processed by longer residence time hyporheic flow paths. In contrast, higher fluxes through short residence time hyporheic flow paths may be inefficient because of the repeated surface-subsurface exchanges required to complete the reaction. Using NEXSS we found that reaction efficiency may be high in both small streams and large rivers, although for different reasons. In small streams reaction progress generally is dominated by faster pathways of vertical exchange beneath submerged bedforms. Slower exchange beneath meandering river banks mainly has importance only in large rivers. For solutes entering networks in proportion to water inputs it is the lower order streams that tend to dominate cumulative reaction progress.

Mechanisms regulating skin immunity and inflammation.

PubMed

Pasparakis, Manolis; Haase, Ingo; Nestle, Frank O

2014-05-01

Immune responses in the skin are important for host defence against pathogenic microorganisms. However, dysregulated immune reactions can cause chronic inflammatory skin diseases. Extensive crosstalk between the different cellular and microbial components of the skin regulates local immune responses to ensure efficient host defence, to maintain and restore homeostasis, and to prevent chronic disease. In this Review, we discuss recent findings that highlight the complex regulatory networks that control skin immunity, and we provide new paradigms for the mechanisms that regulate skin immune responses in host defence and in chronic inflammation.

Time-Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery.

PubMed

Gong, Anmin; Liu, Jianping; Chen, Si; Fu, Yunfa

2018-01-01

To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.

Eccentricity Effects on the Efficiency of Attentional Networks: Evidence From a Modified Attention Network Test.

PubMed

Wang, Bin; Zhao, Jingjing; Wu, Zheng; Shang, Wei; Xiang, Jie; Cao, Rui; Li, Haifang; Chen, Junjie; Zhang, Hui; Yan, Ting

2016-07-05

The effects of eccentricity on the attentional modulation of visual discrimination have been widely studied; however, the substrate of this complex phenomenon is poorly understood. Here, we provided a measure of the effects of eccentricity on three attentional networks: alerting, orienting, and executive attention. Participants (N = 63) were tested with a modified attention network test that included an additional eccentricity variation; this test allowed us to investigate the efficiency of the attentional networks at near and far eccentricities. Compared with targets at the near eccentricity, targets at the far eccentricity generally elicited significantly longer reaction times. We also found the far eccentricity was associated with smaller orienting effect scores and larger executive control scores than the near eccentricity. Interestingly, at the near eccentricity, executive control scores were larger when the spatial information was neutral (no cue, center cue, and double cue), but at the far eccentricity, the scores were larger when the spatial information was valid (spatial cue). We propose that the allocation of attentional resources differed among these cue conditions and influenced the interference caused by conflicting information. © The Author(s) 2016.

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

Chen, Zhi-Hao; Zhao, Yue; Chen, Shui-Sheng

Seven new coordination polymers [Zn(H{sub 2}L)(mbdc)] (1), [Zn(H{sub 3}L)(btc)] (2), [Zn(H{sub 2}L)(Hbtc)] (3), [Zn(H{sub 2}L)(Hbtc)]·H{sub 2}O (4), [Zn{sub 2}(H{sub 2}L)(btc)(μ{sub 2}-OH)] (5), [Cd(H{sub 2}L)(mbdc)] (6) and [Cd{sub 3}(H{sub 2}L){sub 2}(btc){sub 2}(H{sub 2}O)]·5H{sub 2}O (7) were synthesized by reactions of the corresponding metal salt with rigid ligand 1,3-di(1H-imidazol-4-yl)benzene (H{sub 2}L) and different carboxylic acids of 1,3-benzenedicarboxylic acid (H{sub 2}mbdc) and benzene-1,3,5-tricarboxylic acid (H{sub 3}btc), respectively. The results of X-ray crystallographic analysis indicate that complex 1 is 1D chain while 2 is a (3,3)-connected 2D network with Point (Schläfli) symbol of (4,8{sup 2}). Complexes 3 and 6 are 2D networks, 4 ismore » a 3-fold interpenetrating 3D framework with Point (Schläfli) symbol of (6{sup 5},8) and 5 is a (3,8)-connected 2D network with Point (Schläfli) symbol of (3,4{sup 2}){sub 2}(3{sup 4},4{sup 6},5{sup 6},6{sup 8},7{sup 3},8), while 7 is a (3,10)-connected 3D net with Schläfli symbol of (3,4,5){sub 2}(3{sup 4},4{sup 8},5{sup 18},6{sup 12},7{sup 2},8). The thermal stability and photoluminescence of the complexes were investigated. Furthermore, DFT calculations were performed for 2–4 to discuss the temperature controlled self-assembly of the complexes. - Graphical abstract: Seven new coordination polymers with multicarboxylate and rigid ditopic 4-imidazole containing ligands have been obtained and found to show different structures and topologies. - Highlights: • Metal complexes with diverse structures of 1D chain, 2D network and 3D framework. • Mixed ligands of 1,3-di(1H-imidazol-4-yl)benzene and multicarboxylate. • Photoluminescence property.« less

Computational catalyst screening: Scaling, bond-order and catalysis

DOE PAGES

Abild-Pedersen, Frank

2015-10-01

Here, the design of new and better heterogeneous catalysts needed to accommodate the growing demand for energy from renewable sources is an important challenge for coming generations. Most surface catalyzed processes involve a large number of complex reaction networks and the energetics ultimately defines the turn-over-frequency and the selectivity of the process. In order not to get lost in the large quantities of data, simplification schemes that still contain the key elements of the reaction are required. Adsorption and transition state scaling relations constitutes such a scheme that not only maps the reaction relevant information in terms of few parametersmore » but also provides an efficient way of screening for new materials in a continuous multi-dimensional energy space. As with all relations they impose certain restrictions on what can be achieved and in this paper, I show why these limitations exist and how we can change the behavior through an energy-resolved approach that still maintains the screening capabilities needed in computational catalysis.« less

Evaluation of an S-system root-finding method for estimating parameters in a metabolic reaction model.

PubMed

Iwata, Michio; Miyawaki-Kuwakado, Atsuko; Yoshida, Erika; Komori, Soichiro; Shiraishi, Fumihide

2018-02-02

In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given metabolic reaction system and to then characterize it efficiently. In this paper, we discuss the use of an S-system root-finding method (S-system method) to estimate parameters from time-series data of metabolite concentrations. We demonstrate that the S-system method is superior to the Newton-Raphson method in terms of the convergence region and iteration number. We also investigate the usefulness of a translocation technique and a complex-step differentiation method toward the practical application of the S-system method. The results indicate that the S-system method is useful to construct mathematical models for a variety of metabolic reaction networks. Copyright © 2018 Elsevier Inc. All rights reserved.

Continuum-based DFN-consistent numerical framework for the simulation of oxygen infiltration into fractured crystalline rocks

NASA Astrophysics Data System (ADS)

Trinchero, Paolo; Puigdomenech, Ignasi; Molinero, Jorge; Ebrahimi, Hedieh; Gylling, Björn; Svensson, Urban; Bosbach, Dirk; Deissmann, Guido

2017-05-01

We present an enhanced continuum-based approach for the modelling of groundwater flow coupled with reactive transport in crystalline fractured rocks. In the proposed formulation, flow, transport and geochemical parameters are represented onto a numerical grid using Discrete Fracture Network (DFN) derived parameters. The geochemical reactions are further constrained by field observations of mineral distribution. To illustrate how the approach can be used to include physical and geochemical complexities into reactive transport calculations, we have analysed the potential ingress of oxygenated glacial-meltwater in a heterogeneous fractured rock using the Forsmark site (Sweden) as an example. The results of high-performance reactive transport calculations show that, after a quick oxygen penetration, steady state conditions are attained where abiotic reactions (i.e. the dissolution of chlorite and the homogeneous oxidation of aqueous iron(II) ions) counterbalance advective oxygen fluxes. The results show that most of the chlorite becomes depleted in the highly conductive deformation zones where higher mineral surface areas are available for reactions.

Sugar Influx Sensing by the Phosphotransferase System of Escherichia coli

PubMed Central

Somavanshi, Rahul; Ghosh, Bhaswar; Sourjik, Victor

2016-01-01

The phosphotransferase system (PTS) plays a pivotal role in the uptake of multiple sugars in Escherichia coli and many other bacteria. In the cell, individual sugar-specific PTS branches are interconnected through a series of phosphotransfer reactions, thus creating a global network that not only phosphorylates incoming sugars but also regulates a number of cellular processes. Despite the apparent importance of the PTS network in bacterial physiology, the holistic function of the network in the cell remains unclear. Here we used Förster resonance energy transfer (FRET) to investigate the PTS network in E. coli, including the dynamics of protein interactions and the processing of different stimuli and their transmission to the chemotaxis pathway. Our results demonstrate that despite the seeming complexity of the cellular PTS network, its core part operates in a strikingly simple way, sensing the overall influx of PTS sugars irrespective of the sugar identity and distributing this information equally through all studied branches of the network. Moreover, it also integrates several other specific metabolic inputs. The integrated output of the PTS network is then transmitted linearly to the chemotaxis pathway, in stark contrast to the amplification of conventional chemotactic stimuli. Finally, we observe that default uptake through the uninduced PTS network correlates well with the quality of the carbon source, apparently representing an optimal regulatory strategy. PMID:27557415

Modeling biological pathway dynamics with timed automata.

PubMed

Schivo, Stefano; Scholma, Jetse; Wanders, Brend; Urquidi Camacho, Ricardo A; van der Vet, Paul E; Karperien, Marcel; Langerak, Rom; van de Pol, Jaco; Post, Janine N

2014-05-01

Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires capturing their dynamics through computational support and models. ANIMO (analysis of Networks with Interactive Modeling) is a tool that enables the construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analyzed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signaling networks. This enforces precision and uniformity in the definition of signaling pathways, contributing to the integration of isolated signaling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behavior of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behavior of the network components is displayed graphically, allowing for an intuitive and interactive modeling experience.

DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems.

PubMed

Smith, Robert W; van Rosmalen, Rik P; Martins Dos Santos, Vitor A P; Fleck, Christian

2018-06-19

Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.

Hybrid function projective synchronization in complex dynamical networks

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

Wei, Qiang; Wang, Xing-yuan, E-mail: wangxy@dlut.edu.cn; Hu, Xiao-peng

2014-02-15

This paper investigates hybrid function projective synchronization in complex dynamical networks. When the complex dynamical networks could be synchronized up to an equilibrium or periodic orbit, a hybrid feedback controller is designed to realize the different component of vector of node could be synchronized up to different desired scaling function in complex dynamical networks with time delay. Hybrid function projective synchronization (HFPS) in complex dynamical networks with constant delay and HFPS in complex dynamical networks with time-varying coupling delay are researched, respectively. Finally, the numerical simulations show the effectiveness of theoretical analysis.

A lightning strike to the head causing a visual cortex defect with simple and complex visual hallucinations

PubMed Central

Kleiter, Ingo; Luerding, Ralf; Diendorfer, Gerhard; Rek, Helga; Bogdahn, Ulrich; Schalke, Berthold

2007-01-01

The case of a 23‐year‐old mountaineer who was hit by a lightning strike to the occiput causing a large central visual field defect and bilateral tympanic membrane ruptures is described. Owing to extreme agitation, the patient was set to a drug‐induced coma for 3 days. After extubation, she experienced simple and complex visual hallucinations for several days, but otherwise recovered largely. Neuropsychological tests revealed deficits in fast visual detection tasks and non‐verbal learning, and indicated a right temporal lobe dysfunction, consistent with a right temporal focus on electroencephalography. Four months after the accident, she developed a psychological reaction consisting of nightmares with reappearance of the complex visual hallucinations and a depressive syndrome. Using the European Cooperation for Lightning Detection network, a meteorological system for lightning surveillance, the exact geographical location and nature of the lightning flash were retrospectively retraced. PMID:17369595

A lightning strike to the head causing a visual cortex defect with simple and complex visual hallucinations

PubMed Central

Kleiter, Ingo; Luerding, Ralf; Diendorfer, Gerhard; Rek, Helga; Bogdahn, Ulrich; Schalke, Berthold

2009-01-01

The case of a 23-year-old mountaineer who was hit by a lightning strike to the occiput causing a large central visual field defect and bilateral tympanic membrane ruptures is described. Owing to extreme agitation, the patient was sent into a drug-induced coma for 3 days. After extubation, she experienced simple and complex visual hallucinations for several days, but otherwise largely recovered. Neuropsychological tests revealed deficits in fast visual detection tasks and non-verbal learning and indicated a right temporal lobe dysfunction, consistent with a right temporal focus on electroencephalography. At 4 months after the accident, she developed a psychological reaction consisting of nightmares, with reappearance of the complex visual hallucinations and a depressive syndrome. Using the European Cooperation for Lightning Detection network, a meteorological system for lightning surveillance, the exact geographical location and nature of the lightning strike were retrospectively retraced PMID:21734915

Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.

PubMed

Hamilton, Joshua J; Reed, Jennifer L

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

PubMed Central

Hamilton, Joshua J.; Reed, Jennifer L.

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here. PMID:22666308

DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia

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

Heirendt, Laurent; Thiele, Ines; Fleming, Ronan M. T.

Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations. DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes. DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on amore » subset or all the reactions of large and huge-scale networks, on any number of threads or nodes.« less

DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia

DOE PAGES

Heirendt, Laurent; Thiele, Ines; Fleming, Ronan M. T.

2017-01-16

Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations. DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes. DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on amore » subset or all the reactions of large and huge-scale networks, on any number of threads or nodes.« less

General solution of the chemical master equation and modality of marginal distributions for hierarchic first-order reaction networks.

PubMed

Reis, Matthias; Kromer, Justus A; Klipp, Edda

2018-01-20

Multimodality is a phenomenon which complicates the analysis of statistical data based exclusively on mean and variance. Here, we present criteria for multimodality in hierarchic first-order reaction networks, consisting of catalytic and splitting reactions. Those networks are characterized by independent and dependent subnetworks. First, we prove the general solvability of the Chemical Master Equation (CME) for this type of reaction network and thereby extend the class of solvable CME's. Our general solution is analytical in the sense that it allows for a detailed analysis of its statistical properties. Given Poisson/deterministic initial conditions, we then prove the independent species to be Poisson/binomially distributed, while the dependent species exhibit generalized Poisson/Khatri Type B distributions. Generalized Poisson/Khatri Type B distributions are multimodal for an appropriate choice of parameters. We illustrate our criteria for multimodality by several basic models, as well as the well-known two-stage transcription-translation network and Bateman's model from nuclear physics. For both examples, multimodality was previously not reported.

The effects of protein crowding in bacterial photosynthetic membranes on the flow of quinone redox species between the photochemical reaction center and the ubiquinol-cytochrome c2 oxidoreductase.

