RMBNToolbox: random models for biochemical networks
Niemi Jari
2007-05-01
Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.
Modelling of biochemical reaction networks
Gloppen Jørgensen, Arne Gunnar
2011-01-01
This report investigates signalling in reaction kinetic networks. The main topic is signalling between a substance being controlled by another substance and how this can be related to control theory. Different types of so-called natural controllers are compared and certain properties are investigated. Natural controllers are models on how a catalyst enzyme controls, for example the concentration, of a substance. There are sixteen different combinations of signalling between these substanc...
Elston Timothy C
2004-03-01
Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.
Modeling stochasticity in biochemical reaction networks
Constantino, P. H.; Vlysidis, M.; Smadbeck, P.; Kaznessis, Y. N.
2016-03-01
Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts.
Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices
Schmidt, Karsten; Carlsen, Morten; Nielsen, Jens Bredal;
1997-01-01
Within the last decades NMR spectroscopy has undergone tremendous development and has become a powerful analytical tool for the investigation of intracellular flux distributions in biochemical networks using C-13-labeled substrates. Not only are the experiments much easier to conduct than...... experiments employing radioactive tracer elements, but NMR spectroscopy also provides additional information on the labeling pattern of the metabolites. Whereas the maximum amount of information obtainable with C-14-labeled substrates is the fractional enrichment in the individual carbon atom positions, NMR...... spectroscopy can also provide information on the degree of labeling at neighboring carbon atom positions by analyzing multiplet patterns in NMR spectra or using 2-dimensional NMR spectra. It is possible to quantify the mole fractions of molecules that show a specific labeling pattern, i.e., information of the...
Breitling, Rainer; Gilbert, David; Heiner, Monika; Orton, Richard
2008-01-01
Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. Here we provide a general introduction to the field of formal modelling, which emphasizes the intuitive biochemical basis of the modelling process, but is also a...
A New Approach for Modeling Procaryotic Biochemical Networks With Differential Equations
Gebert, Jutta; Radde, Nicole
2006-06-01
One major challenge in Computational Biology is the simulation of the processes in a biological cell, which makes it necessary to understand the interactions between cell components. It is convenient to model the entirety of such interactions as biochemical networks. In this paper we present our novel approach to describe these biochemical networks with piecewise linear differential equations and analyze it theoretically. Then we will discuss methods for the parameter estimation from time series measurements including inference of the network topology. Finally we show an application of our model for the bacterium Corynebacterium glutamicum.
Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki
2014-09-01
Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction. PMID:24623466
Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach
Oliveira Rui
2010-09-01
Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.
S-system-based analysis of the robust properties common to many biochemical network models.
Matsuoka, Yu; Jahan, Nusrat; Kurata, Hiroyuki
2016-05-01
Robustness is a key feature to characterize the adaptation of organisms to changes in their internal and external environments. A broad range of kinetic or dynamic models of biochemical systems have been developed. Robustness analyses are attractive for exploring some common properties of many biochemical models. To reveal such features, we transform different types of mathematical equations into a standard or intelligible formula and use the multiple parameter sensitivity (MPS) to identify some factors critically responsible for the total robustness to many perturbations. The MPS would be determined by the top quarter of the highly sensitive parameters rather than the single parameter with the maximum sensitivity. The MPS did not show any correlation to the network size. The MPS is closely related to the standard deviation of the sensitivity profile. A decrease in the standard deviation enhanced the total robustness, which shows the hallmark of distributed robustness that many factors (pathways) involve the total robustness. PMID:26861555
Dynamic analysis of biochemical network using complex network method
Wang Shuqiang; Shen Yanyan; Hu Jinxing; Li Ning; Zeng Dewei
2015-01-01
In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger ...
Combining flux and energy balance analysis to model large-scale biochemical networks.
Heuett, William J; Qian, Hong
2006-12-01
Stoichiometric Network Theory is a constraints-based, optimization approach for quantitative analysis of the phenotypes of large-scale biochemical networks that avoids the use of detailed kinetics. This approach uses the reaction stoichiometric matrix in conjunction with constraints provided by flux balance and energy balance to guarantee mass conserved and thermodynamically allowable predictions. However, the flux and energy balance constraints have not been effectively applied simultaneously on the genome scale because optimization under the combined constraints is non-linear. In this paper, a sequential quadratic programming algorithm that solves the non-linear optimization problem is introduced. A simple example and the system of fermentation in Saccharomyces cerevisiae are used to illustrate the new method. The algorithm allows the use of non-linear objective functions. As a result, we suggest a novel optimization with respect to the heat dissipation rate of a system. We also emphasize the importance of incorporating interactions between a model network and its surroundings. PMID:17245812
Dynamic analysis of biochemical network using complex network method
Wang Shuqiang
2015-01-01
Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.
Ruess, Jakob
2015-12-01
Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
Ye, Jianxiong; Feng, Enmin; Wang, Lei; Xiu, Zhilong; Sun, Yaqin
Glycerol bioconversion to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulatory. To date, there still exist some uncertain factors in this complex network because of the limitation in bio-techniques, especially in measuring techniques for intracellular substances. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of glycerol and 1,3-PD across the cell membrane, which have received intensive interest in recent years. On the basis of different inferences of the transport mechanisms, we reconstruct various metabolic networks correspondingly and subsequently develop their dynamical systems (S-systems). To determine the most reasonable metabolic network from all possible ones, we establish a quantitative definition of biological robustness and undertake parameter identification and robustness analysis for each system. Numerical results show that it is most possible that both glycerol and 1,3-PD pass the cell membrane by active transport and passive diffusion.
Robust simplifications of multiscale biochemical networks
Zinovyev Andrei
2008-10-01
Full Text Available Abstract Background Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. Results We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-κB pathway. Conclusion Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.
BNDB – The Biochemical Network Database
Kaufmann Michael
2007-10-01
Full Text Available Abstract Background Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources. Description We present the Biochemical Network Database (BNDB, a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB. Conclusion BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org.
Reduction of dynamical biochemical reactions networks in computational biology
Radulescu, O.; Gorban, A.N.; Zinovyev, A.; Noel, V.
2012-01-01
Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to...
Automatic Verification of Biochemical Network Using Model Checking Method%基于模型校核的生化网络自动辨别方法
Jinkyung Kim; Younghee Lee; Il Moon
2008-01-01
This study focuses on automatic searching and verifying methods for the reachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic search and verification for alternative paths within complex and large networks in biological process can provide a consid-erable amount of solutions, which is difficult to handle manually. Model checking is an automatic method for veri-fying if a circuit or a condition, expressed as a concurrent transition system, satisfies a set of properties expressed ina temporal logic, such as computational tree logic (CTL). This article represents that model checking is feasible in biochemical network verification and it shows certain advantages over simulation for querying and searching of special behavioral properties in biochemical processes.
LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
Shi Guiyang
2010-01-01
Full Text Available Abstract Background Biochemical networks play an essential role in systems biology. Rapidly growing network data and versatile research activities call for convenient visualization tools to aid intuitively perceiving abstract structures of networks and gaining insights into the functional implications of networks. There are various kinds of network visualization software, but they are usually not adequate for visual analysis of complex biological networks mainly because of the two reasons: 1 most existing drawing methods suitable for biochemical networks have high computation loads and can hardly achieve near real-time visualization; 2 available network visualization tools are designed for working in certain network modeling platforms, so they are not convenient for general analyses due to lack of broader range of readily accessible numerical utilities. Results We present LucidDraw as a visual analysis tool, which features (a speed: typical biological networks with several hundreds of nodes can be drawn in a few seconds through a new layout algorithm; (b ease of use: working within MATLAB makes it convenient to manipulate and analyze the network data using a broad spectrum of sophisticated numerical functions; (c flexibility: layout styles and incorporation of other available information about functional modules can be controlled by users with little effort, and the output drawings are interactively modifiable. Conclusions Equipped with a new grid layout algorithm proposed here, LucidDraw serves as an auxiliary network analysis tool capable of visualizing complex biological networks in near real-time with controllable layout styles and drawing details. The framework of the algorithm enables easy incorporation of extra biological information, if available, to influence the output layouts with predefined node grouping features.
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
SIMULATING BIOCHEMICAL SIGNALING NETWORKS IN COMPLEX MOVING GEOMETRIES.
Strychalski, Wanda; Adalsteinsson, David; Elston, Timothy C
2010-01-01
Signaling networks regulate cellular responses to environmental stimuli through cascades of protein interactions. External signals can trigger cells to polarize and move in a specific direction. During migration, spatially localized activity of proteins is maintained. To investigate the effects of morphological changes on intracellular signaling, we developed a numerical scheme consisting of a cut cell finite volume spatial discretization coupled with level set methods to simulate the resulting advection-reaction-diffusion system. We then apply the method to several biochemical reaction networks in changing geometries. We found that a Turing instability can develop exclusively by cell deformations that maintain constant area. For a Turing system with a geometry-dependent single or double peak solution, simulations in a dynamically changing geometry suggest that a single peak solution is the only stable one, independent of the oscillation frequency. The method is also applied to a model of a signaling network in a migrating fibroblast. PMID:24086102
A feedback approach to bifurcation analysis in biochemical networks with many parameters
Waldherr, Steffen; Allgower, Frank
2007-01-01
Feedback circuits in biochemical networks which underly cellular signaling pathways are important elements in creating complex behavior. A specific aspect thereof is how stability of equilibrium points depends on model parameters. For biochemical networks, which are modelled using many parameters, it is typically very difficult to estimate the influence of parameters on stability. Finding parameters which result in a change in stability is a key step for a meaningful bifurcation analysis. We ...
Turing-Hopf instability in biochemical reaction networks arising from pairs of subnetworks.
Mincheva, Maya; Roussel, Marc R
2012-11-01
Network conditions for Turing instability in biochemical systems with two biochemical species are well known and involve autocatalysis or self-activation. On the other hand general network conditions for potential Turing instabilities in large biochemical reaction networks are not well developed. A biochemical reaction network with any number of species where only one species moves is represented by a simple digraph and is modeled by a reaction-diffusion system with non-mass action kinetics. A graph-theoretic condition for potential Turing-Hopf instability that arises when a spatially homogeneous equilibrium loses its stability via a single pair of complex eigenvalues is obtained. This novel graph-theoretic condition is closely related to the negative cycle condition for oscillations in ordinary differential equation models and its generalizations, and requires the existence of a pair of subnetworks, each containing an even number of positive cycles. The technique is illustrated with a double-cycle Goodwin type model. PMID:22698892
In silico evolution of biochemical networks
Francois, Paul
2010-03-01
We use computational evolution to select models of genetic networks that can be built from a predefined set of parts to achieve a certain behavior. Selection is made with the help of a fitness defining biological functions in a quantitative way. This fitness has to be specific to a process, but general enough to find processes common to many species. Computational evolution favors models that can be built by incremental improvements in fitness rather than via multiple neutral steps or transitions through less fit intermediates. With the help of these simulations, we propose a kinetic view of evolution, where networks are rapidly selected along a fitness gradient. This mathematics recapitulates Darwin's original insight that small changes in fitness can rapidly lead to the evolution of complex structures such as the eye, and explain the phenomenon of convergent/parallel evolution of similar structures in independent lineages. We will illustrate these ideas with networks implicated in embryonic development and patterning of vertebrates and primitive insects.
Chemical reaction network approaches to Biochemical Systems Theory.
Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R
2015-11-01
This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. PMID:26363083
田允; 黄继云; 王锐; 陶蓉蓉; 卢应梅; 廖美华; 陆楠楠; 李静; 芦博; 韩峰
2012-01-01
Autism is a highly heritable neurodevelopmental condition characterized by impaired social interaction and communication. However, the role of synaptic dysfunction during development of autism remains unclear. In the present study, we address the alterations of biochemical signaling in hippocampal network following induction of the autism in experimental animals. Here, the an- imal disease model and DNA array being used to investigate the differences in transcriptome or- ganization between autistic and normal brain by gene co--expression network analysis.
Insights into the organization of biochemical regulatory networks using graph theory analyses.
Ma'ayan, Avi
2009-02-27
Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks. PMID:18940806
An effective method for computing the noise in biochemical networks
Zhang, Jiajun; Nie, Qing; He, Miao; Zhou, Tianshou
2013-02-01
We present a simple yet effective method, which is based on power series expansion, for computing exact binomial moments that can be in turn used to compute steady-state probability distributions as well as the noise in linear or nonlinear biochemical reaction networks. When the method is applied to representative reaction networks such as the ON-OFF models of gene expression, gene models of promoter progression, gene auto-regulatory models, and common signaling motifs, the exact formulae for computing the intensities of noise in the species of interest or steady-state distributions are analytically given. Interestingly, we find that positive (negative) feedback does not enlarge (reduce) noise as claimed in previous works but has a counter-intuitive effect and that the multi-OFF (or ON) mechanism always attenuates the noise in contrast to the common ON-OFF mechanism and can modulate the noise to the lowest level independently of the mRNA mean. Except for its power in deriving analytical expressions for distributions and noise, our method is programmable and has apparent advantages in reducing computational cost.
Unveiling the hidden structure of complex stochastic biochemical networks
Valleriani, Angelo; Li, Xin; Kolomeisky, Anatoly B.
2014-02-01
Complex Markov models are widely used and powerful predictive tools to analyze stochastic biochemical processes. However, when the network of states is unknown, it is necessary to extract information from the data to partially build the network and estimate the values of the rates. The short-time behavior of the first-passage time distributions between two states in linear chains has been shown recently to behave as a power of time with an exponent equal to the number of intermediate states. For a general Markov model we derive the complete Taylor expansion of the first-passage time distribution between two arbitrary states. By combining algebraic methods and graph theory approaches it is shown that the first term of the Taylor expansion is determined by the shortest path from the initial state to the final state. When this path is unique, we prove that the coefficient of the first term can be written in terms of the product of the transition rates along the path. It is argued that the application of our results to first-return times may be used to estimate the dependence of rates on external parameters in experimentally measured time distributions.
Unveiling the hidden structure of complex stochastic biochemical networks
Complex Markov models are widely used and powerful predictive tools to analyze stochastic biochemical processes. However, when the network of states is unknown, it is necessary to extract information from the data to partially build the network and estimate the values of the rates. The short-time behavior of the first-passage time distributions between two states in linear chains has been shown recently to behave as a power of time with an exponent equal to the number of intermediate states. For a general Markov model we derive the complete Taylor expansion of the first-passage time distribution between two arbitrary states. By combining algebraic methods and graph theory approaches it is shown that the first term of the Taylor expansion is determined by the shortest path from the initial state to the final state. When this path is unique, we prove that the coefficient of the first term can be written in terms of the product of the transition rates along the path. It is argued that the application of our results to first-return times may be used to estimate the dependence of rates on external parameters in experimentally measured time distributions
Mean field interaction in biochemical reaction networks
Tembine, Hamidou
2011-09-01
In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits and illustrate them with numerical examples. © 2011 IEEE.
Trade-Offs in Delayed Information Transmission in Biochemical Networks
Mancini, F.; Marsili, M.; Walczak, A. M.
2016-03-01
In order to transmit biochemical signals, biological regulatory systems dissipate energy with concomitant entropy production. Additionally, signaling often takes place in challenging environmental conditions. In a simple model regulatory circuit given by an input and a delayed output, we explore the trade-offs between information transmission and the system's energetic efficiency. We determine the maximally informative network, given a fixed amount of entropy production and a delayed response, exploring both the case with and without feedback. We find that feedback allows the circuit to overcome energy constraints and transmit close to the maximum available information even in the dissipationless limit. Negative feedback loops, characteristic of shock responses, are optimal at high dissipation. Close to equilibrium positive feedback loops, known for their stability, become more informative. Asking how the signaling network should be constructed to best function in the worst possible environment, rather than an optimally tuned one or in steady state, we discover that at large dissipation the same universal motif is optimal in all of these conditions.
Transient amplification limits noise suppression in biochemical networks
Dixon, John; Lindemann, Anika; McCoy, Jonathan H.
2016-01-01
Cell physiology is orchestrated, on a molecular level, through complex networks of biochemical reactions. The propagation of random fluctuations through these networks can significantly impact cell behavior, raising challenging questions about how network design shapes the cell's ability to suppress or exploit these fluctuations. Here, drawing on insights from statistical physics, fluid dynamics, and systems biology, we explore how transient amplification phenomena arising from network connectivity naturally limit a biochemical system's ability to suppress small fluctuations around steady-state behaviors. We find that even a simple system consisting of two variables linked by a single interaction is capable of amplifying small fluctuations orders of magnitude beyond the levels predicted by linear stability theory. We also find that adding additional interactions can promote further amplification, even when these interactions implement classic design strategies known to suppress fluctuations. These results establish that transient amplification is an essential factor determining baseline noise levels in stable intracellular networks. Significantly, our analysis is not bound to specific systems or interaction mechanisms: we find that noise amplification is an emergent phenomenon found near steady states in any network containing sufficiently strong interactions, regardless of its form or function.
