Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools.

Science.gov (United States)

Miryala, Sravan Kumar; Anbarasu, Anand; Ramaiah, Sudha

2018-02-05

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks. Copyright © 2017 Elsevier B.V. All rights reserved.

Membrane-based biomolecular smart materials

International Nuclear Information System (INIS)

Sarles, Stephen A; Leo, Donald J

2011-01-01

Membrane-based biomolecular materials are a new class of smart material that feature networks of artificial lipid bilayers contained within durable synthetic substrates. Bilayers contained within this modular material platform provide an environment that can be tailored to host an enormous diversity of functional biomolecules, where the functionality of the global material system depends on the type(s) and organization(s) of the biomolecules that are chosen. In this paper, we review a series of biomolecular material platforms developed recently within the Leo Group at Virginia Tech and we discuss several novel coupling mechanisms provided by these hybrid material systems. The platforms developed demonstrate that the functions of biomolecules and the properties of synthetic materials can be combined to operate in concert, and the examples provided demonstrate how the formation and properties of a lipid bilayer can respond to a variety of stimuli including mechanical forces and electric fields

Thermodynamics of random reaction networks.

Science.gov (United States)

Fischer, Jakob; Kleidon, Axel; Dittrich, Peter

2015-01-01

Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha) and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.

Thermodynamics of random reaction networks.

Directory of Open Access Journals (Sweden)

Jakob Fischer

Full Text Available Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

Science.gov (United States)

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data.

Science.gov (United States)

Cho, Kwang-Hyun; Choo, Sang-Mok; Wellstead, Peter; Wolkenhauer, Olaf

2005-08-15

We propose a unified framework for the identification of functional interaction structures of biomolecular networks in a way that leads to a new experimental design procedure. In developing our approach, we have built upon previous work. Thus we begin by pointing out some of the restrictions associated with existing structure identification methods and point out how these restrictions may be eased. In particular, existing methods use specific forms of experimental algebraic equations with which to identify the functional interaction structure of a biomolecular network. In our work, we employ an extended form of these experimental algebraic equations which, while retaining their merits, also overcome some of their disadvantages. Experimental data are required in order to estimate the coefficients of the experimental algebraic equation set associated with the structure identification task. However, experimentalists are rarely provided with guidance on which parameters to perturb, and to what extent, to perturb them. When a model of network dynamics is required then there is also the vexed question of sample rate and sample time selection to be resolved. Supplying some answers to these questions is the main motivation of this paper. The approach is based on stationary and/or temporal data obtained from parameter perturbations, and unifies the previous approaches of Kholodenko et al. (PNAS 99 (2002) 12841-12846) and Sontag et al. (Bioinformatics 20 (2004) 1877-1886). By way of demonstration, we apply our unified approach to a network model which cannot be properly identified by existing methods. Finally, we propose an experiment design methodology, which is not limited by the amount of parameter perturbations, and illustrate its use with an in numero example.

Biomolecular Sciences: uniting Biology and Chemistry

NARCIS (Netherlands)

Vrieling, Engel

2017-01-01

Biomolecular Sciences: uniting Biology and Chemistry www.rug.nl/research/gbb The scientific discoveries in biomolecular sciences have benefitted enormously from technological innovations. At the Groningen Biomolecular Science and Biotechnology Institute (GBB) we now sequence a genome in days,

Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems

International Nuclear Information System (INIS)

Zhou, Peipei; Cai, Shuiming; Liu, Zengrong; Chen, Luonan; Wang, Ruiqi

2013-01-01

To understand how a complex biomolecular network functions, a decomposition or a reconstruction process of the network is often needed so as to provide new insights into the regulatory mechanisms underlying various dynamical behaviors and also to gain qualitative knowledge of the network. Unfortunately, it seems that there are still no general rules on how to decompose a complex network into simple modules. An alternative resolution is to decompose a complex network into small modules or subsystems with specified functions such as switches and oscillators and then integrate them by analyzing the interactions between them. The main idea of this approach can be illustrated by considering a bidirectionally coupled network in this paper, i.e., coupled Toggle switch and Repressilator, and analyzing the occurrence of various dynamics, although the theoretical principle may hold for a general class of networks. We show that various biomolecular signals can be shaped by regulating the coupling between the subsystems. The approach presented here can be expected to simplify and analyze even more complex biological networks

Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems

Energy Technology Data Exchange (ETDEWEB)

Zhou, Peipei [Institute of Systems Biology, Shanghai University, Shanghai 200444 (China); Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu 212013 (China); Cai, Shuiming [Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu 212013 (China); Liu, Zengrong [Institute of Systems Biology, Shanghai University, Shanghai 200444 (China); Chen, Luonan [Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Center for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031 (China); Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505 (Japan); Wang, Ruiqi [Institute of Systems Biology, Shanghai University, Shanghai 200444 (China)

2013-05-15

To understand how a complex biomolecular network functions, a decomposition or a reconstruction process of the network is often needed so as to provide new insights into the regulatory mechanisms underlying various dynamical behaviors and also to gain qualitative knowledge of the network. Unfortunately, it seems that there are still no general rules on how to decompose a complex network into simple modules. An alternative resolution is to decompose a complex network into small modules or subsystems with specified functions such as switches and oscillators and then integrate them by analyzing the interactions between them. The main idea of this approach can be illustrated by considering a bidirectionally coupled network in this paper, i.e., coupled Toggle switch and Repressilator, and analyzing the occurrence of various dynamics, although the theoretical principle may hold for a general class of networks. We show that various biomolecular signals can be shaped by regulating the coupling between the subsystems. The approach presented here can be expected to simplify and analyze even more complex biological networks.

Modeling of fluctuating reaction networks

International Nuclear Information System (INIS)

Lipshtat, A.; Biham, O.

2004-01-01

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

A network dynamics approach to chemical reaction networks

Science.gov (United States)

van der Schaft, A. J.; Rao, S.; Jayawardhana, B.

2016-04-01

A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a 'zero' complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.

Prediction of Biomolecular Complexes

KAUST Repository

Vangone, Anna; Oliva, Romina; Cavallo, Luigi; Bonvin, Alexandre M. J. J.

2017-01-01

Almost all processes in living organisms occur through specific interactions between biomolecules. Any dysfunction of those interactions can lead to pathological events. Understanding such interactions is therefore a crucial step in the investigation of biological systems and a starting point for drug design. In recent years, experimental studies have been devoted to unravel the principles of biomolecular interactions; however, due to experimental difficulties in solving the three-dimensional (3D) structure of biomolecular complexes, the number of available, high-resolution experimental 3D structures does not fulfill the current needs. Therefore, complementary computational approaches to model such interactions are necessary to assist experimentalists since a full understanding of how biomolecules interact (and consequently how they perform their function) only comes from 3D structures which provide crucial atomic details about binding and recognition processes. In this chapter we review approaches to predict biomolecular complexesBiomolecular complexes, introducing the concept of molecular dockingDocking, a technique which uses a combination of geometric, steric and energetics considerations to predict the 3D structure of a biological complex starting from the individual structures of its constituent parts. We provide a mini-guide about docking concepts, its potential and challenges, along with post-docking analysis and a list of related software.

Prediction of Biomolecular Complexes

KAUST Repository

Vangone, Anna

2017-04-12

Almost all processes in living organisms occur through specific interactions between biomolecules. Any dysfunction of those interactions can lead to pathological events. Understanding such interactions is therefore a crucial step in the investigation of biological systems and a starting point for drug design. In recent years, experimental studies have been devoted to unravel the principles of biomolecular interactions; however, due to experimental difficulties in solving the three-dimensional (3D) structure of biomolecular complexes, the number of available, high-resolution experimental 3D structures does not fulfill the current needs. Therefore, complementary computational approaches to model such interactions are necessary to assist experimentalists since a full understanding of how biomolecules interact (and consequently how they perform their function) only comes from 3D structures which provide crucial atomic details about binding and recognition processes. In this chapter we review approaches to predict biomolecular complexesBiomolecular complexes, introducing the concept of molecular dockingDocking, a technique which uses a combination of geometric, steric and energetics considerations to predict the 3D structure of a biological complex starting from the individual structures of its constituent parts. We provide a mini-guide about docking concepts, its potential and challenges, along with post-docking analysis and a list of related software.

A network dynamics approach to chemical reaction networks

NARCIS (Netherlands)

van der Schaft, Abraham; Rao, S.; Jayawardhana, B.

2016-01-01

A treatment of chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a

Biomolecular Science (Fact Sheet)

Energy Technology Data Exchange (ETDEWEB)

2012-04-01

A brief fact sheet about NREL Photobiology and Biomolecular Science. The research goal of NREL's Biomolecular Science is to enable cost-competitive advanced lignocellulosic biofuels production by understanding the science critical for overcoming biomass recalcitrance and developing new product and product intermediate pathways. NREL's Photobiology focuses on understanding the capture of solar energy in photosynthetic systems and its use in converting carbon dioxide and water directly into hydrogen and advanced biofuels.

Converting biomolecular modelling data based on an XML representation.

Science.gov (United States)

Sun, Yudong; McKeever, Steve

2008-08-25

Biomolecular modelling has provided computational simulation based methods for investigating biological processes from quantum chemical to cellular levels. Modelling such microscopic processes requires atomic description of a biological system and conducts in fine timesteps. Consequently the simulations are extremely computationally demanding. To tackle this limitation, different biomolecular models have to be integrated in order to achieve high-performance simulations. The integration of diverse biomolecular models needs to convert molecular data between different data representations of different models. This data conversion is often non-trivial, requires extensive human input and is inevitably error prone. In this paper we present an automated data conversion method for biomolecular simulations between molecular dynamics and quantum mechanics/molecular mechanics models. Our approach is developed around an XML data representation called BioSimML (Biomolecular Simulation Markup Language). BioSimML provides a domain specific data representation for biomolecular modelling which can effciently support data interoperability between different biomolecular simulation models and data formats.

Converting Biomolecular Modelling Data Based on an XML Representation

Directory of Open Access Journals (Sweden)

Sun Yudong

2008-06-01

Full Text Available Biomolecular modelling has provided computational simulation based methods for investigating biological processes from quantum chemical to cellular levels. Modelling such microscopic processes requires atomic description of a biological system and conducts in fine timesteps. Consequently the simulations are extremely computationally demanding. To tackle this limitation, different biomolecular models have to be integrated in order to achieve high-performance simulations. The integration of diverse biomolecular models needs to convert molecular data between different data representations of different models. This data conversion is often non-trivial, requires extensive human input and is inevitably error prone. In this paper we present an automated data conversion method for biomolecular simulations between molecular dynamics and quantum mechanics/molecular mechanics models. Our approach is developed around an XML data representation called BioSimML (Biomolecular Simulation Markup Language. BioSimML provides a domain specific data representation for biomolecular modelling which can effciently support data interoperability between different biomolecular simulation models and data formats.

A model system for targeted drug release triggered by biomolecular signals logically processed through enzyme logic networks.

Science.gov (United States)

Mailloux, Shay; Halámek, Jan; Katz, Evgeny

2014-03-07

A new Sense-and-Act system was realized by the integration of a biocomputing system, performing analytical processes, with a signal-responsive electrode. A drug-mimicking release process was triggered by biomolecular signals processed by different logic networks, including three concatenated AND logic gates or a 3-input OR logic gate. Biocatalytically produced NADH, controlled by various combinations of input signals, was used to activate the electrochemical system. A biocatalytic electrode associated with signal-processing "biocomputing" systems was electrically connected to another electrode coated with a polymer film, which was dissolved upon the formation of negative potential releasing entrapped drug-mimicking species, an enzyme-antibody conjugate, operating as a model for targeted immune-delivery and consequent "prodrug" activation. The system offers great versatility for future applications in controlled drug release and personalized medicine.

Conservation Laws in Biochemical Reaction Networks

DEFF Research Database (Denmark)

Mahdi, Adam; Ferragut, Antoni; Valls, Claudia

2017-01-01

We study the existence of linear and nonlinear conservation laws in biochemical reaction networks with mass-action kinetics. It is straightforward to compute the linear conservation laws as they are related to the left null-space of the stoichiometry matrix. The nonlinear conservation laws...... are difficult to identify and have rarely been considered in the context of mass-action reaction networks. Here, using the Darboux theory of integrability, we provide necessary structural (i.e., parameterindependent) conditions on a reaction network to guarantee the existence of nonlinear conservation laws...

Markovian dynamics on complex reaction networks

Energy Technology Data Exchange (ETDEWEB)

Goutsias, J., E-mail: goutsias@jhu.edu; Jenkinson, G., E-mail: jenkinson@jhu.edu

2013-08-10

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.

Markovian dynamics on complex reaction networks

International Nuclear Information System (INIS)

Goutsias, J.; Jenkinson, G.

2013-01-01

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples

SPATKIN: a simulator for rule-based modeling of biomolecular site dynamics on surfaces.

Science.gov (United States)

Kochanczyk, Marek; Hlavacek, William S; Lipniacki, Tomasz

2017-11-15

Rule-based modeling is a powerful approach for studying biomolecular site dynamics. Here, we present SPATKIN, a general-purpose simulator for rule-based modeling in two spatial dimensions. The simulation algorithm is a lattice-based method that tracks Brownian motion of individual molecules and the stochastic firing of rule-defined reaction events. Because rules are used as event generators, the algorithm is network-free, meaning that it does not require to generate the complete reaction network implied by rules prior to simulation. In a simulation, each molecule (or complex of molecules) is taken to occupy a single lattice site that cannot be shared with another molecule (or complex). SPATKIN is capable of simulating a wide array of membrane-associated processes, including adsorption, desorption and crowding. Models are specified using an extension of the BioNetGen language, which allows to account for spatial features of the simulated process. The C ++ source code for SPATKIN is distributed freely under the terms of the GNU GPLv3 license. The source code can be compiled for execution on popular platforms (Windows, Mac and Linux). An installer for 64-bit Windows and a macOS app are available. The source code and precompiled binaries are available at the SPATKIN Web site (http://pmbm.ippt.pan.pl/software/spatkin). spatkin.simulator@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A Networks Approach to Modeling Enzymatic Reactions.

Science.gov (United States)

Imhof, P

2016-01-01

Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.

Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

Directory of Open Access Journals (Sweden)

St Laurent Georges

2010-03-01

signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology, but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes. Conclusions Overall, our in silico study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.

Biomolecular engineering for nanobio/bionanotechnology

Science.gov (United States)

Nagamune, Teruyuki

2017-04-01

Biomolecular engineering can be used to purposefully manipulate biomolecules, such as peptides, proteins, nucleic acids and lipids, within the framework of the relations among their structures, functions and properties, as well as their applicability to such areas as developing novel biomaterials, biosensing, bioimaging, and clinical diagnostics and therapeutics. Nanotechnology can also be used to design and tune the sizes, shapes, properties and functionality of nanomaterials. As such, there are considerable overlaps between nanotechnology and biomolecular engineering, in that both are concerned with the structure and behavior of materials on the nanometer scale or smaller. Therefore, in combination with nanotechnology, biomolecular engineering is expected to open up new fields of nanobio/bionanotechnology and to contribute to the development of novel nanobiomaterials, nanobiodevices and nanobiosystems. This review highlights recent studies using engineered biological molecules (e.g., oligonucleotides, peptides, proteins, enzymes, polysaccharides, lipids, biological cofactors and ligands) combined with functional nanomaterials in nanobio/bionanotechnology applications, including therapeutics, diagnostics, biosensing, bioanalysis and biocatalysts. Furthermore, this review focuses on five areas of recent advances in biomolecular engineering: (a) nucleic acid engineering, (b) gene engineering, (c) protein engineering, (d) chemical and enzymatic conjugation technologies, and (e) linker engineering. Precisely engineered nanobiomaterials, nanobiodevices and nanobiosystems are anticipated to emerge as next-generation platforms for bioelectronics, biosensors, biocatalysts, molecular imaging modalities, biological actuators, and biomedical applications.

Multilayer Network Analysis of Nuclear Reactions

Science.gov (United States)

Zhu, Liang; Ma, Yu-Gang; Chen, Qu; Han, Ding-Ding

2016-08-01

The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however, we adopt the methods from both multilayer and reaction networks, and obtain a distinctive view by mapping all the nuclear reactions in JINA REACLIB database into a directed network with 4 layers: neutron, proton, 4He and the remainder. The layer names correspond to reaction types decided by the currency particles consumed. This combined approach reveals that, in the remainder layer, the β-stability has high correlation with node degree difference and overlapping coefficient. Moreover, when reaction rates are considered as node strength, we find that, at lower temperatures, nuclide half-life scales reciprocally with its out-strength. The connection between physical properties and topological characteristics may help to explore the boundary of the nuclide chart.

Neutral theory of chemical reaction networks

International Nuclear Information System (INIS)

Lee, Sang Hoon; Holme, Petter; Minnhagen, Petter; Bernhardsson, Sebastian; Kim, Beom Jun

2012-01-01

To what extent do the characteristic features of a chemical reaction network reflect its purpose and function? In general, one argues that correlations between specific features and specific functions are key to understanding a complex structure. However, specific features may sometimes be neutral and uncorrelated with any system-specific purpose, function or causal chain. Such neutral features are caused by chance and randomness. Here we compare two classes of chemical networks: one that has been subjected to biological evolution (the chemical reaction network of metabolism in living cells) and one that has not (the atmospheric planetary chemical reaction networks). Their degree distributions are shown to share the very same neutral system-independent features. The shape of the broad distributions is to a large extent controlled by a single parameter, the network size. From this perspective, there is little difference between atmospheric and metabolic networks; they are just different sizes of the same random assembling network. In other words, the shape of the degree distribution is a neutral characteristic feature and has no functional or evolutionary implications in itself; it is not a matter of life and death. (paper)

A statistical nanomechanism of biomolecular patterning actuated by surface potential

Science.gov (United States)

Lin, Chih-Ting; Lin, Chih-Hao

2011-02-01

Biomolecular patterning on a nanoscale/microscale on chip surfaces is one of the most important techniques used in vitro biochip technologies. Here, we report upon a stochastic mechanics model we have developed for biomolecular patterning controlled by surface potential. The probabilistic biomolecular surface adsorption behavior can be modeled by considering the potential difference between the binding and nonbinding states. To verify our model, we experimentally implemented a method of electroactivated biomolecular patterning technology and the resulting fluorescence intensity matched the prediction of the developed model quite well. Based on this result, we also experimentally demonstrated the creation of a bovine serum albumin pattern with a width of 200 nm in 5 min operations. This submicron noncovalent-binding biomolecular pattern can be maintained for hours after removing the applied electrical voltage. These stochastic understandings and experimental results not only prove the feasibility of submicron biomolecular patterns on chips but also pave the way for nanoscale interfacial-bioelectrical engineering.

Thermodynamic properties of water solvating biomolecular surfaces

Science.gov (United States)

Heyden, Matthias

Changes in the potential energy and entropy of water molecules hydrating biomolecular interfaces play a significant role for biomolecular solubility and association. Free energy perturbation and thermodynamic integration methods allow calculations of free energy differences between two states from simulations. However, these methods are computationally demanding and do not provide insights into individual thermodynamic contributions, i.e. changes in the solvent energy or entropy. Here, we employ methods to spatially resolve distributions of hydration water thermodynamic properties in the vicinity of biomolecular surfaces. This allows direct insights into thermodynamic signatures of the hydration of hydrophobic and hydrophilic solvent accessible sites of proteins and small molecules and comparisons to ideal model surfaces. We correlate dynamic properties of hydration water molecules, i.e. translational and rotational mobility, to their thermodynamics. The latter can be used as a guide to extract thermodynamic information from experimental measurements of site-resolved water dynamics. Further, we study energy-entropy compensations of water at different hydration sites of biomolecular surfaces. This work is supported by the Cluster of Excellence RESOLV (EXC 1069) funded by the Deutsche Forschungsgemeinschaft.

Biomolecular EPR spectroscopy

CERN Document Server

Hagen, Wilfred Raymond

2008-01-01

Comprehensive, Up-to-Date Coverage of Spectroscopy Theory and its Applications to Biological SystemsAlthough a multitude of books have been published about spectroscopy, most of them only occasionally refer to biological systems and the specific problems of biomolecular EPR (bioEPR). Biomolecular EPR Spectroscopy provides a practical introduction to bioEPR and demonstrates how this remarkable tool allows researchers to delve into the structural, functional, and analytical analysis of paramagnetic molecules found in the biochemistry of all species on the planet. A Must-Have Reference in an Intrinsically Multidisciplinary FieldThis authoritative reference seamlessly covers all important bioEPR applications, including low-spin and high-spin metalloproteins, spin traps and spin lables, interaction between active sites, and redox systems. It is loaded with practical tricks as well as do's and don'ts that are based on the author's 30 years of experience in the field. The book also comes with an unprecedented set of...

Open complex-balanced mass action chemical reaction networks

NARCIS (Netherlands)

Rao, Shodhan; van der Schaft, Arjan; Jayawardhana, Bayu

We consider open chemical reaction networks, i.e. ones with inflows and outflows. We assume that all the inflows to the network are constant and all outflows obey the mass action kinetics rate law. We define a complex-balanced open reaction network as one that admits a complex-balanced steady state.

Reaction Hamiltonian and state-to-state description of chemical reactions

International Nuclear Information System (INIS)

Ruf, B.A.; Kresin, V.Z.; Lester, W.A. Jr.

1985-08-01

A chemical reaction is treated as a quantum transition from reactants to products. A specific reaction Hamiltonian (in second quantization formalism) is introduced. The approach leads to Franck-Condon-like factor, and adiabatic method in the framework of the nuclear motion problems. The influence of reagent vibrational state on the product energy distribution has been studied following the reaction Hamiltonian method. Two different cases (fixed available energy and fixed translational energy) are distinguished. Results for several biomolecular reactions are presented. 40 refs., 5 figs

Biomolecular condensates: organizers of cellular biochemistry.

Science.gov (United States)

Banani, Salman F; Lee, Hyun O; Hyman, Anthony A; Rosen, Michael K

2017-05-01

Biomolecular condensates are micron-scale compartments in eukaryotic cells that lack surrounding membranes but function to concentrate proteins and nucleic acids. These condensates are involved in diverse processes, including RNA metabolism, ribosome biogenesis, the DNA damage response and signal transduction. Recent studies have shown that liquid-liquid phase separation driven by multivalent macromolecular interactions is an important organizing principle for biomolecular condensates. With this physical framework, it is now possible to explain how the assembly, composition, physical properties and biochemical and cellular functions of these important structures are regulated.

Extracting reaction networks from databases-opening Pandora's box.

Science.gov (United States)

Fearnley, Liam G; Davis, Melissa J; Ragan, Mark A; Nielsen, Lars K

2014-11-01

Large quantities of information describing the mechanisms of biological pathways continue to be collected in publicly available databases. At the same time, experiments have increased in scale, and biologists increasingly use pathways defined in online databases to interpret the results of experiments and generate hypotheses. Emerging computational techniques that exploit the rich biological information captured in reaction systems require formal standardized descriptions of pathways to extract these reaction networks and avoid the alternative: time-consuming and largely manual literature-based network reconstruction. Here, we systematically evaluate the effects of commonly used knowledge representations on the seemingly simple task of extracting a reaction network describing signal transduction from a pathway database. We show that this process is in fact surprisingly difficult, and the pathway representations adopted by various knowledge bases have dramatic consequences for reaction network extraction, connectivity, capture of pathway crosstalk and in the modelling of cell-cell interactions. Researchers constructing computational models built from automatically extracted reaction networks must therefore consider the issues we outline in this review to maximize the value of existing pathway knowledge. © The Author 2013. Published by Oxford University Press.

Improvements to the APBS biomolecular solvation software suite.

Science.gov (United States)

Jurrus, Elizabeth; Engel, Dave; Star, Keith; Monson, Kyle; Brandi, Juan; Felberg, Lisa E; Brookes, David H; Wilson, Leighton; Chen, Jiahui; Liles, Karina; Chun, Minju; Li, Peter; Gohara, David W; Dolinsky, Todd; Konecny, Robert; Koes, David R; Nielsen, Jens Erik; Head-Gordon, Teresa; Geng, Weihua; Krasny, Robert; Wei, Guo-Wei; Holst, Michael J; McCammon, J Andrew; Baker, Nathan A

2018-01-01

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that have provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses the three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this article, we discuss the models and capabilities that have recently been implemented within the APBS software package including a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory-based algorithm for determining pK a values, and an improved web-based visualization tool for viewing electrostatics. © 2017 The Protein Society.

On the Complexity of Reconstructing Chemical Reaction Networks

DEFF Research Database (Denmark)

Fagerberg, Rolf; Flamm, Christoph; Merkle, Daniel

2013-01-01

The analysis of the structure of chemical reaction networks is crucial for a better understanding of chemical processes. Such networks are well described as hypergraphs. However, due to the available methods, analyses regarding network properties are typically made on standard graphs derived from...... the full hypergraph description, e.g. on the so-called species and reaction graphs. However, a reconstruction of the underlying hypergraph from these graphs is not necessarily unique. In this paper, we address the problem of reconstructing a hypergraph from its species and reaction graph and show NP...

Graphical reduction of reaction networks by linear elimination of species

DEFF Research Database (Denmark)

Saez Cornellana, Meritxell; Wiuf, Carsten; Feliu, Elisenda

2017-01-01

We give a graphically based procedure to reduce a reaction network to a smaller reaction network with fewer species after linear elimination of a set of noninteracting species. We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties...

Structural parameter identifiability analysis for dynamic reaction networks

DEFF Research Database (Denmark)

Davidescu, Florin Paul; Jørgensen, Sten Bay

2008-01-01

method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...

Consumer Activities and Reactions to Social Network Marketing

OpenAIRE

Bistra Vassileva

2017-01-01

The purpose of this paper is to understand consumer behavioural models with respect to their reactions to social network marketing. Theoretical background is focused on online and social network usage, motivations and behaviour. The research goal is to explore consumer reactions to the exposure of social network marketing based on the following criteria: level of brand engagement, word-of-mouth (WOM) referral behaviour, and purchase intentions. Consumers are investigated ...

Biomolecular electrostatics and solvation: a computational perspective.

Science.gov (United States)

Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G; Schnieders, Michael J; Marucho, Marcelo; Zhang, Jiajing; Baker, Nathan A

2012-11-01

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

Modular verification of chemical reaction network encodings via serializability analysis

Science.gov (United States)

Lakin, Matthew R.; Stefanovic, Darko; Phillips, Andrew

2015-01-01

Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a “commit reaction” that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of “extra tolerance”, which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of any system constructed using verified encodings. As an example, we show how this approach may be used to verify two- and four-domain DNA strand displacement encodings of chemical reaction networks, and we generalize our result to the limit where the populations of helper species are unlimited. PMID:27325906

Conditions for extinction events in chemical reaction networks with discrete state spaces.

Science.gov (United States)

Johnston, Matthew D; Anderson, David F; Craciun, Gheorghe; Brijder, Robert

2018-05-01

We study chemical reaction networks with discrete state spaces and present sufficient conditions on the structure of the network that guarantee the system exhibits an extinction event. The conditions we derive involve creating a modified chemical reaction network called a domination-expanded reaction network and then checking properties of this network. Unlike previous results, our analysis allows algorithmic implementation via systems of equalities and inequalities and suggests sequences of reactions which may lead to extinction events. We apply the results to several networks including an EnvZ-OmpR signaling pathway in Escherichia coli.

Characterizing multistationarity regimes in biochemical reaction networks.

Directory of Open Access Journals (Sweden)

Irene Otero-Muras

Full Text Available Switch like responses appear as common strategies in the regulation of cellular systems. Here we present a method to characterize bistable regimes in biochemical reaction networks that can be of use to both direct and reverse engineering of biological switches. In the design of a synthetic biological switch, it is important to study the capability for bistability of the underlying biochemical network structure. Chemical Reaction Network Theory (CRNT may help at this level to decide whether a given network has the capacity for multiple positive equilibria, based on their structural properties. However, in order to build a working switch, we also need to ensure that the bistability property is robust, by studying the conditions leading to the existence of two different steady states. In the reverse engineering of biological switches, knowledge collected about the bistable regimes of the underlying potential model structures can contribute at the model identification stage to a drastic reduction of the feasible region in the parameter space of search. In this work, we make use and extend previous results of the CRNT, aiming not only to discriminate whether a biochemical reaction network can exhibit multiple steady states, but also to determine the regions within the whole space of parameters capable of producing multistationarity. To that purpose we present and justify a condition on the parameters of biochemical networks for the appearance of multistationarity, and propose an efficient and reliable computational method to check its satisfaction through the parameter space.