PubMed

Woronowicz, Kamil; Sha, Daniel; Frese, Raoul N; Sturgis, James N; Nanda, Vikas; Niederman, Robert A

2011-08-01

Atomic force microscopy (AFM) of the native architecture of the intracytoplasmic membrane (ICM) of a variety of species of purple photosynthetic bacteria, obtained at submolecular resolution, shows a tightly packed arrangement of light harvesting (LH) and reaction center (RC) complexes. Since there are no unattributed structures or gaps with space sufficient for the cytochrome bc(1) or ATPase complexes, they are localized in membrane domains distinct from the flat regions imaged by AFM. This has generated a renewed interest in possible long-range pathways for lateral diffusion of UQ redox species that functionally link the RC and the bc(1) complexes. Recent proposals to account for UQ flow in the membrane bilayer are reviewed, along with new experimental evidence provided from an analysis of intrinsic near-IR fluorescence emission that has served to test these hypotheses. The results suggest that different mechanism of UQ flow exist between species such as Rhodobacter sphaeroides, with a highly organized arrangement of LH and RC complexes and fast RC electron transfer turnover, and Phaeospirillum molischianum with a more random organization and slower RC turnover. It is concluded that packing density of the peripheral LH2 antenna in the Rba. sphaeroides ICM imposes constraints that significantly slow the diffusion of UQ redox species between the RC and cytochrome bc(1) complex, while in Phs. molischianum, the crowding of the ICM with LH3 has little effect upon UQ diffusion. This supports the proposal that in this type of ICM, a network of RC-LH1 core complexes observed in AFM provides a pathway for long-range quinone diffusion that is unaffected by differences in LH complex composition or organization.

Investigations of photosynthetic light harvesting by two-dimensional electronic spectroscopy

NASA Astrophysics Data System (ADS)

Read, Elizabeth Louise

Photosynthesis begins with the harvesting of sunlight by antenna pigments, organized in a network of pigment-protein complexes that rapidly funnel energy to photochemical reaction centers. The intricate design of these systems---the widely varying structural motifs of pigment organization within proteins and protein organization within a larger, cooperative network---underlies the remarkable speed and efficiency of light harvesting. Advances in femtosecond laser spectroscopy have enabled researchers to follow light energy on its course through the energetic levels of photosynthetic systems. Now, newly-developed femtosecond two-dimensional electronic spectroscopy reveals deeper insight into the fundamental molecular interactions and dynamics that emerge in these structures. The following chapters present investigations of a number of natural light-harvesting complexes using two-dimensional electronic spectroscopy. These studies demonstrate the various types of information contained in experimental two-dimensional spectra, and they show that the technique makes it possible to probe pigment-protein complexes on the length- and time-scales relevant to their functioning. New methods are described that further extend the capabilities of two-dimensional electronic spectroscopy, for example, by independently controlling the excitation laser pulse polarizations. The experiments, coupled with theoretical simulation, elucidate spatial pathways of energy flow, unravel molecular and electronic structures, and point to potential new quantum mechanical mechanisms of light harvesting.

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

Wu, Fuke, E-mail: wufuke@mail.hust.edu.cn; Tian, Tianhai, E-mail: tianhai.tian@sci.monash.edu.au; Rawlings, James B., E-mail: james.rawlings@wisc.edu

The frequently used reduction technique is based on the chemical master equation for stochastic chemical kinetics with two-time scales, which yields the modified stochastic simulation algorithm (SSA). For the chemical reaction processes involving a large number of molecular species and reactions, the collection of slow reactions may still include a large number of molecular species and reactions. Consequently, the SSA is still computationally expensive. Because the chemical Langevin equations (CLEs) can effectively work for a large number of molecular species and reactions, this paper develops a reduction method based on the CLE by the stochastic averaging principle developed in themore » work of Khasminskii and Yin [SIAM J. Appl. Math. 56, 1766–1793 (1996); ibid. 56, 1794–1819 (1996)] to average out the fast-reacting variables. This reduction method leads to a limit averaging system, which is an approximation of the slow reactions. Because in the stochastic chemical kinetics, the CLE is seen as the approximation of the SSA, the limit averaging system can be treated as the approximation of the slow reactions. As an application, we examine the reduction of computation complexity for the gene regulatory networks with two-time scales driven by intrinsic noise. For linear and nonlinear protein production functions, the simulations show that the sample average (expectation) of the limit averaging system is close to that of the slow-reaction process based on the SSA. It demonstrates that the limit averaging system is an efficient approximation of the slow-reaction process in the sense of the weak convergence.« less

Comprehensive mechanism and structure-sensitivity of ethanol oxidation on platinum: new transition-state searching method for resolving the complex reaction network.

PubMed

Wang, Hui-Fang; Liu, Zhi-Pan

2008-08-20

Ethanol oxidation on Pt is a typical multistep and multiselectivity heterogeneous catalytic process. A comprehensive understanding of this fundamental reaction would greatly benefit design of catalysts for use in direct ethanol fuel cells and the degradation of biomass-derived oxygenates. In this work, the reaction network of ethanol oxidation on different Pt surfaces, including close-packed Pt{111}, stepped Pt{211}, and open Pt{100}, is explored thoroughly with an efficient reaction path searching method, which integrates our new transition-state searching technique with periodic density functional theory calculations. Our new technique enables the location of the transition state and saddle points for most surface reactions simply and efficiently by optimization of local minima. We show that the selectivity of ethanol oxidation on Pt depends markedly on the surface structure, which can be attributed to the structure-sensitivity of two key reaction steps: (i) the initial dehydrogenation of ethanol and (ii) the oxidation of acetyl (CH3CO). On open surface sites, ethanol prefers C-C bond cleavage via strongly adsorbed intermediates (CH2CO or CHCO), which leads to complete oxidation to CO2. However, only partial oxidizations to CH3CHO and CH3COOH occur on Pt{111}. Our mechanism points out that the open surface Pt{100} is the best facet to fully oxidize ethanol at low coverages, which sheds light on the origin of the remarkable catalytic performance of Pt tetrahexahedra nanocrystals found recently. The physical origin of the structure-selectivity is rationalized in terms of both thermodynamics and kinetics. Two fundamental quantities that dictate the selectivity of ethanol oxidation are identified: (i) the ability of surface metal atoms to bond with unsaturated C-containing fragments and (ii) the relative stability of hydroxyl at surface atop sites with respect to other sites.

First examples of ternary lanthanide 5-aminoisophthalate complexes: Hydrothermal syntheses and structures of lanthanide coordination polymers with 5-aminoisophthalate and oxalate

NASA Astrophysics Data System (ADS)

Liu, Chong-Bo; Wen, Hui-Liang; Tan, Sheng-Shui; Yi, Xiu-Guang

2008-05-01

Two new lanthanide coordination polymers with mixed-carboxylates, [Ln(OX)(HAPA)(H 2O)] n[Ln = Eu ( 1), Ho ( 2); H 2APA = 5-aminoisophthalic acid; OX = oxalate] were obtained by hydrothermal reactions, and characterized by single crystal X-ray diffraction, elemental analysis and IR spectra. Complexes 1 and 2 are both 3-D supramolecular structure, in which lanthanide ions are bridged by oxalate and 5-aminoisophthalate ligands forming 2-D metal-organic framework, and 2-D networks are further architectured to form 3-D supramolecular structures by hydrogen bonds. The two carboxylate groups of H 2APA ligand are all deprotonated and exhibit chelating and bridging bidentate coordination modes, respectively, and the amino group in HAPA presents - NH3+ in the titled complexes. The thermogravimetric analysis was carried out to examine the thermal stability of the titled complexes. And the photoluminescence property of 1 was investigated.

The Biomolecular Interaction Network Database and related tools 2005 update

PubMed Central

Alfarano, C.; Andrade, C. E.; Anthony, K.; Bahroos, N.; Bajec, M.; Bantoft, K.; Betel, D.; Bobechko, B.; Boutilier, K.; Burgess, E.; Buzadzija, K.; Cavero, R.; D'Abreo, C.; Donaldson, I.; Dorairajoo, D.; Dumontier, M. J.; Dumontier, M. R.; Earles, V.; Farrall, R.; Feldman, H.; Garderman, E.; Gong, Y.; Gonzaga, R.; Grytsan, V.; Gryz, E.; Gu, V.; Haldorsen, E.; Halupa, A.; Haw, R.; Hrvojic, A.; Hurrell, L.; Isserlin, R.; Jack, F.; Juma, F.; Khan, A.; Kon, T.; Konopinsky, S.; Le, V.; Lee, E.; Ling, S.; Magidin, M.; Moniakis, J.; Montojo, J.; Moore, S.; Muskat, B.; Ng, I.; Paraiso, J. P.; Parker, B.; Pintilie, G.; Pirone, R.; Salama, J. J.; Sgro, S.; Shan, T.; Shu, Y.; Siew, J.; Skinner, D.; Snyder, K.; Stasiuk, R.; Strumpf, D.; Tuekam, B.; Tao, S.; Wang, Z.; White, M.; Willis, R.; Wolting, C.; Wong, S.; Wrong, A.; Xin, C.; Yao, R.; Yates, B.; Zhang, S.; Zheng, K.; Pawson, T.; Ouellette, B. F. F.; Hogue, C. W. V.

2005-01-01

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. PMID:15608229

Detecting breakdown points in metabolic networks.

PubMed

Tagore, Somnath; De, Rajat K

2011-12-14

A complex network of biochemical reactions present in an organism generates various biological moieties necessary for its survival. It is seen that biological systems are robust to genetic and environmental changes at all levels of organization. Functions of various organisms are sustained against mutational changes by using alternative pathways. It is also seen that if any one of the paths for production of the same metabolite is hampered, an alternate path tries to overcome this defect and helps in combating the damage. Certain physical, chemical or genetic change in any of the precursor substrate of a biochemical reaction may damage the production of the ultimate product. We employ a quantitative approach for simulating this phenomena of causing a physical change in the biochemical reactions by performing external perturbations to 12 metabolic pathways under carbohydrate metabolism in Saccharomyces cerevisae as well as 14 metabolic pathways under carbohydrate metabolism in Homo sapiens. Here, we investigate the relationship between structure and degree of compatibility of metabolites against external perturbations, i.e., robustness. Robustness can also be further used to identify the extent to which a metabolic pathway can resist a mutation event. Biological networks with a certain connectivity distribution may be very resilient to a particular attack but not to another. The goal of this work is to determine the exact boundary of network breakdown due to both random and targeted attack, thereby analyzing its robustness. We also find that compared to various non-standard models, metabolic networks are exceptionally robust. Here, we report the use of a 'Resilience-based' score for enumerating the concept of 'network-breakdown'. We also use this approach for analyzing metabolite essentiality providing insight into cellular robustness that can be further used for future drug development. We have investigated the behavior of metabolic pathways under carbohydrate metabolism in S. cerevisae and H. sapiens against random and targeted attack. Both random as well as targeted resilience were calculated by formulating a measure, that we termed as 'Resilience score'. Datasets of metabolites were collected for 12 metabolic pathways belonging to carbohydrate metabolism in S. cerevisae and 14 metabolic pathways belonging to carbohydrate metabolism in H. sapiens from Kyoto Encyclopedia for Genes and Genomes (KEGG). Copyright © 2011 Elsevier Ltd. All rights reserved.

Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network.

PubMed

Griffith, Mark; Courtney, Tod; Peccoud, Jean; Sanders, William H

2006-11-15

The stochastic kinetics of a well-mixed chemical system, governed by the chemical Master equation, can be simulated using the exact methods of Gillespie. However, these methods do not scale well as systems become more complex and larger models are built to include reactions with widely varying rates, since the computational burden of simulation increases with the number of reaction events. Continuous models may provide an approximate solution and are computationally less costly, but they fail to capture the stochastic behavior of small populations of macromolecules. In this article we present a hybrid simulation algorithm that dynamically partitions the system into subsets of continuous and discrete reactions, approximates the continuous reactions deterministically as a system of ordinary differential equations (ODE) and uses a Monte Carlo method for generating discrete reaction events according to a time-dependent propensity. Our approach to partitioning is improved such that we dynamically partition the system of reactions, based on a threshold relative to the distribution of propensities in the discrete subset. We have implemented the hybrid algorithm in an extensible framework, utilizing two rigorous ODE solvers to approximate the continuous reactions, and use an example model to illustrate the accuracy and potential speedup of the algorithm when compared with exact stochastic simulation. Software and benchmark models used for this publication can be made available upon request from the authors.

Capturing the essence of a metabolic network: a flux balance analysis approach.

PubMed

Murabito, Ettore; Simeonidis, Evangelos; Smallbone, Kieran; Swinton, Jonathan

2009-10-07

As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux balance analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented. Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here, we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of an FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole. Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined.

[Attentional impairment in children with attention deficit and hyperactivity disorder].

PubMed

Abbes, Zeineb; Bouden, Asma; Amado, Isabelle; Chantal Bourdel, Marie; Tabbane, Karim; Béchir Halayem, Mohamed

2009-10-01

Attention-deficit hyperactivity disorder (ADHD) is a heterogeneous disorder currently defined by clinical history and behavioral report of impairment. The Attention Network test (ANT) gives measures of different aspects of the complex process of attention. We ask if children with Attention Deficit Hyperactivity Disorder (ADHD) will show a characteristic pattern of deficits on this test. The sample included 40 children (M=9 years) who performed the "Attention network test". Children with an ADHD diagnosis (N=20) were compared to a control group (N=20). The group of children with ADHD showed slower reaction times in all conditions (mean RT=866 ms; SD=234,063). Children with ADHD showed a significant impairment in their executive control system compared to healthy subjects, with slower reaction times in incongruent conditions and lower accuracy scores (RT=1064 ms; F(1.38) p=0.02). Our results showed that spatial orienting and alerting in ADHD was no different than controls (p=0,68). ADHD group showed a greater variable response (p=0,0001). The present study showed that impairment in executive control system and variability measures are the characteristic pattern of deficits in children with ADHD.

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Arene-chromium tricarbonyl complexes in the Pauson-Khand reaction.

PubMed

Rosillo, Marta; Domínguez, Gema; Casarrubios, Luis; Pérez-Castells, Javier

2005-12-09

[reactions: see text] We show the use of arene-chromium tricarbonyl complexes in intra- and intermolecular Pauson-Khand reactions. Both styrene and ethynylbenzene complexes react with alkynes and olefins. The synthesis of enynes connected through chromium-complexed aromatic rings is developed. The intramolecular Pauson-Khand reaction occurs in a totally diastereoselective manner.

Identifying apparent local stable isotope equilibrium in a complex non-equilibrium system.