Finite time thermodynamic coupling in a biochemical network
Dasgupta, Anjan Kr
2014-01-01
The paper describes some thermodynamic constrains and relations in biochemical or metabolic network and provides a basis for entropy enthalpy compensation. Conventional definition of macroscopic forces and fluxes leads to a paradox namely, non-existence of positive efficiency of a chemically driven process. This paradox is resolved by deriving an appropriate definition of macroscopic force using the local balance equations. Entropy enthalpy compensation, whose thermodynamic basis is so far un...
Furusawa, Chikara; Kaneko, Kunihiko
2005-01-01
The evolutionary origin of universal statistics in biochemical reaction network is studied, to explain the power-law distribution of reaction links and the power-law distributions of chemical abundances. Using cell models with catalytic reaction network, we find evidence that the power-law distribution in abundances of chemicals emerges by the selection of cells with higher growth speeds. Through the further evolution, this inhomogeneity in chemical abundances is shown to be embedded in the d...
Feedback Regulation and Its Efficiency in Biochemical Networks
Kobayashi, Tetsuya J.; Yokota, Ryo; Aihara, Kazuyuki
2016-03-01
Intracellular biochemical networks fluctuate dynamically due to various internal and external sources of fluctuation. Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and harnesses noise and how information is transferred over apparently noisy intracellular networks. While substantial theoretical and experimental advancement on the decomposition of fluctuation was achieved for feedforward networks without any loop, we still lack a theoretical basis that can consistently extend such advancement to feedback networks. The main obstacle that hampers is the circulative propagation of fluctuation by feedback loops. In order to define the relevant quantity for the impact of feedback loops for fluctuation, disentanglement of the causally interlocked influences between the components is required. In addition, we also lack an approach that enables us to infer non-perturbatively the influence of the feedback to fluctuation in the same way as the dual reporter system does in the feedforward networks. In this work, we address these problems by extending the work on the fluctuation decomposition and the dual reporter system. For a single-loop feedback network with two components, we define feedback loop gain as the feedback efficiency that is consistent with the fluctuation decomposition for feedforward networks. Then, we clarify the relation of the feedback efficiency with the fluctuation propagation in an open-looped FF network. Finally, by extending the dual reporter system, we propose a conjugate feedback and feedforward system for estimating the feedback efficiency non-perturbatively only from the statistics of the system.
Chang Yu-Te
2008-11-01
Full Text Available Abstract Background Gene networks in nanoscale are of nonlinear stochastic process. Time delays are common and substantial in these biochemical processes due to gene transcription, translation, posttranslation protein modification and diffusion. Molecular noises in gene networks come from intrinsic fluctuations, transmitted noise from upstream genes, and the global noise affecting all genes. Knowledge of molecular noise filtering and biochemical process delay compensation in gene networks is crucial to understand the signal processing in gene networks and the design of noise-tolerant and delay-robust gene circuits for synthetic biology. Results A nonlinear stochastic dynamic model with multiple time delays is proposed for describing a gene network under process delays, intrinsic molecular fluctuations, and extrinsic molecular noises. Then, the stochastic biochemical processing scheme of gene regulatory networks for attenuating these molecular noises and compensating process delays is investigated from the nonlinear signal processing perspective. In order to improve the robust stability for delay toleration and noise filtering, a robust gene circuit for nonlinear stochastic time-delay gene networks is engineered based on the nonlinear robust H∞ stochastic filtering scheme. Further, in order to avoid solving these complicated noise-tolerant and delay-robust design problems, based on Takagi-Sugeno (T-S fuzzy time-delay model and linear matrix inequalities (LMIs technique, a systematic gene circuit design method is proposed to simplify the design procedure. Conclusion The proposed gene circuit design method has much potential for application to systems biology, synthetic biology and drug design when a gene regulatory network has to be designed for improving its robust stability and filtering ability of disease-perturbed gene network or when a synthetic gene network needs to perform robustly under process delays and molecular noises.
Tanase-Nicola, Sorin; Warren, Patrick B.; Wolde, Pieter Rein ten
2005-01-01
Understanding cell function requires an accurate description of how noise is transmitted through biochemical networks. We present an analytical result for the power spectrum of the output signal of a biochemical network that takes into account the correlations between the noise in the input signal (the extrinsic noise) and the noise in the reactions that constitute the network (the intrinsic noise). These correlations arise from the fact that the reactions by which biochemical signals are det...
Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations
Nemenman, Ilya [Los Alamos National Laboratory; Sinitsyn, Nikolai [Los Alamos National Laboratory; Hengartner, Nick [Los Alamos National Laboratory
2008-01-01
We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, follows from the stochastic path Integral representation of the cumulant generating function of reaction events. In applications with a small number of chemIcal reactions, It produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, Interpretable representation and can be used for coarse-grained numerical simulation schemes with a small computational complexity and yet high accuracy. As an example, we derive the coarse-grained description for a chain of biochemical reactions, and show that the coarse-grained and the microscopic simulations are in an agreement, but the coarse-gralned simulations are three orders of magnitude faster.
Model-Based Design of Biochemical Microreactors.
Elbinger, Tobias; Gahn, Markus; Neuss-Radu, Maria; Hante, Falk M; Voll, Lars M; Leugering, Günter; Knabner, Peter
2016-01-01
Mathematical modeling of biochemical pathways is an important resource in Synthetic Biology, as the predictive power of simulating synthetic pathways represents an important step in the design of synthetic metabolons. In this paper, we are concerned with the mathematical modeling, simulation, and optimization of metabolic processes in biochemical microreactors able to carry out enzymatic reactions and to exchange metabolites with their surrounding medium. The results of the reported modeling approach are incorporated in the design of the first microreactor prototypes that are under construction. These microreactors consist of compartments separated by membranes carrying specific transporters for the input of substrates and export of products. Inside the compartments of the reactor multienzyme complexes assembled on nano-beads by peptide adapters are used to carry out metabolic reactions. The spatially resolved mathematical model describing the ongoing processes consists of a system of diffusion equations together with boundary and initial conditions. The boundary conditions model the exchange of metabolites with the neighboring compartments and the reactions at the surface of the nano-beads carrying the multienzyme complexes. Efficient and accurate approaches for numerical simulation of the mathematical model and for optimal design of the microreactor are developed. As a proof-of-concept scenario, a synthetic pathway for the conversion of sucrose to glucose-6-phosphate (G6P) was chosen. In this context, the mathematical model is employed to compute the spatio-temporal distributions of the metabolite concentrations, as well as application relevant quantities like the outflow rate of G6P. These computations are performed for different scenarios, where the number of beads as well as their loading capacity are varied. The computed metabolite distributions show spatial patterns, which differ for different experimental arrangements. Furthermore, the total output of G6P
A MULTILAYER BIOCHEMICAL DRY DEPOSITION MODEL 2. MODEL EVALUATION
The multilayer biochemical dry deposition model (MLBC) described in the accompanying paper was tested against half-hourly eddy correlation data from six field sites under a wide range of climate conditions with various plant types. Modeled CO2, O3, SO2<...
A MULTILAYER BIOCHEMICAL DRY DEPOSITION MODEL 1. MODEL FORMULATION
A multilayer biochemical dry deposition model has been developed based on the NOAA Multilayer Model (MLM) to study gaseous exchanges between the soil, plants, and the atmosphere. Most of the parameterizations and submodels have been updated or replaced. The numerical integration ...
Collaborative networks: Reference modeling
L.M. Camarinha-Matos; H. Afsarmanesh
2008-01-01
Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of
Exact results for noise power spectra in linear biochemical reaction networks
Warren, Patrick B.; Tanase-Nicola, Sorin; Wolde, Pieter Rein ten
2005-01-01
We present a simple method for determining the exact noise power spectra in linear chemical reaction networks. We apply the method to networks which are representative of biochemical processes such as gene expression and signal detection. Our results clarify how noise is transmitted by signal detection motifs, and indicate how to coarse-grain networks by the elimination of fast reactions.
Pugacheva, E
2015-01-01
In the offered review some key issues of social network analysis are discussed. This is a brief summary of social network characteristics, models of network formation, and the network perspective. The aim of this overview is to contribute to interdisciplinary dialogue among researchers in physics, mathematics, sociology, who share a common interest in understanding the network phenomena.
Efficient Characterization of Parametric Uncertainty of Complex (Biochemical Networks.
Claudia Schillings
2015-08-01
Full Text Available Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.
A moment-convergence method for stochastic analysis of biochemical reaction networks
Zhang, Jiajun; Nie, Qing; Zhou, Tianshou
2016-05-01
Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.
Genetic programming-based approach to elucidate biochemical interaction networks from data.
Kandpal, Manoj; Kalyan, Chakravarthy Mynampati; Samavedham, Lakshminarayanan
2013-02-01
Biochemical systems are characterised by cyclic/reversible reciprocal actions, non-linear interactions and a mixed relationship structures (linear and non-linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive-based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming-based causality detection (GPCD) methodology which blends evolutionary computation-based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the 'interaction gaps' which were missed by other methods. PMID:23848052
Simplifying biochemical models with intermediate species
Feliu, Elisenda; Wiuf, Carsten
2013-01-01
canonical model that characterizes crucial dynamical properties, such as mono- and multistationarity and stability of steady states, of all models in the class. We show that if the core model does not have conservation laws, then the introduction of intermediates does not change the steady...
Casey, Fergal
2011-08-22
Abstract Background Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks. Results We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter. Conclusion We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.
A coupled mechano-biochemical model for bone adaptation
Klika, Václav; Pérez, M. A.; García-Aznar, J. M.; Maršík, F.; Doblaré, M.
2014-01-01
Roč. 69, 6-7 (2014), s. 1383-1429. ISSN 0303-6812 Institutional support: RVO:61388998 Keywords : mechano-biochemical model * bone remodelling * BMU Subject RIV: BJ - Thermodynamics Impact factor: 1.846, year: 2014 http://link.springer.com/article/10.1007%2Fs00285-013-0736-9
Model Based Monitoring and Control of Chemical and Biochemical Processes
Huusom, Jakob Kjøbsted
This presentation will give an overview of the work performed at the department of Chemical and Biochemical Engineering related to process control. A research vision is formulated and related to a number of active projects at the department. In more detail a project describing model estimation and...
ADVANCES ON BILINEAR MODELING OF BIOCHEMICAL BATCH PROCESSES
GONZÁLEZ MARTÍNEZ, JOSÉ MARÍA
2015-01-01
[EN] This thesis is aimed to study the implications of the statistical modeling approaches proposed for the bilinear modeling of batch processes, develop new techniques to overcome some of the problems that have not been yet solved and apply them to data of biochemical processes. The study, discussion and development of the new methods revolve around the four steps of the modeling cycle, from the alignment, preprocessing and calibration of batch data to the monitoring of batches trajectories....
Andersen, Kasper Winther
Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...
Biochemical correlates in an animal model of depression
A valid animal model of depression was used to explore specific adrenergic receptor differences between rats exhibiting aberrant behavior and control groups. Preliminary experiments revealed a distinct upregulation of hippocampal beta-receptors (as compared to other brain regions) in those animals acquiring a response deficit as a result of exposure to inescapable footshock. Concurrent studies using standard receptor binding techniques showed no large changes in the density of alpha-adrenergic, serotonergic, or dopaminergic receptor densities. This led to the hypothesis that the hippocampal beta-receptor in responses deficient animals could be correlated with the behavioral changes seen after exposure to the aversive stimulus. Normalization of the behavior through the administration of antidepressants could be expected to reverse the biochemical changes if these are related to the mechanism of action of antidepressant drugs. This study makes three important points: (1) there is a relevant biochemical change in the hippocampus of response deficient rats which occurs in parallel to a well-defined behavior, (2) the biochemical and behavioral changes are normalized by antidepressant treatments exhibiting both serotonergic and adrenergic mechanisms of action, and (3) the mode of action of antidepressants in this model is probably a combination of serotonergic and adrenergic influences modulating the hippocampal beta-receptor. These results are discussed in relation to anatomical and biochemical aspects of antidepressant action
Modeling Epidemic Network Failures
Ruepp, Sarah Renée; Fagertun, Anna Manolova
2013-01-01
This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
Modelling biochemical reaction systems by stochastic differential equations with reflection.
Niu, Yuanling; Burrage, Kevin; Chen, Luonan
2016-05-01
In this paper, we gave a new framework for modelling and simulating biochemical reaction systems by stochastic differential equations with reflection not in a heuristic way but in a mathematical way. The model is computationally efficient compared with the discrete-state Markov chain approach, and it ensures that both analytic and numerical solutions remain in a biologically plausible region. Specifically, our model mathematically ensures that species numbers lie in the domain D, which is a physical constraint for biochemical reactions, in contrast to the previous models. The domain D is actually obtained according to the structure of the corresponding chemical Langevin equations, i.e., the boundary is inherent in the biochemical reaction system. A variant of projection method was employed to solve the reflected stochastic differential equation model, and it includes three simple steps, i.e., Euler-Maruyama method was applied to the equations first, and then check whether or not the point lies within the domain D, and if not perform an orthogonal projection. It is found that the projection onto the closure D¯ is the solution to a convex quadratic programming problem. Thus, existing methods for the convex quadratic programming problem can be employed for the orthogonal projection map. Numerical tests on several important problems in biological systems confirmed the efficiency and accuracy of this approach. PMID:26920245
Hysteresis-driven structure formation in biochemical networks
Klein
1998-09-21
A mechanism of structure formation, based on hysteresis behaviour is presented. A bisubstrate kinetic system with substrate inhibition, discussed previously in the context of Turing structure formation, may show hysteresis behaviour, when embedded in a metabolic network: the system may possess multiple steady states and may be switched from one stable fixpoint to the other. When cells containing this type of system are diffusively coupled, under certain conditions patterns result, which, as is demonstrated, are not of the Turing type. The main difference to diffusion-driven (Turing) structures is the fact that the hysteresis-driven patterns emerge under diffusive conditions, under which both the homogeneous and the asymmetrical steady state is stable. The resulting special properties and biological implications are discussed.Copyright 1998 Academic Press Limited PMID:9778438
Boolean network model predicts knockout mutant phenotypes of fission yeast.
Maria I Davidich
Full Text Available BOOLEAN NETWORKS (OR: networks of switches are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.
Sensitivity analysis for computational models of biochemical systems
Maj,
2014-01-01
Systems biology is an integrated area of science which aims at the analysis of biochemical systems using an holistic perspective. In this context, sensitivity analysis, a technique studying how the output variation of a computational model can be associated to its input state plays a pivotal role. In the thesis it is described how to properly apply the different sensitivity analysis techniques according to the specific case study (i.e., continuous deterministic rather than discrete stochastic...
Evaluation and Development of Methods for Identification of Biochemical Networks
Jauhiainen, Alexandra
2005-01-01
Systems biology is an area concerned with understanding biology on a systems level, where structure and dynamics of the system is in focus. Knowledge about structure and dynamics of biological systems is fundamental information about cells and interactions within cells and also play an increasingly important role in medical applications. System identification deals with the problem of constructing a model of a system from data and an extensive theory of particularly identification of linear ...
Biochemical Models for S-Rnase-Based Self-Incompatibility
Zhi-Hua Hua; Allison Fields; Teh-hui Kao
2008-01-01
S-RNase-based self-incompatibility (SI) is a genetically determined self/non-self-recognition process employed by many flowering plant species to prevent inbreeding and promote outcrosses.For the Plantaginaceae,Rosa-ceae and Solanaceae,it is now known that S-RNase and S-Iocu F-box(two multiple allelic genes at the S-locus)determine the female and male specificity,respectively,during SI interactions.However,how allelic products of these two genes interact inside pollen tubes to result in specific growth inhibition of self-pollen tubes remains to be investigated.Here,we review all the previously proposed biochemical models and discuss whether their predictions are consistent with all SI phenomena,including competitive jnteraction where SI breaks down in pollen that carries two different pollen 5-alleles.We also discuss these models in Iight of the recent findings of compartmentalization of S-RNases in both incompatible and compatible pollen tubes.Lastly,we summarize the results from our recent biochemical studies of PiSLF(Petunia inflata SLF)and S-RNase.and present a new model for the biochemical mechanism of SI in the Solanaceae.The tenet of this model is that a PiSLF preferentially interacts with its non-self S-RNases in the cytoplasm of a pollen tube to result in the assembly of an E3-like complex,which then mediates ubiquitination and degradation of non-self S-RNases through the ubiquitin-26S proteasome pathway.This model can explain all SI phenomena and,at the same time,has raised new questions for further study.