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Review of MEMS differential scanning calorimetry for biomolecular study

Science.gov (United States)

Yu, Shifeng; Wang, Shuyu; Lu, Ming; Zuo, Lei

2017-12-01

Differential scanning calorimetry (DSC) is one of the few techniques that allow direct determination of enthalpy values for binding reactions and conformational transitions in biomolecules. It provides the thermodynamics information of the biomolecules which consists of Gibbs free energy, enthalpy and entropy in a straightforward manner that enables deep understanding of the structure function relationship in biomolecules such as the folding/unfolding of protein and DNA, and ligand bindings. This review provides an up to date overview of the applications of DSC in biomolecular study such as the bovine serum albumin denaturation study, the relationship between the melting point of lysozyme and the scanning rate. We also introduce the recent advances of the development of micro-electro-mechanic-system (MEMS) based DSCs.

Multiscale simulations of anisotropic particles combining molecular dynamics and Green's function reaction dynamics

Science.gov (United States)

Vijaykumar, Adithya; Ouldridge, Thomas E.; ten Wolde, Pieter Rein; Bolhuis, Peter G.

2017-03-01

The modeling of complex reaction-diffusion processes in, for instance, cellular biochemical networks or self-assembling soft matter can be tremendously sped up by employing a multiscale algorithm which combines the mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit stochastic Brownian, Langevin, or deterministic molecular dynamics to treat reactants at the microscopic scale [A. Vijaykumar, P. G. Bolhuis, and P. R. ten Wolde, J. Chem. Phys. 143, 214102 (2015)]. Here we extend this multiscale MD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We present the novel algorithm focusing on Brownian dynamics only, although the methodology is generic. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm, we discuss its performance. The rotational Brownian dynamics MD-GFRD multiscale method will open up the possibility for large scale simulations of protein signalling networks.

Conducting polymer based biomolecular electronic devices

Indian Academy of Sciences (India)

Conducting polymers; LB films; biosensor microactuators; monolayers. ... have been projected for applications for a wide range of biomolecular electronic devices such as optical, electronic, drug-delivery, memory and biosensing devices.

SkyNet: Modular nuclear reaction network library

Science.gov (United States)

Lippuner, Jonas; Roberts, Luke F.

2017-10-01

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

Molecular codes in biological and chemical reaction networks.

Directory of Open Access Journals (Sweden)

Dennis Görlich

Full Text Available Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio- chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries, biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades, an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.

Biomolecular simulation: historical picture and future perspectives.

Science.gov (United States)

van Gunsteren, Wilfred F; Dolenc, Jozica

2008-02-01

Over the last 30 years, computation based on molecular models is playing an increasingly important role in biology, biological chemistry and biophysics. Since only a very limited number of properties of biomolecular systems are actually accessible to measurement by experimental means, computer simulation complements experiments by providing not only averages, but also distributions and time series of any definable, observable or non-observable, quantity. Biomolecular simulation may be used (i) to interpret experimental data, (ii) to provoke new experiments, (iii) to replace experiments and (iv) to protect intellectual property. Progress over the last 30 years is sketched and perspectives are outlined for the future.

Estimation of parameter sensitivities for stochastic reaction networks

KAUST Repository

Gupta, Ankit

2016-01-07

Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.

Consumer Activities and Reactions to Social Network Marketing

Directory of Open Access Journals (Sweden)

Bistra Vassileva

2017-06-01

Full Text Available The purpose of this paper is to understand consumer behavioural models with respect to their reactions to social network marketing. Theoretical background is focused on online and social network usage, motivations and behaviour. The research goal is to explore consumer reactions to the exposure of social network marketing based on the following criteria: level of brand engagement, word-of-mouth (WOM referral behaviour, and purchase intentions. Consumers are investigated based on their attitudes toward social network marketing and basic socio-demographic covariates using data from a sample size of 700 Bulgarian respondents (age group 21–54 years, Internet users, urban inhabitants. Factor and cluster analyses are applied. It is found that consumers are willing to receive information about brands and companies through social networks. They like to talk in social networks about these brands and companies and to share information as well (factor 2, brand engagement. Internet users are willing to share information received through social network advertising (factor 1, wom referral behaviour but they would not buy a certain brand as a result of brand communication activities in social networks (factor 3, purchase intention. Several practical implications regarding marketing activities through social networks are drawn.

Application of Nanodiamonds in Biomolecular Mass Spectrometry

Directory of Open Access Journals (Sweden)

Ping Cheng

2010-03-01

Full Text Available The combination of nanodiamond (ND with biomolecular mass spectrometry (MS makes rapid, sensitive detection of biopolymers from complex biosamples feasible. Due to its chemical inertness, optical transparency and biocompatibility, the advantage of NDs in MS study is unique. Furthermore, functionalization on the surfaces of NDs expands their application in the fields of proteomics and genomics for specific requirements greatly. This review presents methods of MS analysis based on solid phase extraction and elution on NDs and different application examples including peptide, protein, DNA, glycan and others. Owing to the quick development of nanotechnology, surface chemistry, new MS methods and the intense interest in proteomics and genomics, a huge increase of their applications in biomolecular MS analysis in the near future can be predicted.

Laser photodissociation and spectroscopy of mass-separated biomolecular ions

CERN Document Server

Polfer, Nicolas C

2014-01-01

This lecture notes book presents how enhanced structural information of biomolecular ions can be obtained from interaction with photons of specific frequency - laser light. The methods described in the book ""Laser photodissociation and spectroscopy of mass-separated biomolecular ions"" make use of the fact that the discrete energy and fast time scale of photoexcitation can provide more control in ion activation. This activation is the crucial process producing structure-informative product ions that cannot be generated with more conventional heating methods, such as collisional activation. Th

PREFACE: Radiation Damage in Biomolecular Systems (RADAM07)

Science.gov (United States)

McGuigan, Kevin G.

2008-03-01

, which include: theoretical, experimental, medical and computational physicists, radiation chemists, radiation biologists and microbiologists, among others. An important aspect of the previous 3 conferences in this series was to remove barriers between the different working groups and to encourage a more interdisciplinary approach to research collaborations. During RADAM_07 we could observe the success of these efforts. A large number of presentations were based on new collaborations, many funded under the COST STSM programme, and all presentations led to lively discussions. It is clear from the discussions following many of the presentations and at the poster sessions that Radiation Damage in Biomolecular Systems remains a topic of increasing interest, relevance and importance. The success of this conference as well as of the whole RADAM conference series reflects the growing international interest in the area of interactions of ionizing radiation with biomolecules. Despite the scheduled conclusion in September 2007 of COST Action P9 which has part-funded this, and previous RADAM meetings, the nature of the cross-disciplinary interactions and opportunities for collaborative research was deemed so successful and valuable by the assembled delegates that it was agreed that another such meeting should be held in Debrecen in Hungary from 13-15 June 2008 http://www.isa.au.dk/meetings/radam2008/programme.html. Additional information about RADAM07 programme is available on the conference web-page http://www.isa.au.dk/networks/cost/radam07/index.html. The Organizing Committee would like to thank all speakers, contributors, session chairs, referees and meeting staff for their efforts in making the RADAM07 successful. The local Organization Committee would like to thank Lorraine Monard and Margaret Nolan for their invaluable expertise in conference management. We also gratefully acknowledge the financial support of our sponsors - The Royal, College of Surgeons in Ireland (RCSI

On the network thermodynamics of mass action chemical reaction networks

NARCIS (Netherlands)

Schaft, A.J. van der; Rao, S.; Jayawardhana, B.

In this paper we elaborate on the mathematical formulation of mass action chemical reaction networks as recently given in van der Schaft, Rao, Jayawardhana (2012). We show how the reference chemical potentials define a specific thermodynamical equilibrium, and we discuss the port-Hamiltonian

THz time domain spectroscopy of biomolecular conformational modes

International Nuclear Information System (INIS)

Markelz, Andrea; Whitmire, Scott; Hillebrecht, Jay; Birge, Robert

2002-01-01

We discuss the use of terahertz time domain spectroscopy for studies of conformational flexibility and conformational change in biomolecules. Protein structural dynamics are vital to biological function with protein flexibility affecting enzymatic reaction rates and sensory transduction cycling times. Conformational mode dynamics occur on the picosecond timescale and with the collective vibrational modes associated with these large scale structural motions in the 1-100 cm -1 range. We have performed THz time domain spectroscopy (TTDS) of several biomolecular systems to explore the sensitivity of TTDS to distinguish different molecular species, different mutations within a single species and different conformations of a given biomolecule. We compare the measured absorbances to normal mode calculations and find that the TTDS absorbance reflects the density of normal modes determined by molecular mechanics calculations, and is sensitive to both conformation and mutation. These early studies demonstrate some of the advantages and limitations of using TTDS for the study of biomolecules

Integration of metabolome data with metabolic networks reveals reporter reactions

DEFF Research Database (Denmark)

Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen

2006-01-01

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...... network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...

Formal balancing of chemical reaction networks

NARCIS (Netherlands)

van der Schaft, Abraham; Rao, S.; Jayawardhana, B.

2016-01-01

In this paper we recall and extend the main results of Van der Schaft, Rao, Jayawardhana (2015) concerning the use of Kirchhoff’s Matrix Tree theorem in the explicit characterization of complex-balanced reaction networks and the notion of formal balancing. The notion of formal balancing corresponds

Perspective: Watching low-frequency vibrations of water in biomolecular recognition by THz spectroscopy

Science.gov (United States)

Xu, Yao; Havenith, Martina

2015-11-01

Terahertz (THz) spectroscopy has turned out to be a powerful tool which is able to shed new light on the role of water in biomolecular processes. The low frequency spectrum of the solvated biomolecule in combination with MD simulations provides deep insights into the collective hydrogen bond dynamics on the sub-ps time scale. The absorption spectrum between 1 THz and 10 THz of solvated biomolecules is sensitive to changes in the fast fluctuations of the water network. Systematic studies on mutants of antifreeze proteins indicate a direct correlation between biological activity and a retardation of the (sub)-ps hydration dynamics at the protein binding site, i.e., a "hydration funnel." Kinetic THz absorption studies probe the temporal changes of THz absorption during a biological process, and give access to the kinetics of the coupled protein-hydration dynamics. When combined with simulations, the observed results can be explained in terms of a two-tier model involving a local binding and a long range influence on the hydration bond dynamics of the water around the binding site that highlights the significance of the changes in the hydration dynamics at recognition site for biomolecular recognition. Water is shown to assist molecular recognition processes.

SkyNet: A Modular Nuclear Reaction Network Library

Science.gov (United States)

Lippuner, Jonas; Roberts, Luke F.

2017-12-01

Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear reaction network that evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open source, and aims to be easy to use and flexible. Any list of isotopes can be evolved, and SkyNet supports different types of nuclear reactions. SkyNet is modular so that new or existing physics, like nuclear reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear reaction networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.

NMRbox: A Resource for Biomolecular NMR Computation.

Science.gov (United States)

Maciejewski, Mark W; Schuyler, Adam D; Gryk, Michael R; Moraru, Ion I; Romero, Pedro R; Ulrich, Eldon L; Eghbalnia, Hamid R; Livny, Miron; Delaglio, Frank; Hoch, Jeffrey C

2017-04-25

Advances in computation have been enabling many recent advances in biomolecular applications of NMR. Due to the wide diversity of applications of NMR, the number and variety of software packages for processing and analyzing NMR data is quite large, with labs relying on dozens, if not hundreds of software packages. Discovery, acquisition, installation, and maintenance of all these packages is a burdensome task. Because the majority of software packages originate in academic labs, persistence of the software is compromised when developers graduate, funding ceases, or investigators turn to other projects. To simplify access to and use of biomolecular NMR software, foster persistence, and enhance reproducibility of computational workflows, we have developed NMRbox, a shared resource for NMR software and computation. NMRbox employs virtualization to provide a comprehensive software environment preconfigured with hundreds of software packages, available as a downloadable virtual machine or as a Platform-as-a-Service supported by a dedicated compute cloud. Ongoing development includes a metadata harvester to regularize, annotate, and preserve workflows and facilitate and enhance data depositions to BioMagResBank, and tools for Bayesian inference to enhance the robustness and extensibility of computational analyses. In addition to facilitating use and preservation of the rich and dynamic software environment for biomolecular NMR, NMRbox fosters the development and deployment of a new class of metasoftware packages. NMRbox is freely available to not-for-profit users. Copyright © 2017 Biophysical Society. All rights reserved.

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

Science.gov (United States)

Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

2016-04-01

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

Mean field interaction in biochemical reaction networks

KAUST Repository

Tembine, Hamidou; Tempone, Raul; Vilanova, Pedro

2011-01-01

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

A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks

OpenAIRE

Harirchi, Farshad; Khalil, Omar A.; Liu, Sijia; Elvati, Paolo; Violi, Angela; Hero, Alfred O.

2017-01-01

In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational comple...

Quantifying the topography of the intrinsic energy landscape of flexible biomolecular recognition

Science.gov (United States)

Chu, Xiakun; Gan, Linfeng; Wang, Erkang; Wang, Jin

2013-01-01

Biomolecular functions are determined by their interactions with other molecules. Biomolecular recognition is often flexible and associated with large conformational changes involving both binding and folding. However, the global and physical understanding for the process is still challenging. Here, we quantified the intrinsic energy landscapes of flexible biomolecular recognition in terms of binding–folding dynamics for 15 homodimers by exploring the underlying density of states, using a structure-based model both with and without considering energetic roughness. By quantifying three individual effective intrinsic energy landscapes (one for interfacial binding, two for monomeric folding), the association mechanisms for flexible recognition of 15 homodimers can be classified into two-state cooperative “coupled binding–folding” and three-state noncooperative “folding prior to binding” scenarios. We found that the association mechanism of flexible biomolecular recognition relies on the interplay between the underlying effective intrinsic binding and folding energy landscapes. By quantifying the whole global intrinsic binding–folding energy landscapes, we found strong correlations between the landscape topography measure Λ (dimensionless ratio of energy gap versus roughness modulated by the configurational entropy) and the ratio of the thermodynamic stable temperature versus trapping temperature, as well as between Λ and binding kinetics. Therefore, the global energy landscape topography determines the binding–folding thermodynamics and kinetics, crucial for the feasibility and efficiency of realizing biomolecular function. We also found “U-shape” temperature-dependent kinetic behavior and a dynamical cross-over temperature for dividing exponential and nonexponential kinetics for two-state homodimers. Our study provides a unique way to bridge the gap between theory and experiments. PMID:23754431

Integration of biomolecular logic gates with field-effect transducers

Energy Technology Data Exchange (ETDEWEB)

Poghossian, A., E-mail: a.poghossian@fz-juelich.de [Institute of Nano- and Biotechnologies, Aachen University of Applied Sciences, Campus Juelich, Heinrich-Mussmann-Str. 1, D-52428 Juelich (Germany); Institute of Bio- and Nanosystems, Research Centre Juelich GmbH, D-52425 Juelich (Germany); Malzahn, K. [Institute of Nano- and Biotechnologies, Aachen University of Applied Sciences, Campus Juelich, Heinrich-Mussmann-Str. 1, D-52428 Juelich (Germany); Abouzar, M.H. [Institute of Nano- and Biotechnologies, Aachen University of Applied Sciences, Campus Juelich, Heinrich-Mussmann-Str. 1, D-52428 Juelich (Germany); Institute of Bio- and Nanosystems, Research Centre Juelich GmbH, D-52425 Juelich (Germany); Mehndiratta, P. [Institute of Nano- and Biotechnologies, Aachen University of Applied Sciences, Campus Juelich, Heinrich-Mussmann-Str. 1, D-52428 Juelich (Germany); Katz, E. [Department of Chemistry and Biomolecular Science, NanoBio Laboratory (NABLAB), Clarkson University, Potsdam, NY 13699-5810 (United States); Schoening, M.J. [Institute of Nano- and Biotechnologies, Aachen University of Applied Sciences, Campus Juelich, Heinrich-Mussmann-Str. 1, D-52428 Juelich (Germany); Institute of Bio- and Nanosystems, Research Centre Juelich GmbH, D-52425 Juelich (Germany)

2011-11-01

Highlights: > Enzyme-based AND/OR logic gates are integrated with a capacitive field-effect sensor. > The AND/OR logic gates compose of multi-enzyme system immobilised on sensor surface. > Logic gates were activated by different combinations of chemical inputs (analytes). > The logic output (pH change) produced by the enzymes was read out by the sensor. - Abstract: The integration of biomolecular logic gates with field-effect devices - the basic element of conventional electronic logic gates and computing - is one of the most attractive and promising approaches for the transformation of biomolecular logic principles into macroscopically useable electrical output signals. In this work, capacitive field-effect EIS (electrolyte-insulator-semiconductor) sensors based on a p-Si-SiO{sub 2}-Ta{sub 2}O{sub 5} structure modified with a multi-enzyme membrane have been used for electronic transduction of biochemical signals processed by enzyme-based OR and AND logic gates. The realised OR logic gate composes of two enzymes (glucose oxidase and esterase) and was activated by ethyl butyrate or/and glucose. The AND logic gate composes of three enzymes (invertase, mutarotase and glucose oxidase) and was activated by two chemical input signals: sucrose and dissolved oxygen. The developed integrated enzyme logic gates produce local pH changes at the EIS sensor surface as a result of biochemical reactions activated by different combinations of chemical input signals, while the pH value of the bulk solution remains unchanged. The pH-induced charge changes at the gate-insulator (Ta{sub 2}O{sub 5}) surface of the EIS transducer result in an electronic signal corresponding to the logic output produced by the immobilised enzymes. The logic output signals have been read out by means of a constant-capacitance method.

Integration of biomolecular logic gates with field-effect transducers

International Nuclear Information System (INIS)

Poghossian, A.; Malzahn, K.; Abouzar, M.H.; Mehndiratta, P.; Katz, E.; Schoening, M.J.

2011-01-01

Highlights: → Enzyme-based AND/OR logic gates are integrated with a capacitive field-effect sensor. → The AND/OR logic gates compose of multi-enzyme system immobilised on sensor surface. → Logic gates were activated by different combinations of chemical inputs (analytes). → The logic output (pH change) produced by the enzymes was read out by the sensor. - Abstract: The integration of biomolecular logic gates with field-effect devices - the basic element of conventional electronic logic gates and computing - is one of the most attractive and promising approaches for the transformation of biomolecular logic principles into macroscopically useable electrical output signals. In this work, capacitive field-effect EIS (electrolyte-insulator-semiconductor) sensors based on a p-Si-SiO 2 -Ta 2 O 5 structure modified with a multi-enzyme membrane have been used for electronic transduction of biochemical signals processed by enzyme-based OR and AND logic gates. The realised OR logic gate composes of two enzymes (glucose oxidase and esterase) and was activated by ethyl butyrate or/and glucose. The AND logic gate composes of three enzymes (invertase, mutarotase and glucose oxidase) and was activated by two chemical input signals: sucrose and dissolved oxygen. The developed integrated enzyme logic gates produce local pH changes at the EIS sensor surface as a result of biochemical reactions activated by different combinations of chemical input signals, while the pH value of the bulk solution remains unchanged. The pH-induced charge changes at the gate-insulator (Ta 2 O 5 ) surface of the EIS transducer result in an electronic signal corresponding to the logic output produced by the immobilised enzymes. The logic output signals have been read out by means of a constant-capacitance method.

Biomolecular modelling and simulations

CERN Document Server

Karabencheva-Christova, Tatyana

2014-01-01

Published continuously since 1944, the Advances in Protein Chemistry and Structural Biology series is the essential resource for protein chemists. Each volume brings forth new information about protocols and analysis of proteins. Each thematically organized volume is guest edited by leading experts in a broad range of protein-related topics. Describes advances in biomolecular modelling and simulations Chapters are written by authorities in their field Targeted to a wide audience of researchers, specialists, and students The information provided in the volume is well supported by a number of high quality illustrations, figures, and tables.

Biomolecular structure refinement using the GROMOS simulation software

International Nuclear Information System (INIS)

Schmid, Nathan; Allison, Jane R.; Dolenc, Jožica; Eichenberger, Andreas P.; Kunz, Anna-Pitschna E.; Gunsteren, Wilfred F. van

2011-01-01

For the understanding of cellular processes the molecular structure of biomolecules has to be accurately determined. Initial models can be significantly improved by structure refinement techniques. Here, we present the refinement methods and analysis techniques implemented in the GROMOS software for biomolecular simulation. The methodology and some implementation details of the computation of NMR NOE data, 3 J-couplings and residual dipolar couplings, X-ray scattering intensities from crystals and solutions and neutron scattering intensities used in GROMOS is described and refinement strategies and concepts are discussed using example applications. The GROMOS software allows structure refinement combining different types of experimental data with different types of restraining functions, while using a variety of methods to enhance conformational searching and sampling and the thermodynamically calibrated GROMOS force field for biomolecular simulation.

Biomolecular structure refinement using the GROMOS simulation software

Energy Technology Data Exchange (ETDEWEB)

Schmid, Nathan; Allison, Jane R.; Dolenc, Jozica; Eichenberger, Andreas P.; Kunz, Anna-Pitschna E.; Gunsteren, Wilfred F. van, E-mail: wfvgn@igc.phys.chem.ethz.ch [Swiss Federal Institute of Technology ETH, Laboratory of Physical Chemistry (Switzerland)

2011-11-15

For the understanding of cellular processes the molecular structure of biomolecules has to be accurately determined. Initial models can be significantly improved by structure refinement techniques. Here, we present the refinement methods and analysis techniques implemented in the GROMOS software for biomolecular simulation. The methodology and some implementation details of the computation of NMR NOE data, {sup 3}J-couplings and residual dipolar couplings, X-ray scattering intensities from crystals and solutions and neutron scattering intensities used in GROMOS is described and refinement strategies and concepts are discussed using example applications. The GROMOS software allows structure refinement combining different types of experimental data with different types of restraining functions, while using a variety of methods to enhance conformational searching and sampling and the thermodynamically calibrated GROMOS force field for biomolecular simulation.

Reaction mechanism and reaction coordinates from the viewpoint of energy flow

Energy Technology Data Exchange (ETDEWEB)

Li, Wenjin; Ma, Ao, E-mail: aoma@uic.edu [Department of Bioengineering, The University of Illinois at Chicago, 851 South Morgan Street, Chicago, Illinois 60607 (United States)

2016-03-21

Reaction coordinates are of central importance for correct understanding of reaction dynamics in complex systems, but their counter-intuitive nature made it a daunting challenge to identify them. Starting from an energetic view of a reaction process as stochastic energy flows biased towards preferred channels, which we deemed the reaction coordinates, we developed a rigorous scheme for decomposing energy changes of a system, both potential and kinetic, into pairwise components. The pairwise energy flows between different coordinates provide a concrete statistical mechanical language for depicting reaction mechanisms. Application of this scheme to the C{sub 7eq} → C{sub 7ax} transition of the alanine dipeptide in vacuum revealed novel and intriguing mechanisms that eluded previous investigations of this well studied prototype system for biomolecular conformational dynamics. Using a cost function developed from the energy decomposition components by proper averaging over the transition path ensemble, we were able to identify signatures of the reaction coordinates of this system without requiring any input from human intuition.

Reaction mechanism and reaction coordinates from the viewpoint of energy flow

International Nuclear Information System (INIS)

Li, Wenjin; Ma, Ao

2016-01-01

Reaction coordinates are of central importance for correct understanding of reaction dynamics in complex systems, but their counter-intuitive nature made it a daunting challenge to identify them. Starting from an energetic view of a reaction process as stochastic energy flows biased towards preferred channels, which we deemed the reaction coordinates, we developed a rigorous scheme for decomposing energy changes of a system, both potential and kinetic, into pairwise components. The pairwise energy flows between different coordinates provide a concrete statistical mechanical language for depicting reaction mechanisms. Application of this scheme to the C 7eq → C 7ax transition of the alanine dipeptide in vacuum revealed novel and intriguing mechanisms that eluded previous investigations of this well studied prototype system for biomolecular conformational dynamics. Using a cost function developed from the energy decomposition components by proper averaging over the transition path ensemble, we were able to identify signatures of the reaction coordinates of this system without requiring any input from human intuition.

Elimination of intermediate species in multiscale stochastic reaction networks

DEFF Research Database (Denmark)

Cappelletti, Daniele; Wiuf, Carsten

2016-01-01

such as the substrate-enzyme complex in the Michaelis-Menten mechanism. Such species are virtually in all real-world networks, they are typically short-lived, degraded at a fast rate and hard to observe experimentally. We provide conditions under which the Markov process of a multiscale reaction network...

A compact hard X-ray source for medical imaging and biomolecular studies

International Nuclear Information System (INIS)

Cline, D.B.; Green, M.A.; Kolonko, J.

1995-01-01

There are a large number of synchrotron light sources in the world. However, these sources are designed for physics, chemistry, and engineering studies. To our knowledge, none have been optimized for either medical imaging or biomolecular studies. There are special needs for these applications. We present here a preliminary design of a very compact source, small enough for a hospital or a biomolecular laboratory, that is suitable for these applications. (orig.)

Model reduction of detailed-balanced reaction networks by clustering linkage classes

NARCIS (Netherlands)

Rao, Shodhan; Jayawardhana, Bayu; van der Schaft, Abraham; Findeisen, Rolf; Bullinger, Eric; Balsa-Canto, Eva; Bernaerts, Kristel

2016-01-01

We propose a model reduction method that involves sequential application of clustering of linkage classes and Kron reduction. This approach is specifically useful for chemical reaction networks with each linkage class having less number of reactions. In case of detailed balanced chemical reaction

Mean field interaction in biochemical reaction networks

KAUST Repository

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.

Hybrid Quantum Mechanics/Molecular Mechanics/Coarse Grained Modeling: A Triple-Resolution Approach for Biomolecular Systems.

Science.gov (United States)

Sokkar, Pandian; Boulanger, Eliot; Thiel, Walter; Sanchez-Garcia, Elsa

2015-04-14

We present a hybrid quantum mechanics/molecular mechanics/coarse-grained (QM/MM/CG) multiresolution approach for solvated biomolecular systems. The chemically important active-site region is treated at the QM level. The biomolecular environment is described by an atomistic MM force field, and the solvent is modeled with the CG Martini force field using standard or polarizable (pol-CG) water. Interactions within the QM, MM, and CG regions, and between the QM and MM regions, are treated in the usual manner, whereas the CG-MM and CG-QM interactions are evaluated using the virtual sites approach. The accuracy and efficiency of our implementation is tested for two enzymes, chorismate mutase (CM) and p-hydroxybenzoate hydroxylase (PHBH). In CM, the QM/MM/CG potential energy scans along the reaction coordinate yield reaction energies that are too large, both for the standard and polarizable Martini CG water models, which can be attributed to adverse effects of using large CG water beads. The inclusion of an atomistic MM water layer (10 Å for uncharged CG water and 5 Å for polarizable CG water) around the QM region improves the energy profiles compared to the reference QM/MM calculations. In analogous QM/MM/CG calculations on PHBH, the use of the pol-CG description for the outer water does not affect the stabilization of the highly charged FADHOOH-pOHB transition state compared to the fully atomistic QM/MM calculations. Detailed performance analysis in a glycine-water model system indicates that computation times for QM energy and gradient evaluations at the density functional level are typically reduced by 40-70% for QM/MM/CG relative to fully atomistic QM/MM calculations.

Safe design and operation of tank reactors for multiple-reaction networks: uniqueness and multiplicity

NARCIS (Netherlands)

Westerterp, K.R.; Westerink, E.J.

1990-01-01

A method is developed to design a tank reactor in which a network of reactions is carried out. The network is a combination of parallel and consecutive reactions. The method ensures unique operation. Dimensionless groups are used which are either representative of properties of the reaction system

Recent developments in research on catalytic reaction networks

Directory of Open Access Journals (Sweden)

Roberto Serra

2013-09-01

Full Text Available Over the last years, analyses performed on a stochastic model of catalytic reaction networks have provided some indications about the reasons why wet-lab experiments hardly ever comply with the phase transition typically predicted by theoretical models with regard to the emergence of collectively self-replicating sets of molecule (also defined as autocatalytic sets, ACSs, a phenomenon that is often observed in nature and that is supposed to have played a major role in the emergence of the primitive forms of life. The model at issue has allowed to reveal that the emerging ACSs are characterized by a general dynamical fragility, which might explain the difficulty to observe them in lab experiments. In this work, the main results of the various analyses are reviewed, with particular regard to the factors able to affect the generic properties of catalytic reactions network, for what concerns, not only the probability of ACSs to be observed, but also the overall activity of the system, in terms of production of new species, reactions and matter.

Comparing chemical reaction networks

DEFF Research Database (Denmark)

Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max

2017-01-01

We study chemical reaction networks (CRNs) as a kernel model of concurrency provided with semantics based on ordinary differential equations. We investigate the problem of comparing two CRNs, i.e., to decide whether the solutions of a source and of a target CRN can be matched for an appropriate...... choice of initial conditions. Using a categorical framework, we extend and unify model-comparison approaches based on dynamical (semantic) and structural (syntactic) properties of CRNs. Then, we provide an algorithm to compare CRNs, running linearly in time with respect to the cardinality of all possible...... comparisons. Finally, using a prototype implementation, CAGE, we apply our results to biological models from the literature....