PubMed

He, Yuyang; Cao, Xiaobin; Wang, Jianwei; Bao, Huiming

2018-02-28

Although being out of equilibrium, biomolecules in organisms have the potential to approach isotope equilibrium locally because enzymatic reactions are intrinsically reversible. A rigorous approach that can describe isotope distribution among biomolecules and their apparent deviation from equilibrium state is lacking, however. Applying the concept of distance matrix in graph theory, we propose that apparent local isotope equilibrium among a subset of biomolecules can be assessed using an apparent fractionation difference (|Δα|) matrix, in which the differences between the observed isotope composition (δ') and the calculated equilibrium fractionation factor (1000lnβ) can be more rigorously evaluated than by using a previous approach for multiple biomolecules. We tested our |Δα| matrix approach by re-analyzing published data of different amino acids (AAs) in potato and in green alga. Our re-analysis shows that biosynthesis pathways could be the reason for an apparently close-to-equilibrium relationship inside AA families in potato leaves. Different biosynthesis/degradation pathways in tubers may have led to the observed isotope distribution difference between potato leaves and tubers. The analysis of data from green algae does not support the conclusion that AAs are further from equilibrium in glucose-cultured green algae than in the autotrophic ones. Application of the |Δα| matrix can help us to locate potential reversible reactions or reaction networks in a complex system such as a metabolic system. The same approach can be broadly applied to all complex systems that have multiple components, e.g. geochemical or atmospheric systems of early Earth or other planets. Copyright © 2017 John Wiley & Sons, Ltd.

Complex quantum network geometries: Evolution and phase transitions

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

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

Complex quantum network geometries: Evolution and phase transitions.

PubMed

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

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

Thinking on building the network cardiovasology of Chinese medicine.

PubMed

Yu, Gui; Wang, Jie

2012-11-01

With advances in complex network theory, the thinking and methods regarding complex systems have changed revolutionarily. Network biology and network pharmacology were built by applying network-based approaches in biomedical research. The cardiovascular system may be regarded as a complex network, and cardiovascular diseases may be taken as the damage of structure and function of the cardiovascular network. Although Chinese medicine (CM) is effective in treating cardiovascular diseases, its mechanisms are still unclear. With the guidance of complex network theory, network biology and network pharmacology, network-based approaches could be used in the study of CM in preventing and treating cardiovascular diseases. A new discipline-network cardiovasology of CM was, therefore, developed. In this paper, complex network theory, network biology and network pharmacology were introduced and the connotation of "disease-syndrome-formula-herb" was illustrated from the network angle. Network biology could be used to analyze cardiovascular diseases and syndromes and network pharmacology could be used to analyze CM formulas and herbs. The "network-network"-based approaches could provide a new view for elucidating the mechanisms of CM treatment.

An information dimension of weighted complex networks

NASA Astrophysics Data System (ADS)

Wen, Tao; Jiang, Wen

2018-07-01

The fractal and self-similarity are important properties in complex networks. Information dimension is a useful dimension for complex networks to reveal these properties. In this paper, an information dimension is proposed for weighted complex networks. Based on the box-covering algorithm for weighted complex networks (BCANw), the proposed method can deal with the weighted complex networks which appear frequently in the real-world, and it can get the influence of the number of nodes in each box on the information dimension. To show the wide scope of information dimension, some applications are illustrated, indicating that the proposed method is effective and feasible.

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Impulsive synchronization of stochastic reaction-diffusion neural networks with mixed time delays.

PubMed

Sheng, Yin; Zeng, Zhigang

2018-07-01

This paper discusses impulsive synchronization of stochastic reaction-diffusion neural networks with Dirichlet boundary conditions and hybrid time delays. By virtue of inequality techniques, theories of stochastic analysis, linear matrix inequalities, and the contradiction method, sufficient criteria are proposed to ensure exponential synchronization of the addressed stochastic reaction-diffusion neural networks with mixed time delays via a designed impulsive controller. Compared with some recent studies, the neural network models herein are more general, some restrictions are relaxed, and the obtained conditions enhance and generalize some published ones. Finally, two numerical simulations are performed to substantiate the validity and merits of the developed theoretical analysis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Robustness and structure of complex networks

NASA Astrophysics Data System (ADS)

Shao, Shuai

This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks are much more vulnerable to localized attack compared with random attack. In the second part, we extend the tree-like generating function method to incorporating clustering structure in complex networks. We study the robustness of a complex network system, especially a network of networks (NON) with clustering structure in each network. We find that the system becomes less robust as we increase the clustering coefficient of each network. For a partially dependent network system, we also find that the influence of the clustering coefficient on network robustness decreases as we decrease the coupling strength, and the critical coupling strength qc, at which the first-order phase transition changes to second-order, increases as we increase the clustering coefficient.

New Class of Hybrid Materials for Detection, Capture, and "On-Demand" Release of Carbon Monoxide.

PubMed

Pitto-Barry, Anaïs; Lupan, Alexandru; Ellingford, Christopher; Attia, Amr A A; Barry, Nicolas P E

2018-04-25

Carbon monoxide (CO) is both a substance hazardous to health and a side product of a number of industrial processes, such as methanol steam reforming and large-scale oxidation reactions. The separation of CO from nitrogen (N 2 ) in industrial processes is considered to be difficult because of the similarities of their electronic structures, sizes, and physicochemical properties (e.g., boiling points). Carbon monoxide is also a major poison in fuel cells because of its adsorption onto the active sites of the catalysts. It is therefore of the utmost economic importance to discover new materials that enable effective CO capture and release under mild conditions. However, methods to specifically absorb and easily release CO in the presence of contaminants, such as water, nitrogen, carbon dioxide, and oxygen, at ambient temperature are not available. Here, we report the simple and versatile fabrication of a new class of hybrid materials that allows capture and release of carbon monoxide under mild conditions. We found that carborane-containing metal complexes encapsulated in networks made of poly(dimethylsiloxane) react with CO, even when immersed in water, leading to dramatic color and infrared signature changes. Furthermore, we found that the CO can be easily released from the materials by simply dipping the networks into an organic solvent for less than 1 min, at ambient temperature and pressure, which not only offers a straightforward recycling method, but also a new method for the "on-demand" release of carbon monoxide. We illustrated the utilization of the on-demand release of CO from the networks by carrying out a carbonylation reaction on an electron-deficient metal complex that led to the formation of the CO-adduct, with concomitant recycling of the gel. We anticipate that our sponge-like materials and scalable methodology will open up new avenues for the storage, transport, and controlled release of CO, the silent killer and a major industrial poison.

Tailoring ZSM-5 Zeolites for the Fast Pyrolysis of Biomass to Aromatic Hydrocarbons.

PubMed

Hoff, Thomas C; Gardner, David W; Thilakaratne, Rajeeva; Wang, Kaige; Hansen, Thomas W; Brown, Robert C; Tessonnier, Jean-Philippe

2016-06-22

The production of aromatic hydrocarbons from cellulose by zeolite-catalyzed fast pyrolysis involves a complex reaction network sensitive to the zeolite structure, crystallinity, elemental composition, porosity, and acidity. The interplay of these parameters under the reaction conditions represents a major roadblock that has hampered significant improvement in catalyst design for over a decade. Here, we studied commercial and laboratory-synthesized ZSM-5 zeolites and combined data from 10 complementary characterization techniques in an attempt to identify parameters common to high-performance catalysts. Crystallinity and framework aluminum site accessibility were found to be critical to achieve high aromatic yields. These findings enabled us to synthesize a ZSM-5 catalyst with enhanced activity, which offers the highest aromatic hydrocarbon yield reported to date. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SSER: Species specific essential reactions database.

PubMed

Labena, Abraham A; Ye, Yuan-Nong; Dong, Chuan; Zhang, Fa-Z; Guo, Feng-Biao

2017-04-19

Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction would be a better option than targeting the enzymes or the corresponding enzyme-encoding gene. The existing databases such as BRENDA, BiGG, KEGG, Bio-models, Biosilico, and many others offer useful and comprehensive information on biochemical reactions. But none of these databases especially focus on essential reactions. Therefore, building a centralized repository for this class of reactions would be of great value. Here, we present a species-specific essential reactions database (SSER). The current version comprises essential biochemical and transport reactions of twenty-six organisms which are identified via flux balance analysis (FBA) combined with manual curation on experimentally validated metabolic network models. Quantitative data on the number of essential reactions, number of the essential reactions associated with their respective enzyme-encoding genes and shared essential reactions across organisms are the main contents of the database. SSER would be a prime source to obtain essential reactions data and related gene and metabolite information and it can significantly facilitate the metabolic network models reconstruction and analysis, and drug target discovery studies. Users can browse, search, compare and download the essential reactions of organisms of their interest through the website http://cefg.uestc.edu.cn/sser .

Reaction kinetics in open reactors and serial transfers between closed reactors

NASA Astrophysics Data System (ADS)

Blokhuis, Alex; Lacoste, David; Gaspard, Pierre

2018-04-01

Kinetic theory and thermodynamics of reaction networks are extended to the out-of-equilibrium dynamics of continuous-flow stirred tank reactors (CSTR) and serial transfers. On the basis of their stoichiometry matrix, the conservation laws and the cycles of the network are determined for both dynamics. It is shown that the CSTR and serial transfer dynamics are equivalent in the limit where the time interval between the transfers tends to zero proportionally to the ratio of the fractions of fresh to transferred solutions. These results are illustrated with a finite cross-catalytic reaction network and an infinite reaction network describing mass exchange between polymers. Serial transfer dynamics is typically used in molecular evolution experiments in the context of research on the origins of life. The present study is shedding a new light on the role played by serial transfer parameters in these experiments.

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

NASA Astrophysics Data System (ADS)

Zhang, Liang; Lu, Na

2017-12-01

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

Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model.

PubMed

Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van den Berg, Helma; Conner, Mark; van der Maas, Han L J

2016-01-01

This article introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions between these reactions arise through direct causal influences (e.g., the belief that snakes are dangerous causes fear of snakes) and mechanisms that support evaluative consistency between related contents of evaluative reactions (e.g., people tend to align their belief that snakes are useful with their belief that snakes help maintain ecological balance). In the CAN model, the structure of attitude networks conforms to a small-world structure: evaluative reactions that are similar to each other form tight clusters, which are connected by a sparser set of "shortcuts" between them. We argue that the CAN model provides a realistic formalized measurement model of attitudes and therefore fills a crucial gap in the attitude literature. Furthermore, the CAN model provides testable predictions for the structure of attitudes and how they develop, remain stable, and change over time. Attitude strength is conceptualized in terms of the connectivity of attitude networks and we show that this provides a parsimonious account of the differences between strong and weak attitudes. We discuss the CAN model in relation to possible extensions, implication for the assessment of attitudes, and possibilities for further study. (c) 2015 APA, all rights reserved).

Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity

NASA Astrophysics Data System (ADS)

Zhang, Jihui; Xu, Junqin

Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.

Thermodynamically Feasible Kinetic Models of Reaction Networks

PubMed Central

Ederer, Michael; Gilles, Ernst Dieter

2007-01-01

The dynamics of biological reaction networks are strongly constrained by thermodynamics. An holistic understanding of their behavior and regulation requires mathematical models that observe these constraints. However, kinetic models may easily violate the constraints imposed by the principle of detailed balance, if no special care is taken. Detailed balance demands that in thermodynamic equilibrium all fluxes vanish. We introduce a thermodynamic-kinetic modeling (TKM) formalism that adapts the concepts of potentials and forces from irreversible thermodynamics to kinetic modeling. In the proposed formalism, the thermokinetic potential of a compound is proportional to its concentration. The proportionality factor is a compound-specific parameter called capacity. The thermokinetic force of a reaction is a function of the potentials. Every reaction has a resistance that is the ratio of thermokinetic force and reaction rate. For mass-action type kinetics, the resistances are constant. Since it relies on the thermodynamic concept of potentials and forces, the TKM formalism structurally observes detailed balance for all values of capacities and resistances. Thus, it provides an easy way to formulate physically feasible, kinetic models of biological reaction networks. The TKM formalism is useful for modeling large biological networks that are subject to many detailed balance relations. PMID:17208985

Modeling cellular compartmentation in one-carbon metabolism

PubMed Central

Scotti, Marco; Stella, Lorenzo; Shearer, Emily J.; Stover, Patrick J.

2015-01-01

Folate-mediated one-carbon metabolism (FOCM) is associated with risk for numerous pathological states including birth defects, cancers, and chronic diseases. Although the enzymes that constitute the biological pathways have been well described and their interdependency through the shared use of folate cofactors appreciated, the biological mechanisms underlying disease etiologies remain elusive. The FOCM network is highly sensitive to nutritional status of several B-vitamins and numerous penetrant gene variants that alter network outputs, but current computational approaches do not fully capture the dynamics and stochastic noise of the system. Combining the stochastic approach with a rule-based representation will help model the intrinsic noise displayed by FOCM, address the limited flexibility of standard simulation methods for coarse-graining the FOCM-associated biochemical processes, and manage the combinatorial complexity emerging from reactions within FOCM that would otherwise be intractable. PMID:23408533

A Digitally Programmable Cytomorphic Chip for Simulation of Arbitrary Biochemical Reaction Networks.

PubMed

Woo, Sung Sik; Kim, Jaewook; Sarpeshkar, Rahul

2018-04-01

Prior work has shown that compact analog circuits can faithfully represent and model fundamental biomolecular circuits via efficient log-domain cytomorphic transistor equivalents. Such circuits have emphasized basis functions that are dominant in genetic transcription and translation networks and deoxyribonucleic acid (DNA)-protein binding. Here, we report a system featuring digitally programmable 0.35 μm BiCMOS analog cytomorphic chips that enable arbitrary biochemical reaction networks to be exactly represented thus enabling compact and easy composition of protein networks as well. Since all biomolecular networks can be represented as chemical reaction networks, our protein networks also include the former genetic network circuits as a special case. The cytomorphic analog protein circuits use one fundamental association-dissociation-degradation building-block circuit that can be configured digitally to exactly represent any zeroth-, first-, and second-order reaction including loading, dynamics, nonlinearity, and interactions with other building-block circuits. To address a divergence issue caused by random variations in chip fabrication processes, we propose a unique way of performing computation based on total variables and conservation laws, which we instantiate at both the circuit and network levels. Thus, scalable systems that operate with finite error over infinite time can be built. We show how the building-block circuits can be composed to form various network topologies, such as cascade, fan-out, fan-in, loop, dimerization, or arbitrary networks using total variables. We demonstrate results from a system that combines interacting cytomorphic chips to simulate a cancer pathway and a glycolysis pathway. Both simulations are consistent with conventional software simulations. Our highly parallel digitally programmable analog cytomorphic systems can lead to a useful design, analysis, and simulation tool for studying arbitrary large-scale biological networks in systems and synthetic biology.