Relationship between cellular response models and biochemical mechanisms
In most cellular response experiments, survival reflects the kinetics of a variety of damage and repair processes. Unfortunately, biochemical studies of molecular repair deal with mechanisms which cannot be readily correlated with these kinetic observations. The difference in these approaches sometimes leads to confusion over terms such as potentially-lethal and sublethal damage. These terms were introduced with operation definitions, derived from kinetic studies of cell survival, but some researchers have since attempted to associate them with specific biochemical mechanisms. Consequently, the terms are often used in totally different ways be different investigators. The use of carefully constructed models originating either out of assumptions based on mechanisms, or on kinetics, can be used to design experiments to eliminate some alternative kinetic schemes. In turn, some mechanisms may also be eliminated, resulting in a reduction in the number of mechanisms which must be investigated biochemically. One must take advantage of a wide range of specialized radiation procedures in order to accomplish this. Examples of the use of such specialized experimental designs, which have led to a more detailed understanding of the kinetics of both algal and mammalian cell responses, are discussed
Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)
2001-01-01
Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.
A general method for modeling biochemical and biomedical response
Ortiz, Roberto; Lerd Ng, Jia; Hughes, Tyler; Abou Ghantous, Michel; Bouhali, Othmane; Arredouani, Abdelilah; Allen, Roland
2012-10-01
The impressive achievements of biomedical science have come mostly from experimental research with human subjects, animal models, and sophisticated laboratory techniques. Additionally, theoretical chemistry has been a major aid in designing new drugs. Here we introduce a method which is similar to others already well known in theoretical systems biology, but which specifically addresses biochemical changes as the human body responds to medical interventions. It is common in systems biology to use first-order differential equations to model the time evolution of various chemical concentrations, and we as physicists can make a significant impact through designing realistic models and then solving the resulting equations. Biomedical research is rapidly advancing, and the technique presented in this talk can be applied in arbitrarily large models containing tens, hundreds, or even thousands of interacting species, to determine what beneficial effects and side effects may result from pharmaceuticals or other medical interventions.
Lisa M. Bishop
2010-09-01
Full Text Available We develop the stochastic, chemical master equation as a unifying approach to the dynamics of biochemical reaction systems in a mesoscopic volume under a living environment. A living environment provides a continuous chemical energy input that sustains the reaction system in a nonequilibrium steady state with concentration fluctuations. We discuss the linear, unimolecular single-molecule enzyme kinetics, phosphorylation-dephosphorylation cycle (PdPC with bistability, and network exhibiting oscillations. Emphasis is paid to the comparison between the stochastic dynamics and the prediction based on the traditional approach based on the Law of Mass Action. We introduce the difference between nonlinear bistability and stochastic bistability, the latter has no deterministic counterpart. For systems with nonlinear bistability, there are three different time scales: (a individual biochemical reactions, (b nonlinear network dynamics approaching to attractors, and (c cellular evolution. For mesoscopic systems with size of a living cell, dynamics in (a and (c are stochastic while that with (b is dominantly deterministic. Both (b and (c are emergent properties of a dynamic biochemical network; We suggest that the (c is most relevant to major cellular biochemical processes such as epi-genetic regulation, apoptosis, and cancer immunoediting. The cellular evolution proceeds with transitions among the attractors of (b in a “punctuated equilibrium” manner.
Qian, Hong; Bishop, Lisa M
2010-01-01
We develop the stochastic, chemical master equation as a unifying approach to the dynamics of biochemical reaction systems in a mesoscopic volume under a living environment. A living environment provides a continuous chemical energy input that sustains the reaction system in a nonequilibrium steady state with concentration fluctuations. We discuss the linear, unimolecular single-molecule enzyme kinetics, phosphorylation-dephosphorylation cycle (PdPC) with bistability, and network exhibiting oscillations. Emphasis is paid to the comparison between the stochastic dynamics and the prediction based on the traditional approach based on the Law of Mass Action. We introduce the difference between nonlinear bistability and stochastic bistability, the latter has no deterministic counterpart. For systems with nonlinear bistability, there are three different time scales: (a) individual biochemical reactions, (b) nonlinear network dynamics approaching to attractors, and (c) cellular evolution. For mesoscopic systems with size of a living cell, dynamics in (a) and (c) are stochastic while that with (b) is dominantly deterministic. Both (b) and (c) are emergent properties of a dynamic biochemical network; We suggest that the (c) is most relevant to major cellular biochemical processes such as epi-genetic regulation, apoptosis, and cancer immunoediting. The cellular evolution proceeds with transitions among the attractors of (b) in a "punctuated equilibrium" manner. PMID:20957107
Thomas Philipp
2012-05-01
Full Text Available Abstract 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
Jao, Tun; Schröter, Manuel; Chen, Chao-Long; Cheng, Yu-Fan; Lo, Chun-Yi Zac; Chou, Kun-Hsien; Patel, Ameera X; Lin, Wei-Che; Lin, Ching-Po; Bullmore, Edward T
2015-11-15
Functional properties of the brain may be associated with changes in complex brain networks. However, little is known about how properties of large-scale functional brain networks may be altered stepwise in patients with disturbance of consciousness, e.g., an encephalopathy. We used resting-state fMRI data on patients suffering from various degrees of hepatic encephalopathy (HE) to explore how topological and spatial network properties of functional brain networks changed at different cognitive and consciousness states. Severity of HE was measured clinically and by neuropsychological tests. Fifty-eight non-alcoholic liver cirrhosis patients and 62 normal controls were studied. Patients were subdivided into liver cirrhosis with no outstanding HE (NoHE, n=23), minimal HE with cognitive impairment only detectable by neuropsychological tests (MHE, n=28), and clinically overt HE (OHE, n=7). From the earliest stage, the NoHE, functional brain networks were progressively more random, less clustered, and less modular. Since the intermediate stage (MHE), increased ammonia level was accompanied by concomitant exponential decay of mean connectivity strength, especially in the primary cortical areas and midline brain structures. Finally, at the OHE stage, there were radical reorganization of the topological centrality-i.e., the relative importance-of the hubs and reorientation of functional connections between nodes. In summary, this study illustrated progressively greater abnormalities in functional brain network organization in patients with clinical and biochemical evidence of more severe hepatic encephalopathy. The early-than-expected brain network dysfunction in cirrhotic patients suggests that brain functional connectivity and network analysis may provide useful and complementary biomarkers for more aggressive and earlier intervention of hepatic encephalopathy. Moreover, the stepwise deterioration of functional brain networks in HE patients may suggest that hierarchical
A Network-of-Networks Model for Electrical Infrastructure Networks
Halappanavar, Mahantesh; Hogan, Emilie; Duncan, Daniel; Zhenyu,; Huang,; Hines, Paul D H
2015-01-01
Modeling power transmission networks is an important area of research with applications such as vulnerability analysis, study of cascading failures, and location of measurement devices. Graph-theoretic approaches have been widely used to solve these problems, but are subject to several limitations. One of the limitations is the ability to model a heterogeneous system in a consistent manner using the standard graph-theoretic formulation. In this paper, we propose a {\\em network-of-networks} approach for modeling power transmission networks in order to explicitly incorporate heterogeneity in the model. This model distinguishes between different components of the network that operate at different voltage ratings, and also captures the intra and inter-network connectivity patterns. By building the graph in this fashion we present a novel, and fundamentally different, perspective of power transmission networks. Consequently, this novel approach will have a significant impact on the graph-theoretic modeling of powe...
Extracting protein regulatory networks with graphical models.
Grzegorczyk, Marco
2007-09-01
During the last decade the development of high-throughput biotechnologies has resulted in the production of exponentially expanding quantities of biological data, such as genomic and proteomic expression data. One fundamental problem in systems biology is to learn the architecture of biochemical pathways and regulatory networks in an inferential way from such postgenomic data. Along with the increasing amount of available data, a lot of novel statistical methods have been developed and proposed in the literature. This article gives a non-mathematical overview of three widely used reverse engineering methods, namely relevance networks, graphical Gaussian models, and Bayesian networks, whereby the focus is on their relative merits and shortcomings. In addition the reverse engineering results of these graphical methods on cytometric protein data from the RAF-signalling network are cross-compared via AUROC scatter plots. PMID:17893851
A microfluidic platform for controlled biochemical stimulation of twin neuronal networks.
Biffi, Emilia; Piraino, Francesco; Pedrocchi, Alessandra; Fiore, Gianfranco B; Ferrigno, Giancarlo; Redaelli, Alberto; Menegon, Andrea; Rasponi, Marco
2012-06-01
Spatially and temporally resolved delivery of soluble factors is a key feature for pharmacological applications. In this framework, microfluidics coupled to multisite electrophysiology offers great advantages in neuropharmacology and toxicology. In this work, a microfluidic device for biochemical stimulation of neuronal networks was developed. A micro-chamber for cell culturing, previously developed and tested for long term neuronal growth by our group, was provided with a thin wall, which partially divided the cell culture region in two sub-compartments. The device was reversibly coupled to a flat micro electrode array and used to culture primary neurons in the same microenvironment. We demonstrated that the two fluidically connected compartments were able to originate two parallel neuronal networks with similar electrophysiological activity but functionally independent. Furthermore, the device allowed to connect the outlet port to a syringe pump and to transform the static culture chamber in a perfused one. At 14 days invitro, sub-networks were independently stimulated with a test molecule, tetrodotoxin, a neurotoxin known to block action potentials, by means of continuous delivery. Electrical activity recordings proved the ability of the device configuration to selectively stimulate each neuronal network individually. The proposed microfluidic approach represents an innovative methodology to perform biological, pharmacological, and electrophysiological experiments on neuronal networks. Indeed, it allows for controlled delivery of substances to cells, and it overcomes the limitations due to standard drug stimulation techniques. Finally, the twin network configuration reduces biological variability, which has important outcomes on pharmacological and drug screening. PMID:22655017
Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling.
Brown, Sherry-Ann; Moraru, Ion I; Schaff, James C; Loew, Leslie M
2011-10-01
Because of its highly branched dendrite, the Purkinje neuron requires significant computational resources if coupled electrical and biochemical activity are to be simulated. To address this challenge, we developed a scheme for reducing the geometric complexity; while preserving the essential features of activity in both the soma and a remote dendritic spine. We merged our previously published biochemical model of calcium dynamics and lipid signaling in the Purkinje neuron, developed in the Virtual Cell modeling and simulation environment, with an electrophysiological model based on a Purkinje neuron model available in NEURON. A novel reduction method was applied to the Purkinje neuron geometry to obtain a model with fewer compartments that is tractable in Virtual Cell. Most of the dendritic tree was subject to reduction, but we retained the neuron's explicit electrical and geometric features along a specified path from spine to soma. Further, unlike previous simplification methods, the dendrites that branch off along the preserved explicit path are retained as reduced branches. We conserved axial resistivity and adjusted passive properties and active channel conductances for the reduction in surface area, and cytosolic calcium for the reduction in volume. Rallpacks are used to validate the reduction algorithm and show that it can be generalized to other complex neuronal geometries. For the Purkinje cell, we found that current injections at the soma were able to produce similar trains of action potentials and membrane potential propagation in the full and reduced models in NEURON; the reduced model produces identical spiking patterns in NEURON and Virtual Cell. Importantly, our reduced model can simulate communication between the soma and a distal spine; an alpha function applied at the spine to represent synaptic stimulation gave similar results in the full and reduced models for potential changes associated with both the spine and the soma. Finally, we combined
Inverse fracture network modelling
The basic problem in analyzing flow and transport in fractured rock is that the flow may be largely governed by a poorly connected network of fractures. Flow in such a system cannot be modeled with traditional modelling techniques. Fracture network models also have a limitation, in that they are based on geological data on fracture geometry even though it is known that only a small portion of fractures observed is hydraulically active. This paper discusses a new technique developed for treating the problem as well as presents a modelling example carried out to apply it. The approach is developed in Lawrence Berkeley Laboratory and it treats the fracture zone as an 'equivalent discontinuum'. The discontinuous nature of the problem is represented through flow on a partially filled lattice. An equivalent discontinuum model is constructed by adding and removing conductive elements through a statistical inverse technique called 'simulated annealing'. The fracture network model is 'annealed' until the modified systems behaves like the observed. The further development of the approach continues at LBL and in a joint LBL/VTT collaboration project the possibilities to apply the technique in Finnish conditions are investigated
Cheali, Peam; Gernaey, Krist; Sin, Gürkan
2013-01-01
This study presents the development of an expanded biorefinery processing network for producing biofuels that combines biochemical and thermochemical conversion platforms. The expanded network is coupled to a framework that uses a superstructure based optimization approach to generate and compare...... for identifying at early stage optimal biorefinery concept with respect to technical, economic and environmental criteria....
Exact hybrid particle/population simulation of rule-based models of biochemical systems.
Justin S Hogg
2014-04-01
Full Text Available Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that
Exact hybrid particle/population simulation of rule-based models of biochemical systems.
Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R
2014-04-01
Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings
SBMLsqueezer: A CellDesigner plug-in to generate kinetic rate equations for biochemical networks
Schröder Adrian
2008-04-01
Full Text Available Abstract Background The development of complex biochemical models has been facilitated through the standardization of machine-readable representations like SBML (Systems Biology Markup Language. This effort is accompanied by the ongoing development of the human-readable diagrammatic representation SBGN (Systems Biology Graphical Notation. The graphical SBML editor CellDesigner allows direct translation of SBGN into SBML, and vice versa. For the assignment of kinetic rate laws, however, this process is not straightforward, as it often requires manual assembly and specific knowledge of kinetic equations. Results SBMLsqueezer facilitates exactly this modeling step via automated equation generation, overcoming the highly error-prone and cumbersome process of manually assigning kinetic equations. For each reaction the kinetic equation is derived from the stoichiometry, the participating species (e.g., proteins, mRNA or simple molecules as well as the regulatory relations (activation, inhibition or other modulations of the SBGN diagram. Such information allows distinctions between, for example, translation, phosphorylation or state transitions. The types of kinetics considered are numerous, for instance generalized mass-action, Hill, convenience and several Michaelis-Menten-based kinetics, each including activation and inhibition. These kinetics allow SBMLsqueezer to cover metabolic, gene regulatory, signal transduction and mixed networks. Whenever multiple kinetics are applicable to one reaction, parameter settings allow for user-defined specifications. After invoking SBMLsqueezer, the kinetic formulas are generated and assigned to the model, which can then be simulated in CellDesigner or with external ODE solvers. Furthermore, the equations can be exported to SBML, LaTeX or plain text format. Conclusion SBMLsqueezer considers the annotation of all participating reactants, products and regulators when generating rate laws for reactions. Thus, for
Qian Hong
2008-05-01
Full Text Available Abstract Background: Several approaches, including metabolic control analysis (MCA, flux balance analysis (FBA, correlation metric construction (CMC, and biochemical circuit theory (BCT, have been developed for the quantitative analysis of complex biochemical networks. Here, we present a comprehensive theory of linear analysis for nonequilibrium steady-state (NESS biochemical reaction networks that unites these disparate approaches in a common mathematical framework and thermodynamic basis. Results: In this theory a number of relationships between key matrices are introduced: the matrix A obtained in the standard, linear-dynamic-stability analysis of the steady-state can be decomposed as A = SRT where R and S are directly related to the elasticity-coefficient matrix for the fluxes and chemical potentials in MCA, respectively; the control-coefficients for the fluxes and chemical potentials can be written in terms of RT BS and ST BS respectively where matrix B is the inverse of A; the matrix S is precisely the stoichiometric matrix in FBA; and the matrix eAt plays a central role in CMC. Conclusion: One key finding that emerges from this analysis is that the well-known summation theorems in MCA take different forms depending on whether metabolic steady-state is maintained by flux injection or concentration clamping. We demonstrate that if rate-limiting steps exist in a biochemical pathway, they are the steps with smallest biochemical conductances and largest flux control-coefficients. We hypothesize that biochemical networks for cellular signaling have a different strategy for minimizing energy waste and being efficient than do biochemical networks for biosynthesis. We also discuss the intimate relationship between MCA and biochemical systems analysis (BSA.
Coevolutionary modeling in network formation
Al-Shyoukh, Ibrahim
2014-12-03
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
Cellular automata modelling of biomolecular networks dynamics.