Electron transfer reactions of metal complexes in solution

International Nuclear Information System (INIS)

Sutin, N.

1977-01-01

A few representative electron-transfer reactions are selected and their kinetic parameters compared with the predictions of activated complex models. Since Taube has presented an elegant treatment of intramolecular electron-transfer reactions, emphasis is on bimolecular reactions. The latter electron-transfer reactions are more complicated to treat theoretically since the geometries of their activated complexes are not as well known as for the intramolecular case. In addition in biomolecular reactions, the work required to bring the two reactants together needs to be calculated. Since both reactants generally carry charges this presents a non-trivial problem at the ionic strengths usually used to study bimolecular electron transfer

A computational framework for the automated construction of glycosylation reaction networks.

Science.gov (United States)

Liu, Gang; Neelamegham, Sriram

2014-01-01

Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS) data. The features described above are illustrated using three case studies that examine: i) O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii) automated N-linked glycosylation pathway construction; and iii) the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme biochemistry. All

A computational framework for the automated construction of glycosylation reaction networks.

Directory of Open Access Journals (Sweden)

Gang Liu

Full Text Available Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS data. The features described above are illustrated using three case studies that examine: i O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii automated N-linked glycosylation pathway construction; and iii the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme

Improvements to the APBS biomolecular solvation software suite: Improvements to the APBS Software Suite

Energy Technology Data Exchange (ETDEWEB)

Jurrus, Elizabeth [Pacific Northwest National Laboratory, Richland Washington; Engel, Dave [Pacific Northwest National Laboratory, Richland Washington; Star, Keith [Pacific Northwest National Laboratory, Richland Washington; Monson, Kyle [Pacific Northwest National Laboratory, Richland Washington; Brandi, Juan [Pacific Northwest National Laboratory, Richland Washington; Felberg, Lisa E. [University of California, Berkeley California; Brookes, David H. [University of California, Berkeley California; Wilson, Leighton [University of Michigan, Ann Arbor Michigan; Chen, Jiahui [Southern Methodist University, Dallas Texas; Liles, Karina [Pacific Northwest National Laboratory, Richland Washington; Chun, Minju [Pacific Northwest National Laboratory, Richland Washington; Li, Peter [Pacific Northwest National Laboratory, Richland Washington; Gohara, David W. [St. Louis University, St. Louis Missouri; Dolinsky, Todd [FoodLogiQ, Durham North Carolina; Konecny, Robert [University of California San Diego, San Diego California; Koes, David R. [University of Pittsburgh, Pittsburgh Pennsylvania; Nielsen, Jens Erik [Protein Engineering, Novozymes A/S, Copenhagen Denmark; Head-Gordon, Teresa [University of California, Berkeley California; Geng, Weihua [Southern Methodist University, Dallas Texas; Krasny, Robert [University of Michigan, Ann Arbor Michigan; Wei, Guo-Wei [Michigan State University, East Lansing Michigan; Holst, Michael J. [University of California San Diego, San Diego California; McCammon, J. Andrew [University of California San Diego, San Diego California; Baker, Nathan A. [Pacific Northwest National Laboratory, Richland Washington; Brown University, Providence Rhode Island

2017-10-24

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that has provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this manuscript, we discuss the models and capabilities that have recently been implemented within the APBS software package including: a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory based algorithm for determining pKa values, and an improved web-based visualization tool for viewing electrostatics.

Estimation of parameter sensitivities for stochastic reaction networks

KAUST Repository

Gupta, Ankit

2016-01-01

Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a

The Forward-Reverse Algorithm for Stochastic Reaction Networks

KAUST Repository

Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2015-01-01

In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem

Nanogap biosensors for electrical and label-free detection of biomolecular interactions

International Nuclear Information System (INIS)

Kyu Kim, Sang; Cho, Hyunmin; Park, Hye-Jung; Kwon, Dohyoung; Min Lee, Jeong; Hyun Chung, Bong

2009-01-01

We demonstrate nanogap biosensors for electrical and label-free detection of biomolecular interactions. Parallel fabrication of nanometer distance gaps has been achieved using a silicon anisotropic wet etching technique on a silicon-on-insulator (SOI) wafer with a finely controllable silicon device layer. Since silicon anisotropic wet etching resulted in a trapezoid-shaped structure whose end became narrower during the etching, the nanogap structure was simply fabricated on the device layer of a SOI wafer. The nanogap devices were individually addressable and a gap size of less than 60 nm was obtained. We demonstrate that the nanogap biosensors can electrically detect biomolecular interactions such as biotin/streptavidin and antigen/antibody pairs. The nanogap devices show a current increase when the proteins are bound to the surface. The current increases proportionally depending upon the concentrations of the molecules in the range of 100 fg ml -1 -100 ng ml -1 at 1 V bias. It is expected that the nanogap developed here could be a highly sensitive biosensor platform for label-free detection of biomolecular interactions.

Development of an informatics infrastructure for data exchange of biomolecular simulations: Architecture, data models and ontology.

Science.gov (United States)

Thibault, J C; Roe, D R; Eilbeck, K; Cheatham, T E; Facelli, J C

2015-01-01

Biomolecular simulations aim to simulate structure, dynamics, interactions, and energetics of complex biomolecular systems. With the recent advances in hardware, it is now possible to use more complex and accurate models, but also reach time scales that are biologically significant. Molecular simulations have become a standard tool for toxicology and pharmacology research, but organizing and sharing data - both within the same organization and among different ones - remains a substantial challenge. In this paper we review our recent work leading to the development of a comprehensive informatics infrastructure to facilitate the organization and exchange of biomolecular simulations data. Our efforts include the design of data models and dictionary tools that allow the standardization of the metadata used to describe the biomedical simulations, the development of a thesaurus and ontology for computational reasoning when searching for biomolecular simulations in distributed environments, and the development of systems based on these models to manage and share the data at a large scale (iBIOMES), and within smaller groups of researchers at laboratory scale (iBIOMES Lite), that take advantage of the standardization of the meta data used to describe biomolecular simulations.

Stochastic analysis of complex reaction networks using binomial moment equations.

Science.gov (United States)

Barzel, Baruch; Biham, Ofer

2012-09-01

The stochastic analysis of complex reaction networks is a difficult problem because the number of microscopic states in such systems increases exponentially with the number of reactive species. Direct integration of the master equation is thus infeasible and is most often replaced by Monte Carlo simulations. While Monte Carlo simulations are a highly effective tool, equation-based formulations are more amenable to analytical treatment and may provide deeper insight into the dynamics of the network. Here, we present a highly efficient equation-based method for the analysis of stochastic reaction networks. The method is based on the recently introduced binomial moment equations [Barzel and Biham, Phys. Rev. Lett. 106, 150602 (2011)]. The binomial moments are linear combinations of the ordinary moments of the probability distribution function of the population sizes of the interacting species. They capture the essential combinatorics of the reaction processes reflecting their stoichiometric structure. This leads to a simple and transparent form of the equations, and allows a highly efficient and surprisingly simple truncation scheme. Unlike ordinary moment equations, in which the inclusion of high order moments is prohibitively complicated, the binomial moment equations can be easily constructed up to any desired order. The result is a set of equations that enables the stochastic analysis of complex reaction networks under a broad range of conditions. The number of equations is dramatically reduced from the exponential proliferation of the master equation to a polynomial (and often quadratic) dependence on the number of reactive species in the binomial moment equations. The aim of this paper is twofold: to present a complete derivation of the binomial moment equations; to demonstrate the applicability of the moment equations for a representative set of example networks, in which stochastic effects play an important role.

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.

Science.gov (United States)

Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán

2014-03-11

While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

Attractor controllability of Boolean networks by flipping a subset of their nodes

Science.gov (United States)

Rafimanzelat, Mohammad Reza; Bahrami, Fariba

2018-04-01

The controllability analysis of Boolean networks (BNs), as models of biomolecular regulatory networks, has drawn the attention of researchers in recent years. In this paper, we aim at governing the steady-state behavior of BNs using an intervention method which can easily be applied to most real system, which can be modeled as BNs, particularly to biomolecular regulatory networks. To this end, we introduce the concept of attractor controllability of a BN by flipping a subset of its nodes, as the possibility of making a BN converge from any of its attractors to any other one, by one-time flipping members of a subset of BN nodes. Our approach is based on the algebraic state-space representation of BNs using semi-tensor product of matrices. After introducing some new matrix tools, we use them to derive necessary and sufficient conditions for the attractor controllability of BNs. A forward search algorithm is then suggested to identify the minimal perturbation set for attractor controllability of a BN. Next, a lower bound is derived for the cardinality of this set. Two new indices are also proposed for quantifying the attractor controllability of a BN and the influence of each network variable on the attractor controllability of the network and the relationship between them is revealed. Finally, we confirm the efficiency of the proposed approach by applying it to the BN models of some real biomolecular networks.

Schreibersite: an effective catalyst in the formose reaction network

Science.gov (United States)

Pallmann, S.; Šteflová (neé Svobodová, J.; Haas, M.; Lamour, S.; Henß, A.; Trapp, O.

2018-05-01

We report on the ability of the meteoritic material schreibersite to catalyze the generation of higher sugars from simple carbohydrates in the formose reaction network. Since the analysis of carbonaceous meteorites like the Murchison meteorite it has become generally accepted that a substantial amount of organic material has been delivered to the early earth and, therefore, ought to be considered in scenarios for the origin(s) of life. Also for the open question of accessible phosphorus sources, an extraterrestrial material called schreibersite has been identified that is capable of releasing soluble and reactive phosphorus oxyanions that would react with organics to form for instance nucleotides and membrane associated molecules. We have reinvestigated this material using capillary electrophoresis to monitor its corrosion process in water and probed its ability to phosphorylate a wide range of organics. Although showing a poor reactivity of schreibersite, we have found that the material catalyzes the aldol reaction of small carbohydrates forming larger sugar molecules. This reaction in the formose reaction network is a prebiotically likely route to biologically relevant sugars. The results of our study present one of the first instances of connecting extraterrestrial material to prebiotic chemistry on the early earth.

COEL: A Cloud-based Reaction Network Simulator

Directory of Open Access Journals (Sweden)

Peter eBanda

2016-04-01

Full Text Available Chemical Reaction Networks (CRNs are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework that does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL's key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm-based optimization toolbox for rate constants.COEL is an open-source project hosted on GitHub (http://dx.doi.org/10.5281/zenodo.46544, which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at http://coel-sim.org. The framework is ideally suited for a collaborative use in both research and education.

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

Science.gov (United States)

Sheng, Yin; Zhang, Hao; Zeng, Zhigang

2017-10-01

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

Selected topics in solution-phase biomolecular NMR spectroscopy

Science.gov (United States)

Kay, Lewis E.; Frydman, Lucio

2017-05-01

Solution bio-NMR spectroscopy continues to enjoy a preeminent role as an important tool in elucidating the structure and dynamics of a range of important biomolecules and in relating these to function. Equally impressive is how NMR continues to 'reinvent' itself through the efforts of many brilliant practitioners who ask increasingly demanding and increasingly biologically relevant questions. The ability to manipulate spin Hamiltonians - almost at will - to dissect the information of interest contributes to the success of the endeavor and ensures that the NMR technology will be well poised to contribute to as yet unknown frontiers in the future. As a tribute to the versatility of solution NMR in biomolecular studies and to the continued rapid advances in the field we present a Virtual Special Issue (VSI) that includes over 40 articles on various aspects of solution-state biomolecular NMR that have been published in the Journal of Magnetic Resonance in the past 7 years. These, in total, help celebrate the achievements of this vibrant field.

Injectivity, multiple zeros, and multistationarity in reaction networks

DEFF Research Database (Denmark)

Feliu, Elisenda

2015-01-01

Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems are typically parametrized by many (unknown) parameters...

Toward a self-consistent and unitary reaction network for big bang nucleosynthesis

International Nuclear Information System (INIS)

Paris, Mark W.; Brown, Lowell S.; Hale, Gerald M.; Hayes-Sterbenz, Anna C.; Jungman, Gerard; Kawano, Toshihiko; Fuller, George M.; Grohs, Evan B.; Kunieda, Satoshi

2014-01-01

Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a 'unitary reaction network' for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the 9 B compound nuclear system in the resonant destruction of 7 Li during primordial nucleosynthesis. We have studied reactions in the 9 B compound system with a multichannel, two-body unitary R-matrix code (EDA) using the known elastic and reaction data, in a four-channel treatment. The data include elastic 6 Li( 3 He, 3 He) 6 Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for 6 Li( 3 He,p) 8 Be* and from 0.4 to 5.0 MeV for the 6 Li( 3 He,γ) 7 Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for 6 Li( 3 He,γ) 9 B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized. (author)

Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks

DEFF Research Database (Denmark)

Anderson, David F; Craciun, Gheorghe; Gopalkrishnan, Manoj

2015-01-01

We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non-equilibrium potent...

On the Mathematical Structure of Balanced Chemical Reaction Networks Governed by Mass Action Kinetics

NARCIS (Netherlands)

Schaft, Arjan van der; Rao, Shodhan; Jayawardhana, Bayu

2013-01-01

Motivated by recent progress on the interplay between graph theory, dynamics, and systems theory, we revisit the analysis of chemical reaction networks described by mass action kinetics. For reaction networks possessing a thermodynamic equilibrium we derive a compact formulation exhibiting at the

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

International Nuclear Information System (INIS)

Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.

2017-01-01

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Lastly, propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.

Bio-orthogonal Fluorescent Labelling of Biopolymers through Inverse-Electron-Demand Diels-Alder Reactions.

Science.gov (United States)

Kozma, Eszter; Demeter, Orsolya; Kele, Péter

2017-03-16

Bio-orthogonal labelling schemes based on inverse-electron-demand Diels-Alder (IEDDA) cycloaddition have attracted much attention in chemical biology recently. The appealing features of this reaction, such as the fast reaction kinetics, fully bio-orthogonal nature and high selectivity, have helped chemical biologists gain deeper understanding of biochemical processes at the molecular level. Listing the components and discussing the possibilities and limitations of these reagents, we provide a recent snapshot of the field of IEDDA-based biomolecular manipulation with special focus on fluorescent modulation approaches through the use of bio-orthogonalized building blocks. At the end, we discuss challenges that need to be addressed for further developments in order to overcome recent limitations and to enable researchers to answer biomolecular questions in more detail. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Preclusion of switch behavior in reaction networks with mass-action kinetics

DEFF Research Database (Denmark)

Feliu, Elisenda; Wiuf, C.

2012-01-01

We study networks taken with mass-action kinetics and provide a Jacobian criterion that applies to an arbitrary network to preclude the existence of multiple positive steady states within any stoichiometric class for any choice of rate constants. We are concerned with the characterization...... precludes the existence of degenerate steady states. Further, we relate injectivity of a network to that of the network obtained by adding outflow, or degradation, reactions for all species....

DNA algorithms of implementing biomolecular databases on a biological computer.

Science.gov (United States)

Chang, Weng-Long; Vasilakos, Athanasios V

2015-01-01

In this paper, DNA algorithms are proposed to perform eight operations of relational algebra (calculus), which include Cartesian product, union, set difference, selection, projection, intersection, join, and division, on biomolecular relational databases.

A scalable computational framework for establishing long-term behavior of stochastic reaction networks.

Directory of Open Access Journals (Sweden)

Ankit Gupta

2014-06-01

Full Text Available Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed.

Toward a self-consistent and unitary reaction network for big bang nucleosynthesis

Energy Technology Data Exchange (ETDEWEB)

Paris, Mark W.; Brown, Lowell S.; Hale, Gerald M.; Hayes-Sterbenz, Anna C.; Jungman, Gerard; Kawano, Toshihiko, E-mail: mparis@lanl.gov [Los Alamos National Laboratory, Los Alamos, New Mexico (United States); Fuller, George M.; Grohs, Evan B. [Department of Physics, University of California, San Diego, La Jolla, CA (United States); Kunieda, Satoshi [Nuclear Data Center, Japan Atomic Energy Agency, Tokai-mura Naka-gun, Ibaraki (Japan)

2014-07-01

Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a 'unitary reaction network' for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the {sup 9}B compound nuclear system in the resonant destruction of {sup 7}Li during primordial nucleosynthesis. We have studied reactions in the {sup 9}B compound system with a multichannel, two-body unitary R-matrix code (EDA) using the known elastic and reaction data, in a four-channel treatment. The data include elastic {sup 6}Li({sup 3}He,{sup 3}He){sup 6}Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for {sup 6}Li({sup 3}He,p){sup 8}Be* and from 0.4 to 5.0 MeV for the {sup 6}Li({sup 3}He,γ){sup 7}Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for {sup 6}Li({sup 3}He,γ){sup 9}B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized. (author)

High energy halogen atom reactions activated by nuclear transformations. Progress report, February 15, 1980-February 14, 1981

International Nuclear Information System (INIS)

1981-02-01

The stereochemistry of high energy 18 F, /sup 34m/Cl, and 76 Br substitution reactions involving enantiomeric molecules in the gas and condensed phase is studied. The gas to condensed state transition in halogen high energy chemistry, involving chlorine, bromine, and iodine activated by the (n,γ) and (I.T.) processes in halomethanes, saturated and unsaturated hydrocarbons is being investigated in more detail. Special attention is given to defining the nature of the enhancement yields in the condensed phase. High energy halogen reactions in liquid and frozen aqueous solutions of organic and biomolecular solutes are studied in an attempt to learn more about these reactions. The applications of high energy chemistry techniques and theory to neutron activation analysis of biological systems are being continued. Special attention is given to developing procedures for trace molecular determinations in biological systems. The applications of hot halogen atoms as indicators of solute-solute interactions in liquid and frozen aqueous solutions of halogenated bases and nucleosides are being developed. Experiments are designed to explain the mechanisms of the radioprotection offered biomolecular solutes trapped within the frozen ice lattice. Reactions of bromine and iodine activated by isomeric transition with halogenated biomolecular solutes in liquid and frozen aqueous solutions are studied. The high energy reactions of iodine with the isomers of pentene have been studied in low pressure gaseous systems employing additives and rare gas moderators and liquid systems. Reactivity of excited complex formation and structural effects of electrophilic iodine attack on the pi-bond systems are studied

High energy halogen atom reactions activated by nuclear transformations. Progress report, February 15, 1980-February 14, 1981

Energy Technology Data Exchange (ETDEWEB)

1981-02-01

The stereochemistry of high energy /sup 18/F, /sup 34m/Cl, and /sup 76/Br substitution reactions involving enantiomeric molecules in the gas and condensed phase is studied. The gas to condensed state transition in halogen high energy chemistry, involving chlorine, bromine, and iodine activated by the (n,..gamma..) and (I.T.) processes in halomethanes, saturated and unsaturated hydrocarbons is being investigated in more detail. Special attention is given to defining the nature of the enhancement yields in the condensed phase. High energy halogen reactions in liquid and frozen aqueous solutions of organic and biomolecular solutes are studied in an attempt to learn more about these reactions. The applications of high energy chemistry techniques and theory to neutron activation analysis of biological systems are being continued. Special attention is given to developing procedures for trace molecular determinations in biological systems. The applications of hot halogen atoms as indicators of solute-solute interactions in liquid and frozen aqueous solutions of halogenated bases and nucleosides are being developed. Experiments are designed to explain the mechanisms of the radioprotection offered biomolecular solutes trapped within the frozen ice lattice. Reactions of bromine and iodine activated by isomeric transition with halogenated biomolecular solutes in liquid and frozen aqueous solutions are studied. The high energy reactions of iodine with the isomers of pentene have been studied in low pressure gaseous systems employing additives and rare gas moderators and liquid systems. Reactivity of excited complex formation and structural effects of electrophilic iodine attack on the pi-bond systems are studied.

Smartphones for cell and biomolecular detection.

Science.gov (United States)

Liu, Xiyuan; Lin, Tung-Yi; Lillehoj, Peter B

2014-11-01

Recent advances in biomedical science and technology have played a significant role in the development of new sensors and assays for cell and biomolecular detection. Generally, these efforts are aimed at reducing the complexity and costs associated with diagnostic testing so that it can be performed outside of a laboratory or hospital setting, requiring minimal equipment and user involvement. In particular, point-of-care (POC) testing offers immense potential for many important applications including medical diagnosis, environmental monitoring, food safety, and biosecurity. When coupled with smartphones, POC systems can offer portability, ease of use and enhanced functionality while maintaining performance. This review article focuses on recent advancements and developments in smartphone-based POC systems within the last 6 years with an emphasis on cell and biomolecular detection. These devices typically comprise multiple components, such as detectors, sample processors, disposable chips, batteries, and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. Researchers have demonstrated several promising approaches employing various detection schemes and device configurations, and it is expected that further developments in biosensors, battery technology and miniaturized electronics will enable smartphone-based POC technologies to become more mainstream tools in the scientific and biomedical communities.

Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory.

Science.gov (United States)

Pantazis, Yannis; Katsoulakis, Markos A; Vlachos, Dionisios G

2013-10-22

Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks. Typically, such mathematical models involve networks of coupled jump stochastic processes with a large number of parameters that need to be suitably calibrated against experimental data. In this direction, the parameter sensitivity analysis of reaction networks is an essential mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, existing sensitivity analysis approaches such as variants of the finite difference method can have an overwhelming computational cost in models with a high-dimensional parameter space. We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. For this reason, we need to work on path-space, i.e., the set consisting of all stochastic trajectories, hence the proposed approach is referred to as "pathwise". The pathwise sensitivity analysis method is realized by employing the rigorously-derived Relative Entropy Rate, which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. In addition, the knowledge of the structure of the FIM can allow to efficiently address

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

Science.gov (United States)

Johnston, Matthew D

2017-12-01

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

Synthetic tetracycline-inducible regulatory networks: computer-aided design of dynamic phenotypes

Directory of Open Access Journals (Sweden)

Kaznessis Yiannis N

2007-01-01

Full Text Available Abstract Background Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA and Tet-On (rtTA. Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. With computer aided design, we aim to improve the synthesis of regulatory networks and propose new designs that enable tighter control of expression. Results In this paper we engineer novel networks by recombining existing genes or part of genes. We synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed. Conclusion The complexity of biomolecular interactions is accurately captured through computer simulations. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory

A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2016-01-01

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.

A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro

2016-07-07

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.

The HADDOCK web server for data-driven biomolecular docking

NARCIS (Netherlands)

de Vries, S.J.|info:eu-repo/dai/nl/304837717; van Dijk, M.|info:eu-repo/dai/nl/325811113; Bonvin, A.M.J.J.|info:eu-repo/dai/nl/113691238

2010-01-01

Computational docking is the prediction or modeling of the three-dimensional structure of a biomolecular complex, starting from the structures of the individual molecules in their free, unbound form. HADDOC K is a popular docking program that takes a datadriven approach to docking, with support for

Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo

2008-01-01

In this paper, the global exponential stability and periodicity for a class of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are addressed by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential converge to 0 of the difference between any two solutions of the original reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Furthermore, we prove periodicity of the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Sufficient conditions ensuring the global exponential stability and the existence of periodic oscillatory solutions for the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are given. These conditions are easy to check and have important leading significance in the design and application of reaction-diffusion recurrent neural networks with delays. Finally, two numerical examples are given to show the effectiveness of the obtained results

Global exponential stability of reaction-diffusion recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Liang Jinling; Cao Jinde

2003-01-01

Employing general Halanay inequality, we analyze the global exponential stability of a class of reaction-diffusion recurrent neural networks with time-varying delays. Several new sufficient conditions are obtained to ensure existence, uniqueness and global exponential stability of the equilibrium point of delayed reaction-diffusion recurrent neural networks. The results extend and improve the earlier publications. In addition, an example is given to show the effectiveness of the obtained result

High energy halogen atom reactions activated by nuclear transformations. Progress report, February 15-December 31, 1984

International Nuclear Information System (INIS)

1985-01-01

Energetic halogen atoms or ions, activated by various nuclear transformations are studied in gas, high pressure and condensed phase saturated and unsaturated hydrocarbons, halomethanes, and liquid and solid aqueous solutions of biomolecular and organic solutes in order to understand better the mechanisms and dynamics of high energy monovalent species. The experimental program and its goals remain the same, consisting of four interrelated areas: (1) The stereochemistry of energetic 18 F, /sup 34m/Cl, and 38 Cl substitution reactions with chiral molecules in the gas and condensed phase is studied. (2) The gas to condensed state transition in halogen high energy chemistry, involving energetic chlorine, bromine, and iodine reactions in halomethanes, saturated and unsaturated hydrocarbons and aqueous solutions of biomolecules and alkyl halides is being investigated in more detail. Current attention is given to defining the nature of the enhancement yields in the condensed phase. Specifically, energetic halogen reactions in liquid and frozen aqueous solutions or organic and biomolecular solutes are studied. (3) Reactions of bromine and iodine activated by isomeric transition with halogenated biomolecular and organic solutes in liquid and frozen aqueous solutions are being studied in an attempt to learn more about the activation events in the condensed phase. (4) The applications of hot chemistry techniques and theory to neutron activation analysis of biological systems are being continued. Current attention is given to developing procedures for trace molecular determinations in biological systems. The applications of hot halogen atoms as site indicators in liquid and frozen aqueous solutions of halogenated bases and nucleosides are currently being developed. 14 references

Nuclear Forensics and Radiochemistry: Reaction Networks

Energy Technology Data Exchange (ETDEWEB)

Rundberg, Robert S. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-11-22

In the intense neutron flux of a nuclear explosion the production of isotopes may occur through successive neutron induced reactions. The pathway to these isotopes illustrates both the complexity of the problem and the need for high quality nuclear data. The growth and decay of radioactive isotopes can follow a similarly complex network. The Bateman equation will be described and modified to apply to the transmutation of isotopes in a high flux reactor. A alternative model of growth and decay, the GD code, that can be applied to fission products will also be described.

GROMOS++Software for the Analysis of Biomolecular Simulation Trajectories

NARCIS (Netherlands)

Eichenberger, A.P.; Allison, J.R.; Dolenc, J.; Geerke, D.P.; Horta, B.A.C.; Meier, K; Oostenbrink, B.C.; Schmid, N.; Steiner, D; Wang, D.; van Gunsteren, W.F.

2011-01-01

GROMOS++ is a set of C++ programs for pre- and postprocessing of molecular dynamics simulation trajectories and as such is part of the GROningen MOlecular Simulation software for (bio)molecular simulation. It contains more than 70 programs that can be used to prepare data for the production of

Poisson-Nernst-Planck Equations for Simulating Biomolecular Diffusion-Reaction Processes I: Finite Element Solutions.

Science.gov (United States)

Lu, Benzhuo; Holst, Michael J; McCammon, J Andrew; Zhou, Y C

2010-09-20

In this paper we developed accurate finite element methods for solving 3-D Poisson-Nernst-Planck (PNP) equations with singular permanent charges for electrodiffusion in solvated biomolecular systems. The electrostatic Poisson equation was defined in the biomolecules and in the solvent, while the Nernst-Planck equation was defined only in the solvent. We applied a stable regularization scheme to remove the singular component of the electrostatic potential induced by the permanent charges inside biomolecules, and formulated regular, well-posed PNP equations. An inexact-Newton method was used to solve the coupled nonlinear elliptic equations for the steady problems; while an Adams-Bashforth-Crank-Nicolson method was devised for time integration for the unsteady electrodiffusion. We numerically investigated the conditioning of the stiffness matrices for the finite element approximations of the two formulations of the Nernst-Planck equation, and theoretically proved that the transformed formulation is always associated with an ill-conditioned stiffness matrix. We also studied the electroneutrality of the solution and its relation with the boundary conditions on the molecular surface, and concluded that a large net charge concentration is always present near the molecular surface due to the presence of multiple species of charged particles in the solution. The numerical methods are shown to be accurate and stable by various test problems, and are applicable to real large-scale biophysical electrodiffusion problems.