Topological and kinetic determinants of the modal matrices of dynamic models of metabolism

PubMed Central

2017-01-01

Large-scale kinetic models of metabolism are becoming increasingly comprehensive and accurate. A key challenge is to understand the biochemical basis of the dynamic properties of these models. Linear analysis methods are well-established as useful tools for characterizing the dynamic response of metabolic networks. Central to linear analysis methods are two key matrices: the Jacobian matrix (J) and the modal matrix (M-1) arising from its eigendecomposition. The modal matrix M-1 contains dynamically independent motions of the kinetic model near a reference state, and it is sparse in practice for metabolic networks. However, connecting the structure of M-1 to the kinetic properties of the underlying reactions is non-trivial. In this study, we analyze the relationship between J, M-1, and the kinetic properties of the underlying network for kinetic models of metabolism. Specifically, we describe the origin of mode sparsity structure based on features of the network stoichiometric matrix S and the reaction kinetic gradient matrix G. First, we show that due to the scaling of kinetic parameters in real networks, diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures with clear biological interpretations. Then, we show that more complicated modes originate from topologically-connected reactions that have similar reaction elasticities in G. These elasticities represent dynamic equilibrium balances within reactions and are key determinants of modal structure. The work presented should prove useful towards obtaining an understanding of the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions. PMID:29267329

Approaching human language with complex networks

NASA Astrophysics Data System (ADS)

Cong, Jin; Liu, Haitao

2014-12-01

The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

PubMed

Megchelenbrink, Wout; Huynen, Martijn; Marchiori, Elena

2014-01-01

Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be obtained. However, sampling a high dimensional network is time-consuming. Furthermore, the constraints imposed on the network give rise to an irregularly shaped solution space. Therefore more tailored, efficient sampling methods are needed. We propose an efficient sampling algorithm (called optGpSampler), which implements the Artificial Centering Hit-and-Run algorithm in a different manner than the sampling algorithm implemented in the COBRA Toolbox for metabolic network analysis, here called gpSampler. Results of extensive experiments on different genome-scale metabolic networks show that optGpSampler is up to 40 times faster than gpSampler. Application of existing convergence diagnostics on small network reconstructions indicate that optGpSampler converges roughly ten times faster than gpSampler towards similar sampling distributions. For networks of higher dimension (i.e. containing more than 500 reactions), we observed significantly better convergence of optGpSampler and a large deviation between the samples generated by the two algorithms. optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.

Creation and perturbation of planar networks of chemical oscillators

PubMed Central

Tompkins, Nathan; Cambria, Matthew Carl; Wang, Adam L.; Heymann, Michael; Fraden, Seth

2015-01-01

Methods for creating custom planar networks of diffusively coupled chemical oscillators and perturbing individual oscillators within the network are presented. The oscillators consist of the Belousov-Zhabotinsky (BZ) reaction contained in an emulsion. Networks of drops of the BZ reaction are created with either Dirichlet (constant-concentration) or Neumann (no-flux) boundary conditions in a custom planar configuration using programmable illumination for the perturbations. The differences between the observed network dynamics for each boundary condition are described. Using light, we demonstrate the ability to control the initial conditions of the network and to cause individual oscillators within the network to undergo sustained period elongation or a one-time phase delay. PMID:26117136

Time-ordered product expansions for computational stochastic system biology.

PubMed

Mjolsness, Eric

2013-06-01

The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie's stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems.

Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

NASA Astrophysics Data System (ADS)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

Co-complexes derived from alkene insertion to alkyne-dicobaltpentacarbonyl complexes: insight into the regioselectivity of pauson-khand reactions of cyclopropenes.

PubMed

Pallerla, Mahesh K; Yap, Glenn P A; Fox, Joseph M

2008-08-15

Described are the X-ray crystallographic and spectral properties of Co-complexes that were isolated from two Pauson-Khand reactions of chiral cyclopropenes. These are the first examples of isolated Co-complexes derived from the putative alkene-insertion intermediates of Pauson-Khand reactions. The binuclear Co-complexes are coordinated to mu-bonded, five-carbon "flyover" carbene ligands. It is proposed that the complexes result from cyclopropane fragmentation subsequent to alkene insertion. The observation of these metal complexes provides a rationale for the origin of regioselectivity in Pauson-Khand reactions of cyclopropenes.

Exploration of cellular reaction systems.

PubMed

Kirkilionis, Markus

2010-01-01

We discuss and review different ways to map cellular components and their temporal interaction with other such components to different non-spatially explicit mathematical models. The essential choices made in the literature are between discrete and continuous state spaces, between rule and event-based state updates and between deterministic and stochastic series of such updates. The temporal modelling of cellular regulatory networks (dynamic network theory) is compared with static network approaches in two first introductory sections on general network modelling. We concentrate next on deterministic rate-based dynamic regulatory networks and their derivation. In the derivation, we include methods from multiscale analysis and also look at structured large particles, here called macromolecular machines. It is clear that mass-action systems and their derivatives, i.e. networks based on enzyme kinetics, play the most dominant role in the literature. The tools to analyse cellular reaction networks are without doubt most complete for mass-action systems. We devote a long section at the end of the review to make a comprehensive review of related tools and mathematical methods. The emphasis is to show how cellular reaction networks can be analysed with the help of different associated graphs and the dissection into modules, i.e. sub-networks.

Path finding methods accounting for stoichiometry in metabolic networks

PubMed Central

2011-01-01

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. PMID:21619601

Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations

NASA Astrophysics Data System (ADS)

Samoilov, Michael; Plyasunov, Sergey; Arkin, Adam P.

2005-02-01

Stochastic effects in biomolecular systems have now been recognized as a major physiologically and evolutionarily important factor in the development and function of many living organisms. Nevertheless, they are often thought of as providing only moderate refinements to the behaviors otherwise predicted by the classical deterministic system description. In this work we show by using both analytical and numerical investigation that at least in one ubiquitous class of (bio)chemical-reaction mechanisms, enzymatic futile cycles, the external noise may induce a bistable oscillatory (dynamic switching) behavior that is both quantitatively and qualitatively different from what is predicted or possible deterministically. We further demonstrate that the noise required to produce these distinct properties can itself be caused by a set of auxiliary chemical reactions, making it feasible for biological systems of sufficient complexity to generate such behavior internally. This new stochastic dynamics then serves to confer additional functional modalities on the enzymatic futile cycle mechanism that include stochastic amplification and signaling, the characteristics of which could be controlled by both the type and parameters of the driving noise. Hence, such noise-induced phenomena may, among other roles, potentially offer a novel type of control mechanism in pathways that contain these cycles and the like units. In particular, observations of endogenous or externally driven noise-induced dynamics in regulatory networks may thus provide additional insight into their topology, structure, and kinetics. network motif | signal transduction | chemical reaction | synthetic biology | systems biology

PENDISC: a simple method for constructing a mathematical model from time-series data of metabolite concentrations.

PubMed

Sriyudthsak, Kansuporn; Iwata, Michio; Hirai, Masami Yokota; Shiraishi, Fumihide

2014-06-01

The availability of large-scale datasets has led to more effort being made to understand characteristics of metabolic reaction networks. However, because the large-scale data are semi-quantitative, and may contain biological variations and/or analytical errors, it remains a challenge to construct a mathematical model with precise parameters using only these data. The present work proposes a simple method, referred to as PENDISC (Parameter Estimation in a N on- DImensionalized S-system with Constraints), to assist the complex process of parameter estimation in the construction of a mathematical model for a given metabolic reaction system. The PENDISC method was evaluated using two simple mathematical models: a linear metabolic pathway model with inhibition and a branched metabolic pathway model with inhibition and activation. The results indicate that a smaller number of data points and rate constant parameters enhances the agreement between calculated values and time-series data of metabolite concentrations, and leads to faster convergence when the same initial estimates are used for the fitting. This method is also shown to be applicable to noisy time-series data and to unmeasurable metabolite concentrations in a network, and to have a potential to handle metabolome data of a relatively large-scale metabolic reaction system. Furthermore, it was applied to aspartate-derived amino acid biosynthesis in Arabidopsis thaliana plant. The result provides confirmation that the mathematical model constructed satisfactorily agrees with the time-series datasets of seven metabolite concentrations.

Modeling reactive transport processes in fractured rock using the time domain random walk approach within a dual-porosity framework

NASA Astrophysics Data System (ADS)

Roubinet, D.; Russian, A.; Dentz, M.; Gouze, P.

2017-12-01

Characterizing and modeling hydrodynamic reactive transport in fractured rock are critical challenges for various research fields and applications including environmental remediation, geological storage, and energy production. To this end, we consider a recently developed time domain random walk (TDRW) approach, which is adapted to reproduce anomalous transport behaviors and capture heterogeneous structural and physical properties. This method is also very well suited to optimize numerical simulations by memory-shared massive parallelization and provide numerical results at various scales. So far, the TDRW approach has been applied for modeling advective-diffusive transport with mass transfer between mobile and immobile regions and simple (theoretical) reactions in heterogeneous porous media represented as single continuum domains. We extend this approach to dual-continuum representations considering a highly permeable fracture network embedded into a poorly permeable rock matrix with heterogeneous geochemical reactions occurring in both geological structures. The resulting numerical model enables us to extend the range of the modeled heterogeneity scales with an accurate representation of solute transport processes and no assumption on the Fickianity of these processes. The proposed model is compared to existing particle-based methods that are usually used to model reactive transport in fractured rocks assuming a homogeneous surrounding matrix, and is used to evaluate the impact of the matrix heterogeneity on the apparent reaction rates for different 2D and 3D simple-to-complex fracture network configurations.

Identification of lethal reactions in the Esherichia coli metabolic network: Graph theory approach

NASA Astrophysics Data System (ADS)

Ghim, C.-M.; Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

As a first step toward holistic modeling of cells, we analyze the biochemical reactions occurring in the genome-scale metabolism of Esherichia coli. To this end, we construct a directed bipartite graph by assigning metabolite or reaction to each node. We apply various measures of centrality, a well-known concept in the graph theory, and their modifications to the metabolic network, finding that there exist lethal reactions involved in the central metabolism. Such lethal reactions or associated enzymes under diverse environments in silico are identified and compared with earlier results obtained from flux balance analysis.

Graphite grain-size spectrum and molecules from core-collapse supernovae

NASA Astrophysics Data System (ADS)

Clayton, Donald D.; Meyer, Bradley S.

2018-01-01

Our goal is to compute the abundances of carbon atomic complexes that emerge from the C + O cores of core-collapse supernovae. We utilize our chemical reaction network in which every atomic step of growth employs a quantum-mechanically guided reaction rate. This tool follows step-by-step the growth of linear carbon chain molecules from C atoms in the oxygen-rich C + O cores. We postulate that once linear chain molecules reach a sufficiently large size, they isomerize to ringed molecules, which serve as seeds for graphite grain growth. We demonstrate our technique for merging the molecular reaction network with a parallel program that can follow 1017 steps of C addition onto the rare seed species. Due to radioactivity within the C + O core, abundant ambient oxygen is unable to convert C to CO, except to a limited degree that actually facilitates carbon molecular ejecta. But oxygen severely minimizes the linear-carbon-chain abundances. Despite the tiny abundances of these linear-carbon-chain molecules, they can give rise to a small abundance of ringed-carbon molecules that serve as the nucleations on which graphite grain growth builds. We expand the C + O-core gas adiabatically from 6000 K for 109 s when reactions have essentially stopped. These adiabatic tracks emulate the actual expansions of the supernova cores. Using a standard model of 1056 atoms of C + O core ejecta having O/C = 3, we calculate standard ejection yields of graphite grains of all sizes produced, of the CO molecular abundance, of the abundances of linear-carbon molecules, and of Buckminsterfullerene. None of these except CO was expected from the C + O cores just a few years past.

Elucidating the design principles of photosynthetic electron-transfer proteins by site-directed spin labeling EPR spectroscopy.

PubMed

Ishara Silva, K; Jagannathan, Bharat; Golbeck, John H; Lakshmi, K V

2016-05-01

Site-directed spin labeling electron paramagnetic resonance (SDSL EPR) spectroscopy is a powerful tool to determine solvent accessibility, side-chain dynamics, and inter-spin distances at specific sites in biological macromolecules. This information provides important insights into the structure and dynamics of both natural and designed proteins and protein complexes. Here, we discuss the application of SDSL EPR spectroscopy in probing the charge-transfer cofactors in photosynthetic reaction centers (RC) such as photosystem I (PSI) and the bacterial reaction center (bRC). Photosynthetic RCs are large multi-subunit proteins (molecular weight≥300 kDa) that perform light-driven charge transfer reactions in photosynthesis. These reactions are carried out by cofactors that are paramagnetic in one of their oxidation states. This renders the RCs unsuitable for conventional nuclear magnetic resonance spectroscopy investigations. However, the presence of native paramagnetic centers and the ability to covalently attach site-directed spin labels in RCs makes them ideally suited for the application of SDSL EPR spectroscopy. The paramagnetic centers serve as probes of conformational changes, dynamics of subunit assembly, and the relative motion of cofactors and peptide subunits. In this review, we describe novel applications of SDSL EPR spectroscopy for elucidating the effects of local structure and dynamics on the electron-transfer cofactors of photosynthetic RCs. Because SDSL EPR Spectroscopy is uniquely suited to provide dynamic information on protein motion, it is a particularly useful method in the engineering and analysis of designed electron transfer proteins and protein networks. This article is part of a Special Issue entitled Biodesign for Bioenergetics--the design and engineering of electronic transfer cofactors, proteins and protein networks, edited by Ronald L. Koder and J.L. Ross Anderson. Copyright © 2016. Published by Elsevier B.V.

Network cosmology.

PubMed

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

Network Cosmology

PubMed Central

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

Approaching human language with complex networks.

PubMed

Cong, Jin; Liu, Haitao

2014-12-01

The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics). Copyright © 2014 Elsevier B.V. All rights reserved.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

NASA Astrophysics Data System (ADS)

Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Visibility in the topology of complex networks

NASA Astrophysics Data System (ADS)

Tsiotas, Dimitrios; Charakopoulos, Avraam

2018-09-01

Taking its inspiration from the visibility algorithm, which was proposed by Lacasa et al. (2008) to convert a time-series into a complex network, this paper develops and proposes a novel expansion of this algorithm that allows generating a visibility graph from a complex network instead of a time-series that is currently applicable. The purpose of this approach is to apply the idea of visibility from the field of time-series to complex networks in order to interpret the network topology as a landscape. Visibility in complex networks is a multivariate property producing an associated visibility graph that maps the ability of a node "to see" other nodes in the network that lie beyond the range of its neighborhood, in terms of a control-attribute. Within this context, this paper examines the visibility topology produced by connectivity (degree) in comparison with the original (source) network, in order to detect what patterns or forces describe the mechanism under which a network is converted to a visibility graph. The overall analysis shows that visibility is a property that increases the connectivity in networks, it may contribute to pattern recognition (among which the detection of the scale-free topology) and it is worth to be applied to complex networks in order to reveal the potential of signal processing beyond the range of its neighborhood. Generally, this paper promotes interdisciplinary research in complex networks providing new insights to network science.