Bonchev, D; Thomas, S; Apte, A; Kier, L B
2010-01-01
The modelling of biological systems dynamics is traditionally performed by ordinary differential equations (ODEs). When dealing with intracellular networks of genes, proteins and metabolites, however, this approach is hindered by network complexity and the lack of experimental kinetic parameters. This opened the field for other modelling techniques, such as cellular automata (CA) and agent-based modelling (ABM). This article reviews this emerging field of studies on network dynamics in molecular biology. The basics of the CA technique are discussed along with an extensive list of related software and websites. The application of CA to networks of biochemical reactions is exemplified in detail by the case studies of the mitogen-activated protein kinase (MAPK) signalling pathway, the FAS-ligand (FASL)-induced and Bcl-2-related apoptosis. The potential of the CA method to model basic pathways patterns, to identify ways to control pathway dynamics and to help in generating strategies to fight with cancer is demonstrated. The different line of CA applications presented includes the search for the best-performing network motifs, an analysis of importance for effective intracellular signalling and pathway cross-talk. PMID:20373215
Hierarchical graphs for rule-based modeling of biochemical systems
Hu Bin
2011-02-01
Full Text Available Abstract Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal of an edge represents a class of association (dissociation reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for
Goldberg, S R; Evans, T S
2014-01-01
The distribution of the number of academic publications as a function of citation count for a given year is remarkably similar from year to year. We measure this similarity as a width of the distribution and find it to be approximately constant from year to year. We show that simple citation models fail to capture this behaviour. We then provide a simple three parameter citation network model using a mixture of local and global search processes which can reproduce the correct distribution over time. We use the citation network of papers from the hep-th section of arXiv to test our model. For this data, around 20% of citations use global information to reference recently published papers, while the remaining 80% are found using local searches. We note that this is consistent with other studies though our motivation is very different from previous work. Finally, we also find that the fluctuations in the size of an academic publication's bibliography is important for the model. This is not addressed in most mode...
A new computational method to split large biochemical networks into coherent subnets
Verwoerd Wynand S
2011-02-01
Full Text Available Abstract Background Compared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation. The method proposed here (Netsplitter allows the user to control separator selection. It combines local connection degree partitioning with global connectivity derived from random walks on the network, to produce a more even distribution of subnetwork sizes. Partitioning is performed progressively and the interactive visual matrix presentation used allows the user considerable control over the process, while incorporating special strategies to maintain the network integrity and minimise the information loss due to partitioning. Results Partitioning of a genome scale network of 1348 metabolites and 1468 reactions for Arabidopsis thaliana encapsulates 66% of the network into 10 medium sized subnets. Applied to the flavonoid subnetwork extracted in this way, it is shown that Netsplitter separates this naturally into four subnets with recognisable functionality, namely synthesis of lignin precursors, flavonoids, coumarin and benzenoids. A quantitative quality measure called efficacy is constructed and shows that the new method gives improved partitioning for several metabolic networks, including bacterial, plant and mammal species. Conclusions For the examples studied the Netsplitter method is a considerable improvement on the performance of connection degree partitioning, giving a better balance of
A Network Synthesis Model for Generating Protein Interaction Network Families
Sayed Mohammad Ebrahim Sahraeian; Byung-Jun Yoon
2012-01-01
In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model ca...
Network model with structured nodes
Frisco, Pierluigi
2011-08-01
We present a network model in which words over a specific alphabet, called structures, are associated to each node and undirected edges are added depending on some distance measure between different structures. This model shifts the underlying principle of network generation from a purely mathematical one to an information-based one. It is shown how this model differs from the Barábasi-Albert and duplication models and how it can generate networks with topological features similar to biological networks: power law degree distribution, low average path length, clustering coefficient independent from the network size, etc. Two biological networks: S. cerevisiae gene network and E. coli protein-protein interaction network, are replicated using this model.
Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong
2016-07-01
This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.
Tewari, Ashuthosh; Gamito, Eduard J; Crawford, E David; Menon, Mani
2004-03-01
A number of new predictive modeling techniques have emerged in the past several years. These methods, which have been developed in fields such as artificial intelligence research, engineering, and meteorology, are now being applied to problems in medicine with promising results. This review outlines our recent work with use of selected advanced techniques such as artificial neural networks, genetic algorithms, and propensity scoring to develop useful models for estimating the risk of biochemical recurrence and long-term survival in men with clinically localized prostate cancer. In addition, we include a description of our efforts to develop a comprehensive prostate cancer database that, along with these novel modeling techniques, provides a powerful research tool that allows for the stratification of risk for treatment failure and survival by such factors as age, race, and comorbidities. Clinical and pathologic data from 1400 patients were used to develop the biochemical recurrence model. The area under the receiver operating characteristic curve for this model was 0.83, with a sensitivity of 85% and specificity of 74%. For the survival model, data from 6149 men were used. Our analysis indicated that age, income, and comorbidities had a statistically significant impact on survival. The effect of race did not reach statistical significance in this regard. The C index value for the model was 0.69 for overall survival. We conclude that these methods, along with a comprehensive database, allow for the development of models that provide estimates of treatment failure risk and survival probability that are more meaningful and clinically useful than those previously developed. PMID:15072605
Modeling biochemical transformation processes and information processing with Narrator
Palfreyman Niall M
2007-03-01
Full Text Available Abstract Background Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs, which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. Results Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. Conclusion Narrator is a
A neighbourhood evolving network model
Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development
Mining and modeling character networks
Bonato, Anthony; Elenberg, Ethan R; Gleich, David F; Hou, Yangyang
2016-01-01
We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of beveridge, we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http://moviegalaxies.com/ database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we ...
St Laurent Georges
2010-03-01
Full Text Available Abstract Background Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors. Methods In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology. Results Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4
Kentaro Inoue
Full Text Available BACKGROUND: For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. RESULTS: We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. CONCLUSIONS: Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.
Modeling Dynamics of Information Networks
Rosvall, Martin; Sneppen, Kim
2003-01-01
We propose an information-based model for network dynamics in which imperfect information leads to networks where the different vertices have widely different number of edges to other vertices, and where the topology has hierarchical features. The possibility to observe scale free networks is linked to a minimally connected system where hubs remain dynamic.
Developing Personal Network Business Models
Saugstrup, Dan; Henten, Anders
2006-01-01
The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports on...
Development of a biochemical switching device: mathematical model.
Okamoto, M
1990-01-01
There are many examples of enzymes that share substrates or cofactors in a cyclic manner. Techniques have been developed that use cyclic enzyme systems to assay quantitatively small amounts of biochemical substances (cofactor, substrate), however, only a few studies of the control of these systems have been published. The author previously showed with computer simulations that cyclic enzyme systems have the reliability of ON-OFF types of operation (McCulloch-Pitts' neuronic equation) and the applicability for a switching circuit in a biocomputer. The switching time was inevitably determined in accordance with the difference in amount between two inputs of the system. A unique switching mechanism of cyclic enzyme systems (basic switching element) and the effects of excitatory stimuli on switching properties of the integrated biochemical switching system are demonstrated. PMID:2082931
Complex Networks in Psychological Models
Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.
We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.
Internet Network Resource Information Model
陈传峰; 李增智; 唐亚哲; 刘康平
2002-01-01
The foundation of any network management systens is a database that con-tains information about the network resources relevant to the management tasks. A networkinformation model is an abstraction of network resources, including both managed resources andmanaging resources. In the SNMP-based management framework, management information isdefined almost exclusively from a "device" viewpoint, namely, managing a network is equiva-lent to managing a collection of individual nodes. Aiming at making use of recent advances indistributed computing and in object-oriented analysis and design, the Internet management ar-chitecture can also be based on the Open Distributed Processing Reference Model (RM-ODP).The purpose of this article is to provide an Internet Network Resource Information Model.First, a layered management information architecture will be discussed. Then the Internetnetwork resource information model is presented. The information model is specified usingObject-Z.
Patterns of Stochastic Behavior in Dynamically Unstable High-Dimensional Biochemical Networks
Rosenfeld, Simon
2009-01-01
The question of dynamical stability and stochastic behavior of large biochemical networks is discussed. It is argued that stringent conditions of asymptotic stability have very little chance to materialize in a multidimensional system described by the differential equations of chemical kinetics. The reason is that the criteria of asymptotic stability (Routh-Hurwitz, Lyapunov criteria, Feinberg’s Deficiency Zero theorem) would impose the limitations of very high algebraic order on the kinetic rates and stoichiometric coefficients, and there are no natural laws that would guarantee their unconditional validity. Highly nonlinear, dynamically unstable systems, however, are not necessarily doomed to collapse, as a simple Jacobian analysis would suggest. It is possible that their dynamics may assume the form of pseudo-random fluctuations quite similar to a shot noise, and, therefore, their behavior may be described in terms of Langevin and Fokker-Plank equations. We have shown by simulation that the resulting pseudo-stochastic processes obey the heavy-tailed Generalized Pareto Distribution with temporal sequence of pulses forming the set of constituent-specific Poisson processes. Being applied to intracellular dynamics, these properties are naturally associated with burstiness, a well documented phenomenon in the biology of gene expression. PMID:19838330
Chen, Bor-Sen; Chen, Po-Wei
2009-12-01
Inherently, biochemical regulatory networks suffer from process delays, internal parametrical perturbations as well as external disturbances. Robustness is the property to maintain the functions of intracellular biochemical regulatory networks despite these perturbations. In this study, system and signal processing theories are employed for measurement of robust stability and filtering ability of linear and nonlinear time-delay biochemical regulatory networks. First, based on Lyapunov stability theory, the robust stability of biochemical network is measured for the tolerance of additional process delays and additive internal parameter fluctuations. Then the filtering ability of attenuating additive external disturbances is estimated for time-delay biochemical regulatory networks. In order to overcome the difficulty of solving the Hamilton Jacobi inequality (HJI), the global linearization technique is employed to simplify the measurement procedure by a simple linear matrix inequality (LMI) method. Finally, an example is given in silico to illustrate how to measure the robust stability and filtering ability of a nonlinear time-delay perturbative biochemical network. This robust stability and filtering ability measurement for biochemical network has potential application to synthetic biology, gene therapy and drug design. PMID:19788895
Telecommunications network modelling, planning and design
Evans, Sharon
2003-01-01
Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.
A new statistical biomechanics modeling of physical and biochemical parameters of bone strength
Soboyejo, A.B.O. [Ohio State Univ., Columbus, OH (United States). Dept. of Aerospace Engineering, Applied Mechanics and Aviation; Nestor, K.E. [Ohio State Univ., Wooster, OH (United States). Dept. of Animal Sciences
2001-07-01
New multiparameter biomechanics models were developed in this work for the characterization of bone strength, as functions of the major physical and biochemical parameters, which can contribute to mechanical properties of bone strength. Theoretical and experimental methods had been developed to model bone strength as functions of (a) the physical parameters and (b) the biochemical parameters, The choice of any particular methodology will depend on the availability of either the physical or biochemical parameters. Experimental data of compressive strength of tibia and femur bones of broiler chickens and turkeys together with their corresponding physical and biochemical parameters were collected and used as examples in this study. These data were used to validate the theoretical principles developed in this work. Useful practical applications of the statistical biomechanics principles developed in this study, particularly in the field of bone strength enhancement in turkeys and broiler chickens are discussed. Similar medical applications to human beings are also highlighted in the discussions. (orig.)
Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.
Claudia Schillings; Mikael Sunnåker; Jörg Stelling; Christoph Schwab
2015-01-01
Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternat...
Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks
Claudia Schillings; Mikael Sunnåker; Jörg Stelling; Christoph Schwab
2015-01-01
Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternat...
Investigating modularity in the analysis of process algebra models of biochemical systems
Ciocchetta, Federica; Hillston, Jane; 10.4204/EPTCS.19.4
2010-01-01
Compositionality is a key feature of process algebras which is often cited as one of their advantages as a modelling technique. It is certainly true that in biochemical systems, as in many other systems, model construction is made easier in a formalism which allows the problem to be tackled compositionally. In this paper we consider the extent to which the compositional structure which is inherent in process algebra models of biochemical systems can be exploited during model solution. In essence this means using the compositional structure to guide decomposed solution and analysis. Unfortunately the dynamic behaviour of biochemical systems exhibits strong interdependencies between the components of the model making decomposed solution a difficult task. Nevertheless we believe that if such decomposition based on process algebras could be established it would demonstrate substantial benefits for systems biology modelling. In this paper we present our preliminary investigations based on a case study of the phero...
Modeling of fluctuating reaction networks
Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Simplified models of biological networks.
Sneppen, Kim; Krishna, Sandeep; Semsey, Szabolcs
2010-01-01
The function of living cells is controlled by complex regulatory networks that are built of a wide diversity of interacting molecular components. The sheer size and intricacy of molecular networks of even the simplest organisms are obstacles toward understanding network functionality. This review discusses the achievements and promise of a bottom-up approach that uses well-characterized subnetworks as model systems for understanding larger networks. It highlights the interplay between the structure, logic, and function of various types of small regulatory circuits. The bottom-up approach advocates understanding regulatory networks as a collection of entangled motifs. We therefore emphasize the potential of negative and positive feedback, as well as their combinations, to generate robust homeostasis, epigenetics, and oscillations. PMID:20192769
Advances in theoretical models of network science
FANG Jin-qing; BI Qiao; LI Yong
2007-01-01
In this review article, we will summarize the main advances in network science investigated by the CIAE Group of Complex Network in this field. Several theoretical models of network science were proposed and their topological and dynamical properties are reviewed and compared with the other models. Our models mainly include a harmonious unifying hybrid preferential model, a large unifying hybrid network model, a quantum interference network, a hexagonal nanowire network, and a small-world network with the same degree. The models above reveal some new phenomena and findings, which are useful for deeply understanding and investigating complex networks and their applications.
Security models for heterogeneous networking.
Mapp, Glenford E.; Aiash, Mahdi; Lasebae, Aboubaker; Phan, Raphael
2010-01-01
Security for Next Generation Networks (NGNs) is an attractive topic for many research groups. The Y-Comm security group believes that a new security approach is needed to address the security challenges in 4G networks. This paper sheds light on our approach of providing security for the Y-Comm architecture as an example of 4G communication frameworks. Our approach proposes a four-layer security integrated module to protect data and three targeted security models to protect different network e...
A Multilayer Model of Computer Networks
Shchurov, Andrey A.
2015-01-01
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...
Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.
Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph
2015-08-01
Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design. PMID:26317784
SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks
Draeger, Andreas; Zielinski, Daniel C.; Keller, Roland;
2015-01-01
simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved...... during kinetic model construction would thus benefit from automated methods for rate law assignment. Results: We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the...... algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when...
Target-Centric Network Modeling
Mitchell, Dr. William L.; Clark, Dr. Robert M.
In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling, and...... collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...
Milias-Argeitis, Andreas; Engblom, Stefan; Bauer, Pavol; Khammash, Mustafa
2015-12-01
Nature presents multiple intriguing examples of processes that proceed with high precision and regularity. This remarkable stability is frequently counter to modellers' experience with the inherent stochasticity of chemical reactions in the regime of low-copy numbers. Moreover, the effects of noise and nonlinearities can lead to 'counterintuitive' behaviour, as demonstrated for a basic enzymatic reaction scheme that can display stochastic focusing (SF). Under the assumption of rapid signal fluctuations, SF has been shown to convert a graded response into a threshold mechanism, thus attenuating the detrimental effects of signal noise. However, when the rapid fluctuation assumption is violated, this gain in sensitivity is generally obtained at the cost of very large product variance, and this unpredictable behaviour may be one possible explanation of why, more than a decade after its introduction, SF has still not been observed in real biochemical systems. In this work, we explore the noise properties of a simple enzymatic reaction mechanism with a small and fluctuating number of active enzymes that behaves as a high-gain, noisy amplifier due to SF caused by slow enzyme fluctuations. We then show that the inclusion of a plausible negative feedback mechanism turns the system from a noisy signal detector to a strong homeostatic mechanism by exchanging high gain with strong attenuation in output noise and robustness to parameter variations. Moreover, we observe that the discrepancy between deterministic and stochastic descriptions of stochastically focused systems in the evolution of the means almost completely disappears, despite very low molecule counts and the additional nonlinearity due to feedback. The reaction mechanism considered here can provide a possible resolution to the apparent conflict between intrinsic noise and high precision in critical intracellular processes. PMID:26609065
Hydraulic Modeling: Pipe Network Analysis
Datwyler, Trevor T.
2012-01-01
Water modeling is becoming an increasingly important part of hydraulic engineering. One application of hydraulic modeling is pipe network analysis. Using programmed algorithms to repeatedly solve continuity and energy equations, computer software can greatly reduce the amount of time required to analyze a closed conduit system. Such hydraulic models can become a valuable tool for cities to maintain their water systems and plan for future growth. The Utah Division of Drinking Water regulations...