Unique temporal and spatial biomolecular emission profile on individual zinc oxide nanorods

Science.gov (United States)

Singh, Manpreet; Song, Sheng; Hahm, Jong-In

2013-12-01

Zinc oxide nanorods (ZnO NRs) have emerged in recent years as extremely useful, optical signal-enhancing platforms in DNA and protein detection. Although the use of ZnO NRs in biodetection has been demonstrated so far in systems involving many ZnO NRs per detection element, their future applications will likely take place in a miniaturized setting while exploiting single ZnO NRs in a low-volume, high-throughput bioanalysis. In this paper, we investigate temporal and spatial characteristics of the biomolecular fluorescence on individual ZnO NR systems. Quantitative and qualitative examinations of the biomolecular intensity and photostability are carried out as a function of two important criteria, the time and position along the long axis (length) of NRs. Photostability profiles are also measured with respect to the position on NRs and compared to those characteristics of biomolecules on polymeric control platforms. Unlike the uniformly distributed signal observed on the control platforms, both the fluorescence intensity and photostability are position-dependent on individual ZnO NRs. We have identified a unique phenomenon of highly localized, fluorescence intensification on the nanorod ends (FINE) of well-characterized, individual ZnO nanostructures. When compared to the polymeric controls, the biomolecular fluorescence intensity and photostability are determined to be higher on individual ZnO NRs regardless of the position on NRs. We have also carried out finite-difference time-domain simulations the results of which are in good agreement with the observed FINE. The outcomes of our investigation will offer a much needed basis for signal interpretation for biodetection devices and platforms consisting of single ZnO NRs and, at the same time, contribute significantly to provide insight in understanding the biomolecular fluorescence observed from ZnO NR ensemble-based systems.Zinc oxide nanorods (ZnO NRs) have emerged in recent years as extremely useful, optical

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

International Nuclear Information System (INIS)

1996-03-01

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

Behavior of ionic species in biomolecular reactions

International Nuclear Information System (INIS)

Seki, Hiroyuki

1974-01-01

Studies were made on short-lived intermediates such as neutral radicals, activated molecules, and radical ions, which appear and disappear accompanying various reactions in living organisms, especially on the behavior of the ionic species of chlorophyl a (Chl. a) and on the reductive cutting of the cobalt-carbon linkage in Vitamin B 12 and coenzyme-type B 12 . A rigid body of MTHF at 77 0 K, containing 3 mM Chl. a, was irradiated with γ ray, and captured electrons which did not react were bleached with near-infrared light. Thus a spectrum of Chl. a - was obtained. The ESR spectrum of Chl. a - was also obtained. Examination of these spectra indicated that not only Chl. a + but also Chl. a - were formed in the course of photochemical synthesis. A methanol rigid body at 77 0 K containing 10 mM VB 12 was irradiated with γ ray, then captured electrons which did not react were bleached. Thus an electron spectrum and an ESR spectrum were obtained. Examination of these spectra indicated that the Co-C linkage was cut off even in a rigid body at 77 0 K when B 12 coenzyme was reduced by electron capture and Bsub(12s) and 5'-deoxyadenosyl radical were formed. (Fukutomi, T.)

Reaction networks as systems for resource allocation: a variational principle for their non-equilibrium steady states.

Directory of Open Access Journals (Sweden)

Andrea De Martino

Full Text Available Within a fully microscopic setting, we derive a variational principle for the non-equilibrium steady states of chemical reaction networks, valid for time-scales over which chemical potentials can be taken to be slowly varying: at stationarity the system minimizes a global function of the reaction fluxes with the form of a Hopfield Hamiltonian with hebbian couplings, that is explicitly seen to correspond to the rate of decay of entropy production over time. Guided by this analogy, we show that reaction networks can be formally re-cast as systems of interacting reactions that optimize the use of the available compounds by competing for substrates, akin to agents competing for a limited resource in an optimal allocation problem. As an illustration, we analyze the scenario that emerges in two simple cases: that of toy (random reaction networks and that of a metabolic network model of the human red blood cell.

Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.

Directory of Open Access Journals (Sweden)

Andrea Ciliberto

2007-03-01

Full Text Available In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis-Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme-substrate complex (C is much less than the free substrate concentration (S0. However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter-Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter-Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1 it unveils the modular structure of the enzymatic reactions, (2 it suggests a simple algorithm to formulate correct kinetic equations, and (3 contrary to classical Michaelis-Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively.

Chemical reaction networks as a model to describe UVC- and radiolytically-induced reactions of simple compounds.

Science.gov (United States)

Dondi, Daniele; Merli, Daniele; Albini, Angelo; Zeffiro, Alberto; Serpone, Nick

2012-05-01

When a chemical system is submitted to high energy sources (UV, ionizing radiation, plasma sparks, etc.), as is expected to be the case of prebiotic chemistry studies, a plethora of reactive intermediates could form. If oxygen is present in excess, carbon dioxide and water are the major products. More interesting is the case of reducing conditions where synthetic pathways are also possible. This article examines the theoretical modeling of such systems with random-generated chemical networks. Four types of random-generated chemical networks were considered that originated from a combination of two connection topologies (viz., Poisson and scale-free) with reversible and irreversible chemical reactions. The results were analyzed taking into account the number of the most abundant products required for reaching 50% of the total number of moles of compounds at equilibrium, as this may be related to an actual problem of complex mixture analysis. The model accounts for multi-component reaction systems with no a priori knowledge of reacting species and the intermediates involved if system components are sufficiently interconnected. The approach taken is relevant to an earlier study on reactions that may have occurred in prebiotic systems where only a few compounds were detected. A validation of the model was attained on the basis of results of UVC and radiolytic reactions of prebiotic mixtures of low molecular weight compounds likely present on the primeval Earth.

A cascade reaction network mimicking the basic functional steps of acquired immune response

Science.gov (United States)

Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

2015-01-01

Biological systems use complex ‘information processing cores’ composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS which we call Adaptive Immune Response Simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system which responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner which is superficially similar to the most basic responses of the vertebrate acquired immune system, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices. PMID:26391084

A cascade reaction network mimicking the basic functional steps of adaptive immune response.

Science.gov (United States)

Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

2015-10-01

Biological systems use complex 'information-processing cores' composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS that we call an adaptive immune response simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system that responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner that is superficially similar to the most basic responses of the vertebrate AIS, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices.

Physics at the biomolecular interface fundamentals for molecular targeted therapy

CERN Document Server

Fernández, Ariel

2016-01-01

This book focuses primarily on the role of interfacial forces in understanding biological phenomena at the molecular scale. By providing a suitable statistical mechanical apparatus to handle the biomolecular interface, the book becomes uniquely positioned to address core problems in molecular biophysics. It highlights the importance of interfacial tension in delineating a solution to the protein folding problem, in unravelling the physico-chemical basis of enzyme catalysis and protein associations, and in rationally designing molecular targeted therapies. Thus grounded in fundamental science, the book develops a powerful technological platform for drug discovery, while it is set to inspire scientists at any level in their careers determined to address the major challenges in molecular biophysics. The acknowledgment of how exquisitely the structure and dynamics of proteins and their aqueous environment are related attests to the overdue recognition that biomolecular phenomena cannot be effectively understood w...

Reaction network modelling for kinetic parameters of pyrolytic reactions of CHON extractants in nuclear fuel processing waste management. Contributed Paper IT-07

International Nuclear Information System (INIS)

Gaikar, Vilas G.; Thaore, Vaishali

2014-01-01

The recovery and purification of plutonium (Pu) from uranium (U) and of U from Thorium (Th) in spent nuclear fuel reprocessing is accomplished by processes that employ organophosphorous compounds as extractants.The main objective of the present work was to develop a suitable kinetic model and to determine the kinetic parameters of the set of reactions involved in the pyrolysis of amides by fitting the experimental data in the reaction network model. The experimental data and analysis are expected to be useful in the steam pyrolysis of amide waste in fuel reprocessing in the nuclear industry. The basic approach was to understand the reaction mechanism of the steam pyrolysis of amides and then to estimate the reaction rate constants for the generation and consumption of different species by solving the model equations, allowing for the determination of important species in the reaction network

Electron-correlated fragment-molecular-orbital calculations for biomolecular and nano systems.

Science.gov (United States)

Tanaka, Shigenori; Mochizuki, Yuji; Komeiji, Yuto; Okiyama, Yoshio; Fukuzawa, Kaori

2014-06-14

Recent developments in the fragment molecular orbital (FMO) method for theoretical formulation, implementation, and application to nano and biomolecular systems are reviewed. The FMO method has enabled ab initio quantum-mechanical calculations for large molecular systems such as protein-ligand complexes at a reasonable computational cost in a parallelized way. There have been a wealth of application outcomes from the FMO method in the fields of biochemistry, medicinal chemistry and nanotechnology, in which the electron correlation effects play vital roles. With the aid of the advances in high-performance computing, the FMO method promises larger, faster, and more accurate simulations of biomolecular and related systems, including the descriptions of dynamical behaviors in solvent environments. The current status and future prospects of the FMO scheme are addressed in these contexts.

hPDB – Haskell library for processing atomic biomolecular structures in protein data bank format

OpenAIRE

Gajda, Michał Jan

2013-01-01

Background Protein DataBank file format is used for the majority of biomolecular data available today. Haskell is a lazy functional language that enjoys a high-level class-based type system, a growing collection of useful libraries and a reputation for efficiency. Findings I present a fast library for processing biomolecular data in the Protein Data Bank format. I present benchmarks indicating that this library is faster than other frequently used Protein Data Bank parsing programs. The propo...

Interplay between excitation kinetics and reaction-center dynamics in purple bacteria

International Nuclear Information System (INIS)

Caycedo-Soler, Felipe; RodrIguez, Ferney J; Quiroga, Luis; Johnson, Neil F

2010-01-01

Photosynthesis is arguably the fundamental process of life, since it enables energy from the Sun to enter the food chain on the Earth. It is a remarkable non-equilibrium process in which photons are converted to many-body excitations, which traverse a complex biomolecular membrane, where they are captured and fuel chemical reactions within a reaction center (RC) in order to produce nutrients. The precise nature of these dynamical processes-which lie at the interface between quantum and classical behavior and involve both noise and coordination-is still being explored. Here, we focus on a striking recent empirical finding concerning an illumination-driven transition in the biomolecular membrane architecture of the purple bacteria Rsp. photometricum. Using stochastic realizations to describe a hopping rate model for excitation transfer, we show numerically and analytically that this surprising shift in preferred architectures can be traced to the interplay between the excitation kinetics and the RC dynamics. The net effect is that the bacteria profit from efficient metabolism at low illumination intensities while using dissipation to avoid an oversupply of energy at high illumination intensities.

Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models

KAUST Repository

Moraes, Alvaro

2015-01-01

Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions

An Overview of Biomolecular Event Extraction from Scientific Documents.

Science.gov (United States)

Vanegas, Jorge A; Matos, Sérgio; González, Fabio; Oliveira, José L

2015-01-01

This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.

An Overview of Biomolecular Event Extraction from Scientific Documents

Directory of Open Access Journals (Sweden)

Jorge A. Vanegas

2015-01-01

Full Text Available This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.

Photochirogenesis: Photochemical Models on the Origin of Biomolecular Homochirality

Directory of Open Access Journals (Sweden)

Cornelia Meinert

2010-05-01

Full Text Available Current research focuses on a better understanding of the origin of biomolecular asymmetry by the identification and detection of the possibly first chiral molecules that were involved in the appearance and evolution of life on Earth. We have reasons to assume that these molecules were specific chiral amino acids. Chiral amino acids have been identified in both chondritic meteorites and simulated interstellar ices. Present research reasons that circularly polarized electromagnetic radiation was identified in interstellar environments and an asymmetric interstellar photon-molecule interaction might have triggered biomolecular symmetry breaking. We review on the possible prebiotic interaction of ‘chiral photons’ in the form of circularly polarized light, with early chiral organic molecules. We will highlight recent studies on enantioselective photolysis of racemic amino acids by circularly polarized light and experiments on the asymmetric photochemical synthesis of amino acids from only one C and one N containing molecules by simulating interstellar environments. Both approaches are based on circular dichroic transitions of amino acids that will be presented as well.

REVIEW ARTICLE: How do biomolecular systems speed up and regulate rates?

Science.gov (United States)

Zhou, Huan-Xiang

2005-09-01

The viability of a biological system depends upon careful regulation of the rates of various processes. These rates have limits imposed by intrinsic chemical or physical steps (e.g., diffusion). These limits can be expanded by interactions and dynamics of the biomolecules. For example, (a) a chemical reaction is catalyzed when its transition state is preferentially bound to an enzyme; (b) the folding of a protein molecule is speeded up by specific interactions within the transition-state ensemble and may be assisted by molecular chaperones; (c) the rate of specific binding of a protein molecule to a cellular target can be enhanced by mechanisms such as long-range electrostatic interactions, nonspecific binding and folding upon binding; (d) directional movement of motor proteins is generated by capturing favorable Brownian motion through intermolecular binding energy; and (e) conduction and selectivity of ions through membrane channels are controlled by interactions and the dynamics of channel proteins. Simple physical models are presented here to illustrate these processes and provide a unifying framework for understanding speed attainment and regulation in biomolecular systems.

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

Science.gov (United States)

Wang, Ting; Plecháč, Petr

2017-12-01

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

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

Science.gov (United States)

Wang, Ting; Plecháč, Petr

2017-12-21

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

Micro and Nanotechnologies Enhanced Biomolecular Sensing

Directory of Open Access Journals (Sweden)

Tza-Huei Wang

2013-07-01

Full Text Available This editorial summarizes some of the recent advances of micro and nanotechnology-based tools and devices for biomolecular detection. These include the incorporation of nanomaterials into a sensor surface or directly interfacing with molecular probes to enhance target detection via more rapid and sensitive responses, and the use of self-assembled organic/inorganic nanocomposites that inhibit exceptional spectroscopic properties to enable facile homogenous assays with efficient binding kinetics. Discussions also include some insight into microfluidic principles behind the development of an integrated sample preparation and biosensor platform toward a miniaturized and fully functional system for point of care applications.

Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Jian; Lu Junguo

2008-01-01

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits

From dynamics to structure and function of model biomolecular systems

NARCIS (Netherlands)

Fontaine-Vive-Curtaz, F.

2007-01-01

The purpose of this thesis was to extend recent works on structure and dynamics of hydrogen bonded crystals to model biomolecular systems and biological processes. The tools that we have used are neutron scattering (NS) and density functional theory (DFT) and force field (FF) based simulation

Single-molecule imaging and manipulation of biomolecular machines and systems.

Science.gov (United States)

Iino, Ryota; Iida, Tatsuya; Nakamura, Akihiko; Saita, Ei-Ichiro; You, Huijuan; Sako, Yasushi

2018-02-01

Biological molecular machines support various activities and behaviors of cells, such as energy production, signal transduction, growth, differentiation, and migration. We provide an overview of single-molecule imaging methods involving both small and large probes used to monitor the dynamic motions of molecular machines in vitro (purified proteins) and in living cells, and single-molecule manipulation methods used to measure the forces, mechanical properties and responses of biomolecules. We also introduce several examples of single-molecule analysis, focusing primarily on motor proteins and signal transduction systems. Single-molecule analysis is a powerful approach to unveil the operational mechanisms both of individual molecular machines and of systems consisting of many molecular machines. Quantitative, high-resolution single-molecule analyses of biomolecular systems at the various hierarchies of life will help to answer our fundamental question: "What is life?" This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato. Copyright © 2017 Elsevier B.V. All rights reserved.

Changes in biomolecular profile in a single nucleolus during cell fixation.

Science.gov (United States)

Kuzmin, Andrey N; Pliss, Artem; Prasad, Paras N

2014-11-04

Fixation of biological sample is an essential technique applied in order to "freeze" in time the intracellular molecular content. However, fixation induces changes of the cellular molecular structure, which mask physiological distribution of biomolecules and bias interpretation of results. Accurate, sensitive, and comprehensive characterization of changes in biomolecular composition, occurring during fixation, is crucial for proper analysis of experimental data. Here we apply biomolecular component analysis for Raman spectra measured in the same nucleoli of HeLa cells before and after fixation by either formaldehyde solution or by chilled ethanol. It is found that fixation in formaldehyde does not strongly affect the Raman spectra of nucleolar biomolecular components, but may significantly decrease the nucleolar RNA concentration. At the same time, ethanol fixation leads to a proportional increase (up to 40%) in concentrations of nucleolar proteins and RNA, most likely due to cell shrinkage occurring in the presence of coagulant fixative. Ethanol fixation also triggers changes in composition of nucleolar proteome, as indicated by an overall reduction of the α-helical structure of proteins and increase in the concentration of proteins containing the β-sheet conformation. We conclude that cross-linking fixation is a more appropriate protocol for mapping of proteins in situ. At the same time, ethanol fixation is preferential for studies of RNA-containing macromolecules. We supplemented our quantitative Raman spectroscopic measurements with mapping of the protein and lipid macromolecular groups in live and fixed cells using coherent anti-Stokes Raman scattering nonlinear optical imaging.

Event-triggered synchronization for reaction-diffusion complex networks via random sampling

Science.gov (United States)

Dong, Tao; Wang, Aijuan; Zhu, Huiyun; Liao, Xiaofeng

2018-04-01

In this paper, the synchronization problem of the reaction-diffusion complex networks (RDCNs) with Dirichlet boundary conditions is considered, where the data is sampled randomly. An event-triggered controller based on the sampled data is proposed, which can reduce the number of controller and the communication load. Under this strategy, the synchronization problem of the diffusion complex network is equivalently converted to the stability of a of reaction-diffusion complex dynamical systems with time delay. By using the matrix inequality technique and Lyapunov method, the synchronization conditions of the RDCNs are derived, which are dependent on the diffusion term. Moreover, it is found the proposed control strategy can get rid of the Zeno behavior naturally. Finally, a numerical example is given to verify the obtained results.

Electrochemical sensor for multiplex screening of genetically modified DNA: identification of biotech crops by logic-based biomolecular analysis.

Science.gov (United States)

Liao, Wei-Ching; Chuang, Min-Chieh; Ho, Ja-An Annie

2013-12-15

Genetically modified (GM) technique, one of the modern biomolecular engineering technologies, has been deemed as profitable strategy to fight against global starvation. Yet rapid and reliable analytical method is deficient to evaluate the quality and potential risk of such resulting GM products. We herein present a biomolecular analytical system constructed with distinct biochemical activities to expedite the computational detection of genetically modified organisms (GMOs). The computational mechanism provides an alternative to the complex procedures commonly involved in the screening of GMOs. Given that the bioanalytical system is capable of processing promoter, coding and species genes, affirmative interpretations succeed to identify specified GM event in terms of both electrochemical and optical fashions. The biomolecular computational assay exhibits detection capability of genetically modified DNA below sub-nanomolar level and is found interference-free by abundant coexistence of non-GM DNA. This bioanalytical system, furthermore, sophisticates in array fashion operating multiplex screening against variable GM events. Such a biomolecular computational assay and biosensor holds great promise for rapid, cost-effective, and high-fidelity screening of GMO. Copyright © 2013 Elsevier B.V. All rights reserved.

Switching dynamics in reaction networks induced by molecular discreteness

International Nuclear Information System (INIS)

Togashi, Yuichi; Kaneko, Kunihiko

2007-01-01

To study the fluctuations and dynamics in chemical reaction processes, stochastic differential equations based on the rate equation involving chemical concentrations are often adopted. When the number of molecules is very small, however, the discreteness in the number of molecules cannot be neglected since the number of molecules must be an integer. This discreteness can be important in biochemical reactions, where the total number of molecules is not significantly larger than the number of chemical species. To elucidate the effects of such discreteness, we study autocatalytic reaction systems comprising several chemical species through stochastic particle simulations. The generation of novel states is observed; it is caused by the extinction of some molecular species due to the discreteness in their number. We demonstrate that the reaction dynamics are switched by a single molecule, which leads to the reconstruction of the acting network structure. We also show the strong dependence of the chemical concentrations on the system size, which is caused by transitions to discreteness-induced novel states

Affinity Capillary Electrophoresis – A Powerful Tool to Investigate Biomolecular Interactions

Czech Academy of Sciences Publication Activity Database

Kašička, Václav

2017-01-01

Roč. 30, č. 5 (2017), s. 248 ISSN 1471-6577 Institutional support: RVO:61388963 Keywords : capillary affinity electrophoresis * biomolecular interactions * binding constants Subject RIV: CB - Analytical Chemistry, Separation OBOR OECD: Analytical chemistry Impact factor: 0.663, year: 2016

Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms

International Nuclear Information System (INIS)

Sheng Li; Yang Huizhong; Lou Xuyang

2009-01-01

This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.

GraphAlignment: Bayesian pairwise alignment of biological networks

Directory of Open Access Journals (Sweden)

Kolář Michal

2012-11-01

Full Text Available Abstract Background With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. Results We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0. On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6. On empirical bacterial protein-protein interaction networks (PIN and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. Conclusions The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.

Modeling evolution of crosstalk in noisy signal transduction networks

Science.gov (United States)

Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-02-01

Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.

HPDB-Haskell library for processing atomic biomolecular structures in Protein Data Bank format.

Science.gov (United States)

Gajda, Michał Jan

2013-11-23

Protein DataBank file format is used for the majority of biomolecular data available today. Haskell is a lazy functional language that enjoys a high-level class-based type system, a growing collection of useful libraries and a reputation for efficiency. I present a fast library for processing biomolecular data in the Protein Data Bank format. I present benchmarks indicating that this library is faster than other frequently used Protein Data Bank parsing programs. The proposed library also features a convenient iterator mechanism, and a simple API modeled after BioPython. I set a new standard for convenience and efficiency of Protein Data Bank processing in a Haskell library, and release it to open source.

International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otsuka, Naohiko; Dunaeva, Svetlana

2010-11-01

The activities of fourteen nuclear data centres are summarized, and their cooperation under the auspices of the International Atomic Energy Agency is described. Each of the centres provides coverage for different geographical zones and/or specific types of nuclear data, thus together providing a complete service for users worldwide. The International Network of Nuclear Reaction Data Centres (NRDC) was established with the objective of providing nuclear physics databases that are required for nuclear technology (encompassing energy and non-energy applications) by coordinating the collection, compilation and dissemination of nuclear data on an international scale. (author)

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

Directory of Open Access Journals (Sweden)

Luca Marchetti

2017-01-01

Full Text Available HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA.

Variable elimination in chemical reaction networks with mass-action kinetics

DEFF Research Database (Denmark)

Feliu, Elisenda; Wiuf, C.

2012-01-01

We consider chemical reaction networks taken with mass-action kinetics. The steady states of such a system are solutions to a system of polynomial equations. Even for small systems the task of finding the solutions is daunting. We develop an algebraic framework and procedure for linear elimination...

Computer Programming and Biomolecular Structure Studies: A Step beyond Internet Bioinformatics

Science.gov (United States)

Likic, Vladimir A.

2006-01-01

This article describes the experience of teaching structural bioinformatics to third year undergraduate students in a subject titled "Biomolecular Structure and Bioinformatics." Students were introduced to computer programming and used this knowledge in a practical application as an alternative to the well established Internet bioinformatics…

Dynamic and label-free high-throughput detection of biomolecular interactions based on phase-shift interferometry

Science.gov (United States)

Li, Qiang; Huang, Guoliang; Gan, Wupeng; Chen, Shengyi

2009-08-01

Biomolecular interactions can be detected by many established technologies such as fluorescence imaging, surface plasmon resonance (SPR)[1-4], interferometry and radioactive labeling of the analyte. In this study, we have designed and constructed a label-free, real-time sensing platform and its operating imaging instrument that detects interactions using optical phase differences from the accumulation of biological material on solid substrates. This system allows us to monitor biomolecular interactions in real time and quantify concentration changes during micro-mixing processes by measuring the changes of the optical path length (OPD). This simple interferometric technology monitors the optical phase difference resulting from accumulated biomolecular mass. A label-free protein chip that forms a 4×4 probe array was designed and fabricated using a commercial microarray robot spotter on solid substrates. Two positive control probe lines of BSA (Bovine Serum Albumin) and two experimental human IgG and goat IgG was used. The binding of multiple protein targets was performed and continuously detected by using this label-free and real-time sensing platform.

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

Science.gov (United States)

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

2016-03-18

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

Scanning probe and optical tweezer investigations of biomolecular interactions

International Nuclear Information System (INIS)

Rigby-Singleton, Shellie

2002-01-01

A complex array of intermolecular forces controls the interactions between and within biological molecules. The desire to empirically explore the fundamental forces has led to the development of several biophysical techniques. Of these, the atomic force microscope (AFM) and the optical tweezers have been employed throughout this thesis to monitor the intermolecular forces involved in biomolecular interactions. The AFM is a well-established force sensing technique capable of measuring biomolecular interactions at a single molecule level. However, its versatility has not been extrapolated to the investigation of a drug-enzyme complex. The energy landscape for the force induced dissociation of the DHFR-methotrexate complex was studied. Revealing an energy barrier to dissociation located ∼0.3 nm from the bound state. Unfortunately, the AFM has a limited range of accessible loading rates and in order to profile the complete energy landscape alternative force sensing instrumentation should be considered, for example the BFP and optical tweezers. Thus, this thesis outlines the development and construction an optical trap capable of measuring intermolecular forces between biomolecules at the single molecule level. To demonstrate the force sensing abilities of the optical set up, proof of principle measurements were performed which investigate the interactions between proteins and polymer surfaces subjected to varying degrees of argon plasma treatment. Complementary data was gained from measurements performed independently by the AFM. Changes in polymer resistance to proteins as a response to changes in polymer surface chemistry were detected utilising both AFM and optical tweezers measurements. Finally, the AFM and optical tweezers were employed as ultrasensitive biosensors. Single molecule investigations of the antibody-antigen interaction between the cardiac troponin I marker and its complementary antibody, reveals the impact therapeutic concentrations of heparin have

Qualitative Assessment of a 3D Simulation Program: Faculty, Students, and Bio-Organic Reaction Animations

Science.gov (United States)

Günersel, Adalet B.; Fleming, Steven A.

2013-01-01

Research shows that computer-based simulations and animations are especially helpful in fields such as chemistry where concepts are abstract and cannot be directly observed. Bio-Organic Reaction Animations (BioORA) is a freely available 3D visualization software program developed to help students understand the chemistry of biomolecular events.…

Application of biomolecular recognition via magnetic nanoparticle in nanobiotechnology

Science.gov (United States)

Shen, Wei-Zheng; Cetinel, Sibel; Montemagno, Carlo

2018-05-01

The marriage of biomolecular recognition and magnetic nanoparticle creates tremendous opportunities in the development of advanced technology both in academic research and in industrial sectors. In this paper, we review current progress on the magnetic nanoparticle-biomolecule hybrid systems, particularly employing the recognition pairs of DNA-DNA, DNA-protein, protein-protein, and protein-inorganics in several nanobiotechnology application areas, including molecular biology, diagnostics, medical treatment, industrial biocatalysts, and environmental separations.

The Forward-Reverse Algorithm for Stochastic Reaction Networks

KAUST Repository

Bayer, Christian

2015-01-07

In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which we solve a set of deterministic optimization problems where the SRNs are replaced by the classical ODE rates; then, during the second phase, the Monte Carlo version of the EM algorithm is applied starting from the output of the previous phase. Starting from a set of over-dispersed seeds, the output of our two-phase method is a cluster of maximum likelihood estimates obtained by using convergence assessment techniques from the theory of Markov chain Monte Carlo.

Scalable Molecular Dynamics for Large Biomolecular Systems

Directory of Open Access Journals (Sweden)

Robert K. Brunner

2000-01-01

Full Text Available We present an optimized parallelization scheme for molecular dynamics simulations of large biomolecular systems, implemented in the production-quality molecular dynamics program NAMD. With an object-based hybrid force and spatial decomposition scheme, and an aggressive measurement-based predictive load balancing framework, we have attained speeds and speedups that are much higher than any reported in literature so far. The paper first summarizes the broad methodology we are pursuing, and the basic parallelization scheme we used. It then describes the optimizations that were instrumental in increasing performance, and presents performance results on benchmark simulations.

Synthetic Approach to biomolecular science by cyborg supramolecular chemistry.

Science.gov (United States)

Kurihara, Kensuke; Matsuo, Muneyuki; Yamaguchi, Takumi; Sato, Sota

2018-02-01

To imitate the essence of living systems via synthetic chemistry approaches has been attempted. With the progress in supramolecular chemistry, it has become possible to synthesize molecules of a size and complexity close to those of biomacromolecules. Recently, the combination of precisely designed supramolecules with biomolecules has generated structural platforms for designing and creating unique molecular systems. Bridging between synthetic chemistry and biomolecular science is also developing methodologies for the creation of artificial cellular systems. This paper provides an overview of the recently expanding interdisciplinary research to fuse artificial molecules with biomolecules, that can deepen our understanding of the dynamical ordering of biomolecules. Using bottom-up approaches based on the precise chemical design, synthesis and hybridization of artificial molecules with biological materials have been realizing the construction of sophisticated platforms having the fundamental functions of living systems. The effective hybrid, molecular cyborg, approaches enable not only the establishment of dynamic systems mimicking nature and thus well-defined models for biophysical understanding, but also the creation of those with highly advanced, integrated functions. This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato. Copyright © 2017 Elsevier B.V. All rights reserved.

Synergy of Two Highly Specific Biomolecular Recognition Events

DEFF Research Database (Denmark)

Ejlersen, Maria; Christensen, Niels Johan; Sørensen, Kasper K

2018-01-01

Two highly specific biomolecular recognition events, nucleic acid duplex hybridization and DNA-peptide recognition in the minor groove, were coalesced in a miniature ensemble for the first time by covalently attaching a natural AT-hook peptide motif to nucleic acid duplexes via a 2'-amino......-LNA scaffold. A combination of molecular dynamics simulations and ultraviolet thermal denaturation studies revealed high sequence-specific affinity of the peptide-oligonucleotide conjugates (POCs) when binding to complementary DNA strands, leveraging the bioinformation encrypted in the minor groove of DNA...