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

PubMed Central

Trame, MN; Lesko, LJ

2015-01-01

A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015 PMID:27548289

Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks.

PubMed

Briat, Corentin; Gupta, Ankit; Khammash, Mustafa

2018-06-01

The ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called homeostasis and this ability is crucial for the cell's survival and proper functioning. Understanding how cells can achieve homeostasis, despite the intrinsic noise or randomness in their dynamics, is fundamentally important for both systems and synthetic biology. In this context, a significant development is the proposed antithetic integral feedback (AIF) motif, which is found in natural systems, and is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. From the standpoint of applications, one drawback of this motif is that it often leads to an increased cell-to-cell heterogeneity or variance when compared to a constitutive (i.e. open-loop) control strategy. Our goal in this paper is to show that this performance deterioration can be countered by combining the AIF motif and a negative feedback strategy. Using a tailored moment closure method, we derive approximate expressions for the stationary variance for the controlled network that demonstrate that increasing the strength of the negative feedback can indeed decrease the variance, sometimes even below its constitutive level. Numerical results verify the accuracy of these results and we illustrate them by considering three biomolecular networks with two types of negative feedback strategies. Our computational analysis indicates that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time. © 2018 The Author(s).

Thermodynamics of stoichiometric biochemical networks in living systems far from equilibrium.

PubMed

Qian, Hong; Beard, Daniel A

2005-04-22

The principles of thermodynamics apply to both equilibrium and nonequilibrium biochemical systems. The mathematical machinery of the classic thermodynamics, however, mainly applies to systems in equilibrium. We introduce a thermodynamic formalism for the study of metabolic biochemical reaction (open, nonlinear) networks in both time-dependent and time-independent nonequilibrium states. Classical concepts in equilibrium thermodynamics-enthalpy, entropy, and Gibbs free energy of biochemical reaction systems-are generalized to nonequilibrium settings. Chemical motive force, heat dissipation rate, and entropy production (creation) rate, key concepts in nonequilibrium systems, are introduced. Dynamic equations for the thermodynamic quantities are presented in terms of the key observables of a biochemical network: stoichiometric matrix Q, reaction fluxes J, and chemical potentials of species mu without evoking empirical rate laws. Energy conservation and the Second Law are established for steady-state and dynamic biochemical networks. The theory provides the physiochemical basis for analyzing large-scale metabolic networks in living organisms.

Full-dimensional and reduced-dimensional calculations of initial state-selected reaction probabilities studying the H + CH{sub 4} → H{sub 2} + CH{sub 3} reaction on a neural network PES

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

Welsch, Ralph, E-mail: rwelsch@uni-bielefeld.de; Manthe, Uwe, E-mail: uwe.manthe@uni-bielefeld.de

2015-02-14

Initial state-selected reaction probabilities of the H + CH{sub 4} → H{sub 2} + CH{sub 3} reaction are calculated in full and reduced dimensionality on a recent neural network potential [X. Xu, J. Chen, and D. H. Zhang, Chin. J. Chem. Phys. 27, 373 (2014)]. The quantum dynamics calculation employs the quantum transition state concept and the multi-layer multi-configurational time-dependent Hartree approach and rigorously studies the reaction for vanishing total angular momentum (J = 0). The calculations investigate the accuracy of the neutral network potential and study the effect resulting from a reduced-dimensional treatment. Very good agreement is found betweenmore » the present results obtained on the neural network potential and previous results obtained on a Shepard interpolated potential energy surface. The reduced-dimensional calculations only consider motion in eight degrees of freedom and retain the C{sub 3v} symmetry of the methyl fragment. Considering reaction starting from the vibrational ground state of methane, the reaction probabilities calculated in reduced dimensionality are moderately shifted in energy compared to the full-dimensional ones but otherwise agree rather well. Similar agreement is also found if reaction probabilities averaged over similar types of vibrational excitation of the methane reactant are considered. In contrast, significant differences between reduced and full-dimensional results are found for reaction probabilities starting specifically from symmetric stretching, asymmetric (f{sub 2}-symmetric) stretching, or e-symmetric bending excited states of methane.« less

Stochastic theory of large-scale enzyme-reaction networks: Finite copy number corrections to rate equation models

NASA Astrophysics Data System (ADS)

Thomas, Philipp; Straube, Arthur V.; Grima, Ramon

2010-11-01

Chemical reactions inside cells occur in compartment volumes in the range of atto- to femtoliters. Physiological concentrations realized in such small volumes imply low copy numbers of interacting molecules with the consequence of considerable fluctuations in the concentrations. In contrast, rate equation models are based on the implicit assumption of infinitely large numbers of interacting molecules, or equivalently, that reactions occur in infinite volumes at constant macroscopic concentrations. In this article we compute the finite-volume corrections (or equivalently the finite copy number corrections) to the solutions of the rate equations for chemical reaction networks composed of arbitrarily large numbers of enzyme-catalyzed reactions which are confined inside a small subcellular compartment. This is achieved by applying a mesoscopic version of the quasisteady-state assumption to the exact Fokker-Planck equation associated with the Poisson representation of the chemical master equation. The procedure yields impressively simple and compact expressions for the finite-volume corrections. We prove that the predictions of the rate equations will always underestimate the actual steady-state substrate concentrations for an enzyme-reaction network confined in a small volume. In particular we show that the finite-volume corrections increase with decreasing subcellular volume, decreasing Michaelis-Menten constants, and increasing enzyme saturation. The magnitude of the corrections depends sensitively on the topology of the network. The predictions of the theory are shown to be in excellent agreement with stochastic simulations for two types of networks typically associated with protein methylation and metabolism.

Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

PubMed

Scheler, Gabriele

2013-01-01

We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species) with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.

Graph distance for complex networks

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Hirata, Yoshito; Ikeguchi, Tohru; Aihara, Kazuyuki

2016-10-01

Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

PubMed Central

2014-01-01

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614

Linguistic complex networks: Rationale, application, interpretation, and directions. Reply to comments on "Approaching human language with complex networks"

NASA Astrophysics Data System (ADS)

Cong, Jin; Liu, Haitao

2014-12-01

Amid the enthusiasm for real-world networks of the new millennium, the enquiry into linguistic networks is flourishing not only as a productive branch of the new networks science but also as a promising approach to linguistic research. Although the complex network approach constitutes a potential opportunity to make linguistics a science, the world of linguistics seems unprepared to embrace it. For one thing, linguistics has been largely unaffected by quantitative methods. Those who are accustomed to qualitative linguistic methods may find it hard to appreciate the application of quantitative properties of language such as frequency and length, not to mention quantitative properties of language modeled as networks. With this in mind, in our review [1] we restrict ourselves to the basics of complex networks and the new insights into human language with the application of complex networks. For another, while breaking new grounds and posing new challenges for linguistics, the complex network approach to human language as a new tradition of linguistic research is faced with challenges and unsolved issues of its own. It is no surprise that the comments on our review, especially their skepticism and suggestions, focus on various different aspects of the complex network approach to human language. We are grateful to all the insightful and penetrating comments, which, together with our review, mark a significant impetus to linguistic research from the complex network approach. In this reply, we would like to address four major issues of the complex network approach to human language, namely, a) its theoretical rationale, b) its application in linguistic research, c) interpretation of the results, and d) directions of future research.

Co-complexes Derived from Alkene Insertion to Alkyne-dicobaltpentacarbonyl complexes: Insight into the Regioselectivity of Pauson-Khand Reactions of Cyclopropenes

PubMed Central

Pallerla, Mahesh K.; Yap, Glenn P. A.; Fox, Joseph M.

2009-01-01

Described are the X-ray crystallographic and spectral properties of Co-complexes that were isolated from two Pauson-Khand reactions of chiral cyclopropenes. These are the first examples of isolated Co-complexes derived from the putative alkene-insertion intermediates of Pauson-Khand reactions. The binuclear Co-complexes are coordinated to μ-bonded, five-carbon “flyover” carbene ligands. It is proposed that the complexes result from cyclopropane fragmentation subsequent to alkene insertion. The observation of these metal complexes provides a rationale for the origin of regioselectivity in Pauson-Khand reactions of cyclopropenes. PMID:18637694

Metabolism and evolution: A comparative study of reconstructed genome-level metabolic networks

NASA Astrophysics Data System (ADS)

Almaas, Eivind

2008-03-01

The availability of high-quality annotations of sequenced genomes has made it possible to generate organism-specific comprehensive maps of cellular metabolism. Currently, more than twenty such metabolic reconstructions are publicly available, with the majority focused on bacteria. A typical metabolic reconstruction for a bacterium results in a complex network containing hundreds of metabolites (nodes) and reactions (links), while some even contain more than a thousand. The constrain-based optimization approach of flux-balance analysis (FBA) is used to investigate the functional characteristics of such large-scale metabolic networks, making it possible to estimate an organism's growth behavior in a wide variety of nutrient environments, as well as its robustness to gene loss. We have recently completed the genome-level metabolic reconstruction of Yersinia pseudotuberculosis, as well as the three Yersinia pestis biovars Antiqua, Mediaevalis, and Orientalis. While Y. pseudotuberculosis typically only causes fever and abdominal pain that can mimic appendicitis, the evolutionary closely related Y. pestis strains are the aetiological agents of the bubonic plague. In this presentation, I will discuss our results and conclusions from a comparative study on the evolution of metabolic function in the four Yersiniae networks using FBA and related techniques, and I will give particular focus to the interplay between metabolic network topology and evolutionary flexibility.

The Topology of a Discussion: The #Occupy Case.

PubMed

Gargiulo, Floriana; Bindi, Jacopo; Apolloni, Andrea

2015-01-01

We analyse a large sample of the Twitter activity that developed around the social movement 'Occupy Wall Street', to study the complex interactions between the human communication activity and the semantic content of a debate. We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes are hashtags, and the 'users interest network', whose nodes are users. In the first instance, we find out that discussion topics (#hashtags) present a high structural heterogeneity, with a relevant role played by the semantic hubs that are responsible to guarantee the continuity of the debate. In the users' case, the self-organisation process of users' activity, leads to the emergence of two classes of communicators: the 'professionals' and the 'amateurs'. Both the networks present a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when certain sets of topics are censored and/or accounts are removed. By analysing the characteristics of the dynamical networks we can distinguish three phases of the discussion about the movement. Each phase corresponds to a specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of prototypical #hashtags in the discussion.

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks.

PubMed

Meng, X Flora; Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M

2017-05-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. © 2017 The Author(s).

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks

PubMed Central

Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M.

2017-01-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. PMID:28566513

Improvement of proteolytic efficiency towards low-level proteins by an antifouling surface of alumina gel in a microchannel.

PubMed

Liu, Yun; Wang, Huixiang; Liu, Qingping; Qu, Haiyun; Liu, Baohong; Yang, Pengyuan

2010-11-07

A microfluidic reactor has been developed for rapid enhancement of protein digestion by constructing an alumina network within a poly(ethylene terephthalate) (PET) microchannel. Trypsin is stably immobilized in a sol-gel network on the PET channel surface after pretreatment, which produces a protein-resistant interface to reduce memory effects, as characterized by X-ray fluorescence spectrometry and electroosmotic flow. The gel-derived network within a microchannel provides a large surface-to-volume ratio stationary phase for highly efficient proteolysis of proteins existing both at a low level and in complex extracts. The maximum reaction rate of the encapsulated trypsin reactor, measured by kinetic analysis, is much faster than in bulk solution. Due to the microscopic confinement effect, high levels of enzyme entrapment and the biocompatible microenvironment provided by the alumina gel network, the low-level proteins can be efficiently digested using such a microreactor within a very short residence time of a few seconds. The on-chip microreactor is further applied to the identification of a mixture of proteins extracted from normal mouse liver cytoplasm sample via integration with 2D-LC-ESI-MS/MS to show its potential application for large-scale protein identification.

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

PubMed Central

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

The role of syntax in complex networks: Local and global importance of verbs in a syntactic dependency network

NASA Astrophysics Data System (ADS)

Čech, Radek; Mačutek, Ján; Žabokrtský, Zdeněk

2011-10-01

Syntax of natural language has been the focus of linguistics for decades. The complex network theory, being one of new research tools, opens new perspectives on syntax properties of the language. Despite numerous partial achievements, some fundamental problems remain unsolved. Specifically, although statistical properties typical for complex networks can be observed in all syntactic networks, the impact of syntax itself on these properties is still unclear. The aim of the present study is to shed more light on the role of syntax in the syntactic network structure. In particular, we concentrate on the impact of the syntactic function of a verb in the sentence on the complex network structure. Verbs play the decisive role in the sentence structure (“local” importance). From this fact we hypothesize the importance of verbs in the complex network (“global” importance). The importance of verb in the complex network is assessed by the number of links which are directed from the node representing verb to other nodes in the network. Six languages (Catalan, Czech, Dutch, Hungarian, Italian, Portuguese) were used for testing the hypothesis.

Integrated, systems metabolic picture of acetone-butanol-ethanol fermentation by Clostridium acetobutylicum.

PubMed

Liao, Chen; Seo, Seung-Oh; Celik, Venhar; Liu, Huaiwei; Kong, Wentao; Wang, Yi; Blaschek, Hans; Jin, Yong-Su; Lu, Ting

2015-07-07

Microbial metabolism involves complex, system-level processes implemented via the orchestration of metabolic reactions, gene regulation, and environmental cues. One canonical example of such processes is acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum, during which cells convert carbon sources to organic acids that are later reassimilated to produce solvents as a strategy for cellular survival. The complexity and systems nature of the process have been largely underappreciated, rendering challenges in understanding and optimizing solvent production. Here, we present a system-level computational framework for ABE fermentation that combines metabolic reactions, gene regulation, and environmental cues. We developed the framework by decomposing the entire system into three modules, building each module separately, and then assembling them back into an integrated system. During the model construction, a bottom-up approach was used to link molecular events at the single-cell level into the events at the population level. The integrated model was able to successfully reproduce ABE fermentations of the WT C. acetobutylicum (ATCC 824), as well as its mutants, using data obtained from our own experiments and from literature. Furthermore, the model confers successful predictions of the fermentations with various network perturbations across metabolic, genetic, and environmental aspects. From foundation to applications, the framework advances our understanding of complex clostridial metabolism and physiology and also facilitates the development of systems engineering strategies for the production of advanced biofuels.