Biochemical Space: A Framework for Systemic Annotation of Biological Models
Klement, M.; Děd, T.; Šafránek, D.; Červený, Jan; Müller, Stefan; Steuer, Ralf
2014-01-01
Roč. 306, JUL (2014), s. 31-44. ISSN 1571-0661 R&D Projects: GA MŠk(CZ) EE2.3.20.0256 Institutional support: RVO:67179843 Keywords : biological models * model annotation * systems biology * cyanobacteria Subject RIV: EH - Ecology, Behaviour
Spatial Models for Virtual Networks
Janssen, Jeannette
This paper discusses the use of spatial graph models for the analysis of networks that do not have a direct spatial reality, such as web graphs, on-line social networks, or citation graphs. In a spatial graph model, nodes are embedded in a metric space, and link formation depends on the relative position of nodes in the space. It is argued that spatial models form a good basis for link mining: assuming a spatial model, the link information can be used to infer the spatial position of the nodes, and this information can then be used for clustering and recognition of node similarity. This paper gives a survey of spatial graph models, and discusses their suitability for link mining.
Biochemical Production of Ethanol from Corn Stover: 2007 State of Technology Model
Aden, Andy [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2008-05-01
Since 2001, NREL has kept track of technical research progress in the biochemical process through what are known as “State of Technology” (SOT) assessments. The purpose of this report is to update the FY 2005 SOT model with the latest research results from the past two years.
Gray box modeling of MSW degradation: Revealing its dominant (bio)chemical mechanism
Van Turnhout, A.G.; Heimovaara, T.J.; Kleerebezem, R.
2013-01-01
In this paper we present an approach to describe organic degradation within immobile water regions of Municipal Solid Waste (MSW) landfills which is best described by the term “gray box” model. We use a simplified set of dominant (bio)chemical and physical reactions and realistic environmental condi
Xin, Q.; Gong, P.; Li, W.
2015-02-01
Modeling vegetation photosynthesis is essential for understanding carbon exchanges between terrestrial ecosystems and the atmosphere. The radiative transfer process within plant canopies is one of the key drivers that regulate canopy photosynthesis. Most vegetation cover consists of discrete plant crowns, of which the physical observation departs from the underlying assumption of a homogenous and uniform medium in classic radiative transfer theory. Here we advance the Geometric Optical Radiative Transfer (GORT) model to simulate photosynthesis activities for discontinuous plant canopies. We separate radiation absorption into two components that are absorbed by sunlit and shaded leaves, and derive analytical solutions by integrating over the canopy layer. To model leaf-level and canopy-level photosynthesis, leaf light absorption is then linked to the biochemical process of gas diffusion through leaf stomata. The canopy gap probability derived from GORT differs from classic radiative transfer theory, especially when the leaf area index is high, due to leaf clumping effects. Tree characteristics such as tree density, crown shape, and canopy length affect leaf clumping and regulate radiation interception. Modeled gross primary production (GPP) for two deciduous forest stands could explain more than 80% of the variance of flux tower measurements at both near hourly and daily time scales. We also demonstrate that the ambient CO2 concentration influences daytime vegetation photosynthesis, which needs to be considered in state-of-the-art biogeochemical models. The proposed model is complementary to classic radiative transfer theory and shows promise in modeling the radiative transfer process and photosynthetic activities over discontinuous forest canopies.
Senkevitch, Emilee; Cabrera-Luque, Juan; Morizono, Hiroki; Caldovic, Ljubica; Tuchman, Mendel
2012-01-01
All knockout mouse models of urea cycle disorders die in the neonatal period or shortly thereafter. Since N-acetylglutamate synthase (NAGS) deficiency in humans can be effectively treated with N-carbamyl-L-glutamate (NCG), we sought to develop a mouse model of this disorder that could be rescued by biochemical intervention, reared to adulthood, reproduce, and become a novel animal model for hyperammonemia. Founder NAGS knockout heterozygous mice were obtained from the trans-NIH Knock-Out Mous...
On Robustness Analysis of Stochastic Biochemical Systems by Probabilistic Model Checking
Brim, Lubos; Ceska, Milan; Drazan, Sven; Safranek, David
2013-01-01
This report proposes a novel framework for a rigorous robustness analysis of stochastic biochemical systems. The technique is based on probabilistic model checking. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems modelled as continuous time Markov Chains in order to extensively analyse and compare robustness of biological models with uncertain parameters. The framework utilises novel computational methods that enable to effectively evaluat...
Osseointegration of biochemically modified implants in an osteoporosis rodent model
Stadlinger, B.; P Korn; Tödtmann, N; Eckelt, U; Range, U.; Bürki, A.; SJ Ferguson; Kramer, I; Kautz, A.; Schnabelrauch, M; M Kneissel; F Schlottig
2013-01-01
The present study examined the impact of implant surface modifications on osseointegration in an osteoporotic rodent model. Sandblasted, acid-etched titanium implants were either used directly (control) or were further modified by surface conditioning with NaOH or by coating with one of the following active agents: collagen/chondroitin sulphate, simvastatin, or zoledronic acid. Control and modified implants were inserted into the proximal tibia of aged ovariectomised (OVX) osteoporotic rats (...
Molecular paleontology: a biochemical model of the ancestral ribosome
Hsiao, Chiaolong; Lenz, Timothy K.; Peters, Jessica K; Fang, Po-Yu; Schneider, Dana M.; Anderson, Eric J.; Preeprem, Thanawadee; Bowman, Jessica C.; O'Neill, Eric B.; Lie, Lively; Athavale, Shreyas S.; Gossett, J. Jared; Trippe, Catherine; Murray, Jason; Anton S. Petrov
2013-01-01
Ancient components of the ribosome, inferred from a consensus of previous work, were constructed in silico, in vitro and in vivo. The resulting model of the ancestral ribosome presented here incorporates ∼20% of the extant 23S rRNA and fragments of five ribosomal proteins. We test hypotheses that ancestral rRNA can: (i) assume canonical 23S rRNA-like secondary structure, (ii) assume canonical tertiary structure and (iii) form native complexes with ribosomal protein fragments. Footprinting exp...
A review of dissolved oxygen and biochemical oxygen demand models for large rivers
Development and modifications of mathematical models for Dissolved Oxygen (DO) are reviewed in this paper. The field and laboratory methods to estimate the kinetics of Carbonaceous Biochemical Oxygen Demand (CBOD) and Nitrogenous Biochemical Oxygen Demand (NBOD) are also presented. This review also includes recent approaches of BOD and DO modeling beside the conventional ones along with their applicability to the natural rivers. The most frequently available public domain computer models and their applications in real life projects are also briefly covered. The literature survey reveals that currently there is more emphasis on solution techniques rather than understanding the mechanisms and processes that control DO in large rivers. The DO modeling software contains inbuilt coefficients and parameters that may not reflect the specific conditions under study. It is therefore important that the selected mathematical and computer models must incorporate the relevant processes specific to the river under study and be within the available resources in term of data collection. (author)
Osseointegration of biochemically modified implants in an osteoporosis rodent model
B Stadlinger
2013-07-01
Full Text Available The present study examined the impact of implant surface modifications on osseointegration in an osteoporotic rodent model. Sandblasted, acid-etched titanium implants were either used directly (control or were further modified by surface conditioning with NaOH or by coating with one of the following active agents: collagen/chondroitin sulphate, simvastatin, or zoledronic acid. Control and modified implants were inserted into the proximal tibia of aged ovariectomised (OVX osteoporotic rats (n = 32/group. In addition, aged oestrogen competent animals received either control or NaOH conditioned implants. Animals were sacrificed 2 and 4 weeks post-implantation. The excised tibiae were utilised for biomechanical and morphometric readouts (n = 8/group/readout. Biomechanical testing revealed at both time points dramatically reduced osseointegration in the tibia of oestrogen deprived osteoporotic animals compared to intact controls irrespective of NaOH exposure. Consistently, histomorphometric and microCT analyses demonstrated diminished bone-implant contact (BIC, peri-implant bone area (BA, bone volume/tissue volume (BV/TV and bone-mineral density (BMD in OVX animals. Surface coating with collagen/chondroitin sulphate had no detectable impact on osseointegration. Interestingly, statin coating resulted in a transient increase in BIC 2 weeks post-implantation; which, however, did not correspond to improvement of biomechanical readouts. Local exposure to zoledronic acid increased BIC, BA, BV/TV and BMD at 4 weeks. Yet this translated only into a non-significant improvement of biomechanical properties. In conclusion, this study presents a rodent model mimicking severely osteoporotic bone. Contrary to the other bioactive agents, locally released zoledronic acid had a positive impact on osseointegration albeit to a lesser extent than reported in less challenging models.
Biochemical switching device: biomimetic approach and application to neural network study.
Okamoto, M
1992-06-01
There are many examples of enzymes that share substrates or cofactors in a cyclic manner. Techniques have been developed that use cyclic enzyme systems to assay quantitatively small amounts of biochemical substances (cofactor, substrate), however, only a few studies of the control of these systems have been published. The author previously showed with computer simulations that cyclic enzyme systems have the reliability of ON-OFF types of operation (McCulloch-Pitts' neuronic equation) capable of storing short-memory, and the applicability for a switching circuit in a biocomputer. This paper introduces a unique switching mechanism of cyclic enzyme system (basic switching element), and next, building the integrated biochemical switching system being composed of the basic switching element, shows the physiological phenomenon termed 'selective elimination of synapses' generally produced as a result of low-frequency train of electrical stimuli to the synapses (Kuroda, Y. 1989) Neurochem. Int. 14, 309-319). PMID:1368350
Modelling and Analysis of Biochemical Signalling Pathway Cross-talk
Robin Donaldson
2010-02-01
Full Text Available Signalling pathways are abstractions that help life scientists structure the coordination of cellular activity. Cross-talk between pathways accounts for many of the complex behaviours exhibited by signalling pathways and is often critical in producing the correct signal-response relationship. Formal models of signalling pathways and cross-talk in particular can aid understanding and drive experimentation. We define an approach to modelling based on the concept that a pathway is the (synchronising parallel composition of instances of generic modules (with internal and external labels. Pathways are then composed by (synchronising parallel composition and renaming; different types of cross-talk result from different combinations of synchronisation and renaming. We define a number of generic modules in PRISM and five types of cross-talk: signal flow, substrate availability, receptor function, gene expression and intracellular communication. We show that Continuous Stochastic Logic properties can both detect and distinguish the types of cross-talk. The approach is illustrated with small examples and an analysis of the cross-talk between the TGF-b/BMP, WNT and MAPK pathways.
Ising model for distribution networks
Hooyberghs, H; Giuraniuc, C; Van Schaeybroeck, B; Indekeu, J O
2012-01-01
An elementary Ising spin model is proposed for demonstrating cascading failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in realistic networks for distribution and delivery by suppliers to consumers. A ferromagnetic Hamiltonian with quenched random fields results from policies that maximize the gap between demand and delivery. Such policies can arise in a competitive market where firms artificially create new demand, or in a solidary environment where too high a demand cannot reasonably be met. Network failure in the context of a policy of solidarity is possible when an initially active state becomes metastable and decays to a stable inactive state. We explore the characteristics of the demand and delivery, as well as the topological properties, which make the distribution network susceptible of failure. An effective temperature is defined, which governs the strength of the activity fluctuations which can induce a collapse. Numerical results, obtained by Monte Carlo simulations of t...
Modeling integrated cellular machinery using hybrid Petri-Boolean networks.
Natalie Berestovsky
Full Text Available 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
Nonparametric Bayesian Modeling of Complex Networks
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....
Research on the model of home networking
Yun, Xiang; Feng, Xiancheng
2007-11-01
It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network
Mathematical Modelling Plant Signalling Networks
Muraro, D.
2013-01-01
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.
The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks.
Chevalier, Michael; Venturelli, Ophelia; El-Samad, Hana
2015-10-01
Stochastic fluctuations in signaling and gene expression limit the ability of cells to sense the state of their environment, transfer this information along cellular pathways, and respond to it with high precision. Mutual information is now often used to quantify the fidelity with which information is transmitted along a cellular pathway. Mutual information calculations from experimental data have mostly generated low values, suggesting that cells might have relatively low signal transmission fidelity. In this work, we demonstrate that mutual information calculations might be artificially lowered by cell-to-cell variability in both initial conditions and slowly fluctuating global factors across the population. We carry out our analysis computationally using a simple signaling pathway and demonstrate that in the presence of slow global fluctuations, every cell might have its own high information transmission capacity but that population averaging underestimates this value. We also construct a simple synthetic transcriptional network and demonstrate using experimental measurements coupled to computational modeling that its operation is dominated by slow global variability, and hence that its mutual information is underestimated by a population averaged calculation. PMID:26484538
Probabilistic logic modeling of network reliability for hybrid network architectures
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
Energy modelling in sensor networks
D. Schmidt
2007-06-01
Full Text Available Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.
Plant Growth Models Using Artificial Neural Networks
Bubenheim, David
1997-01-01
In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.
Hierarchical Models for Independence Structures of Networks
Sadeghi, Kayvan; Rinaldo, Alessandro
2016-01-01
We introduce a new family of network models, called hierarchical network models, that allow to represent in an explicit manner the stochastic dependence among the edges. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the edges of the network, called the dependency graph. Every network model of dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, w...
Assimilation of ocean colour data into a Biochemical Flux Model of the Eastern Mediterranean Sea
G. Triantafyllou
2006-09-01
Full Text Available Within the framework of the European MFSTEP project, an advanced multivariate sequential data assimilation system has been implemented to assimilate real chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS into a three-dimensional biochemical model of the Eastern Mediterranean. The physical ocean is described through the Princeton Ocean Model (POM while the biochemistry of the ecosystem is tackled with the Biochemical Flux Model (BFM. The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK filter, in which the error statistics were parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs. A radius of influence was further selected around every data point to limit the range of the EOFs spatial correlations. The assimilation experiment was performed for one year over 1999 and forced with ECMWF 6 hour atmospheric fields. The accuracy of the ecological state identification by the assimilation system is assessed by the relevance of the system in fitting the data, and through the impact of the assimilation on non-observed biochemical processes. Assimilation of SeaWiFS data significantly improves the forecasting capability of the BFM model. Results, however, indicate the necessity of subsurface data to enhance the controllability of the ecosystem model in the deep layers.
An evolving network model with community structure
Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties
Brand Marketing Model on Social Networks
Jolita Jezukevičiūtė
2014-04-01
Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.
Modeling the Dynamics of Compromised Networks
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
Information Network Model Query Processing
Song, Xiaopu
Information Networking Model (INM) [31] is a novel database model for real world objects and relationships management. It naturally and directly supports various kinds of static and dynamic relationships between objects. In INM, objects are networked through various natural and complex relationships. INM Query Language (INM-QL) [30] is designed to explore such information network, retrieve information about schema, instance, their attributes, relationships, and context-dependent information, and process query results in the user specified form. INM database management system has been implemented using Berkeley DB, and it supports INM-QL. This thesis is mainly focused on the implementation of the subsystem that is able to effectively and efficiently process INM-QL. The subsystem provides a lexical and syntactical analyzer of INM-QL, and it is able to choose appropriate evaluation strategies and index mechanism to process queries in INM-QL without the user's intervention. It also uses intermediate result structure to hold intermediate query result and other helping structures to reduce complexity of query processing.
Modified Step Variational Iteration Method for Solving Fractional Biochemical Reaction Model
R. Yulita Molliq
2011-01-01
Full Text Available A new method called the modification of step variational iteration method (MoSVIM is introduced and used to solve the fractional biochemical reaction model. The MoSVIM uses general Lagrange multipliers for construction of the correction functional for the problems, and it runs by step approach, which is to divide the interval into subintervals with time step, and the solutions are obtained at each subinterval as well adopting a nonzero auxiliary parameter ℏ to control the convergence region of series' solutions. The MoSVIM yields an analytical solution of a rapidly convergent infinite power series with easily computable terms and produces a good approximate solution on enlarged intervals for solving the fractional biochemical reaction model. The accuracy of the results obtained is in a excellent agreement with the Adam Bashforth Moulton method (ABMM.
Multilayer weighted social network model
Murase, Yohsuke; Török, János; Jo, Hang-Hyun; Kaski, Kimmo; Kertész, János
2014-11-01
Recent empirical studies using large-scale data sets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multilayered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.
Gray box modeling of MSW degradation: Revealing its dominant (bio)chemical mechanism
Van Turnhout, A.G.; Heimovaara, T.J.; Kleerebezem, R.
2013-01-01
In this paper we present an approach to describe organic degradation within immobile water regions of Municipal Solid Waste (MSW) landfills which is best described by the term “gray box” model. We use a simplified set of dominant (bio)chemical and physical reactions and realistic environmental conditions. All equations, relationships and inhibitions are based on semi-empirical or fundamental relationships which have proven to be applicable in the peer reviewed literature. As much as possible ...