Identification of alterations in the Jacobian of biochemical reaction networks from steady state covariance data at two conditions.

Science.gov (United States)

Kügler, Philipp; Yang, Wei

2014-06-01

Model building of biochemical reaction networks typically involves experiments in which changes in the behavior due to natural or experimental perturbations are observed. Computational models of reaction networks are also used in a systems biology approach to study how transitions from a healthy to a diseased state result from changes in genetic or environmental conditions. In this paper we consider the nonlinear inverse problem of inferring information about the Jacobian of a Langevin type network model from covariance data of steady state concentrations associated to two different experimental conditions. Under idealized assumptions on the Langevin fluctuation matrices we prove that relative alterations in the network Jacobian can be uniquely identified when comparing the two data sets. Based on this result and the premise that alteration is locally confined to separable parts due to network modularity we suggest a computational approach using hybrid stochastic-deterministic optimization for the detection of perturbations in the network Jacobian using the sparsity promoting effect of [Formula: see text]-penalization. Our approach is illustrated by means of published metabolomic and signaling reaction networks.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

International Nuclear Information System (INIS)

Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

2016-01-01

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.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

Energy Technology Data Exchange (ETDEWEB)

Marchetti, Luca, E-mail: marchetti@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); University of Trento, Department of Mathematics (Italy); Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)

2016-07-15

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

A mathematical analysis of Prx2-STAT3 disulfide exchange rate constants for a bimolecular reaction mechanism.

Science.gov (United States)

Langford, Troy F; Deen, William M; Sikes, Hadley D

2018-03-22

Appreciation of peroxiredoxins as the major regulators of H 2 O 2 concentrations in human cells has led to a new understanding of redox signaling. In addition to their status as the primary reducers of H 2 O 2 to water, the oxidized peroxiredoxin byproduct of this reaction has recently been shown capable of participation in H 2 O 2 -mediated signaling pathways through disulfide exchange reactions with the transcription factor STAT3. The dynamics of peroxidase-transcription factor disulfide exchange reactions have not yet been considered in detail with respect to how these reactions fit into the larger network of competing reactions in human cells. In this study, we used a kinetic model of oxidation and reduction reactions related to H 2 O 2 metabolism in the cytosol of human cells to study the dynamics of peroxiredoxin-2 mediated oxidation of the redox-regulated transcription factor STAT3. In combination with previously reported experimental data, the model was used to estimate the rate coefficient of a biomolecular reaction between Prx2 and STAT3 for two sets of assumptions that constitute lower and upper bound cases. Using these estimates, we calculated the relative rates of the reaction of oxidized peroxiredoxin-2 and STAT3 and other competing reactions in the cytosol. These calculations revealed that peroxiredoxin-2-mediated oxidation of STAT3 likely occurs at a much slower rate than competing reactions in the cytosol. This analysis suggests the existence of more complex mechanisms, potentially involving currently unknown protein-protein recognition partners, which facilitate disulfide exchange reactions between peroxiredoxin-2 and STAT3. Copyright © 2018 Elsevier Inc. All rights reserved.

ATR-FTIR Spectroscopic Evidence for Biomolecular Phosphorus and Carboxyl Groups Facilitating Bacterial Adhesion to Iron Oxides

Science.gov (United States)

Parikh, Sanjai J.; Mukome, Fungai N.D.; Zhang, Xiaoming

2014-01-01

Attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy has been used to probe the binding of bacteria to hematite (α-Fe2O3) and goethite (α-FeOOH). In situ ATR-FTIR experiments with bacteria (Pseudomonas putida, P. aeruginosa, Escherichia coli), mixed amino acids, polypeptide extracts, deoxyribonucleic acid (DNA), and a suite of model compounds were conducted. These compounds represent carboxyl, catecholate, amide, and phosphate groups present in siderophores, amino acids, polysaccharides, phospholipids, and DNA. Due in part to the ubiquitous presence of carboxyl groups in biomolecules, numerous IR peaks corresponding to outer-sphere or unbound (1400 cm−1) and inner-sphere (1310-1320 cm−1) coordinated carboxyl groups are noted following reaction of bacteria and biomolecules with α-Fe2O3 and α-FeOOH. However, the data also reveal that the presence of low-level amounts (i.e., 0.45-0.79%) of biomolecular phosphorous groups result in strong IR bands at ~1043 cm−1, corresponding to inner-sphere Fe-O-P bonds, underscoring the importance of bacteria associated P-containing groups in biomolecule and cell adhesion. Spectral comparisons also reveal slightly greater P-O-Fe contributions for bacteria (Pseudomonad, E. coli) deposited on α-FeOOH, as compared to α-Fe2O3. This data demonstrates that slight differences in bacterial adhesion to Fe oxides can be attributed to bacterial species and Fe-oxide minerals. However, more importantly, the strong binding affinity of phosphate in all bacteria samples to both Fe-oxides results in the formation of inner-sphere Fe-O-P bonds, signifying the critical role of biomolecular P in the initiation of bacterial adhesion. PMID:24859052

MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations.

Science.gov (United States)

Vergara-Perez, Sandra; Marucho, Marcelo

2016-01-01

One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post- analysis of structural and electrical properties of biomolecules.

MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations

Science.gov (United States)

Vergara-Perez, Sandra; Marucho, Marcelo

2016-01-01

One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson-Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post-analysis of structural and electrical properties of biomolecules.

Critical regimes driven by recurrent mobility patterns of reaction-diffusion processes in networks

Science.gov (United States)

Gómez-Gardeñes, J.; Soriano-Paños, D.; Arenas, A.

2018-04-01

Reaction-diffusion processes1 have been widely used to study dynamical processes in epidemics2-4 and ecology5 in networked metapopulations. In the context of epidemics6, reaction processes are understood as contagions within each subpopulation (patch), while diffusion represents the mobility of individuals between patches. Recently, the characteristics of human mobility7, such as its recurrent nature, have been proven crucial to understand the phase transition to endemic epidemic states8,9. Here, by developing a framework able to cope with the elementary epidemic processes, the spatial distribution of populations and the commuting mobility patterns, we discover three different critical regimes of the epidemic incidence as a function of these parameters. Interestingly, we reveal a regime of the reaction-diffussion process in which, counter-intuitively, mobility is detrimental to the spread of disease. We analytically determine the precise conditions for the emergence of any of the three possible critical regimes in real and synthetic networks.

Application of Hidden Markov Models in Biomolecular Simulations.

Science.gov (United States)

Shukla, Saurabh; Shamsi, Zahra; Moffett, Alexander S; Selvam, Balaji; Shukla, Diwakar

2017-01-01

Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process.

Traveling and Pinned Fronts in Bistable Reaction-Diffusion Systems on Networks

Science.gov (United States)

Kouvaris, Nikos E.; Kori, Hiroshi; Mikhailov, Alexander S.

2012-01-01

Traveling fronts and stationary localized patterns in bistable reaction-diffusion systems have been broadly studied for classical continuous media and regular lattices. Analogs of such non-equilibrium patterns are also possible in networks. Here, we consider traveling and stationary patterns in bistable one-component systems on random Erdös-Rényi, scale-free and hierarchical tree networks. As revealed through numerical simulations, traveling fronts exist in network-organized systems. They represent waves of transition from one stable state into another, spreading over the entire network. The fronts can furthermore be pinned, thus forming stationary structures. While pinning of fronts has previously been considered for chains of diffusively coupled bistable elements, the network architecture brings about significant differences. An important role is played by the degree (the number of connections) of a node. For regular trees with a fixed branching factor, the pinning conditions are analytically determined. For large Erdös-Rényi and scale-free networks, the mean-field theory for stationary patterns is constructed. PMID:23028746

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

Science.gov (United States)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

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

Biomolecular solid state NMR with magic-angle spinning at 25K.

Science.gov (United States)

Thurber, Kent R; Tycko, Robert

2008-12-01

A magic-angle spinning (MAS) probe has been constructed which allows the sample to be cooled with helium, while the MAS bearing and drive gases are nitrogen. The sample can be cooled to 25K using roughly 3 L/h of liquid helium, while the 4-mm diameter rotor spins at 6.7 kHz with good stability (+/-5 Hz) for many hours. Proton decoupling fields up to at least 130 kHz can be applied. This helium-cooled MAS probe enables a variety of one-dimensional and two-dimensional NMR experiments on biomolecular solids and other materials at low temperatures, with signal-to-noise proportional to 1/T. We show examples of low-temperature (13)C NMR data for two biomolecular samples, namely the peptide Abeta(14-23) in the form of amyloid fibrils and the protein HP35 in frozen glycerol/water solution. Issues related to temperature calibration, spin-lattice relaxation at low temperatures, paramagnetic doping of frozen solutions, and (13)C MAS NMR linewidths are discussed.

Investigating biomolecular recognition at the cell surface using atomic force microscopy.

Science.gov (United States)

Wang, Congzhou; Yadavalli, Vamsi K

2014-05-01

Probing the interaction forces that drive biomolecular recognition on cell surfaces is essential for understanding diverse biological processes. Force spectroscopy has been a widely used dynamic analytical technique, allowing measurement of such interactions at the molecular and cellular level. The capabilities of working under near physiological environments, combined with excellent force and lateral resolution make atomic force microscopy (AFM)-based force spectroscopy a powerful approach to measure biomolecular interaction forces not only on non-biological substrates, but also on soft, dynamic cell surfaces. Over the last few years, AFM-based force spectroscopy has provided biophysical insight into how biomolecules on cell surfaces interact with each other and induce relevant biological processes. In this review, we focus on describing the technique of force spectroscopy using the AFM, specifically in the context of probing cell surfaces. We summarize recent progress in understanding the recognition and interactions between macromolecules that may be found at cell surfaces from a force spectroscopy perspective. We further discuss the challenges and future prospects of the application of this versatile technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

Integrative NMR for biomolecular research

International Nuclear Information System (INIS)

Lee, Woonghee; Cornilescu, Gabriel; Dashti, Hesam; Eghbalnia, Hamid R.; Tonelli, Marco; Westler, William M.; Butcher, Samuel E.; Henzler-Wildman, Katherine A.; Markley, John L.

2016-01-01

NMR spectroscopy is a powerful technique for determining structural and functional features of biomolecules in physiological solution as well as for observing their intermolecular interactions in real-time. However, complex steps associated with its practice have made the approach daunting for non-specialists. We introduce an NMR platform that makes biomolecular NMR spectroscopy much more accessible by integrating tools, databases, web services, and video tutorials that can be launched by simple installation of NMRFAM software packages or using a cross-platform virtual machine that can be run on any standard laptop or desktop computer. The software package can be downloaded freely from the NMRFAM software download page ( http://pine.nmrfam.wisc.edu/download-packages.html http://pine.nmrfam.wisc.edu/download_packages.html ), and detailed instructions are available from the Integrative NMR Video Tutorial page ( http://pine.nmrfam.wisc.edu/integrative.html http://pine.nmrfam.wisc.edu/integrative.html ).

Integrative NMR for biomolecular research

Energy Technology Data Exchange (ETDEWEB)

Lee, Woonghee, E-mail: whlee@nmrfam.wisc.edu; Cornilescu, Gabriel; Dashti, Hesam; Eghbalnia, Hamid R.; Tonelli, Marco; Westler, William M.; Butcher, Samuel E.; Henzler-Wildman, Katherine A.; Markley, John L., E-mail: markley@nmrfam.wisc.edu [University of Wisconsin-Madison, National Magnetic Resonance Facility at Madison and Biochemistry Department (United States)

2016-04-15

NMR spectroscopy is a powerful technique for determining structural and functional features of biomolecules in physiological solution as well as for observing their intermolecular interactions in real-time. However, complex steps associated with its practice have made the approach daunting for non-specialists. We introduce an NMR platform that makes biomolecular NMR spectroscopy much more accessible by integrating tools, databases, web services, and video tutorials that can be launched by simple installation of NMRFAM software packages or using a cross-platform virtual machine that can be run on any standard laptop or desktop computer. The software package can be downloaded freely from the NMRFAM software download page ( http://pine.nmrfam.wisc.edu/download-packages.html http://pine.nmrfam.wisc.edu/download{sub p}ackages.html ), and detailed instructions are available from the Integrative NMR Video Tutorial page ( http://pine.nmrfam.wisc.edu/integrative.html http://pine.nmrfam.wisc.edu/integrative.html ).

Simulation of noise-assisted transport via optical cavity networks

International Nuclear Information System (INIS)

Caruso, Filippo; Plenio, Martin B.; Spagnolo, Nicolo; Vitelli, Chiara; Sciarrino, Fabio

2011-01-01

Recently, the presence of noise has been found to play a key role in assisting the transport of energy and information in complex quantum networks and even in biomolecular systems. Here we propose an experimentally realizable optical network scheme for the demonstration of the basic mechanisms underlying noise-assisted transport. The proposed system consists of a network of coupled quantum-optical cavities, injected with a single photon, whose transmission efficiency can be measured. Introducing dephasing in the photon path, this system exhibits a characteristic enhancement of the transport efficiency that can be observed with presently available technology.

Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction

Directory of Open Access Journals (Sweden)

Lin JianJhong

2009-01-01

Full Text Available Szekeley observed that the dynamic pattern of the locomotion of salamanders can be explained by periodic vector sequences generated by logical neural networks. Such sequences can mathematically be described by "doubly periodic traveling waves" and therefore it is of interest to propose dynamic models that may produce such waves. One such dynamic network model is built here based on reaction-diffusion principles and a complete discussion is given for the existence of doubly periodic waves as outputs. Since there are 2 parameters in our model and 4 a priori unknown parameters involved in our search of solutions, our results are nontrivial. The reaction term in our model is a linear function and hence our results can also be interpreted as existence criteria for solutions of a nontrivial linear problem depending on 6 parameters.

Stability analysis of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Li Zuoan; Li Kelin

2009-01-01

In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.

On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

Energy Technology Data Exchange (ETDEWEB)

Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Zunino, Roberto, E-mail: roberto.zunino@unitn.it [Department of Mathematics, University of Trento, Trento (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Department of Mathematics, University of Trento, Trento (Italy)

2015-06-28

Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

Impact parameter determination for 40Ca + 40Ca reactions using a neural network

International Nuclear Information System (INIS)

Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

1995-01-01

A neural network is used for the impact parameter determination in 40 Ca + 40 Ca reactions at energies between 35 and 70 AMeV. A special attention is devoted to the effect of experimental constraints such as the detection efficiency. An overall improvement of the impact parameter determination of 25% is obtained with the neural network. The neural network technique is then used in the analysis of the Ca+Ca data at 35 AMeV and allows separation of three different class of events among the selected 'complete' events. (authors). 8 refs., 5 figs

On the graph and systems analysis of reversible chemical reaction networks with mass action kinetics

NARCIS (Netherlands)

Rao, Shodhan; Jayawardhana, Bayu; Schaft, Arjan van der

2012-01-01

Motivated by the recent progresses on the interplay between the graph theory and systems theory, we revisit the analysis of reversible chemical reaction networks described by mass action kinetics by reformulating it using the graph knowledge of the underlying networks. Based on this formulation, we

Versatile single-molecule multi-color excitation and detection fluorescence setup for studying biomolecular dynamics

KAUST Repository

Sobhy, M. A.; Elshenawy, M. M.; Takahashi, Masateru; Whitman, B. H.; Walter, N. G.; Hamdan, S. M.

2011-01-01

Single-molecule fluorescence imaging is at the forefront of tools applied to study biomolecular dynamics both in vitro and in vivo. The ability of the single-molecule fluorescence microscope to conduct simultaneous multi-color excitation

Report on the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Forrest, R.; Dunaeva, S.; Otsuka, N.

2010-07-01

This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. (author)

Variable elimination in post-translational modification reaction networks with mass-action kinetics

DEFF Research Database (Denmark)

Feliu, Elisenda; Wiuf, Carsten

2013-01-01

We define a subclass of chemical reaction networks called post-translational modification systems. Important biological examples of such systems include MAPK cascades and two-component systems which are well-studied experimentally as well as theoretically. The steady states of such a system...

Noise-Induced Modulation of the Relaxation Kinetics around a Non-Equilibrium Steady State of Non-Linear Chemical Reaction Networks

OpenAIRE

Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido

2011-01-01

Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confi...

Ion induced fragmentation of biomolecular systems at low collision energies

International Nuclear Information System (INIS)

Bernigaud, V; Adoui, L; Chesnel, J Y; Rangama, J; Huber, B A; Manil, B; Alvarado, F; Bari, S; Hoekstra, R; Postma, J; Schlathoelter, T

2009-01-01

In this paper, we present results of different collision experiments between multiply charged ions at low collision energies (in the keV-region) and biomolecular systems. This kind of interaction allows to remove electrons form the biomolecule without transferring a large amount of vibrational excitation energy. Nevertheless, following the ionization of the target, fragmentation of biomolecular species may occur. It is the main objective of this work to study the physical processes involved in the dissociation of highly electronically excited systems. In order to elucidate the intrinsic properties of certain biomolecules (porphyrins and amino acids) we have performed experiments in the gas phase with isolated systems. The obtained results demonstrate the high stability of porphyrins after electron removal. Furthermore, a dependence of the fragmentation pattern produced by multiply charged ions on the isomeric structure of the alanine molecule has been shown. By considering the presence of other surrounding biomolecules (clusters of nucleobases), a strong influence of the environment of the biomolecule on the fragmentation channels and their modification, has been clearly proven. This result is explained, in the thymine and uracil case, by the formation of hydrogen bonds between O and H atoms, which is known to favor planar cluster geometries.

Biomolecular simulations on petascale: promises and challenges

International Nuclear Information System (INIS)

Agarwal, Pratul K; Alam, Sadaf R

2006-01-01

Proteins work as highly efficient machines at the molecular level and are responsible for a variety of processes in all living cells. There is wide interest in understanding these machines for implications in biochemical/biotechnology industries as well as in health related fields. Over the last century, investigations of proteins based on a variety of experimental techniques have provided a wealth of information. More recently, theoretical and computational modeling using large scale simulations is providing novel insights into the functioning of these machines. The next generation supercomputers with petascale computing power, hold great promises as well as challenges for the biomolecular simulation scientists. We briefly discuss the progress being made in this area

Survey of Network-Based Approaches to Research of Cardiovascular Diseases

Directory of Open Access Journals (Sweden)

Anida Sarajlić

2014-01-01

Full Text Available Cardiovascular diseases (CVDs are the leading health problem worldwide. Investigating causes and mechanisms of CVDs calls for an integrative approach that would take into account its complex etiology. Biological networks generated from available data on biomolecular interactions are an excellent platform for understanding interconnectedness of all processes within a living cell, including processes that underlie diseases. Consequently, topology of biological networks has successfully been used for identifying genes, pathways, and modules that govern molecular actions underlying various complex diseases. Here, we review approaches that explore and use relationships between topological properties of biological networks and mechanisms underlying CVDs.

Electrostatics in biomolecular simulations : where are we now and where are we heading?

NARCIS (Netherlands)

Karttunen, M.E.J.; Rottler, J.; Vattulainen, I.; Sagui, C.

2008-01-01

Chapter 2. In this review, we discuss current methods and developments in the treatment of electrostatic interactions in biomolecular and soft matter simulations. We review the current ‘work horses’, namely, Ewald summation based methods such the Particle-Mesh Ewald, and others, and also newer

Stationary patterns in star networks of bistable units: Theory and application to chemical reactions.

Science.gov (United States)

Kouvaris, Nikos E; Sebek, Michael; Iribarne, Albert; Díaz-Guilera, Albert; Kiss, István Z

2017-04-01

We present theoretical and experimental studies on pattern formation with bistable dynamical units coupled in a star network configuration. By applying a localized perturbation to the central or the peripheral elements, we demonstrate the subsequent spreading, pinning, or retraction of the activations; such analysis enables the characterization of the formation of stationary patterns of localized activity. The results are interpreted with a theoretical analysis of a simplified bistable reaction-diffusion model. Weak coupling results in trivial pinned states where the activation cannot propagate. At strong coupling, a uniform state is expected with active or inactive elements at small or large degree networks, respectively. A nontrivial stationary spatial pattern, corresponding to an activation pinning, is predicted to occur at an intermediate number of peripheral elements and at intermediate coupling strengths, where the central activation of the network is pinned, but the peripheral activation propagates toward the center. The results are confirmed in experiments with star networks of bistable electrochemical reactions. The experiments confirm the existence of the stationary spatial patterns and the dependence of coupling strength on the number of peripheral elements for transitions between pinned and retreating or spreading fronts in forced network configurations (where the central or periphery elements are forced to maintain their states).

A compact imaging spectroscopic system for biomolecular detections on plasmonic chips.

Science.gov (United States)

Lo, Shu-Cheng; Lin, En-Hung; Wei, Pei-Kuen; Tsai, Wan-Shao

2016-10-17

In this study, we demonstrate a compact imaging spectroscopic system for high-throughput detection of biomolecular interactions on plasmonic chips, based on a curved grating as the key element of light diffraction and light focusing. Both the curved grating and the plasmonic chips are fabricated on flexible plastic substrates using a gas-assisted thermal-embossing method. A fiber-coupled broadband light source and a camera are included in the system. Spectral resolution within 1 nm is achieved in sensing environmental index solutions and protein bindings. The detected sensitivities of the plasmonic chip are comparable with a commercial spectrometer. An extra one-dimensional scanning stage enables high-throughput detection of protein binding on a designed plasmonic chip consisting of several nanoslit arrays with different periods. The detected resonance wavelengths match well with the grating equation under an air environment. Wavelength shifts between 1 and 9 nm are detected for antigens of various concentrations binding with antibodies. A simple, mass-productive and cost-effective method has been demonstrated on the imaging spectroscopic system for real-time, label-free, highly sensitive and high-throughput screening of biomolecular interactions.

Hierarchical feedback modules and reaction hubs in cell signaling networks.

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical feedback modules and reaction hubs in cell signaling networks.

Directory of Open Access Journals (Sweden)

Jianfeng Xu

Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Xiaohu; Xu Daoyi

2009-01-01

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.

Rapid prototyping of nanofluidic systems using size-reduced electrospun nanofibers for biomolecular analysis.

Science.gov (United States)

Park, Seung-Min; Huh, Yun Suk; Szeto, Kylan; Joe, Daniel J; Kameoka, Jun; Coates, Geoffrey W; Edel, Joshua B; Erickson, David; Craighead, Harold G

2010-11-05

Biomolecular transport in nanofluidic confinement offers various means to investigate the behavior of biomolecules in their native aqueous environments, and to develop tools for diverse single-molecule manipulations. Recently, a number of simple nanofluidic fabrication techniques has been demonstrated that utilize electrospun nanofibers as a backbone structure. These techniques are limited by the arbitrary dimension of the resulting nanochannels due to the random nature of electrospinning. Here, a new method for fabricating nanofluidic systems from size-reduced electrospun nanofibers is reported and demonstrated. As it is demonstrated, this method uses the scanned electrospinning technique for generation of oriented sacrificial nanofibers and exposes these nanofibers to harsh, but isotropic etching/heating environments to reduce their cross-sectional dimension. The creation of various nanofluidic systems as small as 20 nm is demonstrated, and practical examples of single biomolecular handling, such as DNA elongation in nanochannels and fluorescence correlation spectroscopic analysis of biomolecules passing through nanochannels, are provided.

A reaction-diffusion model of ROS-induced ROS release in a mitochondrial network.

Directory of Open Access Journals (Sweden)

Lufang Zhou

2010-01-01

Full Text Available Loss of mitochondrial function is a fundamental determinant of cell injury and death. In heart cells under metabolic stress, we have previously described how the abrupt collapse or oscillation of the mitochondrial energy state is synchronized across the mitochondrial network by local interactions dependent upon reactive oxygen species (ROS. Here, we develop a mathematical model of ROS-induced ROS release (RIRR based on reaction-diffusion (RD-RIRR in one- and two-dimensional mitochondrial networks. The nodes of the RD-RIRR network are comprised of models of individual mitochondria that include a mechanism of ROS-dependent oscillation based on the interplay between ROS production, transport, and scavenging; and incorporating the tricarboxylic acid (TCA cycle, oxidative phosphorylation, and Ca(2+ handling. Local mitochondrial interaction is mediated by superoxide (O2.- diffusion and the O2.(--dependent activation of an inner membrane anion channel (IMAC. In a 2D network composed of 500 mitochondria, model simulations reveal DeltaPsi(m depolarization waves similar to those observed when isolated guinea pig cardiomyocytes are subjected to a localized laser-flash or antioxidant depletion. The sensitivity of the propagation rate of the depolarization wave to O(2.- diffusion, production, and scavenging in the reaction-diffusion model is similar to that observed experimentally. In addition, we present novel experimental evidence, obtained in permeabilized cardiomyocytes, confirming that DeltaPsi(m depolarization is mediated specifically by O2.-. The present work demonstrates that the observed emergent macroscopic properties of the mitochondrial network can be reproduced in a reaction-diffusion model of RIRR. Moreover, the findings have uncovered a novel aspect of the synchronization mechanism, which is that clusters of mitochondria that are oscillating can entrain mitochondria that would otherwise display stable dynamics. The work identifies the

An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks

KAUST Repository

Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2016-01-01

In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem

A coarse-grained model for the simulations of biomolecular interactions in cellular environments

International Nuclear Information System (INIS)

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2014-01-01

The interactions of bio-molecules constitute the key steps of cellular functions. However, in vivo binding properties differ significantly from their in vitro measurements due to the heterogeneity of cellular environments. Here we introduce a coarse-grained model based on rigid-body representation to study how factors such as cellular crowding and membrane confinement affect molecular binding. The macroscopic parameters such as the equilibrium constant and the kinetic rate constant are calibrated by adjusting the microscopic coefficients used in the numerical simulations. By changing these model parameters that are experimentally approachable, we are able to study the kinetic and thermodynamic properties of molecular binding, as well as the effects caused by specific cellular environments. We investigate the volumetric effects of crowded intracellular space on bio-molecular diffusion and diffusion-limited reactions. Furthermore, the binding constants of membrane proteins are currently difficult to measure. We provide quantitative estimations about how the binding of membrane proteins deviates from soluble proteins under different degrees of membrane confinements. The simulation results provide biological insights to the functions of membrane receptors on cell surfaces. Overall, our studies establish a connection between the details of molecular interactions and the heterogeneity of cellular environments

Instrumental biosensors: new perspectives for the analysis of biomolecular interactions.

Science.gov (United States)

Nice, E C; Catimel, B

1999-04-01

The use of instrumental biosensors in basic research to measure biomolecular interactions in real time is increasing exponentially. Applications include protein-protein, protein-peptide, DNA-protein, DNA-DNA, and lipid-protein interactions. Such techniques have been applied to, for example, antibody-antigen, receptor-ligand, signal transduction, and nuclear receptor studies. This review outlines the principles of two of the most commonly used instruments and highlights specific operating parameters that will assist in optimising experimental design, data generation, and analysis.

Development and testing of a compartmentalized reaction network model for redox zones in contaminated aquifers

Science.gov (United States)

Abrams , Robert H.; Loague, Keith; Kent, Douglas B.

1998-01-01

The work reported here is the first part of a larger effort focused on efficient numerical simulation of redox zone development in contaminated aquifers. The sequential use of various electron acceptors, which is governed by the energy yield of each reaction, gives rise to redox zones. The large difference in energy yields between the various redox reactions leads to systems of equations that are extremely ill-conditioned. These equations are very difficult to solve, especially in the context of coupled fluid flow, solute transport, and geochemical simulations. We have developed a general, rational method to solve such systems where we focus on the dominant reactions, compartmentalizing them in a manner that is analogous to the redox zones that are often observed in the field. The compartmentalized approach allows us to easily solve a complex geochemical system as a function of time and energy yield, laying the foundation for our ongoing work in which we couple the reaction network, for the development of redox zones, to a model of subsurface fluid flow and solute transport. Our method (1) solves the numerical system without evoking a redox parameter, (2) improves the numerical stability of redox systems by choosing which compartment and thus which reaction network to use based upon the concentration ratios of key constituents, (3) simulates the development of redox zones as a function of time without the use of inhibition factors or switching functions, and (4) can reduce the number of transport equations that need to be solved in space and time. We show through the use of various model performance evaluation statistics that the appropriate compartment choice under different geochemical conditions leads to numerical solutions without significant error. The compartmentalized approach described here facilitates the next phase of this effort where we couple the redox zone reaction network to models of fluid flow and solute transport.

Optimization Models for Reaction Networks: Information Divergence, Quadratic Programming and Kirchhoff’s Laws

Directory of Open Access Journals (Sweden)

Julio Michael Stern

2014-03-01

Full Text Available This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

Passivity analysis for uncertain BAM neural networks with time delays and reaction-diffusions

Science.gov (United States)

Zhou, Jianping; Xu, Shengyuan; Shen, Hao; Zhang, Baoyong

2013-08-01

This article deals with the problem of passivity analysis for delayed reaction-diffusion bidirectional associative memory (BAM) neural networks with weight uncertainties. By using a new integral inequality, we first present a passivity condition for the nominal networks, and then extend the result to the case with linear fractional weight uncertainties. The proposed conditions are expressed in terms of linear matrix inequalities, and thus can be checked easily. Examples are provided to demonstrate the effectiveness of the proposed results.