Integrated, systems metabolic picture of acetone-butanol-ethanol fermentation by Clostridium acetobutylicum

PubMed Central

Liao, Chen; Seo, Seung-Oh; Celik, Venhar; Liu, Huaiwei; Kong, Wentao; Wang, Yi; Blaschek, Hans; Jin, Yong-Su; Lu, Ting

2015-01-01

Microbial metabolism involves complex, system-level processes implemented via the orchestration of metabolic reactions, gene regulation, and environmental cues. One canonical example of such processes is acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum, during which cells convert carbon sources to organic acids that are later reassimilated to produce solvents as a strategy for cellular survival. The complexity and systems nature of the process have been largely underappreciated, rendering challenges in understanding and optimizing solvent production. Here, we present a system-level computational framework for ABE fermentation that combines metabolic reactions, gene regulation, and environmental cues. We developed the framework by decomposing the entire system into three modules, building each module separately, and then assembling them back into an integrated system. During the model construction, a bottom-up approach was used to link molecular events at the single-cell level into the events at the population level. The integrated model was able to successfully reproduce ABE fermentations of the WT C. acetobutylicum (ATCC 824), as well as its mutants, using data obtained from our own experiments and from literature. Furthermore, the model confers successful predictions of the fermentations with various network perturbations across metabolic, genetic, and environmental aspects. From foundation to applications, the framework advances our understanding of complex clostridial metabolism and physiology and also facilitates the development of systems engineering strategies for the production of advanced biofuels. PMID:26100881

Fate and transport of uranium (VI) in weathered saprolite

DOE PAGES

Kim, Young-Jin; Brooks, Scott C.; Zhang, Fan; ...

2014-11-09

We conducted batch and column experiments to investigate sorption and transport of uranium (U) in the presence of saprolite derived from interbedded shale, limestone, and sandstone sequences. Sorption kinetics were measured at two initial concentrations (C0; 1, 10 mM) and three soil:solution ratios (Rs/w; 0.005, 0.25, 2 kg/L) at pH 4.5 (pH of the saprolite). The rate of U loss from solution (mmole/L/h) increased with increasing Rs/w. Uranium sorption exhibited a fast phase with 80% sorption in the first eight hours for all C0 and Rs/w values and a slow phase during which the reaction slowly approached (pseudo) equilibrium overmore » the next seven days. The pH-dependency of U sorption was apparent in pH sorption edges. U(VI) sorption increased over the pH range 4e6, then decreased sharply at pH > 7.5. U(VI) sorption edges were well described by a surface complexation model using calibrated parameters and the reaction network proposed by Waite et al. (1994). Sorption isotherms measured using the same Rs/w and pH values showed a solids concentration effect where U(VI) sorption capacity and affinity decreased with increasing solids concentration. Moreover, this effect may have been due to either particle aggregation or competition between U(VI) and exchangeable cations for sorption sites. The surface complexation model with calibrated parameters was able to predict the general sorption behavior relatively well, but failed to reproduce solid concentration effects, implying the importance of appropriate design if batch experiments are to be utilized for dynamic systems. Transport of U(VI) through the packed column was significantly retarded. We also conducted transport simulations using the reactive transport model HydroGeoChem (HGC) v5.0 that incorporated the surface complexation reaction network used to model the batch data. Model parameters reported by Waite et al. (1994) provided a better prediction of U transport than optimized parameters derived from our sorption edges. The results presented in this study highlight the challenges in defining appropriate conditions for batch-type experiments used to extrapolate parameters for transport models, and also underline a gap in our ability to transfer batch results to transport simulations.« less

Oral insulin delivery using P(MAA-g-EG) hydrogels: effects of network morphology on insulin delivery characteristics.

PubMed

Nakamura, Koji; Murray, Robert J; Joseph, Jeffrey I; Peppas, Nicholas A; Morishita, Mariko; Lowman, Anthony M

2004-03-24

Hydrogels of poly(methacrylic acid-g-ethylene glycol) were prepared using different reaction water contents in order to vary the network mesh size, swelling behavior and insulin loading/release kinetics. Gels prepared with greater reaction solvent contents swelled to a greater degree and had a larger network mesh size. All of the hydrogels were able to incorporate insulin and protected it from release in acidic media. At higher pH (7.4), the release rates increased with reaction solvent content. Using a closed loop animal model, all of the insulin loaded formulations produced significant insulin absorption in the upper small intestine combined with hypoglycemic effects. In these studies, bioavailabilities ranged from 4.6% to 7.2% and were dependent on reaction solvent content.

Introduction: Cancer Gene Networks.

PubMed

Clarke, Robert

2017-01-01

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary differential equations and related tools to create dynamic, semi-mechanistic models of low dimensional data including gene/protein signaling as a function of time/dose. More recently, the integration of imaging technologies into predictive multiscale modeling has begun to extend further the scales across which data can be obtained and used to gain insight into system function.There are several goals for predictive multiscale modeling including the more academic pursuit of understanding how the system or local feature thereof is regulated or functions, to the more practical or translational goals of identifying predictive (selecting which patient should receive which drug/therapy) or prognostic (disease progress and outcome in an individual patient) biomarkers and/or identifying network vulnerabilities that represent potential targets for therapeutic benefit with existing drugs (including drug repurposing) or for the development of new drugs. These various goals are not necessarily mutually exclusive or inclusive. Within this volume, readers will find examples of many of the activities noted above. Each chapter contains practical and/or methodological insights to guide readers in the design and interpretation of their own and published work.

Ullmann-like reactions for the synthesis of complex two-dimensional materials

NASA Astrophysics Data System (ADS)

Quardokus, Rebecca C.; Tewary, V. K.; DelRio, Frank W.

2016-11-01

Engineering two-dimensional materials through surface-confined synthetic techniques is a promising avenue for designing new materials with tailored properties. Developing and understanding reaction mechanisms for surface-confined synthesis of two-dimensional materials requires atomic-level characterization and chemical analysis. Beggan et al (2015 Nanotechnology 26 365602) used scanning tunneling microscopy and x-ray photoelectron spectroscopy to elucidate the formation mechanism of surface-confined Ullmann-like coupling of thiophene substituted porphyrins on Ag(111). Upon surface deposition, bromine is dissociated and the porphyrins couple with surface adatoms to create linear strands and hexagonally packed molecules. Annealing the sample results in covalently-bonded networks of thienylporphyrin derivatives. A deeper understanding of surface-confined Ullmann-like coupling has the potential to lead to precision-engineered nano-structures through synthetic techniques. Contribution of the National Institute of Standards and Technology, not subject to copyright in the United States of America.

Multidimensional MnO2 nanohair-decorated hybrid multichannel carbon nanofiber as an electrode material for high-performance supercapacitors.

PubMed

Jun, Jaemoon; Lee, Jun Seop; Shin, Dong Hoon; Kim, Sung Gun; Jang, Jyongsik

2015-10-14

One-dimensional (1D)-structured nanomaterials represent one of the most attractive candidates for energy-storage systems due to their contribution to design simplicity, fast charge-transportation network, and their allowance for more accessible ion diffusion. In particular, 1D-structured nanomaterials with a highly complex inner-pore configuration enhance functionality by taking advantage of both the hollow and 1D structures. In this study, we report a MnO2 nanohair-decorated, hybrid multichannel carbon nanofiber (Mn_MCNF) fabricated via single-nozzle co-electrospinning of two immiscible polymer solutions, followed by carbonization and redox reactions. With improved ion accessibility, the optimized Mn_MCNF sample (Mn_MCNF_60 corresponding to a reaction duration time of 60 min for optimal MnO2 nanohair growth) exhibited a high specific capacitance of 855 F g(-1) and excellent cycling performance with ∼87.3% capacitance retention over 5000 cycles.

Digital microfluidics: A promising technique for biochemical applications

NASA Astrophysics Data System (ADS)

Wang, He; Chen, Liguo; Sun, Lining

2017-12-01

Digital microfluidics (DMF) is a versatile microfluidics technology that has significant application potential in the areas of automation and miniaturization. In DMF, discrete droplets containing samples and reagents are controlled to implement a series of operations via electrowetting-on-dielectric. This process works by applying electrical potentials to an array of electrodes coated with a hydrophobic dielectric layer. Unlike microchannels, DMF facilitates precise control over multiple reaction processes without using complex pump, microvalve, and tubing networks. DMF also presents other distinct features, such as portability, less sample consumption, shorter chemical reaction time, flexibility, and easier combination with other technology types. Due to its unique advantages, DMF has been applied to a broad range of fields (e.g., chemistry, biology, medicine, and environment). This study reviews the basic principles of droplet actuation, configuration design, and fabrication of the DMF device, as well as discusses the latest progress in DMF from the biochemistry perspective.

Binuclear cyclometalated organoplatinum complexes containing 1,1'-bis(diphenylphosphino)ferrocene as spacer ligand: kinetics and mechanism of MeI oxidative addition.

PubMed

Jamali, Sirous; Nabavizadeh, S Masoud; Rashidi, Mehdi

2008-06-16

The binuclear complex [Pt2Me2(ppy)2(mu-dppf)], 1, in which ppy = deprotonated 2-phenylpyridyl and dppf = 1,1'-bis(diphenylphosphino)ferrocene, was synthesized by the reaction of [PtMe(SMe2)(ppy)] with 0.5 equiv of dppf at room temperature. In this reaction when 1 equiv of dppf was used, the dppf chelating complex 2, [PtMe(dppf)(ppy-kappa1C)], was obtained. The reaction of Pt(II)-Pt(II) complex 1 with excess MeI gave the Pt(IV)-Pt(IV) complex [Pt2I2Me4(ppy)2(mu-dppf)], 3. When the reaction was performed with 1 equiv of MeI, a mixture containing unreacted complex 1, a mixed-valence Pt(II)-Pt(IV) complex [PtMe(ppy)(mu-dppf)PtIMe2(ppy)], 4, and complex 3 was obtained. In a comparative study, the reaction of [PtMe(SMe2)(ppy)] with 1 equiv of monodentate phosphine PPh3 gave [PtMe(ppy)(PPh3)], A. MeI was reacted with A to give the platinum(IV) complex [PtMe2I(ppy)(PPh3)], C. All the complexes were fully characterized using multinuclear (1H, 31P, 13C, and 195Pt) NMR spectroscopy, and complex 2 was further identified by single crystal X-ray structure determination. The reaction of binuclear Pt(II)-Pt(II) complex 1 with excess MeI was monitored by low temperature 31P NMR spectroscopy and further by 1H NMR spectroscopy, and the kinetics of the reaction was studied by UV-vis spectroscopy. On the basis of the data, a mechanism has been suggested for the reaction which overall involved stepwise oxidative addition of MeI to the two Pt(II) centers. In this suggested mechanism, the reaction proceeded through a number of Pt(II)-Pt(IV) and Pt(IV)-Pt(IV) intermediates. Although MeI in each step was trans oxidatively added to one of the Pt(II) centers, further trans to cis isomerizations of Me and I groups were also identified. A comparative kinetic study of the reaction of monomeric platinum(II) complex A with MeI was also performed. The rate of reaction of MeI with complex 1 was some 3.5 times faster than that with complex A, indicating that dppf in the complex 1, as compared with PPh 3 in the complex A, has significantly enhanced the electron richness of the platinum centers.

Pt-Mechanistic Study of the β-Hydrogen Elimination from Organoplatinum(II) Enolate Complexes

PubMed Central

Alexanian, Erik J.; Hartwig, John F.

2010-01-01

A detailed mechanistic investigation of the thermal reactions of a series of bisphosphine alkylplatinum(II) enolate complexes is reported. The reactions of methylplatinum enolate complexes in the presence of added phosphine form methane and either free or coordinated enone, depending on the steric properties of the enone. Kinetic studies were conducted to determine the relationship between the rates and mechanism of β-hydrogen elimination from enolate complexes and the rates and mechanism of β-hydrogen elimination from alkyl complexes. The rates of reactions of the enolates were inversely dependent on the concentration of added phosphine, indicating that β-hydrogen elimination from the enolate complexes occurs after reversible dissociation of a phosphine. A normal, primary kinetic isotope effect was measured, and this effect was consistent with rate-limiting β-hydrogen elimination or C-H bond-forming reductive elimination to form methane. Reactions of substituted enolate complexes were also studied to determine the effect of the steric and electronic properties of the enolate complexes on the rates of β-hydrogen elimination. These studies showed that reactions of the alkylplatinum enolate complexes were retarded by electron-withdrawing substituents on the enolate and that reactions of enolate complexes possessing alkyl substituents at the β-position occurred at rates that were similar to those of complexes lacking alkyl substituents at this position. Despite the trend in electronic effects on the rates of reactions of enolate complexes and the substantial electronic differences between an enolate and an alkyl ligand, the rates of decomposition of the enolate complexes were similar to those of the analogous alkyl complexes. To the extent that the rates of reaction of the two types of complex are different, those involving β-hydrogen elimination from the enolate ligand were faster. A difference between the identity of the rate-determining step for decomposition of the two classes of complexes and an effect of stereochemistry on the selectivity for β-hydrogen elimination are possible origins of the observed phenomena. PMID:18954048

Efficiency of reactant site sampling in network-free simulation of rule-based models for biochemical systems

PubMed Central

Yang, Jin; Hlavacek, William S.

2011-01-01

Rule-based models, which are typically formulated to represent cell signaling systems, can now be simulated via various network-free simulation methods. In a network-free method, reaction rates are calculated for rules that characterize molecular interactions, and these rule rates, which each correspond to the cumulative rate of all reactions implied by a rule, are used to perform a stochastic simulation of reaction kinetics. Network-free methods, which can be viewed as generalizations of Gillespie’s method, are so named because these methods do not require that a list of individual reactions implied by a set of rules be explicitly generated, which is a requirement of other methods for simulating rule-based models. This requirement is impractical for rule sets that imply large reaction networks (i.e., long lists of individual reactions), as reaction network generation is expensive. Here, we compare the network-free simulation methods implemented in RuleMonkey and NFsim, general-purpose software tools for simulating rule-based models encoded in the BioNetGen language. The method implemented in NFsim uses rejection sampling to correct overestimates of rule rates, which introduces null events (i.e., time steps that do not change the state of the system being simulated). The method implemented in RuleMonkey uses iterative updates to track rule rates exactly, which avoids null events. To ensure a fair comparison of the two methods, we developed implementations of the rejection and rejection-free methods specific to a particular class of kinetic models for multivalent ligand-receptor interactions. These implementations were written with the intention of making them as much alike as possible, minimizing the contribution of irrelevant coding differences to efficiency differences. Simulation results show that performance of the rejection method is equal to or better than that of the rejection-free method over wide parameter ranges. However, when parameter values are such that ligand-induced aggregation of receptors yields a large connected receptor cluster, the rejection-free method is more efficient. PMID:21832806

Tuning the synchronization of a network of weakly coupled self-oscillating gels via capacitors.