Brand Marketing Model on Social Networks
Jolita Jezukevičiūtė; Vida Davidavičienė
2014-01-01
The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
Yoo, Wucherl; Sim, Alex
2014-07-07
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Multiplexing oscillatory biochemical signals.
de Ronde, Wiet; ten Wolde, Pieter Rein
2014-04-01
In recent years it has been increasingly recognized that biochemical signals are not necessarily constant in time and that the temporal dynamics of a signal can be the information carrier. Moreover, it is now well established that the protein signaling network of living cells has a bow-tie structure and that components are often shared between different signaling pathways. Here we show by mathematical modeling that living cells can multiplex a constant and an oscillatory signal: they can transmit these two signals simultaneously through a common signaling pathway, and yet respond to them specifically and reliably. We find that information transmission is reduced not only by noise arising from the intrinsic stochasticity of biochemical reactions, but also by crosstalk between the different channels. Yet, under biologically relevant conditions more than 2 bits of information can be transmitted per channel, even when the two signals are transmitted simultaneously. These observations suggest that oscillatory signals are ideal for multiplexing signals. PMID:24685537
Modelling delay propagation within an airport network
Pyrgiotis, N.; Malone, K.M.; Odoni, A.
2013-01-01
We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and capture
An acoustical model based monitoring network
Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der
2010-01-01
In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the noi
Analysis by fracture network modelling
This report describes the Fracture Network Modelling and Performance Assessment Support performed by Golder Associates Inc. during the Heisei-11 (1999-2000) fiscal year. The primary objective of the Golder Associates work scope during HY-11 was to provide theoretical and review support to the JNC HY-12 Performance assessment effort. In addition, Golder Associates provided technical support to JNC for the Aespoe Project. Major efforts for performance assessment support included analysis of PAWorks pathways and software documentation, verification, and performance assessment visualization. Support for the Aespoe project including 'Task 4' predictive modelling of sorbing tracer transport in TRUE-1 rock block, and integrated hydrogeological and geochemical modelling of Aespoe island for 'Task 5'. Technical information about Golder Associates HY-11 support to JNC is provided in the appendices to this report. (author)
An evolutionary model of social networks
Ludwig, M.; Abell, P.
2007-07-01
Social networks in communities, markets, and societies self-organise through the interactions of many individuals. In this paper we use a well-known mechanism of social interactions — the balance of sentiment in triadic relations — to describe the development of social networks. Our model contrasts with many existing network models, in that people not only establish but also break up relations whilst the network evolves. The procedure generates several interesting network features such as a variety of degree distributions and degree correlations. The resulting network converges under certain conditions to a steady critical state where temporal disruptions in triangles follow a power-law distribution.
Introducing Synchronisation in Deterministic Network Models
Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.;
2006-01-01
The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...
Graph Annotations in Modeling Complex Network Topologies
Dimitropoulos, Xenofontas; Vahdat, Amin; Riley, George
2007-01-01
The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in such a graph possess some non-trivial internal structure that varies across and differentiates among distinct types of links or nodes. In this work, we abstract such additional information as network annotations. We introduce a network topology modeling framework that treats annotations as an extended correlation profile of a network. Assuming we have this profile measured for a given network, we present an algorithm to rescale it in order to construct networks of varying size that still reproduce the original measured annotation profile. Using this methodology, we accurately capture the network properties essential for realistic simulations of network applications and protocols, or any other simulations involving complex network topologies, including modeling and simulation ...
Hybrid modeling of communication networks using Modelica
Färnqvist, Daniel; Strandemar, Katrin; Johansson, Karl Henrik; Hespanha, João Pedro
2002-01-01
Modeling and simulation of communication networks using the modeling language Modelica are discussed. Congestion control in packet-switched networks, such as the Internet, is today mainly analyzed through time-consuming simulations of individual packets. We show, by developing a model library based on a recent hybrid systems model, that Modelica provides an efficient platform for the analysis of communication networks. As an example, a comparison between the two congestion control protocols i...
Hierarchical relational models for document networks
Chang, Jonathan; Blei, David M.
2009-01-01
We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predi...
Multiscaling in the YX model of networks
Holme, Petter; Minnhagen, Petter
2009-01-01
We investigate a Hamiltonian model of networks. The model is a mirror formulation of the XY model (hence the name) -- instead letting the XY spins vary, keeping the coupling topology static, we keep the spins conserved and sample different underlying networks. Our numerical simulations show complex scaling behaviors, but no finite-temperature critical behavior. The ground state and low-order excitations for sparse, finite graphs is a fragmented set of isolated network clusters. Configurations of higher energy are typically more connected. The connected networks of lowest energy are stretched out giving the network large average distances. For the finite sizes we investigate there are three regions -- a low-energy regime of fragmented networks, and intermediate regime of stretched-out networks, and a high-energy regime of compact, disordered topologies. Scaling up the system size, the borders between these regimes approach zero temperature algebraically, but different network structural quantities approach the...
A Computational Model of Cell Migration in Response to Biochemical Diffusion
Dexter, Nicholas C [ORNL; Kruse, Kara L [ORNL; Nutaro, James J [ORNL; Ward, Richard C [ORNL
2009-01-01
The Computational Sciences and Engineering Division of the Oak Ridge National Laboratory is partnering with the University of Tennessee Graduate School of Medicine to design a computational model describing various factors related to the development of intimal hyperplasia (IH) in response to arterial injury. This research focuses on modeling the chemotactic and haptotactic processes that stimulate vascular smooth muscle cell migration into the intima. A hybrid discrete-continuous mathematical model of cell migration in response to biochemical diffusion was developed in C++. Chemoattractant diffusion is modeled as a continuous partial differential equation, whereas migration of the cells is modeled as a series of discrete events. Results obtained from the discrete state model for cell migration agree with those obtained from Boyden chamber experiments.
Network model of bilateral power markets based on complex networks
Wu, Yang; Liu, Junyong; Li, Furong; Yan, Zhanxin; Zhang, Li
2014-06-01
The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.
Localized Modeling of Biochemical and Flow Interactions during Cancer Cell Adhesion.
Julie Behr
Full Text Available This work focuses on one component of a larger research effort to develop a simulation tool to model populations of flowing cells. Specifically, in this study a local model of the biochemical interactions between circulating melanoma tumor cells (TC and substrate adherent polymorphonuclear neutrophils (PMN is developed. This model provides realistic three-dimensional distributions of bond formation and attendant attraction and repulsion forces that are consistent with the time dependent Computational Fluid Dynamics (CFD framework of the full system model which accounts local pressure, shear and repulsion forces. The resulting full dynamics model enables exploration of TC adhesion to adherent PMNs, which is a known participating mechanism in melanoma cell metastasis. The model defines the adhesion molecules present on the TC and PMN cell surfaces, and calculates their interactions as the melanoma cell flows past the PMN. Biochemical rates of reactions between individual molecules are determined based on their local properties. The melanoma cell in the model expresses ICAM-1 molecules on its surface, and the PMN expresses the β-2 integrins LFA-1 and Mac-1. In this work the PMN is fixed to the substrate and is assumed fully rigid and of a prescribed shear-rate dependent shape obtained from micro-PIV experiments. The melanoma cell is transported with full six-degrees-of-freedom dynamics. Adhesion models, which represent the ability of molecules to bond and adhere the cells to each other, and repulsion models, which represent the various physical mechanisms of cellular repulsion, are incorporated with the CFD solver. All models are general enough to allow for future extensions, including arbitrary adhesion molecule types, and the ability to redefine the values of parameters to represent various cell types. The model presented in this study will be part of a clinical tool for development of personalized medical treatment programs.
How to model wireless mesh networks topology
The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches
Model Of Neural Network With Creative Dynamics
Zak, Michail; Barhen, Jacob
1993-01-01
Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.
Magneto-electric network models in electromagnetism
Demenko, A.; Sykulski, J. K.
2006-01-01
Purpose – The aim of this paper is to develop network models of an electromagnetic field containing both eddy and displacement currents. The proposed network models provide good physical insight, help understanding of complicated electromagnetic phenomena and aid explanation of methods of analysis of electromagnetic systems. Design/methodology/approach – The models consist of magnetic and electric networks coupled via sources. The analogy between the finite element method and the loop and nod...
CIMS Network Protocol and Its Net Models
罗军舟; 顾冠群
1997-01-01
Computer communication network architectures for cims are based on the OSI Reference Model.In this paper,CIMS network protocol model is set up on the basis of the corresqonding service model.Then the authors present a formal specification of transport protocols by using an extended Predicate/Transition net system that is briefly introduced in the third part.Finally,the general methods for the Petri nets based formal specification of CIMS network protocols are outlined.
Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
Mehta, Pankaj; Lang, Alex H.; Schwab, David J.
2016-03-01
A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using "post-translational" synthetic biology that exploits reversible protein modification.
Oligopoly Model of a Debit Card Network
Manchev, Peter
2006-01-01
The paper builds an oligopoly model of a debit card network. It examines the competition between debit card issuers. We show that there is an optimal pricing for the debit card network, which maximizes all issuer's revenues. The paper also shows that establishing a link between debit card networks averages the costs provided that there is no growth in the customer's usage of the networks, resulting from the link.
Edge exchangeable models for network data
Crane, Harry
2016-01-01
Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing that edges, not vertices, act as the statistical units in most network datasets, making a theory of edge labeled networks more natural for most applications. Within this context we introduce the new invariance principle of {\\em edge exchangeability}, which unlike its vertex exchangeable counterpart can produce networks with sparse and/or power law structure. We characterize the class of all edge exchangeable network models and identify a particular two parameter family of models with suitable theoretical properties for statistical inference. We discuss issues of estimation from edge exchangeable models and compare our a...
Growth of Lactobacillus paracasei ATCC 334 in a cheese model system: a biochemical approach
Budinich, M.; Diaz-Muniz, I.; Cai, H; Rankin, S. A.; Broadbent, Jeffery R.; Steele, J L
2011-01-01
Growth of Lactobacillus paracasei ATCC 334, in a cheese-ripening model system based upon a medium prepared from ripening Cheddar cheese extract (CCE) was evaluated. Lactobacillus paracasei ATCC 334 grows in CCE made from cheese ripened for 2 (2mCCE), 6 (6mCCE), and 8 (8mCCE) mo, to final cell densities of 5.9 × 108, 1.2 × 108, and 2.1 × 107 cfu/mL, respectively. Biochemical analysis and mass balance equations were used to determine substrate consumption patterns and products formed in 2mCCE. ...
Modeling data throughput on communication networks
Eldridge, J.M.
1993-11-01
New challenges in high performance computing and communications are driving the need for fast, geographically distributed networks. Applications such as modeling physical phenomena, interactive visualization, large data set transfers, and distributed supercomputing require high performance networking [St89][Ra92][Ca92]. One measure of a communication network`s performance is the time it takes to complete a task -- such as transferring a data file or displaying a graphics image on a remote monitor. Throughput, defined as the ratio of the number of useful data bits transmitted per the time required to transmit those bits, is a useful gauge of how well a communication system meets this performance measure. This paper develops and describes an analytical model of throughput. The model is a tool network designers can use to predict network throughput. It also provides insight into those parts of the network that act as a performance bottleneck.
Queuing theory models for computer networks
Galant, David C.
1989-01-01
A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.
A Conceptual Model of Learning Networks
Koper, Rob
In the TENCompetence project a set of UML models (Booch et al. 1999) have been developed to specify the core concepts for Learning Networks Services that support professional competence development. The three most important, high-level models are (a) the use case model, (b) the conceptual model, and (c) the domain model. The first model identifies the primary use cases we need in order to support professional competence development. The second model describes the concept of competence and competence development from a theoretical point of view. What is a competence? How does it relate to the cognitive system of an actor? How are competences developed? The third model is a UML Domain Model that defines, among other things, the components of a Learning Network, defines the concepts and relationships between the concepts in a Learning Network and provides a starting point for the design of the overall architecture for Learning Network Services, including the data model.
Xin-Hai Hu
2014-12-01
Full Text Available Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR. However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP were used to develop an artificial neural network (ANN to predict BCR and to compare it with a logistic regression (LR model using clinical and pathologic parameters, prostate-specific antigen (PSA, margin status (R0/1, pathological stage (pT, and Gleason Score (GS. For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC curve (AUC for the ANN (0.754 and LR models (0.755 calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001, pT or PSA (AUC: 0.619; P always <0.0001 alone. The GS predicted the BCR better than PSA (P = 0.0001, but there was no difference between the ANN and LR models (P = 0.39. Our ANN and LR models predicted individual BCR risk from radical prostatectomy for up to 10 years postoperative. ANN and LR models equally and significantly improved the prediction of BCR compared with PSA and GS alone. When the GS and ANN output values are combined, a more accurate BCR prediction is possible, especially in high-risk patients with GS ≥7.
Hu, Xin-Hai; Cammann, Henning; Meyer, Hellmuth-A; Jung, Klaus; Lu, Hong-Biao; Leva, Natalia; Magheli, Ahmed; Stephan, Carsten; Busch, Jonas
2014-01-01
Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR). However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP) were used to develop an artificial neural network (ANN) to predict BCR and to compare it with a logistic regression (LR) model using clinical and pathologic parameters, prostate-specific antigen (PSA), margin status (R0/1), pathological stage (pT), and Gleason Score (GS). For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC) curve (AUC) for the ANN (0.754) and LR models (0.755) calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001), pT or PSA (AUC: 0.619; P always <0.0001) alone. The GS predicted the BCR better than PSA (P = 0.0001), but there was no difference between the ANN and LR models (P = 0.39). Our ANN and LR models predicted individual BCR risk from radical prostatectomy for up to 10 years postoperative. ANN and LR models equally and significantly improved the prediction of BCR compared with PSA and GS alone. When the GS and ANN output values are combined, a more accurate BCR prediction is possible, especially in high-risk patients with GS ≥7. PMID:25130472
Random graph models for dynamic networks
Zhang, Xiao; Newman, M E J
2016-01-01
We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.
Exponential-family Random Network Models
Fellows, I; Handcock, MS
2012-01-01
Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing com- plex social phenomena. We generalize ERGM by also modeling nodal attributes as random variates, thus creating a random model of the full network, which we call Exponential-family Random Network Models (ERNM). We demonstrate how this framework a...
Sinha, Sarita; Basant, Ankita; Malik, Amrita; Singh, Kunwar P.
2009-01-01
Biochemical changes in the plants of Pistia stratiotes L., a free floating macrophyte exposed to different concentrations of hexavalent chromium (0, 10, 40, 60, 80 and 160 μM) for 48, 96 and 144 h were studied. Chromium-induced oxidative stress in macrophyte was investigated using the multivariate modeling approaches. Cluster analysis rendered two fairly distinct clusters (roots and shoots) of similar characteristics in terms of their biochemical responses. Discriminant analysis identified as...
Diverse Embedding Neural Network Language Models
Audhkhasi, Kartik; Sethy, Abhinav; Ramabhadran, Bhuvana
2014-01-01
We propose Diverse Embedding Neural Network (DENN), a novel architecture for language models (LMs). A DENNLM projects the input word history vector onto multiple diverse low-dimensional sub-spaces instead of a single higher-dimensional sub-space as in conventional feed-forward neural network LMs. We encourage these sub-spaces to be diverse during network training through an augmented loss function. Our language modeling experiments on the Penn Treebank data set show the performance benefit of...
Monochromaticity in Neutral Evolutionary Network Models
Halu, Arda; Bianconi, Ginestra
2012-01-01
Recent studies on epistatic networks of model organisms have unveiled a certain type of modular property called monochromaticity in which the networks are clusterable into functional modules that interact with each other through the same type of epistasis. Here we propose and study three epistatic network models that are inspired by the Duplication-Divergence mechanism to gain insight into the evolutionary basis of monochromaticity and to test if it can be explained as the outcome of a neutra...