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

Science.gov (United States)

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

2008-08-29

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

Applicant reactions to social network web use in personnel selection and assessment

Directory of Open Access Journals (Sweden)

David Aguado

2016-12-01

Full Text Available Human Resource (HR professionals are increasingly using Social Networking Websites (SNWs for personnel recruitment and selection processes. However, evidence is required regarding their psychometric properties and their impact on applicant reactions. In this paper we present and discuss the results of exploring applicant reactions to either the use of a professional SNW (such as LinkedIn or a non-professional SNW (such as Facebook. A scale for assessing applicant reactions was applied to 124 professionals. The results showed more positive attitudes to the use of professional SNWs compared with non-professional SNWs. Both gender and age moderated these results, with females and young applicants having a less positive attitude than males and older participants towards the use of non-professional SNWs.

NMR paves the way for atomic level descriptions of sparsely populated, transiently formed biomolecular conformers.

Science.gov (United States)

Sekhar, Ashok; Kay, Lewis E

2013-08-06

The importance of dynamics to biomolecular function is becoming increasingly clear. A description of the structure-function relationship must, therefore, include the role of motion, requiring a shift in paradigm from focus on a single static 3D picture to one where a given biomolecule is considered in terms of an ensemble of interconverting conformers, each with potentially diverse activities. In this Perspective, we describe how recent developments in solution NMR spectroscopy facilitate atomic resolution studies of sparsely populated, transiently formed biomolecular conformations that exchange with the native state. Examples of how this methodology is applied to protein folding and misfolding, ligand binding, and molecular recognition are provided as a means of illustrating both the power of the new techniques and the significant roles that conformationally excited protein states play in biology.

Drift-Implicit Multi-Level Monte Carlo Tau-Leap Methods for Stochastic Reaction Networks

KAUST Repository

Ben Hammouda, Chiheb

2015-01-01

-space and deterministic ones. These stochastic models constitute the theory of stochastic reaction networks (SRNs). Furthermore, in some cases, the dynamics of fast and slow time scales can be well separated and this is characterized by what is called sti

Biomolecular surface construction by PDE transform.

Science.gov (United States)

Zheng, Qiong; Yang, Siyang; Wei, Guo-Wei

2012-03-01

This work proposes a new framework for the surface generation based on the partial differential equation (PDE) transform. The PDE transform has recently been introduced as a general approach for the mode decomposition of images, signals, and data. It relies on the use of arbitrarily high-order PDEs to achieve the time-frequency localization, control the spectral distribution, and regulate the spatial resolution. The present work provides a new variational derivation of high-order PDE transforms. The fast Fourier transform is utilized to accomplish the PDE transform so as to avoid stringent stability constraints in solving high-order PDEs. As a consequence, the time integration of high-order PDEs can be done efficiently with the fast Fourier transform. The present approach is validated with a variety of test examples in two-dimensional and three-dimensional settings. We explore the impact of the PDE transform parameters, such as the PDE order and propagation time, on the quality of resulting surfaces. Additionally, we utilize a set of 10 proteins to compare the computational efficiency of the present surface generation method and a standard approach in Cartesian meshes. Moreover, we analyze the present method by examining some benchmark indicators of biomolecular surface, that is, surface area, surface-enclosed volume, solvation free energy, and surface electrostatic potential. A test set of 13 protein molecules is used in the present investigation. The electrostatic analysis is carried out via the Poisson-Boltzmann equation model. To further demonstrate the utility of the present PDE transform-based surface method, we solve the Poisson-Nernst-Planck equations with a PDE transform surface of a protein. Second-order convergence is observed for the electrostatic potential and concentrations. Finally, to test the capability and efficiency of the present PDE transform-based surface generation method, we apply it to the construction of an excessively large biomolecule, a

Global exponential stability for reaction-diffusion recurrent neural networks with multiple time varying delays

International Nuclear Information System (INIS)

Lou, X.; Cui, B.

2008-01-01

In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)

Neural network approach to time-dependent dividing surfaces in classical reaction dynamics

Science.gov (United States)

Schraft, Philippe; Junginger, Andrej; Feldmaier, Matthias; Bardakcioglu, Robin; Main, Jörg; Wunner, Günter; Hernandez, Rigoberto

2018-04-01

In a dynamical system, the transition between reactants and products is typically mediated by an energy barrier whose properties determine the corresponding pathways and rates. The latter is the flux through a dividing surface (DS) between the two corresponding regions, and it is exact only if it is free of recrossings. For time-independent barriers, the DS can be attached to the top of the corresponding saddle point of the potential energy surface, and in time-dependent systems, the DS is a moving object. The precise determination of these direct reaction rates, e.g., using transition state theory, requires the actual construction of a DS for a given saddle geometry, which is in general a demanding methodical and computational task, especially in high-dimensional systems. In this paper, we demonstrate how such time-dependent, global, and recrossing-free DSs can be constructed using neural networks. In our approach, the neural network uses the bath coordinates and time as input, and it is trained in a way that its output provides the position of the DS along the reaction coordinate. An advantage of this procedure is that, once the neural network is trained, the complete information about the dynamical phase space separation is stored in the network's parameters, and a precise distinction between reactants and products can be made for all possible system configurations, all times, and with little computational effort. We demonstrate this general method for two- and three-dimensional systems and explain its straightforward extension to even more degrees of freedom.

Systems biology and the origins of life? part II. Are biochemical networks possible ancestors of living systems? networks of catalysed chemical reactions: non-equilibrium, self-organization and evolution.

Science.gov (United States)

Ricard, Jacques

2010-01-01

The present article discusses the possibility that catalysed chemical networks can evolve. Even simple enzyme-catalysed chemical reactions can display this property. The example studied is that of a two-substrate proteinoid, or enzyme, reaction displaying random binding of its substrates A and B. The fundamental property of such a system is to display either emergence or integration depending on the respective values of the probabilities that the enzyme has bound one of its substrate regardless it has bound the other substrate, or, specifically, after it has bound the other substrate. There is emergence of information if p(A)>p(AB) and p(B)>p(BA). Conversely, if p(A)equilibrium. Moreover, in such systems, emergence results in an increase of the energy level of the ternary EAB complex that becomes closer to the transition state of the reaction, thus leading to the enhancement of catalysis. Hence a drift from quasi-equilibrium is, to a large extent, responsible for the production of information and enhancement of catalysis. Non-equilibrium of these simple systems must be an important aspect that leads to both self-organization and evolutionary processes. These conclusions can be extended to networks of catalysed chemical reactions. Such networks are, in fact, networks of networks, viz. meta-networks. In this formal representation, nodes are chemical reactions catalysed by poorly specific proteinoids, and links can be identified to the transport of metabolites from proteinoid to proteinoid. The concepts of integration and emergence can be applied to such situations and can be used to define the identity of these networks and therefore their evolution. Defined as open non-equilibrium structures, such biochemical networks possess two remarkable properties: (1) the probability of occurrence of their nodes is dependant upon the input and output of matter in, and from, the system and (2) the probability of occurrence of the nodes is strictly linked to their degree of

A new approach to implement absorbing boundary condition in biomolecular electrostatics.

Science.gov (United States)

Goni, Md Osman

2013-01-01

This paper discusses a novel approach to employ the absorbing boundary condition in conjunction with the finite-element method (FEM) in biomolecular electrostatics. The introduction of Bayliss-Turkel absorbing boundary operators in electromagnetic scattering problem has been incorporated by few researchers. However, in the area of biomolecular electrostatics, this boundary condition has not been investigated yet. The objective of this paper is twofold. First, to solve nonlinear Poisson-Boltzmann equation using Newton's method and second, to find an efficient and acceptable solution with minimum number of unknowns. In this work, a Galerkin finite-element formulation is used along with a Bayliss-Turkel absorbing boundary operator that explicitly accounts for the open field problem by mapping the Sommerfeld radiation condition from the far field to near field. While the Bayliss-Turkel condition works well when the artificial boundary is far from the scatterer, an acceptable tolerance of error can be achieved with the second order operator. Numerical results on test case with simple sphere show that the treatment is able to reach the same level of accuracy achieved by the analytical method while using a lower grid density. Bayliss-Turkel absorbing boundary condition (BTABC) combined with the FEM converges to the exact solution of scattering problems to within discretization error.

An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks

KAUST Repository

Bayer, Christian

2016-01-06

In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce an efficient two-phase algorithm in which the first phase is deterministic and it is intended to provide a starting point for the second phase which is the Monte Carlo EM Algorithm.

Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

Science.gov (United States)

Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

2013-06-01

Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

Report on the IAEA technical meeting on network of nuclear reaction data centres

Energy Technology Data Exchange (ETDEWEB)

Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria); Henriksson, H [NEA Data Bank, Issy-les-Moulineaux (France)

2005-01-15

This report summarizes the IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Brookhaven National Laboratory, Upton, NY, USA from 4-7 October 2004. The meeting was attended by 20 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, status reports of the participating data centres, and a revised technical protocol for the cooperation of the network. (author)

Report on the IAEA technical meeting on network of nuclear reaction data centres

International Nuclear Information System (INIS)

Schwerer, O.; Henriksson, H.

2005-01-01

This report summarizes the IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Brookhaven National Laboratory, Upton, NY, USA from 4-7 October 2004. The meeting was attended by 20 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, status reports of the participating data centres, and a revised technical protocol for the cooperation of the network. (author)

High-speed AFM for Studying Dynamic Biomolecular Processes

Science.gov (United States)

Ando, Toshio

2008-03-01

Biological molecules show their vital activities only in aqueous solutions. It had been one of dreams in biological sciences to directly observe biological macromolecules (protein, DNA) at work under a physiological condition because such observation is straightforward to understanding their dynamic behaviors and functional mechanisms. Optical microscopy has no sufficient spatial resolution and electron microscopy is not applicable to in-liquid samples. Atomic force microscopy (AFM) can visualize molecules in liquids at high resolution but its imaging rate was too low to capture dynamic biological processes. This slow imaging rate is because AFM employs mechanical probes (cantilevers) and mechanical scanners to detect the sample height at each pixel. It is quite difficult to quickly move a mechanical device of macroscopic size with sub-nanometer accuracy without producing unwanted vibrations. It is also difficult to maintain the delicate contact between a probe tip and fragile samples. Two key techniques are required to realize high-speed AFM for biological research; fast feedback control to maintain a weak tip-sample interaction force and a technique to suppress mechanical vibrations of the scanner. Various efforts have been carried out in the past decade to materialize high-speed AFM. The current high-speed AFM can capture images on video at 30-60 frames/s for a scan range of 250nm and 100 scan lines, without significantly disturbing week biomolecular interaction. Our recent studies demonstrated that this new microscope can reveal biomolecular processes such as myosin V walking along actin tracks and association/dissociation dynamics of chaperonin GroEL-GroES that occurs in a negatively cooperative manner. The capacity of nanometer-scale visualization of dynamic processes in liquids will innovate on biological research. In addition, it will open a new way to study dynamic chemical/physical processes of various phenomena that occur at the liquid-solid interfaces.

Tailoring the Variational Implicit Solvent Method for New Challenges: Biomolecular Recognition and Assembly

Directory of Open Access Journals (Sweden)

Clarisse Gravina Ricci

2018-02-01

Full Text Available Predicting solvation free energies and describing the complex water behavior that plays an important role in essentially all biological processes is a major challenge from the computational standpoint. While an atomistic, explicit description of the solvent can turn out to be too expensive in large biomolecular systems, most implicit solvent methods fail to capture “dewetting” effects and heterogeneous hydration by relying on a pre-established (i.e., guessed solvation interface. Here we focus on the Variational Implicit Solvent Method, an implicit solvent method that adds water “plasticity” back to the picture by formulating the solvation free energy as a functional of all possible solvation interfaces. We survey VISM's applications to the problem of molecular recognition and report some of the most recent efforts to tailor VISM for more challenging scenarios, with the ultimate goal of including thermal fluctuations into the framework. The advances reported herein pave the way to make VISM a uniquely successful approach to characterize complex solvation properties in the recognition and binding of large-scale biomolecular complexes.

Tailoring the Variational Implicit Solvent Method for New Challenges: Biomolecular Recognition and Assembly

Science.gov (United States)

Ricci, Clarisse Gravina; Li, Bo; Cheng, Li-Tien; Dzubiella, Joachim; McCammon, J. Andrew

2018-01-01

Predicting solvation free energies and describing the complex water behavior that plays an important role in essentially all biological processes is a major challenge from the computational standpoint. While an atomistic, explicit description of the solvent can turn out to be too expensive in large biomolecular systems, most implicit solvent methods fail to capture “dewetting” effects and heterogeneous hydration by relying on a pre-established (i.e., guessed) solvation interface. Here we focus on the Variational Implicit Solvent Method, an implicit solvent method that adds water “plasticity” back to the picture by formulating the solvation free energy as a functional of all possible solvation interfaces. We survey VISM's applications to the problem of molecular recognition and report some of the most recent efforts to tailor VISM for more challenging scenarios, with the ultimate goal of including thermal fluctuations into the framework. The advances reported herein pave the way to make VISM a uniquely successful approach to characterize complex solvation properties in the recognition and binding of large-scale biomolecular complexes. PMID:29484300

Effects of network dissolution changes on pore-to-core upscaled reaction rates for kaolinite and anorthite reactions under acidic conditions

KAUST Repository

Kim, Daesang

2013-11-01

We have extended reactive flow simulation in pore-network models to include geometric changes in the medium from dissolution effects. These effects include changes in pore volume and reactive surface area, as well as topological changes that open new connections. The computed changes were based upon a mineral map from an X-ray computed tomography image of a sandstone core. We studied the effect of these changes on upscaled (pore-scale to core-scale) reaction rates and compared against the predictions of a continuum model. Specifically, we modeled anorthite and kaolinite reactions under acidic flow conditions during which the anorthite reactions remain far from equilibrium (dissolution only), while the kaolinite reactions can be near-equilibrium. Under dissolution changes, core-scale reaction rates continuously and nonlinearly evolved in time. At higher injection rates, agreement with predictions of the continuum model degraded significantly. For the far-from-equilibrium reaction, our results indicate that the ability to correctly capture the heterogeneity in dissolution changes in the reactive mineral surface area is critical to accurately predict upscaled reaction rates. For the near-equilibrium reaction, the ability to correctly capture the heterogeneity in the saturation state remains critical. Inclusion of a Nernst-Planck term to ensure neutral ionic currents under differential diffusion resulted in at most a 9% correction in upscaled rates.

Dynamical Behaviors of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Delays

Directory of Open Access Journals (Sweden)

Li Wan

2012-01-01

Full Text Available This paper investigates dynamical behaviors of stochastic Cohen-Grossberg neural network with delays and reaction diffusion. By employing Lyapunov method, Poincaré inequality and matrix technique, some sufficient criteria on ultimate boundedness, weak attractor, and asymptotic stability are obtained. Finally, a numerical example is given to illustrate the correctness and effectiveness of our theoretical results.

Interacting with the biomolecular solvent accessible surface via a haptic feedback device

Directory of Open Access Journals (Sweden)

Hayward Steven

2009-10-01

Full Text Available Abstract Background From the 1950s computer based renderings of molecules have been produced to aid researchers in their understanding of biomolecular structure and function. A major consideration for any molecular graphics software is the ability to visualise the three dimensional structure of the molecule. Traditionally, this was accomplished via stereoscopic pairs of images and later realised with three dimensional display technologies. Using a haptic feedback device in combination with molecular graphics has the potential to enhance three dimensional visualisation. Although haptic feedback devices have been used to feel the interaction forces during molecular docking they have not been used explicitly as an aid to visualisation. Results A haptic rendering application for biomolecular visualisation has been developed that allows the user to gain three-dimensional awareness of the shape of a biomolecule. By using a water molecule as the probe, modelled as an oxygen atom having hard-sphere interactions with the biomolecule, the process of exploration has the further benefit of being able to determine regions on the molecular surface that are accessible to the solvent. This gives insight into how awkward it is for a water molecule to gain access to or escape from channels and cavities, indicating possible entropic bottlenecks. In the case of liver alcohol dehydrogenase bound to the inhibitor SAD, it was found that there is a channel just wide enough for a single water molecule to pass through. Placing the probe coincident with crystallographic water molecules suggests that they are sometimes located within small pockets that provide a sterically stable environment irrespective of hydrogen bonding considerations. Conclusion By using the software, named HaptiMol ISAS (available from http://www.haptimol.co.uk, one can explore the accessible surface of biomolecules using a three-dimensional input device to gain insights into the shape and water

Engineering intracellular active transport systems as in vivo biomolecular tools.

Energy Technology Data Exchange (ETDEWEB)

Bachand, George David; Carroll-Portillo, Amanda

2006-11-01

Active transport systems provide essential functions in terms of cell physiology and metastasis. These systems, however, are also co-opted by invading viruses, enabling directed transport of the virus to and from the cell's nucleus (i.e., the site of virus replication). Based on this concept, fundamentally new approaches for interrogating and manipulating the inner workings of living cells may be achievable by co-opting Nature's active transport systems as an in vivo biomolecular tool. The overall goal of this project was to investigate the ability to engineer kinesin-based transport systems for in vivo applications, specifically the collection of effector proteins (e.g., transcriptional regulators) within single cells. In the first part of this project, a chimeric fusion protein consisting of kinesin and a single chain variable fragment (scFv) of an antibody was successfully produced through a recombinant expression system. The kinesin-scFv retained both catalytic and antigenic functionality, enabling selective capture and transport of target antigens. The incorporation of a rabbit IgG-specific scFv into the kinesin established a generalized system for functionalizing kinesin with a wide range of target-selective antibodies raised in rabbits. The second objective was to develop methods of isolating the intact microtubule network from live cells as a platform for evaluating kinesin-based transport within the cytoskeletal architecture of a cell. Successful isolation of intact microtubule networks from two distinct cell types was demonstrated using glutaraldehyde and methanol fixation methods. This work provides a platform for inferring the ability of kinesin-scFv to function in vivo, and may also serve as a three-dimensional scaffold for evaluating and exploiting kinesin-based transport for nanotechnological applications. Overall, the technology developed in this project represents a first-step in engineering active transport system for in vivo

The Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning

International Nuclear Information System (INIS)

Parete-Koon, Suzanne; Hix, William Raphael; Thielemann, Friedrich-Karl W.

2008-01-01

The nuclei of the 'iron peak' are formed in massive stars shortly before core collapse and during their supernova outbursts as well as during thermonuclear supernovae. Complete and incomplete silicon burning during these events are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present an improvement on our hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. Because the size and membership of these groups vary as the temperature, density and electron faction change, achieving maximal efficiency requires dynamic adjustment of group number and membership. Toward this end, we are implementing a scheme beginning with 2 QSE groups at appropriately high temperature, then progressing through, 3 and 3* group stages (with successively more independent variables) as temperature declines. This combination allows accurate prediction of the nuclear abundance evolution, deleptonization and energy generation at a further reduced computational cost when compared to a conventional nuclear reaction network or our previous 3 fixed group QSE-reduced network. During silicon burning, the resultant QSE-reduced network is up to 20 times faster than the full network it replaces without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multi-dimensional applications

An efficient forward-reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Vilanova, Pedro

2016-01-01

reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ

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

Energy Technology Data Exchange (ETDEWEB)

Do, Phuong T. M. [Univ. of Delaware, Newark, DE (United States); McAtee, Jesse R. [Univ. of Delaware, Newark, DE (United States); Watson, Donald A. [Univ. of Delaware, Newark, DE (United States); Lobo, Raul F. [Univ. of Delaware, Newark, DE (United States)

2012-12-12

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

Limits for Stochastic Reaction Networks

DEFF Research Database (Denmark)

Cappelletti, Daniele

Reaction systems have been introduced in the 70s to model biochemical systems. Nowadays their range of applications has increased and they are fruitfully used in dierent elds. The concept is simple: some chemical species react, the set of chemical reactions form a graph and a rate function...... is associated with each reaction. Such functions describe the speed of the dierent reactions, or their propensities. Two modelling regimes are then available: the evolution of the dierent species concentrations can be deterministically modelled through a system of ODE, while the counts of the dierent species...... at a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along a path...

A new strategy for imaging biomolecular events through interactions between liquid crystals and oil-in-water emulsions.

Science.gov (United States)

Hu, Qiong-Zheng; Jang, Chang-Hyun

2012-11-21

In this study, we demonstrate a new strategy to image biomolecular events through interactions between liquid crystals (LCs) and oil-in-water emulsions. The optical response had a dark appearance when a nematic LC, 4-cyano-4'-pentylbiphenyl (5CB), is in contact with emulsion droplets of glyceryl trioleate (GT). In contrast, the optical response had a bright appearance when 5CB is in contact with GT emulsions decorated with surfactants such as sodium oleate. Since lipase can hydrolyze GT and produce oleic acid, the optical response also displays a bright appearance after 5CB has been in contact with a mixture of lipase and GT emulsions. These results indicate the feasibility of monitoring biomolecular events through interactions between LCs and oil-in-water emulsions.

Explicit integration of extremely stiff reaction networks: partial equilibrium methods

International Nuclear Information System (INIS)

Guidry, M W; Hix, W R; Billings, J J

2013-01-01

In two preceding papers (Guidry et al 2013 Comput. Sci. Disc. 6 015001 and Guidry and Harris 2013 Comput. Sci. Disc. 6 015002), we have shown that when reaction networks are well removed from equilibrium, explicit asymptotic and quasi-steady-state approximations can give algebraically stabilized integration schemes that rival standard implicit methods in accuracy and speed for extremely stiff systems. However, we also showed that these explicit methods remain accurate but are no longer competitive in speed as the network approaches equilibrium. In this paper, we analyze this failure and show that it is associated with the presence of fast equilibration timescales that neither asymptotic nor quasi-steady-state approximations are able to remove efficiently from the numerical integration. Based on this understanding, we develop a partial equilibrium method to deal effectively with the approach to equilibrium and show that explicit asymptotic methods, combined with the new partial equilibrium methods, give an integration scheme that can plausibly deal with the stiffest networks, even in the approach to equilibrium, with accuracy and speed competitive with that of implicit methods. Thus we demonstrate that such explicit methods may offer alternatives to implicit integration of even extremely stiff systems and that these methods may permit integration of much larger networks than have been possible before in a number of fields. (paper)

The universal statistical distributions of the affinity, equilibrium constants, kinetics and specificity in biomolecular recognition.

Directory of Open Access Journals (Sweden)

Xiliang Zheng

2015-04-01

Full Text Available We uncovered the universal statistical laws for the biomolecular recognition/binding process. We quantified the statistical energy landscapes for binding, from which we can characterize the distributions of the binding free energy (affinity, the equilibrium constants, the kinetics and the specificity by exploring the different ligands binding with a particular receptor. The results of the analytical studies are confirmed by the microscopic flexible docking simulations. The distribution of binding affinity is Gaussian around the mean and becomes exponential near the tail. The equilibrium constants of the binding follow a log-normal distribution around the mean and a power law distribution in the tail. The intrinsic specificity for biomolecular recognition measures the degree of discrimination of native versus non-native binding and the optimization of which becomes the maximization of the ratio of the free energy gap between the native state and the average of non-native states versus the roughness measured by the variance of the free energy landscape around its mean. The intrinsic specificity obeys a Gaussian distribution near the mean and an exponential distribution near the tail. Furthermore, the kinetics of binding follows a log-normal distribution near the mean and a power law distribution at the tail. Our study provides new insights into the statistical nature of thermodynamics, kinetics and function from different ligands binding with a specific receptor or equivalently specific ligand binding with different receptors. The elucidation of distributions of the kinetics and free energy has guiding roles in studying biomolecular recognition and function through small-molecule evolution and chemical genetics.

Computational methods to study the structure and dynamics of biomolecules and biomolecular processes from bioinformatics to molecular quantum mechanics

CERN Document Server

2014-01-01

Since the second half of the 20th century machine computations have played a critical role in science and engineering. Computer-based techniques have become especially important in molecular biology, since they often represent the only viable way to gain insights into the behavior of a biological system as a whole. The complexity of biological systems, which usually needs to be analyzed on different time- and size-scales and with different levels of accuracy, requires the application of different approaches, ranging from comparative analysis of sequences and structural databases, to the analysis of networks of interdependence between cell components and processes, through coarse-grained modeling to atomically detailed simulations, and finally to molecular quantum mechanics. This book provides a comprehensive overview of modern computer-based techniques for computing the structure, properties and dynamics of biomolecules and biomolecular processes. The twenty-two chapters, written by scientists from all over t...

Micro- and nanodevices integrated with biomolecular probes.

Science.gov (United States)

Alapan, Yunus; Icoz, Kutay; Gurkan, Umut A

2015-12-01

Understanding how biomolecules, proteins and cells interact with their surroundings and other biological entities has become the fundamental design criterion for most biomedical micro- and nanodevices. Advances in biology, medicine, and nanofabrication technologies complement each other and allow us to engineer new tools based on biomolecules utilized as probes. Engineered micro/nanosystems and biomolecules in nature have remarkably robust compatibility in terms of function, size, and physical properties. This article presents the state of the art in micro- and nanoscale devices designed and fabricated with biomolecular probes as their vital constituents. General design and fabrication concepts are presented and three major platform technologies are highlighted: microcantilevers, micro/nanopillars, and microfluidics. Overview of each technology, typical fabrication details, and application areas are presented by emphasizing significant achievements, current challenges, and future opportunities. Copyright © 2015 Elsevier Inc. All rights reserved.

MSU SINP CDFE nuclear data activities in the nuclear reaction data centres network

International Nuclear Information System (INIS)

Boboshin, I.N.; Varlamov, V.V.; Komarov, S.Yu.; Peskov, N.N.; Semin, S.B.; Stepanov, M.E.; Chesnokov, V.V.

2002-01-01

This paper is the progress report of the Centre for Photonuclear Experiments Data, Moscow. It is a short review of the works carried out by the CDFE concerning the IAEA nuclear reaction data centers network activities from May 2001 until May 2002. and the description of the main results obtained. (a.n.)

Proceedings of the international advisory committee on 'biomolecular dynamics instrument DNA' and the workshop on 'biomolecular dynamics backscattering spectrometers'

International Nuclear Information System (INIS)

Arai, Masatoshi; Aizawa, Kazuya; Nakajima, Kenji; Shibata, Kaoru; Takahashi, Nobuaki

2008-08-01

A workshop entitled 'Biomolecular Dynamics Backscattering Spectrometers' was held on February 27th - 29th, 2008 at J-PARC Center, Japan Atomic Energy Agency. This workshop was planned to be held for aiming to realize an innovative neutron backscattering instrument, namely DNA, in the MLF and thus four leading scientists in the field of neutron backscattering instruments were invited as the International Advisory Committee (IAC member: Dr. Dan Neumann (Chair); Prof. Ferenc Mezei; Dr. Hannu Mutka; Dr. Philip Tregenna-Piggott) for DNA from institutes in the United States, France and Switzerland, where backscattering instruments are in-service. It was therefore held in the form of lecture anterior and then in the form of the committee posterior. This report includes the executive summary of the IAC and materials of the presentations in the IAC and the workshop. (author)

Impulsive Synchronization of Reaction-Diffusion Neural Networks With Mixed Delays and Its Application to Image Encryption.

Science.gov (United States)

Chen, Wu-Hua; Luo, Shixian; Zheng, Wei Xing

2016-12-01

This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functional combined with the use of a new type of integral inequality for treating the reaction-diffusion terms. The impulse-time-dependent feature of the proposed Lyapunov functional can capture more hybrid dynamical behaviors of the impulsive reaction-diffusion neural networks than the conventional impulse-time-independent Lyapunov functions/functionals, while the new integral inequality, which is derived from Wirtinger's inequality, overcomes the conservatism introduced by the integral inequality used in the previous results. Numerical examples demonstrate the effectiveness of the proposed method. Later, the developed impulsive synchronization method is applied to build a spatiotemporal chaotic cryptosystem that can transmit an encrypted image. The experimental results verify that the proposed image-encrypting cryptosystem has the advantages of large key space and high security against some traditional attacks.

A Reaction-Diffusion-Based Coding Rate Control Mechanism for Camera Sensor Networks

Directory of Open Access Journals (Sweden)

Naoki Wakamiya

2010-08-01

Full Text Available A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

A reaction-diffusion-based coding rate control mechanism for camera sensor networks.