PubMed

Fang, Yan; Yashin, Victor V; Dickerson, Samuel J; Balazs, Anna C

2018-05-01

We consider a network of coupled oscillating units, where each unit comprises a self-oscillating polymer gel undergoing the Belousov-Zhabotinsky (BZ) reaction and an overlaying piezoelectric (PZ) cantilever. Through chemo-mechano-electrical coupling, the oscillations of the networked BZ-PZ units achieve in-phase or anti-phase synchronization, enabling, for example, the storage of information within the system. Herein, we develop numerical and computational models to show that the introduction of capacitors into the BZ-PZ system enhances the dynamical behavior of the oscillating network by yielding additional stable synchronization modes. We specifically show that the capacitors lead to a redistribution of charge in the system and alteration of the force that the PZ cantilevers apply to the underlying gel. Hence, the capacitors modify the strength of the coupling between the oscillators in the network. We utilize a linear stability analysis to determine the phase behavior of BZ-PZ networks encompassing different capacitances, force polarities, and number of units and then verify our findings with numerical simulations. Thus, through analytical calculations and numerical simulations, we determine the impact of the capacitors on the existence of the synchronization modes, their stability, and the rate of synchronization within these complex dynamical systems. The findings from our study can be used to design robotic materials that harness the materials' intrinsic, responsive properties to perform such functions as sensing, actuation, and information storage.

Tuning the synchronization of a network of weakly coupled self-oscillating gels via capacitors

NASA Astrophysics Data System (ADS)

Fang, Yan; Yashin, Victor V.; Dickerson, Samuel J.; Balazs, Anna C.

2018-05-01

We consider a network of coupled oscillating units, where each unit comprises a self-oscillating polymer gel undergoing the Belousov-Zhabotinsky (BZ) reaction and an overlaying piezoelectric (PZ) cantilever. Through chemo-mechano-electrical coupling, the oscillations of the networked BZ-PZ units achieve in-phase or anti-phase synchronization, enabling, for example, the storage of information within the system. Herein, we develop numerical and computational models to show that the introduction of capacitors into the BZ-PZ system enhances the dynamical behavior of the oscillating network by yielding additional stable synchronization modes. We specifically show that the capacitors lead to a redistribution of charge in the system and alteration of the force that the PZ cantilevers apply to the underlying gel. Hence, the capacitors modify the strength of the coupling between the oscillators in the network. We utilize a linear stability analysis to determine the phase behavior of BZ-PZ networks encompassing different capacitances, force polarities, and number of units and then verify our findings with numerical simulations. Thus, through analytical calculations and numerical simulations, we determine the impact of the capacitors on the existence of the synchronization modes, their stability, and the rate of synchronization within these complex dynamical systems. The findings from our study can be used to design robotic materials that harness the materials' intrinsic, responsive properties to perform such functions as sensing, actuation, and information storage.

Monte Carlo simulation of a simple gene network yields new evolutionary insights.

PubMed

Andrecut, M; Cloud, D; Kauffman, S A

2008-02-07

Monte Carlo simulations of a genetic toggle switch show that its behavior can be more complex than analytic models would suggest. We show here that as a result of the interplay between frequent and infrequent reaction events, such a switch can have more stable states than an analytic model would predict, and that the number and character of these states depend to a large extent on the propensity of transcription factors to bind to and dissociate from promoters. The effects of gene duplications differ even more; in analytic models, these seem to result in the disappearance of bi-stability and thus a loss of the switching function, but a Monte Carlo simulation shows that they can result in the appearance of new stable states without the loss of old ones, and thus in an increase of the complexity of the switch's behavior which may facilitate the evolution of new cellular functions. These differences are of interest with respect to the evolution of gene networks, particularly in clonal lines of cancer cells, where the duplication of active genes is an extremely common event, and often seems to result in the appearance of viable new cellular phenotypes.

Nearside-farside, local angular momentum and resummation theories: Useful tools for understanding the dynamics of complex-mode reactions

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

Hankel, Marlies, E-mail: m.hankel@uq.edu.au, E-mail: j.n.l.connor@manchester.ac.uk; Connor, J. N. L., E-mail: m.hankel@uq.edu.au, E-mail: j.n.l.connor@manchester.ac.uk

2015-07-15

A valuable tool for understanding the dynamics of direct reactions is Nearside-Farside (NF) scattering theory. It makes a decomposition of the (resummed) partial wave series for the scattering amplitude, both for the differential cross section (DCS) and the Local Angular Momentum (LAM). This paper makes the first combined application of these techniques to complex-mode reactions. We ask if NF theory is a useful tool for their identification, in particular, can it distinguish complex-mode from direct-mode reactions? We also ask whether NF theory can identify NF interference oscillations in the full DCSs of complex-mode reactions. Our investigation exploits the fact thatmore » accurate quantum scattering matrix elements have recently become available for complex-mode reactions. We first apply NF theory to two simple models for the scattering amplitude of a complex-mode reaction: One involves a single Legendre polynomial; the other involves a single Legendre function of the first kind, whose form is suggested by complex angular momentum theory. We then study, at fixed translational energies, four state-to-state complex-mode reactions. They are: S({sup 1}D) + HD → SH + D, S({sup 1}D) + DH → SD + H, N({sup 2}D) +H{sub 2} → NH + H, and H{sup +} + D{sub 2} → HD + D{sup +}. We compare the NF results for the DCSs and LAMs with those for a state-to-state direct reaction, namely, F + H{sub 2} → FH + H. We demonstrate that NF theory is a valuable tool for identifying and analyzing the dynamics of complex-mode reactions.« less

Detecting complexes from edge-weighted PPI networks via genes expression analysis.

PubMed

Zhang, Zehua; Song, Jian; Tang, Jijun; Xu, Xinying; Guo, Fei

2018-04-24

Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization. We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.

A Petri net approach to the study of persistence in chemical reaction networks.

PubMed

Angeli, David; De Leenheer, Patrick; Sontag, Eduardo D

2007-12-01

Persistence is the property, for differential equations in R(n), that solutions starting in the positive orthant do not approach the boundary of the orthant. For chemical reactions and population models, this translates into the non-extinction property: provided that every species is present at the start of the reaction, no species will tend to be eliminated in the course of the reaction. This paper provides checkable conditions for persistence of chemical species in reaction networks, using concepts and tools from Petri net theory, and verifies these conditions on various systems which arise in the modeling of cell signaling pathways.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

Computational analysis of complex systems: Applications to population dynamics and networks

NASA Astrophysics Data System (ADS)

Molnar, Ferenc

In most complex evolving systems, we can often find a critical subset of the constituents that can initiate a global change in the entire system. For example, in complex networks, a critical subset of nodes can efficiently spread information, influence, or control dynamical processes over the entire network. Similarly, in nonlinear dynamics, we can locate key variables, or find the necessary parameters, to reach the attraction basin of a desired global state. In both cases, a fundamental goal is finding the ability to efficiently control these systems. We study two distinct complex systems in this dissertation, exploring these topics. First, we analyze a population dynamics model describing interactions of sex-structured population groups. Specifically, we analyze how a sex-linked genetic trait's ecological consequence (population survival or extinction) can be influenced by the presence of sex-specific cultural mortality traits, motivated by the desire to expand the theoretical understanding of the role of biased sex ratios in organisms. We analyze dynamics within a single population group, as well as between competing groups. We find that there is a finite range of sex ratio bias that can be maintained in stable equilibrium by sex-specific mortalities. We also find that the outcome of an invasion and the ensuing between-group competition depends not on larger equilibrium group densities, but on the higher allocation of sex-ratio genes. When we extend the model with diffusive dispersal, we find that a critical patch size for achieving positive growth only exists if the population expands into an empty environment. If a resident population is already present that can be exploited by the invading group, then any small seed of invader can advance from rarity, in the mean-field approximation, as long as the local competition dynamics favors the invader's survival. Most spatial models assume initial populations with a uniform distribution inside a finite patch; a simple, but not a cost-efficient approach. We show, using a novel application of simulated annealing, that a specific, non-trivial shape of spatial distribution can minimize the total cost of successful invasion, i.e., the cost of ecological restoration. Further, our approach can be generalized to essentially any reaction-diffusion model with diffusive spreading. In the second part of the dissertation we conduct an extensive study of minimum dominating sets (MDS) in complex networks; particularly, in scale-free networks. MDS is the smallest subset of nodes in a network that can reach every other node as nearest neighbors, thus it provides a key subset of nodes that play critical role in controllability and observability of social, biological, and technological networks. Continued interest in network control, monitoring and influencing of complex networks motivates our research of understanding the properties and practical application-related issues of the MDS. Our study of the scaling behavior reveals that the size of MDS always scales linearly with network size, as long as the power-law degree exponent gamma of the degree distribution is larger than 2. However, when gamma<2, a domination transition occurs, allowing the MDS size to become O(1), leading to easily dominated networks, under certain structural conditions. Motivated by practical applicability in large networks, we develop a new dominating set selection method, derived from probabilistic node selection techniques, which can select small dominating sets without complete network topology information. We also show that the effectiveness of our method, as well as the effectiveness of other heuristics of dominating set selection, strongly depends on the assortativity of networks. Finally, we conduct a numerical study to analyze the fraction of nodes that remain dominated, after the network is damaged, and some nodes are removed. We find that dominating sets optimized for small size are particularly vulnerable to damage; a significant amount of "domination coverage" may be lost if key dominator nodes are deleted. However, we also find that increasing the redundancy of dominating sets by adding a few well-picked nodes can successfully increase the post-damage dominated fraction of the network. Based on this idea, we develop two algorithms to build dominating sets with flexible balance between size and damage resilience.

Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

PubMed

Gao, Zhongke; Jin, Ningde

2009-06-01

The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

Boolean logic tree of graphene-based chemical system for molecular computation and intelligent molecular search query.

PubMed

Huang, Wei Tao; Luo, Hong Qun; Li, Nian Bing

2014-05-06

The most serious, and yet unsolved, problem of constructing molecular computing devices consists in connecting all of these molecular events into a usable device. This report demonstrates the use of Boolean logic tree for analyzing the chemical event network based on graphene, organic dye, thrombin aptamer, and Fenton reaction, organizing and connecting these basic chemical events. And this chemical event network can be utilized to implement fluorescent combinatorial logic (including basic logic gates and complex integrated logic circuits) and fuzzy logic computing. On the basis of the Boolean logic tree analysis and logic computing, these basic chemical events can be considered as programmable "words" and chemical interactions as "syntax" logic rules to construct molecular search engine for performing intelligent molecular search query. Our approach is helpful in developing the advanced logic program based on molecules for application in biosensing, nanotechnology, and drug delivery.

Photofunctional Eu3+/Tb3+ hybrids through sulfoxide linkages: coordination bonds construction, characterization and luminescence.

PubMed

Guo, Lei; Yan, Bing; Liu, Jin-Liang

2011-05-14

New kinds of organic-inorganic hybrid materials consisting of rare earth (Eu(3+), Tb(3+)) complexes covalently bonded to a silica-based network have been obtained by a sol-gel approach. Three novel versatile molecular building blocks containing sulfoxide organic units have been synthesized by methylene modification reaction, which are used as the ligands of rare earth ions and also as siloxane network precursors. The obtained hybrids are characterized by chemical analysis and spectroscopic methods such as FTIR and UV; XRD and SEM. Photoluminescence measurements on the prepared hybrids were performed showing the intra-4f(n) emission in the visible (Eu(3+), Tb(3+)) region and in all the cases being sensitized by the sulfoxide ligands. The emission quantum efficiency and the Judd-Ofelt intensity parameters of Eu(3+) hybrid materials were also investigated in detail.

Nano-scale hydrogen-bond network improves the durability of greener cements

PubMed Central

Jacobsen, Johan; Rodrigues, Michelle Santos; Telling, Mark T. F.; Beraldo, Antonio Ludovico; Santos, Sérgio Francisco; Aldridge, Laurence P.; Bordallo, Heloisa N.

2013-01-01

More than ever before, the world's increasing need for new infrastructure demands the construction of efficient, sustainable and durable buildings, requiring minimal climate-changing gas-generation in their production. Maintenance-free “greener” building materials made from blended cements have advantages over ordinary Portland cements, as they are cheaper, generate less carbon dioxide and are more durable. The key for the improved performance of blends (which substitute fine amorphous silicates for cement) is related to their resistance to water penetration. The mechanism of this water resistance is of great environmental and economical impact but is not yet understood due to the complexity of the cement's hydration reactions. Using neutron spectroscopy, we studied a blend where cement was replaced by ash from sugar cane residuals originating from agricultural waste. Our findings demonstrate that the development of a distinctive hydrogen bond network at the nano-scale is the key to the performance of these greener materials. PMID:24036676

A Novel BA Complex Network Model on Color Template Matching

PubMed Central

Han, Risheng; Yue, Guangxue; Ding, Hui

2014-01-01

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching. PMID:25243235

A novel BA complex network model on color template matching.

PubMed

Han, Risheng; Shen, Shigen; Yue, Guangxue; Ding, Hui

2014-01-01

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.

Continuum-based DFN-consistent numerical framework for the simulation of oxygen infiltration into fractured crystalline rocks.

PubMed

Trinchero, Paolo; Puigdomenech, Ignasi; Molinero, Jorge; Ebrahimi, Hedieh; Gylling, Björn; Svensson, Urban; Bosbach, Dirk; Deissmann, Guido

2017-05-01

We present an enhanced continuum-based approach for the modelling of groundwater flow coupled with reactive transport in crystalline fractured rocks. In the proposed formulation, flow, transport and geochemical parameters are represented onto a numerical grid using Discrete Fracture Network (DFN) derived parameters. The geochemical reactions are further constrained by field observations of mineral distribution. To illustrate how the approach can be used to include physical and geochemical complexities into reactive transport calculations, we have analysed the potential ingress of oxygenated glacial-meltwater in a heterogeneous fractured rock using the Forsmark site (Sweden) as an example. The results of high-performance reactive transport calculations show that, after a quick oxygen penetration, steady state conditions are attained where abiotic reactions (i.e. the dissolution of chlorite and the homogeneous oxidation of aqueous iron(II) ions) counterbalance advective oxygen fluxes. The results show that most of the chlorite becomes depleted in the highly conductive deformation zones where higher mineral surface areas are available for reactions. Copyright © 2017 Elsevier B.V. All rights reserved.