Senkevitch, Emilee; Cabrera-Luque, Juan; Morizono, Hiroki; Caldovic, Ljubica; Tuchman, Mendel
2012-06-01
All knockout mouse models of urea cycle disorders die in the neonatal period or shortly thereafter. Since N-acetylglutamate synthase (NAGS) deficiency in humans can be effectively treated with N-carbamyl-l-glutamate (NCG), we sought to develop a mouse model of this disorder that could be rescued by biochemical intervention, reared to adulthood, reproduce, and become a novel animal model for hyperammonemia. Founder NAGS knockout heterozygous mice were obtained from the trans-NIH Knock-Out Mouse Project. Genotyping of the mice was performed by PCR and confirmed by Western blotting of liver and intestine. NCG and L-citrulline (Cit) were used to rescue the NAGS knockout homozygous (Nags(-/-)) pups and the rescued animals were characterized. We observed an 85% survival rate of Nags(-/-) mice when they were given intraperitoneal injections with NCG and Cit during the newborn period until weaning and supplemented subsequently with both compounds in their drinking water. This regimen has allowed for normal development, apparent health, and reproduction. Interruption of this rescue intervention resulted in the development of severe hyperammonemia and death within 48 h. In addition to hyperammonemia, interruption of rescue supplementation was associated with elevated plasma glutamine, glutamate, and lysine, and reduced citrulline, arginine, ornithine and proline levels. We conclude that NAGS deprived mouse model has been developed which can be rescued by NCG and Cit and reared to reproduction and beyond. This biochemically salvageable mouse model recapitulates the clinical phenotype of proximal urea cycle disorders and can be used as a reliable model of induced hyperammonemia by manipulating the administration of the rescue compounds. PMID:22503289
Modelling pollutant deposition to vegetation: scaling down from the canopy to the biochemical level
In the atmosphere, pollutants exist in either the gas, particle or liquid (rain and cloud water) phase. The most important gas-phase pollutants from a biological or ecological perspective are oxides of nitrogen (nitrogen dioxide, nitric acid vapor), oxides of sulfur (sulfur dioxide), ammonia, tropospheric ozone and mercury vapor. For liquid or particle phase pollutants, the suite of pollutants is varied and includes hydrogen ion, multiple heavy metals, and select anions. For many of these pollutants, plant canopies are a major sink within continental landscapes, and deposition is highly dependent on the (i) physical form or phase of the pollutant, (ii) meteorological conditions above and within the plant canopy, and (iii) physiological or biochemical properties of the leaf, both on the leaf surface and within the leaf interior. In large measure, the physical and chemical processes controlling deposition at the meteorological and whole-canopy levels are well characterized and have been mathematically modelled. In contrast, the processes operating on the leaf surface and within the leaf interior are not well understood and are largely specific for individual pollutants. The availability of process-level models to estimate deposition is discussed briefly at the canopy and leaf level; however, the majority of effort is devoted to modelling deposition at the leaf surface and leaf interior using the two-layer stagnant film model. This model places a premium on information of a physiological and biochemical nature, and highlights the need to distinguish clearly between the measurements of atmospheric chemistry and the physiologically effective exposure since the two may be very dissimilar. A case study of deposition in the Los Angeles Basin is used to demonstrate the modelling approach, to present the concept of exposure dynamics in the atmosphere versus that in the leaf interior, and to document the principle that most forest canopies are exposed to multiple chemical
Performance modeling of network data services
Haynes, R.A.; Pierson, L.G.
1997-01-01
Networks at major computational organizations are becoming increasingly complex. The introduction of large massively parallel computers and supercomputers with gigabyte memories are requiring greater and greater bandwidth for network data transfers to widely dispersed clients. For networks to provide adequate data transfer services to high performance computers and remote users connected to them, the networking components must be optimized from a combination of internal and external performance criteria. This paper describes research done at Sandia National Laboratories to model network data services and to visualize the flow of data from source to sink when using the data services.
Unified Hybrid Network Theoretical Model Trilogy
无
2011-01-01
The first of the unified hybrid network theoretical model trilogy (UHNTF) is the harmonious unification hybrid preferential model (HUHPM), seen in the inner loop of Fig. 1, the unified hybrid ratio is defined.
Modeling information flow in biological networks
Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method
Network models in economics and finance
Pardalos, Panos; Rassias, Themistocles
2014-01-01
Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.
Strategic games on a hierarchical network model
无
2008-01-01
Among complex network models, the hierarchical network model is the one most close to such real networks as world trade web, metabolic network, WWW, actor network, and so on. It has not only the property of power-law degree distribution, but growth based on growth and preferential attachment, showing the scale-free degree distribution property. In this paper, we study the evolution of cooperation on a hierarchical network model, adopting the prisoner's dilemma (PD) game and snowdrift game (SG) as metaphors of the interplay between connected nodes. BA model provides a unifying framework for the emergence of cooperation. But interestingly, we found that on hierarchical model, there is no sign of cooperation for PD game, while the frequency of cooperation decreases as the common benefit decreases for SG. By comparing the scaling clustering coefficient properties of the hierarchical network model with that of BA model, we found that the former amplifies the effect of hubs. Considering different performances of PD game and SG on complex network, we also found that common benefit leads to cooperation in the evolution. Thus our study may shed light on the emergence of cooperation in both natural and social environments.
Evaluation of EOR Processes Using Network Models
Larsen, Jens Kjell; Krogsbøll, Anette
1998-01-01
The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels......) including estimation of their "petrophysical" properties (e.g. absolute permeability). 3) Mathematical modelling and computer studies of multiphase transport through pore space using mathematical network models. 4) Investigation of link between pore-scale and macroscopic recovery mechanisms....
Modelling the structure of complex networks
Herlau, Tue
A complex network is a systems in which a discrete set of units interact in a quantifiable manner. Representing systems as complex networks have become increasingly popular in a variety of scientific fields including biology, social sciences and economics. Parallel to this development complex...... next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...
Towards reproducible descriptions of neuronal network models.
Eilen Nordlie
2009-08-01
Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.
Symbolic regression of generative network models
Menezes, Telmo
2014-01-01
Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world netwo...
Complex networks analysis in socioeconomic models
Varela, Luis M; Ausloos, Marcel; Carrete, Jesus
2014-01-01
This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including res...
Reduced models of networks of coupled enzymatic reactions
Kumar, Ajit
2011-01-01
The Michaelis-Menten equation has played a central role in our understanding of biochemical processes. It has long been understood how this equation approximates the dynamics of irreversible enzymatic reactions. However, a similar approximation in the case of networks, where the product of one reaction can act as an enzyme in another, has not been fully developed. Here we rigorously derive such an approximation in a class of coupled enzymatic networks where the individual interactions are of Michaelis-Menten type. We show that the sufficient conditions for the validity of the total quasi steady state assumption (tQSSA), obtained in a single protein case by Borghans, de Boer and Segel can be extended to sufficient conditions for the validity of the tQSSA in a large class of enzymatic networks. Secondly, we derive reduced equations that approximate the network's dynamics and involve only protein concentrations. This significantly reduces the number of equations necessary to model such systems. We prove the vali...
Boolean networks as modelling framework
Florian eGreil
2012-08-01
Full Text Available In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and the immediate application in plant biology, references for more in-depths treatment of the mathematical details are also given.
Implementing network constraints in the EMPS model
Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.
2010-02-15
This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)
Modelling and control of road traffic networks
Haut, Bertrand
2007-01-01
Road traffic networks offer a particularly challenging research subject to the control community. The traffic congestion around big cities is constantly increasing and is now becoming a major problem. However, the dynamics of a road network exhibit some complex behaviours such as nonlinearities, delays and saturation effects that prevent the use of some classical control algorithms. This thesis presents different models and control algorithms used for road traffic networks. The dynamics ar...
Delivery Time Reliability Model of Logistics Network
Liusan Wu; Qingmei Tan; Yuehui Zhang
2013-01-01
Natural disasters like earthquake and flood will surely destroy the existing traffic network, usually accompanied by delivery delay or even network collapse. A logistics-network-related delivery time reliability model defined by a shortest-time entropy is proposed as a means to estimate the actual delivery time reliability. The less the entropy is, the stronger the delivery time reliability remains, and vice versa. The shortest delivery time is computed separately based on two different assum...
Modelling Microwave Devices Using Artificial Neural Networks
Andrius Katkevičius
2012-04-01
Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian
Measuring and modeling switched Ethernet networks
2003-01-01
Switched Ethernet is a dominating technology in local area networks, and a strong contender in system area networks. This thesis describes measurements on switched Ethernets, in the context of high speed system area networks with real time requirements. One such system is the ATLAS level 2 trigger, which formed the basis of the measurements. Further on the thesis describes the design, implementation and results from a simulation model of the measured devic...
Modeling Evolution of Weighted Clique Networks
We propose a weighted clique network evolution model, which expands continuously by the addition of a new clique (maximal complete sub-graph) at each time step. And the cliques in the network overlap with each other. The structural expansion of the weighted clique network is combined with the edges' weight and vertices' strengths dynamical evolution. The model is based on a weight-driven dynamics and a weights' enhancement mechanism combining with the network growth. We study the network properties, which include the distribution of vertices' strength and the distribution of edges' weight, and find that both the distributions follow the scale-free distribution. At the same time, we also find that the relationship between strength and degree of a vertex are linear correlation during the growth of the network. On the basis of mean-field theory, we study the weighted network model and prove that both vertices' strength and edges' weight of this model follow the scale-free distribution. And we exploit an algorithm to forecast the network dynamics, which can be used to reckon the distributions and the corresponding scaling exponents. Furthermore, we observe that mean-field based theoretic results are consistent with the statistical data of the model, which denotes the theoretical result in this paper is effective. (interdisciplinary physics and related areas of science and technology)
Modeling Evolution of Weighted Clique Networks
Yang, Xu-Hua; Jiang, Feng-Ling; Chen, Sheng-Yong; Wang, Wan-Liang
2011-11-01
We propose a weighted clique network evolution model, which expands continuously by the addition of a new clique (maximal complete sub-graph) at each time step. And the cliques in the network overlap with each other. The structural expansion of the weighted clique network is combined with the edges' weight and vertices' strengths dynamical evolution. The model is based on a weight-driven dynamics and a weights' enhancement mechanism combining with the network growth. We study the network properties, which include the distribution of vertices' strength and the distribution of edges' weight, and find that both the distributions follow the scale-free distribution. At the same time, we also find that the relationship between strength and degree of a vertex are linear correlation during the growth of the network. On the basis of mean-field theory, we study the weighted network model and prove that both vertices' strength and edges' weight of this model follow the scale-free distribution. And we exploit an algorithm to forecast the network dynamics, which can be used to reckon the distributions and the corresponding scaling exponents. Furthermore, we observe that mean-field based theoretic results are consistent with the statistical data of the model, which denotes the theoretical result in this paper is effective.
Homophyly/Kinship Model: Naturally Evolving Networks
Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi
2015-10-01
It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.
Fairhead, J.; O'Sullivan, D.
1996-01-01
How and why do some networks develop rapidly and effectively, and others not? This paper draws from two contrasting project case-studies to help us discuss this question. The first case shows a pattern of network development that is highly projectspecific and can best be described by a conventional, essentially linear, 'stages' development model. In such a model, network effectiveness is believed to be a product of repeated social interactions that ultimately result in a high degree of social...
Evaluating Network Models: A Likelihood Analysis
Wang, Wen-Qiang; Zhou, Tao
2011-01-01
Many models are put forward to mimic the evolution of real networked systems. A well-accepted way to judge the validity is to compare the modeling results with real networks subject to several structural features. Even for a specific real network, we cannot fairly evaluate the goodness of different models since there are too many structural features while there is no criterion to select and assign weights on them. Motivated by the studies on link prediction algorithms, we propose a unified method to evaluate the network models via the comparison of the likelihoods of the currently observed network driven by different models, with an assumption that the higher the likelihood is, the better the model is. We test our method on the real Internet at the Autonomous System (AS) level, and the results suggest that the Generalized Linear Preferential (GLP) model outperforms the Tel Aviv Network Generator (Tang), while both two models are better than the Barab\\'asi-Albert (BA) and Erd\\"os-R\\'enyi (ER) models. Our metho...
Designing Network-based Business Model Ontology
Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz
2015-01-01
is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....
An endogenous model of the credit network
He, Jianmin; Sui, Xin; Li, Shouwei
2016-01-01
In this paper, an endogenous credit network model of firm-bank agents is constructed. The model describes the endogenous formation of firm-firm, firm-bank and bank-bank credit relationships. By means of simulations, the model is capable of showing some obvious similarities with empirical evidence found by other scholars: the upper-tail of firm size distribution can be well fitted with a power-law; the bank size distribution can be lognormally distributed with a power-law tail; the bank in-degrees of the interbank credit network as well as the firm-bank credit network fall into two-power-law distributions.
Modelling and designing electric energy networks
The author gives an overview of his research works in the field of electric network modelling. After a brief overview of technological evolutions from the telegraph to the all-electric fly-by-wire aircraft, he reports and describes various works dealing with a simplified modelling of electric systems and with fractal simulation. Then, he outlines the challenges for the design of electric networks, proposes a design process, gives an overview of various design models, methods and tools, and reports an application in the design of electric networks for future jumbo jets
Characterization and Modeling of Network Traffic
Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur;
2011-01-01
This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter......-arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values...
Larsson, Hilde Kristina
Computational fluid dynamics (CFD) is the application of numerical methods to solve systems of partial differential equations related to fluid dynamics. The continuity and the momentum equations are the most commonly applied equations within CFD, and together they can be used to calculate the...... velocity and pressure distributions in a fluid. CFD also enables the modelling of several fluids simultaneously, e.g. gas bubbles in a liquid, as well as the presence of turbulence and dissolved chemicals in a fluid, and many other phenomena. This makes CFD an appreciated tool for studying flow structures......, mixing, and other mass transfer phenomena in chemical and biochemical reactor systems. In this project, four selected case studies are investigated in order to explore the capabilities of CFD. The selected cases are a 1 ml stirred microbioreactor, an 8 ml magnetically stirred reactor, a Rushton impeller...
Multiscaling in an YX model of networks
Holme, Petter; Wu, Zhi-Xi; Minnhagen, Petter
2009-09-01
We investigate a Hamiltonian model of networks. The model is a mirror formulation of the XY model (hence the name)—instead of letting the XY spins vary, keeping the coupling topology static, we keep the spins conserved and sample different underlying networks. Our numerical simulations show complex scaling behaviors with various exponents as the system grows and temperature approaches zero, but no finite-temperature universal critical behavior. The ground-state and low-order excitations for sparse, finite graphs are a fragmented set of isolated network clusters. Configurations of higher energy are typically more connected. The connected networks of lowest energy are stretched out giving the network large average distances. For the finite sizes we investigate, there are three regions—a low-energy regime of fragmented networks, an intermediate regime of stretched-out networks, and a high-energy regime of compact, disordered topologies. Scaling up the system size, the borders between these regimes approach zero temperature algebraically, but different network-structural quantities approach their T=0 values with different exponents. We argue this is a, perhaps rare, example of a statistical-physics model where finite sizes show a more interesting behavior than the thermodynamic limit.
Cyber threat model for tactical radio networks
Kurdziel, Michael T.
2014-05-01
The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.
Modeling GMPLS and Optical MPLS Networks
Christiansen, Henrik Lehrmann; Wessing, Henrik
2003-01-01
. The MPLS concept is attractive because it can work as a unifying control structure. covering all technologies. This paper describes how a novel scheme for optical MPLS and circuit switched GMPLS based networks can incorporated in such multi-domain, MPLS-based scenarios and how it could be modeled. Network...
Stochastic discrete model of karstic networks
Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.
Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.
Histological and biochemical analysis of DNA damage after BNCT in rat model
To understand the mechanism of tumor cell death induced by boron neutron capture therapy (BNCT) and to optimize BNCT condition, we used rat tumor graft models and histological and biochemical analyses were carried out focusing on DNA damage response. Rat lymphosarcoma cells were grafted subcutaneously into male Wister rats. The rats with developed tumors were then treated with neutron beam irradiation 45 min after injection of 330 mg/kg bodyweight boronophenylalanine (10BPA) (+BPA) or saline control (–BPA). BNCT was carried out in the National Nuclear Center of the Republic of Kazakhstan (neutron flux: 1×109 nvt/s, fluence: 6×1011 nvt) with the presence of background γ-irradiation of 33 Gy. 6 and 20 h after BNCT treatment, tumors were resected, fixed and subjected to immunohistochemistry and biochemical analyses. Immunostaining of nuclei showed that double strand break (DSB) marker gamma H2AX staining was high in 20 h/+BPA sample but not in 20 h/–BPA samples. Poly(ADP-ribose), DSB and single strand break markers of DNA, also demonstrated this tendency. These two markers were observed at low levels in unirradiated tissues or 6 h after BNCT either under −BPA and +BPA conditions. HMGB1 level increased in 6 h/+BPA but not in 6 h/−BPA or 20 h/+BPA samples. The persistent staining of γH2AX and poly(ADP-ribose) in +BPA group suggests accumulated DSB damage after BNCT. The early HMGB1 upregulation and γH2AX and poly(ADP-ribose) observed later might be the markers for monitoring the DNA damage induced by BNCT. - Highlights: • We used rat tumor graft models and DNA damage response in BNCT was analyzed. • HMGB1 upregulation was suggested to be an early marker for BNCT. • The persistent presence of γH2AX and PAR in the nuclei might serve as late markers
Model-based control of networked systems
Garcia, Eloy; Montestruque, Luis A
2014-01-01
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled. The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...
Modeling trust context in networks
Adali, Sibel
2013-01-01
We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout
Graphical Model Theory for Wireless Sensor Networks
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm
Graphical Model Theory for Wireless Sensor Networks
Davis, William B.
2002-12-08
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.
Monochromaticity in neutral evolutionary network models.