Science.gov (United States)

Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki

2010-01-01

A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

Raman spectroscopy detects biomolecular changes associated with nanoencapsulated hesperetin treatment in experimental oral carcinogenesis

International Nuclear Information System (INIS)

Gurushankar, K; Gohulkumar, M; Krishnakumar, N; Kumar, Piyush; Murali Krishna, C

2016-01-01

Recently it has been shown that Raman spectroscopy possesses great potential in the investigation of biomolecular changes of tumor tissues with therapeutic drug response in a non-invasive and label-free manner. The present study is designed to investigate the antitumor effect of hespertin-loaded nanoparticles (HETNPs) relative to the efficacy of native hesperetin (HET) in modifying the biomolecular changes during 7,12-dimethyl benz(a)anthracene (DMBA)-induced oral carcinogenesis using a Raman spectroscopic technique. Significant differences in the intensity and shape of the Raman spectra between the control and the experimental tissues at 1800–500 cm −1 were observed. Tumor tissues are characterized by an increase in the relative amount of proteins, nucleic acids, tryptophan and phenylalanine and a decrease in the percentage of lipids when compared to the control tissues. Further, oral administration of HET and its nanoparticulates restored the status of the lipids and significantly decreased the levels of protein and nucleic acid content. Treatment with HETNPs showed a more potent antitumor effect than treatment with native HET, which resulted in an overall reduction in the intensity of several biochemical Raman bands in DMBA-induced oral carcinogenesis being observed. Principal component and linear discriminant analysis (PC–LDA), together with leave-one-out cross validation (LOOCV) on Raman spectra yielded diagnostic sensitivities of 100%, 80%, 91.6% and 65% and specificities of 100%, 65%, 60% and 55% for classification of control versus DMBA, DMBA versus DMBA  +  HET, DMBA versus DMBA  +  HETNPs and DMBA  +  HET versus DMBA  +  HETNPs treated tissue groups, respectively. These results further demonstrate that Raman spectroscopy associated with multivariate statistical algorithms could be a valuable tool for developing a comprehensive understanding of the process of biomolecular changes, and could reveal the signatures of the

The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

Science.gov (United States)

van Zundert, G C P; Rodrigues, J P G L M; Trellet, M; Schmitz, C; Kastritis, P L; Karaca, E; Melquiond, A S J; van Dijk, M; de Vries, S J; Bonvin, A M J J

2016-02-22

The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Segmenting Costumers Based on Their Reactions to Social Networks Marketing on Instagram

Directory of Open Access Journals (Sweden)

rashin ghahreman

2017-09-01

Full Text Available Since customers react differently to business and marketing on social networks, the researcher is looking for segmenting customers into different categories according to their reaction to marketing in social networks. The present study is a descriptive-exploratory research and the data were collected through a questionnaire. The population of 14,000 follower of the researcher’s personal page on Instagram were analyzed and a sample 224 members were randomly selected. To analyze the data, a two-step clustering method was applied. As a result, five distinct clusters (the active, the talker, the hesitant, the passive and the averse were identified. Two segments were reported to be highly influenced by social networks marketing in terms of brand engagement, purchase intention and word of mouth advertisement (WOM. The "Active" are the most influenced group including 18.3% of the population most of whom are single girls or women. The next group that are influenced the most by social networks marketing is the "Talker". This group represents 24.1% of the population, the most populated group. The "Talker" are different from the "Active" in term of their intention to purchase. Totally, 42.2% of the population are reported to be influenced by social networks marketing.

Biomolecular Markers in Cancer of the Tongue

Directory of Open Access Journals (Sweden)

Daris Ferrari

2009-01-01

Full Text Available The incidence of tongue cancer is increasing worldwide, and its aggressiveness remains high regardless of treatment. Genetic changes and the expression of abnormal proteins have been frequently reported in the case of head and neck cancers, but the little information that has been published concerning tongue tumours is often contradictory. This review will concentrate on the immunohistochemical expression of biomolecular markers and their relationships with clinical behaviour and prognosis. Most of these proteins are associated with nodal stage, tumour progression and metastases, but there is still controversy concerning their impact on disease-free and overall survival, and treatment response. More extensive clinical studies are needed to identify the patterns of molecular alterations and the most reliable predictors in order to develop tailored anti-tumour strategies based on the targeting of hypoxia markers, vascular and lymphangiogenic factors, epidermal growth factor receptors, intracytoplasmatic signalling and apoptosis.

Towards sensitive, high-throughput, biomolecular assays based on fluorescence lifetime

Science.gov (United States)

Ioanna Skilitsi, Anastasia; Turko, Timothé; Cianfarani, Damien; Barre, Sophie; Uhring, Wilfried; Hassiepen, Ulrich; Léonard, Jérémie

2017-09-01

Time-resolved fluorescence detection for robust sensing of biomolecular interactions is developed by implementing time-correlated single photon counting in high-throughput conditions. Droplet microfluidics is used as a promising platform for the very fast handling of low-volume samples. We illustrate the potential of this very sensitive and cost-effective technology in the context of an enzymatic activity assay based on fluorescently-labeled biomolecules. Fluorescence lifetime detection by time-correlated single photon counting is shown to enable reliable discrimination between positive and negative control samples at a throughput as high as several hundred samples per second.

Small-angle X-ray scattering investigations of biomolecular confinement, loading, and release from liquid-crystalline nanochannel assemblies

Czech Academy of Sciences Publication Activity Database

Angelova, A.; Angelov, Borislav; Garamus, V. M.; Couvreur, P.; Lesieur, S.

2012-01-01

Roč. 3, č. 3 (2012), s. 445-457 ISSN 1948-7185 Institutional research plan: CEZ:AV0Z40500505 Keywords : nanochannels * biomolecular nanostructures * SAXS Subject RIV: CD - Macromolecular Chemistry Impact factor: 6.585, year: 2012

Flavylium network of chemical reactions in confined media: modulation of 3',4',7-trihydroxyflavilium reactions by host-guest interactions with cucurbit[7]uril.

Science.gov (United States)

Basílio, Nuno; Pina, Fernando

2014-08-04

In moderately acidic aqueous solutions, flavylium compounds undergo a pH-, and in some cases, light-dependent array of reversible chemical reactions. This network can be described as a single acid-base reaction involving a flavylium cation (acidic form) and a mixture of basic forms (quinoidal base, hemiketal and cis and trans chalcones). The apparent pK'a of the system and the relative mole fractions of the basic forms can be modulated by the interaction with cucurbit[7]uril. The system is studied by using (1) H NMR spectroscopy, UV/Vis spectroscopy, flash photolysis, and steady-state irradiation. Of all the network species, the flavylium cation possesses the highest affinity for cucurbit[7]uril. The rate of interconversion between flavylium cation and the basic species (where trans-chalcone is dominant) is approximately nine times lower inside the cucurbit[7]uril. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Experimental (Network) and Evaluated Nuclear Reaction Data at NDS

International Nuclear Information System (INIS)

Otsuka, N.; Semkova, V.; Simakov, S.P.; Zerkin, V.

2011-01-01

Dr Simakov of Nuclear Data Services Unit in the Nuclear Data Section (NDS) gave a brief overview of the data compilation and evaluation activities in the nuclear data community: experimental nuclear reaction data (EXFOR, http://www-nds.iaea.org/exfor/) and evaluated nuclear reaction data (ENDF, http://www-nds.iaea.org/endf). The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by NDS includes 14 Centres in 8 Countries (China, Hungary, India, Japan, Korea, Russian, Ukraine, USA) and 2 International Organizations (NEA, IAEA). It had the first meeting of four core centres (Brookhaven, Saclay, Obninsk, Vienna) in 1966 and the EXFOR was adopted as an official data exchange format. In 2000, IAEA implemented the EXFOR database as a relational multiform database and the EXFOR is a trusted, increasing and living database with 19100 experimental works (as of September 2011) and 141600 data tables. The EXFOR provides a compilation control system for selection of articles and compilation of data and the NRDC home page provides manuals, documents and codes. The nuclear data can be retrieved by the web-retrieval system or distributed on a DVD on request. The EXFOR data play a critical role in the development of evaluated nuclear reaction data. There are several major general purpose libraries: ENDF (US), CENDL (China), JEFF (EU), JENDL (Japan) and RUSFOND (Russia). In addition, there are special libraries for particular applications: EAF (European Activation File), FENDL (Fusion Evaluated Nuclear Data Library for ITER neutronics), IBANDL (Ion Beam Analysis Nuclear Data Library for surface analysis of solids), IRDF, DXS (Dosimetry, radiation damage and gas production data) and Medical portal. Dr V. Zerkin of NDS demonstrated the data retrieval from the EXFOR database and the ENDF library.

Experimental (Network) and Evaluated Nuclear Reaction Data at NDS

Energy Technology Data Exchange (ETDEWEB)

Otsuka, N; Semkova, V; Simakov, S P; Zerkin, V [Nuclear Data Services Unit, Nuclear Data Section, IAEA, Vienna (Austria)

2011-11-15

Dr Simakov of Nuclear Data Services Unit in the Nuclear Data Section (NDS) gave a brief overview of the data compilation and evaluation activities in the nuclear data community: experimental nuclear reaction data (EXFOR, http://www-nds.iaea.org/exfor/) and evaluated nuclear reaction data (ENDF, http://www-nds.iaea.org/endf). The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by NDS includes 14 Centres in 8 Countries (China, Hungary, India, Japan, Korea, Russian, Ukraine, USA) and 2 International Organizations (NEA, IAEA). It had the first meeting of four core centres (Brookhaven, Saclay, Obninsk, Vienna) in 1966 and the EXFOR was adopted as an official data exchange format. In 2000, IAEA implemented the EXFOR database as a relational multiform database and the EXFOR is a trusted, increasing and living database with 19100 experimental works (as of September 2011) and 141600 data tables. The EXFOR provides a compilation control system for selection of articles and compilation of data and the NRDC home page provides manuals, documents and codes. The nuclear data can be retrieved by the web-retrieval system or distributed on a DVD on request. The EXFOR data play a critical role in the development of evaluated nuclear reaction data. There are several major general purpose libraries: ENDF (US), CENDL (China), JEFF (EU), JENDL (Japan) and RUSFOND (Russia). In addition, there are special libraries for particular applications: EAF (European Activation File), FENDL (Fusion Evaluated Nuclear Data Library for ITER neutronics), IBANDL (Ion Beam Analysis Nuclear Data Library for surface analysis of solids), IRDF, DXS (Dosimetry, radiation damage and gas production data) and Medical portal. Dr V. Zerkin of NDS demonstrated the data retrieval from the EXFOR database and the ENDF library.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

Science.gov (United States)

Mistry, Divya; Wise, Roger P; Dickerson, Julie A

2017-01-01

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

Perspective: Markov models for long-timescale biomolecular dynamics

International Nuclear Information System (INIS)

Schwantes, C. R.; McGibbon, R. T.; Pande, V. S.

2014-01-01

Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics

Perspective: Markov models for long-timescale biomolecular dynamics

Energy Technology Data Exchange (ETDEWEB)

Schwantes, C. R.; McGibbon, R. T. [Department of Chemistry, Stanford University, Stanford, California 94305 (United States); Pande, V. S., E-mail: pande@stanford.edu [Department of Chemistry, Stanford University, Stanford, California 94305 (United States); Department of Computer Science, Stanford University, Stanford, California 94305 (United States); Department of Structural Biology, Stanford University, Stanford, California 94305 (United States); Biophysics Program, Stanford University, Stanford, California 94305 (United States)

2014-09-07

Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics.

Techniques of biomolecular quantification through AMS detection of radiocarbon

International Nuclear Information System (INIS)

Vogel, S.J.; Turteltaub, K.W.; Frantz, C.; Felton, J.S.; Gledhill, B.L.

1992-01-01

Accelerator mass spectrometry offers a large gain over scintillation counting in sensitivity for detecting radiocarbon in biomolecular tracing. Application of this sensitivity requires new considerations of procedures to extract or isolate the carbon fraction to be quantified, to inventory all carbon in the sample, to prepare graphite from the sample for use in the spectrometer, and to derive a meaningful quantification from the measured isotope ratio. These procedures need to be accomplished without contaminating the sample with radiocarbon, which may be ubiquitous in laboratories and on equipment previously used for higher dose, scintillation experiments. Disposable equipment, materials and surfaces are used to control these contaminations. Quantification of attomole amounts of labeled substances are possible through these techniques

Noise-induced modulation of the relaxation kinetics around a non-equilibrium steady state of non-linear chemical reaction networks.

Science.gov (United States)

Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido

2011-01-28

Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confinement increases the lifetimes of all species that are involved in any non-linear reaction as a reactant. Burst monotonically increases or decreases lifetimes. Competition between burst-induced and confinement-induced modulation may hence lead to a non-monotonic modulation. We quantify lifetime as the integral of the time autocorrelation function (ACF) of concentration fluctuations around a non-equilibrium steady state of the reaction network. Furthermore, we look at the first and second derivatives of the ACF, each of which is affected in opposite ways by burst and confinement. This allows discriminating between these two noise sources. We analytically derive the ACF from the linear Fokker-Planck approximation of the chemical master equation in order to establish a baseline for the burst-induced modulation at low confinement. Effects of higher confinement are then studied using a partial-propensity stochastic simulation algorithm. The results presented here may help understand the mechanisms that deviate stochastic kinetics from its deterministic counterpart. In addition, they may be instrumental when using fluorescence-lifetime imaging microscopy (FLIM) or fluorescence-correlation spectroscopy (FCS) to measure confinement and burst in systems with known reaction rates, or, alternatively, to correct for the effects of confinement and burst when experimentally measuring reaction rates.

ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design.

Science.gov (United States)

Wood, Christopher W; Heal, Jack W; Thomson, Andrew R; Bartlett, Gail J; Ibarra, Amaurys Á; Brady, R Leo; Sessions, Richard B; Woolfson, Derek N

2017-10-01

The rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users. Here we introduce ISAMBARD, a tool for structural analysis, model building and rational design of biomolecules. ISAMBARD is open-source, modular, computationally scalable and intuitive to use. These features allow non-experts to explore biomolecular design in silico. ISAMBARD addresses a standing issue in protein design, namely, how to introduce backbone variability in a controlled manner. This is achieved through the generalization of tools for parametric modelling, describing the overall shape of proteins geometrically, and without input from experimentally determined structures. This will allow backbone conformations for entire folds and assemblies not observed in nature to be generated de novo, that is, to access the 'dark matter of protein-fold space'. We anticipate that ISAMBARD will find broad applications in biomolecular design, biotechnology and synthetic biology. A current stable build can be downloaded from the python package index (https://pypi.python.org/pypi/isambard/) with development builds available on GitHub (https://github.com/woolfson-group/) along with documentation, tutorial material and all the scripts used to generate the data described in this paper. d.n.woolfson@bristol.ac.uk or chris.wood@bristol.ac.uk. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Integrated Spintronic Platforms for Biomolecular Recognition Detection

Science.gov (United States)

Martins, V. C.; Cardoso, F. A.; Loureiro, J.; Mercier, M.; Germano, J.; Cardoso, S.; Ferreira, R.; Fonseca, L. P.; Sousa, L.; Piedade, M. S.; Freitas, P. P.

2008-06-01

This paper covers recent developments in magnetoresistive based biochip platforms fabricated at INESC-MN, and their application to the detection and quantification of pathogenic waterborn microorganisms in water samples for human consumption. Such platforms are intended to give response to the increasing concern related to microbial contaminated water sources. The presented results concern the development of biological active DNA chips and protein chips and the demonstration of the detection capability of the present platforms. Two platforms are described, one including spintronic sensors only (spin-valve based or magnetic tunnel junction based), and the other, a fully scalable platform where each probe site consists of a MTJ in series with a thin film diode (TFD). Two microfluidic systems are described, for cell separation and concentration, and finally, the read out and control integrated electronics are described, allowing the realization of bioassays with a portable point of care unit. The present platforms already allow the detection of complementary biomolecular target recognition with 1 pM concentration.

Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro

2015-01-07

Stochastic reaction networks (SRNs) is a class of continuous-time Markov chains intended to describe, from the kinetic point of view, the time-evolution of chemical systems in which molecules of different chemical species undergo a finite set of reaction channels. This talk is based on articles [4, 5, 6], where we are interested in the following problem: given a SRN, X, defined though its set of reaction channels, and its initial state, x0, estimate E (g(X(T))); that is, the expected value of a scalar observable, g, of the process, X, at a fixed time, T. This problem lead us to define a series of Monte Carlo estimators, M, such that, with high probability can produce values close to the quantity of interest, E (g(X(T))). More specifically, given a user-selected tolerance, TOL, and a small confidence level, η, find an estimator, M, based on approximate sampled paths of X, such that, P (|E (g(X(T))) − M| ≤ TOL) ≥ 1 − η; even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.

Allosteric transitions of supramolecular systems explored by network models: application to chaperonin GroEL.

Directory of Open Access Journals (Sweden)

Zheng Yang

2009-04-01

Full Text Available Identification of pathways involved in the structural transitions of biomolecular systems is often complicated by the transient nature of the conformations visited across energy barriers and the multiplicity of paths accessible in the multidimensional energy landscape. This task becomes even more challenging in exploring molecular systems on the order of megadaltons. Coarse-grained models that lend themselves to analytical solutions appear to be the only possible means of approaching such cases. Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM, for exploring functional transitions. Application to bacterial chaperonin GroEL and comparisons with experimental data, results from action minimization algorithm, and previous simulations support the utility of aANM as a computationally efficient, yet physically plausible, tool for unraveling potential transition pathways sampled by large complexes/assemblies. An important outcome is the assessment of the critical inter-residue interactions formed/broken near the transition state(s, most of which involve conserved residues.

A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design.

Science.gov (United States)

Alford, Rebecca F; Leaver-Fay, Andrew; Gonzales, Lynda; Dolan, Erin L; Gray, Jeffrey J

2017-12-01

Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.

A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design.

Directory of Open Access Journals (Sweden)

Rebecca F Alford

2017-12-01

Full Text Available Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.

Random catalytic reaction networks

Science.gov (United States)

Stadler, Peter F.; Fontana, Walter; Miller, John H.

1993-03-01

We study networks that are a generalization of replicator (or Lotka-Volterra) equations. They model the dynamics of a population of object types whose binary interactions determine the specific type of interaction product. Such a system always reduces its dimension to a subset that contains production pathways for all of its members. The network equation can be rewritten at a level of collectives in terms of two basic interaction patterns: replicator sets and cyclic transformation pathways among sets. Although the system contains well-known cases that exhibit very complicated dynamics, the generic behavior of randomly generated systems is found (numerically) to be extremely robust: convergence to a globally stable rest point. It is easy to tailor networks that display replicator interactions where the replicators are entire self-sustaining subsystems, rather than structureless units. A numerical scan of random systems highlights the special properties of elementary replicators: they reduce the effective interconnectedness of the system, resulting in enhanced competition, and strong correlations between the concentrations.

Neural Network Control of CSTR for Reversible Reaction Using Reverence Model Approach

Directory of Open Access Journals (Sweden)

Duncan ALOKO

2007-01-01

Full Text Available In this work, non-linear control of CSTR for reversible reaction is carried out using Neural Network as design tool. The Model Reverence approach in used to design ANN controller. The idea is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. We use PID control scheme as benchmark to study the performance of the controller. The comparison shows that ANN controller out perform PID in the extreme range of non-linearity.This paper represents a preliminary effort to design a simplified neutral network control scheme for a class of non-linear process. Future works will involve further investigation of the effectiveness of thin approach for the real industrial chemical process

Noise-induced modulation of the relaxation kinetics around a non-equilibrium steady state of non-linear chemical reaction networks.

Directory of Open Access Journals (Sweden)

Rajesh Ramaswamy

2011-01-01

Full Text Available Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confinement increases the lifetimes of all species that are involved in any non-linear reaction as a reactant. Burst monotonically increases or decreases lifetimes. Competition between burst-induced and confinement-induced modulation may hence lead to a non-monotonic modulation. We quantify lifetime as the integral of the time autocorrelation function (ACF of concentration fluctuations around a non-equilibrium steady state of the reaction network. Furthermore, we look at the first and second derivatives of the ACF, each of which is affected in opposite ways by burst and confinement. This allows discriminating between these two noise sources. We analytically derive the ACF from the linear Fokker-Planck approximation of the chemical master equation in order to establish a baseline for the burst-induced modulation at low confinement. Effects of higher confinement are then studied using a partial-propensity stochastic simulation algorithm. The results presented here may help understand the mechanisms that deviate stochastic kinetics from its deterministic counterpart. In addition, they may be instrumental when using fluorescence-lifetime imaging microscopy (FLIM or fluorescence-correlation spectroscopy (FCS to measure confinement and burst in systems with known reaction rates, or, alternatively, to correct for the effects of confinement and burst when experimentally measuring reaction rates.

Orientation of biomolecular assemblies in a microfluidic jet

International Nuclear Information System (INIS)

Priebe, M; Kalbfleisch, S; Tolkiehn, M; Salditt, T; Koester, S; Abel, B; Davies, R J

2010-01-01

We have investigated multilamellar lipid assemblies in a microfluidic jet, operating at high shear rates of the order of 10 7 s -1 . Compared to classical Couette cells or rheometers, the shear rate was increased by at least 2-3 orders of magnitude, and the sample volume was scaled down correspondingly. At the same time, the jet is characterized by high extensional stress due to elongational flow. A focused synchrotron x-ray beam was used to measure the structure and orientation of the lipid assemblies in the jet. The diffraction patterns indicate conventional multilamellar phases, aligned with the membrane normals oriented along the velocity gradient of the jet. The results indicate that the setup may be well suited for coherent diffractive imaging of oriented biomolecular assemblies and macromolecules at the future x-ray free electron laser (XFEL) sources.

ANCA: Anharmonic Conformational Analysis of Biomolecular Simulations.

Science.gov (United States)

Parvatikar, Akash; Vacaliuc, Gabriel S; Ramanathan, Arvind; Chennubhotla, S Chakra

2018-05-08

Anharmonicity in time-dependent conformational fluctuations is noted to be a key feature of functional dynamics of biomolecules. Although anharmonic events are rare, long-timescale (μs-ms and beyond) simulations facilitate probing of such events. We have previously developed quasi-anharmonic analysis to resolve higher-order spatial correlations and characterize anharmonicity in biomolecular simulations. In this article, we have extended this toolbox to resolve higher-order temporal correlations and built a scalable Python package called anharmonic conformational analysis (ANCA). ANCA has modules to: 1) measure anharmonicity in the form of higher-order statistics and its variation as a function of time, 2) output a storyboard representation of the simulations to identify key anharmonic conformational events, and 3) identify putative anharmonic conformational substates and visualization of transitions between these substates. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Introduction to a Protein Interaction System Used for Quantitative Evaluation of Biomolecular Interactions

OpenAIRE

Yamniuk, Aaron

2013-01-01

A central goal of molecular biology is the determination of biomolecular function. This comes largely from a knowledge of the non-covalent interactions that biological small and macro-molecules experience. The fundamental mission of the Molecular Interactions Research Group (MIRG) of the ABRF is to show how solution biophysical tools are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core t...

Exact model reduction of combinatorial reaction networks

Directory of Open Access Journals (Sweden)

Fey Dirk

2008-08-01

Full Text Available Abstract Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks.

Classification of Modern and Old Río Tinto Sedimentary Deposits Through the Biomolecular Record Using a Life Marker Biochip: Implications for Detecting Life on Mars

Science.gov (United States)

Parro, Victor; Fernández-Remolar, David; Rodríguez-Manfredi, José A.; Cruz-Gil, Patricia; Rivas, Luis A.; Ruiz-Bermejo, Marta; Moreno-Paz, Mercedes; García-Villadangos, Miriam; Gómez-Ortiz, David; Blanco-López, Yolanda; Menor-Salván, César; Prieto-Ballesteros, Olga; Gómez-Elvira, Javier

2011-01-01

The particular mineralogy formed in the acidic conditions of the Río Tinto has proven to be a first-order analogue for the acid-sulfate aqueous environments of Mars. Therefore, studies about the formation and preservation of biosignatures in the Río Tinto will provide insights into equivalent processes on Mars. We characterized the biomolecular patterns recorded in samples of modern and old fluvial sediments along a segment of the river by means of an antibody microarray containing more than 200 antibodies (LDCHIP200, for Life Detector Chip) against whole microorganisms, universal biomolecules, or environmental extracts. Samples containing 0.3-0.5g of solid material were automatically analyzed in situ by the Signs Of LIfe Detector instrument (SOLID2), and the results were corroborated by extensive analysis in the laboratory. Positive antigen-antibody reactions indicated the presence of microbial strains or high-molecular-weight biopolymers that originated from them. The LDCHIP200 results were quantified and subjected to a multivariate analysis for immunoprofiling. We associated similar immunopatterns, and biomolecular markers, to samples with similar sedimentary age. Phyllosilicate-rich samples from modern fluvial sediments gave strong positive reactions with antibodies against bacteria of the genus Acidithiobacillus and against biochemical extracts from Río Tinto sediments and biofilms. These samples contained high amounts of sugars (mostly polysaccharides) with monosaccharides like glucose, rhamnose, fucose, and so on. By contrast, the older deposits, which are a mix of clastic sands and evaporites, showed only a few positives with LDCHIP200, consistent with lower protein and sugar content. We conclude that LDCHIP200 results can establish a correlation between microenvironments, diagenetic stages, and age with the biomarker profile associated with a sample. Our results would help in the search for putative martian biomarkers in acidic deposits with similar

Classification of modern and old Río Tinto sedimentary deposits through the biomolecular record using a life marker biochip: implications for detecting life on Mars.

Science.gov (United States)

Parro, Victor; Fernández-Remolar, David; Rodríguez-Manfredi, José A; Cruz-Gil, Patricia; Rivas, Luis A; Ruiz-Bermejo, Marta; Moreno-Paz, Mercedes; García-Villadangos, Miriam; Gómez-Ortiz, David; Blanco-López, Yolanda; Menor-Salván, César; Prieto-Ballesteros, Olga; Gómez-Elvira, Javier

2011-01-01

The particular mineralogy formed in the acidic conditions of the Río Tinto has proven to be a first-order analogue for the acid-sulfate aqueous environments of Mars. Therefore, studies about the formation and preservation of biosignatures in the Río Tinto will provide insights into equivalent processes on Mars. We characterized the biomolecular patterns recorded in samples of modern and old fluvial sediments along a segment of the river by means of an antibody microarray containing more than 200 antibodies (LDCHIP200, for Life Detector Chip) against whole microorganisms, universal biomolecules, or environmental extracts. Samples containing 0.3-0.5 g of solid material were automatically analyzed in situ by the Signs Of LIfe Detector instrument (SOLID2), and the results were corroborated by extensive analysis in the laboratory. Positive antigen-antibody reactions indicated the presence of microbial strains or high-molecular-weight biopolymers that originated from them. The LDCHIP200 results were quantified and subjected to a multivariate analysis for immunoprofiling. We associated similar immunopatterns, and biomolecular markers, to samples with similar sedimentary age. Phyllosilicate-rich samples from modern fluvial sediments gave strong positive reactions with antibodies against bacteria of the genus Acidithiobacillus and against biochemical extracts from Río Tinto sediments and biofilms. These samples contained high amounts of sugars (mostly polysaccharides) with monosaccharides like glucose, rhamnose, fucose, and so on. By contrast, the older deposits, which are a mix of clastic sands and evaporites, showed only a few positives with LDCHIP200, consistent with lower protein and sugar content. We conclude that LDCHIP200 results can establish a correlation between microenvironments, diagenetic stages, and age with the biomarker profile associated with a sample. Our results would help in the search for putative martian biomarkers in acidic deposits with similar

Effects of Clear Kefir on Biomolecular Aspects of Glycemic Status of Type 2 Diabetes Mellitus (T2DM Patients in Bandung, West Java [Study on Human Blood Glucose, c Peptide and Insulin

Directory of Open Access Journals (Sweden)

Judiono J

2014-08-01

Full Text Available Background: Diabetes Mellitus (DM triggers an excessive reaction of free-radicals. It increases reactive oxygen species and reduces antioxidants status as well as the β cell damage. Clear kefir was used for DM therapies, however it limited biomolecular exploration of its bioactive roles. Research aimed to investigate the effects of clear kefir on the biomolecular nature of the glycemic status of T2DM in Bandung. Methods: The randomized pretest-posttest control group was conducted by 106 T2DM patients. Research was done in several hospitals in Bandung and Cimahi, West Java from 2012–2013. Samples were divided randomly into three groups: (1 T2DM with HbA1c 7 fed standard diet and supplemented 200 ml/day by clear kefir, (3 T2DM with HbA1c was fed a standard diet as a control group. Dose response was obtained from a preeliminary vivo study, and then converted to human dosage by year 2011. Intervention was effectively done for 30 days. HbA1c was measured by HPLC. Fasting blood glucose (FBG and Postprandial blood glucose levels (PBG were measured by enzymes levels. C Peptide and insulin were measured by Elisa. Data was analyzed by a statictics programme by significance p<0,05. Study was approved by ethic committee. Results : HbA1c was significantly reduced in delta level (p<0.01 and FBG (p<0.015 among kefir groups. PBG was not significantly reduced among groups. C-Peptide was significantly increased in delta level, except in control group (p<0.014. Insulin was reduced significantly, except in control group (p<0.003. Conclusions : Supplementation of clear kefir reduced blood glucose levels (HbA1c, FBG, PBG and increased c-peptide. Clear kefir’s biomolecular mechanisms and chemistry characterization is a challenge for future studies.