Covalently crosslinked diels-alder polymer networks.

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

Bowman, Christopher; Adzima, Brian J.; Anderson, Benjamin John

2011-09-01

This project examines the utility of cycloaddition reactions for the synthesis of polymer networks. Cycloaddition reactions are desirable because they produce no unwanted side reactions or small molecules, allowing for the formation of high molecular weight species and glassy crosslinked networks. Both the Diels-Alder reaction and the copper-catalyzed azide-alkyne cycloaddition (CuAAC) were studied. Accomplishments include externally triggered healing of a thermoreversible covalent network via self-limited hysteresis heating, the creation of Diels-Alder based photoresists, and the successful photochemical catalysis of CuAAC as an alternative to the use of ascorbic acid for the generation of Cu(I) in click reactions. An analysis ofmore » the results reveals that these new methods offer the promise of efficiently creating robust, high molecular weight species and delicate three dimensional structures that incorporate chemical functionality in the patterned material. This work was performed under a Strategic Partnerships LDRD during FY10 and FY11 as part of a Sandia National Laboratories/University of Colorado-Boulder Excellence in Science and Engineering Fellowship awarded to Brian J. Adzima, a graduate student at UC-Boulder. Benjamin J. Anderson (Org. 1833) was the Sandia National Laboratories point-of-contact for this fellowship.« less

Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

PubMed Central

Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

2013-01-01

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks. PMID:24386268

Minimal perceptrons for memorizing complex patterns

NASA Astrophysics Data System (ADS)

Pastor, Marissa; Song, Juyong; Hoang, Danh-Tai; Jo, Junghyo

2016-11-01

Feedforward neural networks have been investigated to understand learning and memory, as well as applied to numerous practical problems in pattern classification. It is a rule of thumb that more complex tasks require larger networks. However, the design of optimal network architectures for specific tasks is still an unsolved fundamental problem. In this study, we consider three-layered neural networks for memorizing binary patterns. We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity. We formulated the minimal network size for regular, random, and complex patterns. In particular, the minimal size for complex patterns, which are neither ordered nor disordered, was predicted by measuring their Hamming distances from known ordered patterns. Our predictions agree with simulations based on the back-propagation algorithm.

Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics.

PubMed

Arampatzis, Georgios; Katsoulakis, Markos A; Rey-Bellet, Luc

2016-03-14

We demonstrate that centered likelihood ratio estimators for the sensitivity indices of complex stochastic dynamics are highly efficient with low, constant in time variance and consequently they are suitable for sensitivity analysis in long-time and steady-state regimes. These estimators rely on a new covariance formulation of the likelihood ratio that includes as a submatrix a Fisher information matrix for stochastic dynamics and can also be used for fast screening of insensitive parameters and parameter combinations. The proposed methods are applicable to broad classes of stochastic dynamics such as chemical reaction networks, Langevin-type equations and stochastic models in finance, including systems with a high dimensional parameter space and/or disparate decorrelation times between different observables. Furthermore, they are simple to implement as a standard observable in any existing simulation algorithm without additional modifications.

Complex and differential glial responses in Alzheimer's disease and ageing.

PubMed

Rodríguez, José J; Butt, Arthur M; Gardenal, Emanuela; Parpura, Vladimir; Verkhratsky, Alexei

2016-01-01

Glial cells and their association with neurones are fundamental for brain function. The emergence of complex neurone-glial networks assures rapid information transfer, creating a sophisticated circuitry where both types of neural cells work in concert, serving different activities. All glial cells, represented by astrocytes, oligodendrocytes, microglia and NG2-glia, are essential for brain homeostasis and defence. Thus, glia are key not only for normal central nervous system (CNS) function, but also to its dysfunction, being directly associated with all forms of neuropathological processes. Therefore, the progression and outcome of neurological and neurodegenerative diseases depend on glial reactions. In this review, we provide a concise account of recent data obtained from both human material and animal models demonstrating the pathological involvement of glia in neurodegenerative processes, including Alzheimer's disease (AD), as well as physiological ageing.

Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics

NASA Astrophysics Data System (ADS)

Arampatzis, Georgios; Katsoulakis, Markos A.; Rey-Bellet, Luc

2016-03-01

We demonstrate that centered likelihood ratio estimators for the sensitivity indices of complex stochastic dynamics are highly efficient with low, constant in time variance and consequently they are suitable for sensitivity analysis in long-time and steady-state regimes. These estimators rely on a new covariance formulation of the likelihood ratio that includes as a submatrix a Fisher information matrix for stochastic dynamics and can also be used for fast screening of insensitive parameters and parameter combinations. The proposed methods are applicable to broad classes of stochastic dynamics such as chemical reaction networks, Langevin-type equations and stochastic models in finance, including systems with a high dimensional parameter space and/or disparate decorrelation times between different observables. Furthermore, they are simple to implement as a standard observable in any existing simulation algorithm without additional modifications.

Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics

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

Arampatzis, Georgios; Katsoulakis, Markos A.; Rey-Bellet, Luc

2016-03-14

We demonstrate that centered likelihood ratio estimators for the sensitivity indices of complex stochastic dynamics are highly efficient with low, constant in time variance and consequently they are suitable for sensitivity analysis in long-time and steady-state regimes. These estimators rely on a new covariance formulation of the likelihood ratio that includes as a submatrix a Fisher information matrix for stochastic dynamics and can also be used for fast screening of insensitive parameters and parameter combinations. The proposed methods are applicable to broad classes of stochastic dynamics such as chemical reaction networks, Langevin-type equations and stochastic models in finance, including systemsmore » with a high dimensional parameter space and/or disparate decorrelation times between different observables. Furthermore, they are simple to implement as a standard observable in any existing simulation algorithm without additional modifications.« less

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Advances in the Theory of Complex Networks

NASA Astrophysics Data System (ADS)

Peruani, Fernando

An exhaustive and comprehensive review on the theory of complex networks would imply nowadays a titanic task, and it would result in a lengthy work containing plenty of technical details of arguable relevance. Instead, this chapter addresses very briefly the ABC of complex network theory, visiting only the hallmarks of the theoretical founding, to finally focus on two of the most interesting and promising current research problems: the study of dynamical processes on transportation networks and the identification of communities in complex networks.

The Validity of Quasi-Steady-State Approximations in Discrete Stochastic Simulations

PubMed Central

Kim, Jae Kyoung; Josić, Krešimir; Bennett, Matthew R.

2014-01-01

In biochemical networks, reactions often occur on disparate timescales and can be characterized as either fast or slow. The quasi-steady-state approximation (QSSA) utilizes timescale separation to project models of biochemical networks onto lower-dimensional slow manifolds. As a result, fast elementary reactions are not modeled explicitly, and their effect is captured by nonelementary reaction-rate functions (e.g., Hill functions). The accuracy of the QSSA applied to deterministic systems depends on how well timescales are separated. Recently, it has been proposed to use the nonelementary rate functions obtained via the deterministic QSSA to define propensity functions in stochastic simulations of biochemical networks. In this approach, termed the stochastic QSSA, fast reactions that are part of nonelementary reactions are not simulated, greatly reducing computation time. However, it is unclear when the stochastic QSSA provides an accurate approximation of the original stochastic simulation. We show that, unlike the deterministic QSSA, the validity of the stochastic QSSA does not follow from timescale separation alone, but also depends on the sensitivity of the nonelementary reaction rate functions to changes in the slow species. The stochastic QSSA becomes more accurate when this sensitivity is small. Different types of QSSAs result in nonelementary functions with different sensitivities, and the total QSSA results in less sensitive functions than the standard or the prefactor QSSA. We prove that, as a result, the stochastic QSSA becomes more accurate when nonelementary reaction functions are obtained using the total QSSA. Our work provides an apparently novel condition for the validity of the QSSA in stochastic simulations of biochemical reaction networks with disparate timescales. PMID:25099817

A Novel Approach for Modeling Chemical Reaction in Generalized Fluid System Simulation Program

NASA Technical Reports Server (NTRS)

Sozen, Mehmet; Majumdar, Alok

2002-01-01

The Generalized Fluid System Simulation Program (GFSSP) is a computer code developed at NASA Marshall Space Flight Center for analyzing steady state and transient flow rates, pressures, temperatures, and concentrations in a complex flow network. The code, which performs system level simulation, can handle compressible and incompressible flows as well as phase change and mixture thermodynamics. Thermodynamic and thermophysical property programs, GASP, WASP and GASPAK provide the necessary data for fluids such as helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, water, a hydrogen, isobutane, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, several refrigerants, nitrogen trifluoride and ammonia. The program which was developed out of need for an easy to use system level simulation tool for complex flow networks, has been used for the following purposes to name a few: Space Shuttle Main Engine (SSME) High Pressure Oxidizer Turbopump Secondary Flow Circuits, Axial Thrust Balance of the Fastrac Engine Turbopump, Pressurized Propellant Feed System for the Propulsion Test Article at Stennis Space Center, X-34 Main Propulsion System, X-33 Reaction Control System and Thermal Protection System, and International Space Station Environmental Control and Life Support System design. There has been an increasing demand for implementing a combustion simulation capability into GFSSP in order to increase its system level simulation capability of a liquid rocket propulsion system starting from the propellant tanks up to the thruster nozzle for spacecraft as well as launch vehicles. The present work was undertaken for addressing this need. The chemical equilibrium equations derived from the second law of thermodynamics and the energy conservation equation derived from the first law of thermodynamics are solved simultaneously by a Newton-Raphson method. The numerical scheme was implemented as a User Subroutine in GFSSP.

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

Yang, Dong; Chen, Mingyang; Martinez-Macias, Claudia

In this study, the adsorption of N 2 on structurally well-defined dealuminated HY zeolite-supported iridium diethylene complexes was investigated. Iridium dinitrogen complexes formed when the sample was exposed to N 2 in H 2 at 298 K, as shown by infrared spectra recorded with isotopically labeled N 2. Four supported species formed in various flowing gases: Ir(N 2), Ir(N 2)(N 2), Ir(C 2H 5)(N 2), and Ir(H)(N 2). Their interconversions are summarized in a reaction network, showing, for example, that, in the presence of N 2, Ir(N 2) was the predominant dinitrogen species at temperatures of 273-373 K. Ir(CO)(N 2)more » formed transiently in flowing CO, and in the presence of H 2, rather stable iridium hydride complexes formed. Here, four structural models of each iridium complex bonded at the acidic sites of the zeolite were employed in a computational investigation, showing that the calculated vibrational frequencies agree well with experiment when full calculations are done at the level of density functional theory, independent of the size of the model of the zeolite.« less

Self-assembly of five 8-hydroxyquinolinate-based complexes: tunable core, supramolecular structure, and photoluminescence properties.

PubMed

Yuan, Guozan; Shan, Weilong; Qiao, Xuelong; Ma, Li; Huo, Yanping

2014-07-01

Five new Zn(II) complexes, namely [Zn(3)(L)(6)] (1), [Zn(2)(Cl)(2)(L)(2) (py)(2)] (2), [Zn(2)(Br)(2) (L)(2)(py)(2)] (3), [Zn(L)(2)(py)] (4), and [Zn(2)(OAc)(2)(L)(2)(py)(2)] (5), were prepared by the solvothermal reaction of ZnX(2) (X(-) =Cl(-), Br(-), F(-), and OAc(-)) salts with a 8-hydroxyquinolinate ligand (HL) that contained a trifluorophenyl group. All of the complexes were characterized by elemental analysis, IR spectroscopy, and powder and single-crystal X-ray crystallography. The building blocks exhibited unprecedented structural diversification and their self-assembly afforded one mononuclear, three binuclear, and one trinuclear Zn(II) structures in response to different anions and solvent systems. Complexes 1-5 featured four types of supramolecular network controlled by non-covalent interactions, such as π⋅⋅⋅π-stacking, C-H⋅⋅⋅π, hydrogen-bonding, and halogen-related interactions. Investigation of their photoluminescence properties exhibited disparate emission wavelengths, lifetimes, and quantum yields in the solid state. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Efficient Green's Function Reaction Dynamics (GFRD) simulations for diffusion-limited, reversible reactions

NASA Astrophysics Data System (ADS)

Bashardanesh, Zahedeh; Lötstedt, Per

2018-03-01

In diffusion controlled reversible bimolecular reactions in three dimensions, a dissociation step is typically followed by multiple, rapid re-association steps slowing down the simulations of such systems. In order to improve the efficiency, we first derive an exact Green's function describing the rate at which an isolated pair of particles undergoing reversible bimolecular reactions and unimolecular decay separates beyond an arbitrarily chosen distance. Then the Green's function is used in an algorithm for particle-based stochastic reaction-diffusion simulations for prediction of the dynamics of biochemical networks. The accuracy and efficiency of the algorithm are evaluated using a reversible reaction and a push-pull chemical network. The computational work is independent of the rates of the re-associations.

Elucidation of Diels-Alder Reaction Network of 2,5-Dimethylfuran and Ethylene on HY Zeolite Catalyst

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

Do, Phuong T. M.; McAtee, Jesse R.; Watson, Donald A.

2012-12-12

The reaction of 2,5-dimethylfuran and ethylene to produce p-xylene represents a potentially important route for the conversion of biomass to high-value organic chemicals. Current preparation methods suffer from low selectivity and produce a number of byproducts. Using modern separation and analytical techniques, the structures of many of the byproducts produced in this reaction when HY zeolite is employed as a catalyst have been identified. From these data, a detailed reaction network is proposed, demonstrating that hydrolysis and electrophilic alkylation reactions compete with the desired Diels–Alder/dehydration sequence. This information will allow the rational identification of more selective catalysts and more selectivemore » reaction conditions.« less

Complex network construction based on user group attention sequence

NASA Astrophysics Data System (ADS)

Zhang, Gaowei; Xu, Lingyu; Wang, Lei

2018-04-01

In the traditional complex network construction, it is often to use the similarity between nodes, build the weight of the network, and finally build the network. However, this approach tends to focus only on the coupling between nodes, while ignoring the information transfer between nodes and the transfer of directionality. In the network public opinion space, based on the set of stock series that the network groups pay attention to within a certain period of time, we vectorize the different stocks and build a complex network.

Identification of hybrid node and link communities in complex networks

PubMed Central

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-01-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

Identification of hybrid node and link communities in complex networks.

PubMed

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

NASA Astrophysics Data System (ADS)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Online Community Detection for Large Complex Networks

PubMed Central

Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

2014-01-01

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

Pruning artificial neural networks using neural complexity measures.

PubMed

Jorgensen, Thomas D; Haynes, Barry P; Norlund, Charlotte C F

2008-10-01

This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.