Halu, Arda; Bianconi, Ginestra
2012-12-01
Recent studies on epistatic networks of model organisms have unveiled a certain type of modular property called monochromaticity in which the networks are clustered into functional modules that interact with each other through the same type of epistasis. Here, we propose and study three epistatic network models that are inspired by the duplication-divergence mechanism to gain insight into the evolutionary basis of monochromaticity and to test if it can be explained as the outcome of a neutral evolutionary hypothesis. We show that the epistatic networks formed by these stochastic evolutionary models have monochromaticity conflict distributions that are centered close to zero and are statistically significantly different from their randomized counterparts. In particular, the last model we propose yields a strictly monochromatic solution. Our results agree with the monochromaticity findings in real organisms and point toward the possible role of a neutral mechanism in the evolution of this phenomenon. PMID:23367998
Modeling Network Traffic in Wavelet Domain
Sheng Ma
2004-12-01
Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.
Fracture network modelling and performance assessment support
This report describes the Fracture Network Modelling and Performance Assessment Support performed by Golder Associates Inc. during the Heisei-12 (2000-2001) fiscal year. The primary objective of the work described in this report was to assist JNC in research related to characterization of solute transport pathways in fracture networks by the discrete fracture network (DFN) and channel network (CN) approaches. In addition, Golder supported JNC participation in the Aespoe Modeling Task Force on of Groundwater Flow and Transport (AMTF). Golder carried out extensive analyses of flow and transport for a 5 meter scale fracture network pathway for AMTF Task 4, and assisted in development of AMTF Task 6 which will address the integration of site characterization and repository safety assessment. Technical information about Golder Associates HY-12 support to JNC/Tokai is provided in the appendices to this report. (author)
Modelling subtle growth of linguistic networks
Kulig, Andrzej; Kwapien, Jaroslaw; Oswiecimka, Pawel
2014-01-01
We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest path length. Then, we identify grammar induced local chain-like linear growth as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.
Sparsity in Model Gene Regulatory Networks
We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)
Soliman Sylvain
2012-05-01
Full Text Available Abstract Background We present a way to compute the minimal semi-positive invariants of a Petri net representing a biological reaction system, as resolution of a Constraint Satisfaction Problem. The use of Petri nets to manipulate Systems Biology models and make available a variety of tools is quite old, and recently analyses based on invariant computation for biological models have become more and more frequent, for instance in the context of module decomposition. Results In our case, this analysis brings both qualitative and quantitative information on the models, in the form of conservation laws, consistency checking, etc. thanks to finite domain constraint programming. It is noticeable that some of the most recent optimizations of standard invariant computation techniques in Petri nets correspond to well-known techniques in constraint solving, like symmetry-breaking. Moreover, we show that the simple and natural encoding proposed is not only efficient but also flexible enough to encompass sub/sur-invariants, siphons/traps, etc., i.e., other Petri net structural properties that lead to supplementary insight on the dynamics of the biochemical system under study. Conclusions A simple implementation based on GNU-Prolog's finite domain solver, and including symmetry detection and breaking, was incorporated into the BIOCHAM modelling environment and in the independent tool Nicotine. Some illustrative examples and benchmarks are provided.
Energy-oriented models for WDM networks
Ricciardi, Sergio; Careglio, Davide; Palmieri, Francesco; Fiore, Ugo; Santos Boada, Germán; Solé Pareta, Josep
2010-01-01
A realistic energy-oriented model is necessary to formally characterize the energy consumption and the consequent carbon footprint of actual and future high-capacity WDM networks. The energy model describes the energy consumption of the various network elements (NE) and predicts their energy consumption behavior under different traffic loads and for the diverse traffic types, including all optical and electronic traffic, O/E/O conversions, 3R regenerations, add/drop multiplexing, etc. Besi...
Survey of propagation Model in wireless Network
Hemant Kumar Sharma; Sanjeev Sharma; Krishna Kumar Pandey
2011-01-01
To implementation of mobile ad hoc network wave propagation models are necessary to determine propagation characteristic through a medium. Wireless mobile ad hoc networks are self creating and self organizing entity. Propagation study provides an estimation of signal characteristics. Accurate prediction of radio propagation behaviour for MANET is becoming a difficult task. This paper presents investigation of propagation model. Radio wave propagation mechanisms are absorption, reflection, ref...
Clustered model reduction of positive directed networks
Ishizaki, Takayuki; Kashima, Kenji; Girard, Antoine; Imura, Jun-ichi; Chen, Luonan; Aihara, Kazuyuki
2015-01-01
This paper proposes a clustered model reduction method for semistable positive linear systems evolving over directed networks. In this method, we construct a set of clusters, i.e., disjoint sets of state variables, based on a notion of cluster reducibility, defined as the uncontrollability of local states. By aggregating the reducible clusters with aggregation coefficients associated with the Frobenius eigenvector, we obtain an approximate model that preserves not only a network structure amo...
IP Network Management Model Based on NGOSS
ZHANG Jin-yu; LI Hong-hui; LIU Feng
2004-01-01
This paper addresses a management model for IP network based on Next Generation Operation Support System (NGOSS). It makes the network management on the base of all the operation actions of ISP, It provides QoS to user service through the whole path by providing end-to-end Service Level Agreements (SLA) management through whole path. Based on web and coordination technology, this paper gives an implement architecture of this model.
Model-Based Clustering of Large Networks
Vu, Duy Quang; Schweinberger, Michael
2012-01-01
We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger datasets than those seen elsewhere in the literature. The more flexible modeling framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms, which we show how to adapt to the more complicated optimization requirements introduced by the constraints imposed by the novel parameterizations we propose, are based on variational generalized EM algorithms...
A simple model for studying interacting networks
Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.
2011-03-01
Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.
A survey of statistical network models
Goldenberg, Anna; Fienberg, Stephen E; Airoldi, Edoardo M
2009-01-01
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry poin...
The Network Performance Assessment Model - Regulation with a Reference Network
A new model - the Network Performance Assessment Model - has been developed gradually since 1998, in order to evaluate and benchmark local electricity grids. The model is intended to be a regulation tool for the Swedish local electricity networks, used by the Swedish Energy Agency. At spring 2004 the Network Performance Assessment Model will run into operation, based on the companies' results for 2003. The mission of the Network Performance Assessment Model is to evaluate the networks from a costumers' point of view and establish a fair price level. In order to do that, the performance of the operator is evaluated. The performances are assessed in correspondence to a price level that the consumer is considered to accept, can agree to as fair and is prepared to pay. This price level is based on an average cost, based on the cost of an efficient grid that will be built today, with already known technology. The performances are accounted in Customer Values. Those Customer Values are what can be created by someone but can't be created better by someone else. The starting point is to look upon the companies from a customers' point of view. The factors that can't be influenced by the companies are evaluated by fixed rules, valid to all companies. The rules reflect the differences. The cost for a connection is evaluated from the actual facts, i.e. the distances between the subscribers and the demanded capacity by the subscriber. This is done by the creation of a reference network, with a capacity to fulfill the demand from the subscriber. This is an efficient grid with no spare capacity and no excess capacity. The companies' existing grid are without importance, as well as holds for dimensioning as technology. Those factors which the company can influence, for an example connection reliability, are evaluated from a customer perspective by measuring the actual reliability, measured as the number and length of the interruption. When implemented to the regulation the Network
MODEL FOR NETWORKED BUSINESS: Case study of Application Service Provider's network
Pesonen, Tero
2011-01-01
MODEL FOR NETWORKED BUSINESS Case study of Application Service Provider's network The aim of the research was to create a network business model to optimise benefits for a business network in the area of software industry. The main research questions were: ? What kind of network business models can be found? ? What are the value creation mechanisms as well as advantages and disadvantages of different models? ? How to use former frameworks to develop a network business mode...
Research on Context Aware Network Security Model
XiaoHui Guo
2013-08-01
Full Text Available According to high development of internet and mobile internet technologies, more and more services and applications are researched and are becoming more and more important in people’s life. At the same time, there are still many risks that virus attack from internet. Network security is facing more challenges than before, such as attack method becoming more diversify, attack times are increasing rapidly, and attack behavior are becoming a system to damage network security. What’s more, application data is more changeable that before, which makes it more difficult to judge which behavior is attack and response to the attack. Current network security system can’t prevent a system level attack, what’s more, can’t response to the attack quickly and effectively. Therefore, this paper designed a new context aware network security model to prevent various attack effectively, present the context declaim algorithm to judge network attack, and then designed a data share mechanism to share attack information with peer machine, which can decrease the response time deeply. Finally, this paper designed a set experiment to validate the quality and performance of context based network security model, the result shows this model can prevent network attack effectively and more memory saving in changeable application.
Performance modeling, stochastic networks, and statistical multiplexing
Mazumdar, Ravi R
2013-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan
Modeling, Optimization & Control of Hydraulic Networks
Tahavori, Maryamsadat
2014-01-01
. The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...
'Enzyme Test Bench': A biochemical application of the multi-rate modeling
Rachinskiy, K; Buechs, J [Department of Biochemical Engineering, Sammelbau Biologie, RWTH-Aachen University, D-52074 Aachen (Germany); Schultze, H; Boy, M [BASF Aktiengesellschaft, Ludwigshafen (Germany)], E-mail: buechs@biovt.rwth-aachen.de
2008-11-01
In the expanding field of 'white biotechnology' enzymes are frequently applied to catalyze the biochemical reaction from a resource material to a valuable product. Evolutionary designed to catalyze the metabolism in any life form, they selectively accelerate complex reactions under physiological conditions. Modern techniques, such as directed evolution, have been developed to satisfy the increasing demand on enzymes. Applying these techniques together with rational protein design, we aim at improving of enzymes' activity, selectivity and stability. To tap the full potential of these techniques, it is essential to combine them with adequate screening methods. Nowadays a great number of high throughput colorimetric and fluorescent enzyme assays are applied to measure the initial enzyme activity with high throughput. However, the prediction of enzyme long term stability within short experiments is still a challenge. A new high throughput technique for enzyme characterization with specific attention to the long term stability, called 'Enzyme Test Bench', is presented. The concept of the Enzyme Test Bench consists of short term enzyme tests conducted under partly extreme conditions to predict the enzyme long term stability under moderate conditions. The technique is based on the mathematical modeling of temperature dependent enzyme activation and deactivation. Adapting the temperature profiles in sequential experiments by optimum non-linear experimental design, the long term deactivation effects can be purposefully accelerated and detected within hours. During the experiment the enzyme activity is measured online to estimate the model parameters from the obtained data. Thus, the enzyme activity and long term stability can be calculated as a function of temperature. The results of the characterization, based on micro liter format experiments of hours, are in good agreement with the results of long term experiments in 1L format. Thus, the new
'Enzyme Test Bench': A biochemical application of the multi-rate modeling
In the expanding field of 'white biotechnology' enzymes are frequently applied to catalyze the biochemical reaction from a resource material to a valuable product. Evolutionary designed to catalyze the metabolism in any life form, they selectively accelerate complex reactions under physiological conditions. Modern techniques, such as directed evolution, have been developed to satisfy the increasing demand on enzymes. Applying these techniques together with rational protein design, we aim at improving of enzymes' activity, selectivity and stability. To tap the full potential of these techniques, it is essential to combine them with adequate screening methods. Nowadays a great number of high throughput colorimetric and fluorescent enzyme assays are applied to measure the initial enzyme activity with high throughput. However, the prediction of enzyme long term stability within short experiments is still a challenge. A new high throughput technique for enzyme characterization with specific attention to the long term stability, called 'Enzyme Test Bench', is presented. The concept of the Enzyme Test Bench consists of short term enzyme tests conducted under partly extreme conditions to predict the enzyme long term stability under moderate conditions. The technique is based on the mathematical modeling of temperature dependent enzyme activation and deactivation. Adapting the temperature profiles in sequential experiments by optimum non-linear experimental design, the long term deactivation effects can be purposefully accelerated and detected within hours. During the experiment the enzyme activity is measured online to estimate the model parameters from the obtained data. Thus, the enzyme activity and long term stability can be calculated as a function of temperature. The results of the characterization, based on micro liter format experiments of hours, are in good agreement with the results of long term experiments in 1L format. Thus, the new technique allows for both
'Enzyme Test Bench': A biochemical application of the multi-rate modeling
Rachinskiy, K.; Schultze, H.; Boy, M.; Büchs, J.
2008-11-01
In the expanding field of 'white biotechnology' enzymes are frequently applied to catalyze the biochemical reaction from a resource material to a valuable product. Evolutionary designed to catalyze the metabolism in any life form, they selectively accelerate complex reactions under physiological conditions. Modern techniques, such as directed evolution, have been developed to satisfy the increasing demand on enzymes. Applying these techniques together with rational protein design, we aim at improving of enzymes' activity, selectivity and stability. To tap the full potential of these techniques, it is essential to combine them with adequate screening methods. Nowadays a great number of high throughput colorimetric and fluorescent enzyme assays are applied to measure the initial enzyme activity with high throughput. However, the prediction of enzyme long term stability within short experiments is still a challenge. A new high throughput technique for enzyme characterization with specific attention to the long term stability, called 'Enzyme Test Bench', is presented. The concept of the Enzyme Test Bench consists of short term enzyme tests conducted under partly extreme conditions to predict the enzyme long term stability under moderate conditions. The technique is based on the mathematical modeling of temperature dependent enzyme activation and deactivation. Adapting the temperature profiles in sequential experiments by optimum non-linear experimental design, the long term deactivation effects can be purposefully accelerated and detected within hours. During the experiment the enzyme activity is measured online to estimate the model parameters from the obtained data. Thus, the enzyme activity and long term stability can be calculated as a function of temperature. The results of the characterization, based on micro liter format experiments of hours, are in good agreement with the results of long term experiments in 1L format. Thus, the new technique allows for both
Modeling acquaintance networks based on balance theory
Vukašinović Vida
2014-09-01
Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models
Butovich, Igor A.; Lu, Hua; McMahon, Anne; Eule, J Corinna
2012-01-01
A side by side comparison of the rabbit and the human meibum demonstrated their vast biochemical differences. Thus, the rabbit seems to be a poor animal model of the human tear film studies. Mice and canines, on the other hand, were found to be very similar to humans and should be considered instead.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Modeling Emergence in Neuroprotective Regulatory Networks
Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.; Stevens, S.L.; Stenzel-Poore, Mary
2013-01-05
The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.
International migration network: Topology and modeling
Fagiolo, Giorgio; Mastrorillo, Marina
2013-07-01
This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.
Ignat Drozdov
Full Text Available Small intestinal (SI neuroendocrine tumors (NET are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations. All were up-regulated (p<0.035 with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10(-5 M significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10(-5 M stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D(2 and Serotonin [5-HT(2] receptor agonist, 10(-6 M stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8-2-fold for isoproterenol and forskolin. Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional
E.V.M. Maciel de Carvalho
2010-05-01
Full Text Available The subject was represented and discussed at The National Week of Science and Technology, UFPE, an initiative from The Ministry of Science and Technology to encourage children and people in science and technology activities. The work aimed to renew the importance to transmit knowledge from simple, imaginative, biochemical models and interactive teaching. The stand tool contained an aquarium with fishes, five scale models showing peptide bond, carbohydrate inhibited lectin molecule, hemagglutination reaction, lectin-bacterium surface interaction and enzyme-substract-inhibitor. Posters described tropical fish importance and methods applied to obtain fish serum and organs to purify lectins and protein inhibitors as well as to extract tissue DNA; notions were transmitted on fish immunology and diseases. The students were attracted and impressed with the exotic fishes most cultivated in Brazil; they asked if it is necessary to kill the fish to extract lectin and about lectin importance. Students were also interested to know if all fish enzyme/inhibitors are favorable to the own fish organism. The work succeeded to inform and stimulate future scientists in the field and to awake their scientific curiosity.
Modeling of urban traffic networks with lattice Boltzmann model
Meng, Jian-ping; Qian, Yue-hong; Dai, Shi-qiang
2008-02-01
It is of great importance to uncover the characteristics of traffic networks. However, there have been few researches concerning kinetics models for urban traffic networks. In this work, a lattice Boltzmann model (LBM) for urban traffic networks is proposed by incorporating the ideas of the Biham-Middleton-Levine (BML) model into the LBM for road traffic. In the present model, situations at intersections with the red and green traffic signals are treated as a kind of boundary conditions varying with time. Thus, the urban traffic network could be described in the mesoscopic level. By performing numerical simulations under the periodic boundary conditions, the behavior of average velocity is investigated in detail. The numerical results agree quite well with those given by the Chowdhury-Schadschneider (ChSch) model (Chowdhury D. and Schadschneider A., Phys. Rev. E, 59 (1999) R1311). Furthermore, the statistical noise is reduced in this discrete kinetics model, thus, the present model has considerably high computational efficiency.