Multiscale Persistent Functions for Biomolecular Structure Characterization

Energy Technology Data Exchange (ETDEWEB)

Xia, Kelin [Nanyang Technological University (Singapore). Division of Mathematical Sciences, School of Physical, Mathematical Sciences and School of Biological Sciences; Li, Zhiming [Central China Normal University, Wuhan (China). Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics; Mu, Lin [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division

2017-11-02

Here in this paper, we introduce multiscale persistent functions for biomolecular structure characterization. The essential idea is to combine our multiscale rigidity functions (MRFs) with persistent homology analysis, so as to construct a series of multiscale persistent functions, particularly multiscale persistent entropies, for structure characterization. To clarify the fundamental idea of our method, the multiscale persistent entropy (MPE) model is discussed in great detail. Mathematically, unlike the previous persistent entropy (Chintakunta et al. in Pattern Recognit 48(2):391–401, 2015; Merelli et al. in Entropy 17(10):6872–6892, 2015; Rucco et al. in: Proceedings of ECCS 2014, Springer, pp 117–128, 2016), a special resolution parameter is incorporated into our model. Various scales can be achieved by tuning its value. Physically, our MPE can be used in conformational entropy evaluation. More specifically, it is found that our method incorporates in it a natural classification scheme. This is achieved through a density filtration of an MRF built from angular distributions. To further validate our model, a systematical comparison with the traditional entropy evaluation model is done. Additionally, it is found that our model is able to preserve the intrinsic topological features of biomolecular data much better than traditional approaches, particularly for resolutions in the intermediate range. Moreover, by comparing with traditional entropies from various grid sizes, bond angle-based methods and a persistent homology-based support vector machine method (Cang et al. in Mol Based Math Biol 3:140–162, 2015), we find that our MPE method gives the best results in terms of average true positive rate in a classic protein structure classification test. More interestingly, all-alpha and all-beta protein classes can be clearly separated from each other with zero error only in our model. Finally, a special protein structure index (PSI) is proposed, for the first

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Qiankun Song

2007-06-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Cao Jinde

2007-01-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models

KAUST Repository

Moraes, Alvaro

2015-01-01

Epidemics have shaped, sometimes more than wars and natural disasters, demo- graphic aspects of human populations around the world, their health habits and their economies. Ebola and the Middle East Respiratory Syndrome (MERS) are clear and current examples of potential hazards at planetary scale. During the spread of an epidemic disease, there are phenomena, like the sudden extinction of the epidemic, that can not be captured by deterministic models. As a consequence, stochastic models have been proposed during the last decades. A typical forward problem in the stochastic setting could be the approximation of the expected number of infected individuals found in one month from now. On the other hand, a typical inverse problem could be, given a discretely observed set of epidemiological data, infer the transmission rate of the epidemic or its basic reproduction number. Markovian epidemic models are stochastic models belonging to a wide class of pure jump processes known as Stochastic Reaction Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions but they also have applications in neural networks, virus kinetics, and dynamics of social networks, among others. 4 This PhD thesis is focused on novel fast simulation algorithms and statistical inference methods for SRNs. Our novel Multi-level Monte Carlo (MLMC) hybrid simulation algorithms provide accurate estimates of expected values of a given observable of SRNs at a prescribed final time. They are designed to control the global approximation error up to a user-selected accuracy and up to a certain confidence level, and with near optimal computational work. We also present novel dual-weighted residual expansions for fast estimation of weak and strong errors arising from the MLMC methodology. Regarding the statistical inference

Building a mechanistic biogeochemical reaction network for upscaling : Characterization of mass transport limitation between regions of hydrolysis and methanogenesis

NARCIS (Netherlands)

Van Turnhout, A.G.; Kleerebezem, R.; Heimovaara, T.J.

2015-01-01

In this study, we aim to validate the reaction network with an idealized experiment. We want to show that 1) rate controlling parameters are identifiable from the measured data by inverse modeling, and 2) that this network is able to predict the measured emissions in the experiment given the initial

Design of an Adaptive-Neural Network Attitude Controller of a Satellite using Reaction Wheels

Directory of Open Access Journals (Sweden)

Abbas Ajorkar

2015-04-01

Full Text Available In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, a multilayer neural network with back-propagation law is designed. In this structure, the parameters of the moment of inertia matrix and external disturbances are estimated and used in feedback linearization control law. Finally, the performance of the designed attitude controller is investigated by several simulations.

Metadislocation reactions and metadislocation networks in the complex metallic alloy ξ'-Al-Pd-Mn

International Nuclear Information System (INIS)

Heggen, Marc; Feuerbacher, Michael

2005-01-01

Metadislocations are novel structural defects firstly observed in the complex metallic alloy ξ'-Al-Pd-Mn. We present a transmission electron microscopy study on metadislocation reactions and networks. It is shown that metadislocations can dissociate into partials, which leads to a decrease of the elastic line energy. Connected groups of metadislocations can assume large and complex network structures with large total Burgers vectors. However, the local elastic strain at the individual metadislocation cores as well as the fault-plane energies remain small. By this mechanism, effective large Burgers vectors, contributing massively to plastic strain, can be distributed over a large portion of the material

Native fluorescence detection of biomolecular and pharmaceutical compounds in capillary electrophoresis: detector designs, performance and applications: A review

NARCIS (Netherlands)

de Kort, B.J.; de Jong, G.J.; Somsen, G.W.

2013-01-01

This review treats the coupling of capillary electrophoresis (CE) with fluorescence detection (Flu) for the analysis of natively fluorescent biomolecular and pharmaceutical compounds. CE-Flu combines the excellent separation efficiency of CE with the high selectivity and sensitivity of Flu. In

Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers.

Science.gov (United States)

Stamova, Ivanka; Stamov, Gani

2017-12-01

In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fractional Lyapunov method sufficient conditions are given. We also study the global Mittag-Leffler synchronization of two identical fractional impulsive reaction-diffusion neural networks using linear controllers, which was an open problem even for integer-order models. Since the Mittag-Leffler stability notion is a generalization of the exponential stability concept for fractional-order systems, our results extend and improve the exponential impulsive control theory of neural network system with time-varying delays and reaction-diffusion terms to the fractional-order case. The fractional-order derivatives allow us to model the long-term memory in the neural networks, and thus the present research provides with a conceptually straightforward mathematical representation of rather complex processes. Illustrative examples are presented to show the validity of the obtained results. We show that by means of appropriate impulsive controllers we can realize the stability goal and to control the qualitative behavior of the states. An image encryption scheme is extended using fractional derivatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tibialis anterior muscle needle biopsy and sensitive biomolecular methods: a useful tool in myotonic dystrophy type 1

Directory of Open Access Journals (Sweden)

S. Iachettini

2015-10-01

Full Text Available Myotonic dystrophy type 1 (DM1 is a neuromuscular disorder caused by a CTG repeat expansion in 3’UTR of DMPK gene. This mutation causes accumulation of toxic RNA in nuclear foci leading to splicing misregulation of specific genes. In view of future clinical trials with antisense oligonucleotides in DM1 patients, it is important to set up sensitive and minimally-invasive tools to monitor the efficacy of treatments on skeletal muscle. A tibialis anterior (TA muscle sample of about 60 mg was obtained from 5 DM1 patients and 5 healthy subjects through a needle biopsy. A fragment of about 40 mg was used for histological examination and a fragment of about 20 mg was used for biomolecular analysis. The TA fragments obtained with the minimally-invasive needle biopsy technique is enough to perform all the histopathological and biomolecular evaluations useful to monitor a clinical trial on DM1 patients.

Hybrid organic semiconductor lasers for bio-molecular sensing.

Science.gov (United States)

Haughey, Anne-Marie; Foucher, Caroline; Guilhabert, Benoit; Kanibolotsky, Alexander L; Skabara, Peter J; Burley, Glenn; Dawson, Martin D; Laurand, Nicolas

2014-01-01

Bio-functionalised luminescent organic semiconductors are attractive for biophotonics because they can act as efficient laser materials while simultaneously interacting with molecules. In this paper, we present and discuss a laser biosensor platform that utilises a gain layer made of such an organic semiconductor material. The simple structure of the sensor and its operation principle are described. Nanolayer detection is shown experimentally and analysed theoretically in order to assess the potential and the limits of the biosensor. The advantage conferred by the organic semiconductor is explained, and comparisons to laser sensors using alternative dye-doped materials are made. Specific biomolecular sensing is demonstrated, and routes to functionalisation with nucleic acid probes, and future developments opened up by this achievement, are highlighted. Finally, attractive formats for sensing applications are mentioned, as well as colloidal quantum dots, which in the future could be used in conjunction with organic semiconductors.

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

Science.gov (United States)

Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

2008-08-12

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges

Metal phosphonate coordination networks and frameworks as precursors of electrocatalysts for the hydrogen and oxygen evolution reactions

Science.gov (United States)

Zhang, Rui; El-Refaei, Sayed M.; Russo, Patrícia A.; Pinna, Nicola

2018-05-01

The hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) play key roles in the conversion of energy derived from renewable energy sources into chemical energy. Efficient, robust, and inexpensive electrocatalysts are necessary for driving these reactions at high rates at low overpotentials and minimize energetic losses. Recently, electrocatalysts derived from hybrid metal phosphonate compounds have shown high activity for the HER or OER. We review here the utilization of metal phosphonate coordination networks and metal-organic frameworks as precursors/templates for transition-metal phosphides, phosphates, or oxyhydroxides generated in situ in alkaline solutions, and their electrocatalytic performance in HER or OER.

Design rules for biomolecular adhesion: lessons from force measurements.

Science.gov (United States)

Leckband, Deborah

2010-01-01

Cell adhesion to matrix, other cells, or pathogens plays a pivotal role in many processes in biomolecular engineering. Early macroscopic methods of quantifying adhesion led to the development of quantitative models of cell adhesion and migration. The more recent use of sensitive probes to quantify the forces that alter or manipulate adhesion proteins has revealed much greater functional diversity than was apparent from population average measurements of cell adhesion. This review highlights theoretical and experimental methods that identified force-dependent molecular properties that are central to the biological activity of adhesion proteins. Experimental and theoretical methods emphasized in this review include the surface force apparatus, atomic force microscopy, and vesicle-based probes. Specific examples given illustrate how these tools have revealed unique properties of adhesion proteins and their structural origins.

Report on the IAEA technical meeting on network of nuclear reaction data centres

Energy Technology Data Exchange (ETDEWEB)

Schwerer, O [IAEA Nuclear Data Section, Vienna (Austria)

2006-12-15

An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting) was held at IAEA Headquarters, Vienna, Austria, from 25 to 28 September 2006. The meeting was attended by 19 participants from 10 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

Report on the IAEA technical meeting on network of nuclear reaction data centres

International Nuclear Information System (INIS)

Schwerer, O.

2006-12-01

An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting) was held at IAEA Headquarters, Vienna, Austria, from 25 to 28 September 2006. The meeting was attended by 19 participants from 10 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

A multiscale modeling approach for biomolecular systems

Energy Technology Data Exchange (ETDEWEB)

Bowling, Alan, E-mail: bowling@uta.edu; Haghshenas-Jaryani, Mahdi, E-mail: mahdi.haghshenasjaryani@mavs.uta.edu [The University of Texas at Arlington, Department of Mechanical and Aerospace Engineering (United States)

2015-04-15

This paper presents a new multiscale molecular dynamic model for investigating the effects of external interactions, such as contact and impact, during stepping and docking of motor proteins and other biomolecular systems. The model retains the mass properties ensuring that the result satisfies Newton’s second law. This idea is presented using a simple particle model to facilitate discussion of the rigid body model; however, the particle model does provide insights into particle dynamics at the nanoscale. The resulting three-dimensional model predicts a significant decrease in the effect of the random forces associated with Brownian motion. This conclusion runs contrary to the widely accepted notion that the motor protein’s movements are primarily the result of thermal effects. This work focuses on the mechanical aspects of protein locomotion; the effect ATP hydrolysis is estimated as internal forces acting on the mechanical model. In addition, the proposed model can be numerically integrated in a reasonable amount of time. Herein, the differences between the motion predicted by the old and new modeling approaches are compared using a simplified model of myosin V.

Stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Wang Linshan; Zhang Zhe; Wang Yangfan

2008-01-01

Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities

Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

Science.gov (United States)

Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

2013-01-01

The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Looking for chemical reaction networks exhibiting a drift along a manifold of marginally stable states.

Science.gov (United States)

Brogioli, Doriano

2013-02-07

I recently reported some examples of mass-action equations that have a continuous manifold of marginally stable stationary states [Brogioli, D., 2010. Marginally stable chemical systems as precursors of life. Phys. Rev. Lett. 105, 058102; Brogioli, D., 2011. Marginal stability in chemical systems and its relevance in the origin of life. Phys. Rev. E 84, 031931]. The corresponding chemical reaction networks show nonclassical effects, i.e. a violation of the mass-action equations, under the effect of the concentration fluctuations: the chemical system drifts along the marginally stable states. I proposed that this effect is potentially involved in abiogenesis. In the present paper, I analyze the mathematical properties of mass-action equations of marginally stable chemical reaction networks. The marginal stability implies that the mass-action equations obey some conservation law; I show that the mathematical properties of the conserved quantity characterize the motion along the marginally stable stationary state manifold, i.e. they allow to predict if the fluctuations give rise to a random walk or a drift under the effect of concentration fluctuations. Moreover, I show that the presence of the drift along the manifold of marginally stable stationary-states is a critical property, i.e. at least one of the reaction constants must be fine tuned in order to obtain the drift. Copyright © 2012 Elsevier Ltd. All rights reserved.

A developmental systems perspective on epistasis: computational exploration of mutational interactions in model developmental regulatory networks.

Directory of Open Access Journals (Sweden)

Jayson Gutiérrez

2009-09-01

Full Text Available The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks. Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/- feedback and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1 the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2 the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of

Flows, scaling, and the control of moment hierarchies for stochastic chemical reaction networks

Science.gov (United States)

Smith, Eric; Krishnamurthy, Supriya

2017-12-01

Stochastic chemical reaction networks (CRNs) are complex systems that combine the features of concurrent transformation of multiple variables in each elementary reaction event and nonlinear relations between states and their rates of change. Most general results concerning CRNs are limited to restricted cases where a topological characteristic known as deficiency takes a value 0 or 1, implying uniqueness and positivity of steady states and surprising, low-information forms for their associated probability distributions. Here we derive equations of motion for fluctuation moments at all orders for stochastic CRNs at general deficiency. We show, for the standard base case of proportional sampling without replacement (which underlies the mass-action rate law), that the generator of the stochastic process acts on the hierarchy of factorial moments with a finite representation. Whereas simulation of high-order moments for many-particle systems is costly, this representation reduces the solution of moment hierarchies to a complexity comparable to solving a heat equation. At steady states, moment hierarchies for finite CRNs interpolate between low-order and high-order scaling regimes, which may be approximated separately by distributions similar to those for deficiency-zero networks and connected through matched asymptotic expansions. In CRNs with multiple stable or metastable steady states, boundedness of high-order moments provides the starting condition for recursive solution downward to low-order moments, reversing the order usually used to solve moment hierarchies. A basis for a subset of network flows defined by having the same mean-regressing property as the flows in deficiency-zero networks gives the leading contribution to low-order moments in CRNs at general deficiency, in a 1 /n expansion in large particle numbers. Our results give a physical picture of the different informational roles of mean-regressing and non-mean-regressing flows and clarify the dynamical

Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otsuka, Naohiko

2013-07-01

This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres, held at the IAEA Headquarters in Vienna, Austria from 23 to 25 April 2013. The meeting was attended by 24 participants representing 13 cooperative centres from 8 Member States and 2 International Organisations. A summary of the meeting is given in this report along with the conclusions and actions. (author)

A Practical Quantum Mechanics Molecular Mechanics Method for the Dynamical Study of Reactions in Biomolecules.

Science.gov (United States)

Mendieta-Moreno, Jesús I; Marcos-Alcalde, Iñigo; Trabada, Daniel G; Gómez-Puertas, Paulino; Ortega, José; Mendieta, Jesús

2015-01-01

Quantum mechanics/molecular mechanics (QM/MM) methods are excellent tools for the modeling of biomolecular reactions. Recently, we have implemented a new QM/MM method (Fireball/Amber), which combines an efficient density functional theory method (Fireball) and a well-recognized molecular dynamics package (Amber), offering an excellent balance between accuracy and sampling capabilities. Here, we present a detailed explanation of the Fireball method and Fireball/Amber implementation. We also discuss how this tool can be used to analyze reactions in biomolecules using steered molecular dynamics simulations. The potential of this approach is shown by the analysis of a reaction catalyzed by the enzyme triose-phosphate isomerase (TIM). The conformational space and energetic landscape for this reaction are analyzed without a priori assumptions about the protonation states of the different residues during the reaction. The results offer a detailed description of the reaction and reveal some new features of the catalytic mechanism. In particular, we find a new reaction mechanism that is characterized by the intramolecular proton transfer from O1 to O2 and the simultaneous proton transfer from Glu 165 to C2. Copyright © 2015 Elsevier Inc. All rights reserved.

A biomolecular recognition approach for the functionalization of cellulose with gold nanoparticles.

Science.gov (United States)

Almeida, A; Rosa, A M M; Azevedo, A M; Prazeres, D M F

2017-09-01

Materials with new and improved functionalities can be obtained by modifying cellulose with gold nanoparticles (AuNPs) via the in situ reduction of a gold precursor or the deposition or covalent immobilization of pre-synthesized AuNPs. Here, we present an alternative biomolecular recognition approach to functionalize cellulose with biotin-AuNPs that relies on a complex of 2 recognition elements: a ZZ-CBM3 fusion that combines a carbohydrate-binding module (CBM) with the ZZ fragment of the staphylococcal protein A and an anti-biotin antibody. Paper and cellulose microparticles with AuNPs immobilized via the ZZ-CBM3:anti-biotin IgG supramolecular complex displayed an intense red color, whereas essentially no color was detected when AuNPs were deposited over the unmodified materials. Scanning electron microscopy analysis revealed a homogeneous distribution of AuNPs when immobilized via ZZ-CBM3:anti-biotin IgG complexes and aggregation of AuNPs when deposited over paper, suggesting that color differences are due to interparticle plasmon coupling effects. The approach could be used to functionalize paper substrates and cellulose nanocrystals with AuNPs. More important, however, is the fact that the occurrence of a biomolecular recognition event between the CBM-immobilized antibody and its specific, AuNP-conjugated antigen is signaled by red color. This opens up the way for the development of simple and straightforward paper/cellulose-based tests where detection of a target analyte can be made by direct use of color signaling. Copyright © 2017 John Wiley & Sons, Ltd.

Optimal number of coarse-grained sites in different components of large biomolecular complexes.

Science.gov (United States)

Sinitskiy, Anton V; Saunders, Marissa G; Voth, Gregory A

2012-07-26

The computational study of large biomolecular complexes (molecular machines, cytoskeletal filaments, etc.) is a formidable challenge facing computational biophysics and biology. To achieve biologically relevant length and time scales, coarse-grained (CG) models of such complexes usually must be built and employed. One of the important early stages in this approach is to determine an optimal number of CG sites in different constituents of a complex. This work presents a systematic approach to this problem. First, a universal scaling law is derived and numerically corroborated for the intensity of the intrasite (intradomain) thermal fluctuations as a function of the number of CG sites. Second, this result is used for derivation of the criterion for the optimal number of CG sites in different parts of a large multibiomolecule complex. In the zeroth-order approximation, this approach validates the empirical rule of taking one CG site per fixed number of atoms or residues in each biomolecule, previously widely used for smaller systems (e.g., individual biomolecules). The first-order corrections to this rule are derived and numerically checked by the case studies of the Escherichia coli ribosome and Arp2/3 actin filament junction. In different ribosomal proteins, the optimal number of amino acids per CG site is shown to differ by a factor of 3.5, and an even wider spread may exist in other large biomolecular complexes. Therefore, the method proposed in this paper is valuable for the optimal construction of CG models of such complexes.

Investigation of the Human Disease Osteogenesis Imperfecta: A Research-Based Introduction to Concepts and Skills in Biomolecular Analysis

Science.gov (United States)

Mate, Karen; Sim, Alistair; Weidenhofer, Judith; Milward, Liz; Scott, Judith

2013-01-01

A blended approach encompassing problem-based learning (PBL) and structured inquiry was used in this laboratory exercise based on the congenital disease Osteogenesis imperfecta (OI), to introduce commonly used techniques in biomolecular analysis within a clinical context. During a series of PBL sessions students were presented with several…

Report on the IAEA technical meeting on network of nuclear reaction data centres

International Nuclear Information System (INIS)

Pronyaev, V.G.; Schwerer, O.; Nichols, A.L.

2002-08-01

An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (and the biennial Data Centre Heads' Meeting) was held at the OECD Nuclear Energy Agency, Issy-les-Moulineaux (near Paris), France, from 27 to 30 May 2002. The meeting was attended by 21 participants from 12 co-operating data centres of six Member States and two international organizations. This report contains the meeting summary, conclusions and actions, status reports of the participating data centres, and working papers considered. (author)

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

Science.gov (United States)

Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

2016-06-01

Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

Summary report on [IAEA] technical meeting of the International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Dunaeva, S.; Otsuka, N.; Schwerer, O.

2009-08-01

An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at the IAEA Headquarters in Vienna from 25 to 26 May 2009. The meeting was attended by 23 participants from 13 cooperating data centres. A summary of the meeting is given in this report, along with the conclusions, actions, and status report of the participating data centres. (author)

Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules.

Science.gov (United States)

Xia, Kelin

2017-12-20

In this paper, a multiscale virtual particle based elastic network model (MVP-ENM) is proposed for the normal mode analysis of large-sized biomolecules. The multiscale virtual particle (MVP) model is proposed for the discretization of biomolecular density data. With this model, large-sized biomolecular structures can be coarse-grained into virtual particles such that a balance between model accuracy and computational cost can be achieved. An elastic network is constructed by assuming "connections" between virtual particles. The connection is described by a special harmonic potential function, which considers the influence from both the mass distributions and distance relations of the virtual particles. Two independent models, i.e., the multiscale virtual particle based Gaussian network model (MVP-GNM) and the multiscale virtual particle based anisotropic network model (MVP-ANM), are proposed. It has been found that in the Debye-Waller factor (B-factor) prediction, the results from our MVP-GNM with a high resolution are as good as the ones from GNM. Even with low resolutions, our MVP-GNM can still capture the global behavior of the B-factor very well with mismatches predominantly from the regions with large B-factor values. Further, it has been demonstrated that the low-frequency eigenmodes from our MVP-ANM are highly consistent with the ones from ANM even with very low resolutions and a coarse grid. Finally, the great advantage of MVP-ANM model for large-sized biomolecules has been demonstrated by using two poliovirus virus structures. The paper ends with a conclusion.

LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

Energy Technology Data Exchange (ETDEWEB)

MASLOV,S.SNEPPEN,K.

2003-11-17

Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.

Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otsuka, Naohiko

2012-06-01

This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres, held at the OECD Nuclear Energy Agency (NEA) in Issy-les-Moulineaux, France from 16 to 19 April 2012. The meeting was attended by twenty-three participants representing thirteen cooperative centres from eight Member States and two International Organisations. A summary of the meeting is given in this report along with the conclusions and actions. (author)

Report on the IAEA technical meeting on the network of nuclear reaction data centres

Energy Technology Data Exchange (ETDEWEB)

Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria)

2006-02-15

Results of the IAEA Technical meeting on the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 12 to 14 October 2005, are summarized in this report. The meeting was attended by 16 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, and status reports of the participating data centres. (author)

Report on the IAEA technical meeting on the network of nuclear reaction data centres

International Nuclear Information System (INIS)

Schwerer, O.

2006-02-01

Results of the IAEA Technical meeting on the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 12 to 14 October 2005, are summarized in this report. The meeting was attended by 16 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, and status reports of the participating data centres. (author)

Parity Violation in Chiral Molecules: From Theory towards Spectroscopic Experiment and the Evolution of Biomolecular Homochirality

CERN Multimedia

CERN. Geneva

2016-01-01

The observation of biomolecular homochirality can be considered as a quasi-fossil of the evolution of life [1], the interpretation of which has been an open question for more than a century, with numerous related hypotheses, but no definitive answers. We shall briefly discuss the current status and the relation to the other two questions. The discovery of parity violation led to important developm...

Delay-induced Turing-like waves for one-species reaction-diffusion model on a network

Science.gov (United States)

Petit, Julien; Carletti, Timoteo; Asllani, Malbor; Fanelli, Duccio

2015-09-01

A one-species time-delay reaction-diffusion system defined on a complex network is studied. Traveling waves are predicted to occur following a symmetry-breaking instability of a homogeneous stationary stable solution, subject to an external nonhomogeneous perturbation. These are generalized Turing-like waves that materialize in a single-species populations dynamics model, as the unexpected byproduct of the imposed delay in the diffusion part. Sufficient conditions for the onset of the instability are mathematically provided by performing a linear stability analysis adapted to time-delayed differential equations. The method here developed exploits the properties of the Lambert W-function. The prediction of the theory are confirmed by direct numerical simulation carried out for a modified version of the classical Fisher model, defined on a Watts-Strogatz network and with the inclusion of the delay.

Foreword [IJEGMBE 2015: India-Japan expert group meeting on biomolecular electronics and organic nanotechnology for environment preservation, Fukuoka (Japan), 23-26 December 2015

International Nuclear Information System (INIS)

2016-01-01

There is increased interest in organic nanotechnology and biomolecular electronics for environmental preservation, and in their anticipated impact on the economics of both the developing and the developed world. Keeping this in mind, the Department of Biological Functions, Graduate School of Life Sciences and Systems Engineering, Kyushu Institute of Technology (KIT), Kitakyushu, Japan, and the Department of Science and Technology Centre on Biomolecular Electronics (DSTCBE), National Physical Laboratory (NPL) jointly organized the India-Japan Workshop on Biomolecular Electronics and Organic Nanotechnology for Environmental Preservation (IJWBME 2009) at NPL, New Delhi from 17 th - 19 th December 2009, IJWBME 2011 at EGRET Himeji, Himeji, from 7 th - 10 th December, Japan, and IJWBME 2013 at Delhi Technological University, New Delhi, from 13 th - 15 th December. The India-Japan Expert Group Meeting on Biomolecular Electronics and Organic Nanotechnology for Environment Preservation (IJEGMBE) will be held from 22 th – 25 th , December, 2015, at Nakamura Centenary Memorial Hall, Kyushu Institute of Technology, Kitakyushu, Japan in association with Delhi Technological University, Delhi, India. Recent years have seen rapid growth in the area of Biomolecular Electronics involving the association and expertise of physicists, biologists, chemists, electronics engineers and information technologists. There is increasing interest in the development of nanotechnology and biomolecular electronic devices for the preservation of our precious environment. In this context, the world of the electronics, which developed on Si semiconductors, is going to change drastically. A paradigm shift towards organic or printed electronics is more likely in the future. The field of organic electronics promises exciting new technologies based on inexpensive and mechanically flexible electronic devices, and is now starting to see commercial success. On the sidelines of this increasingly well

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

Directory of Open Access Journals (Sweden)

Recep Colak

2010-10-01

Full Text Available Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

Science.gov (United States)

Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

2010-10-25

Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large

Multi-scale modeling of diffusion-controlled reactions in polymers: renormalisation of reactivity parameters.

Science.gov (United States)

Everaers, Ralf; Rosa, Angelo

2012-01-07

The quantitative description of polymeric systems requires hierarchical modeling schemes, which bridge the gap between the atomic scale, relevant to chemical or biomolecular reactions, and the macromolecular scale, where the longest relaxation modes occur. Here, we use the formalism for diffusion-controlled reactions in polymers developed by Wilemski, Fixman, and Doi to discuss the renormalisation of the reactivity parameters in polymer models with varying spatial resolution. In particular, we show that the adjustments are independent of chain length. As a consequence, it is possible to match reactions times between descriptions with different resolution for relatively short reference chains and to use the coarse-grained model to make quantitative predictions for longer chains. We illustrate our results by a detailed discussion of the classical problem of chain cyclization in the Rouse model, which offers the simplest example of a multi-scale descriptions, if we consider differently discretized Rouse models for the same physical system. Moreover, we are able to explore different combinations of compact and non-compact diffusion in the local and large-scale dynamics by varying the embedding dimension.

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

Energy Technology Data Exchange (ETDEWEB)

Hirdt, J.A. [Department of Mathematics and Computer Science, St. Joseph' s College, Patchogue, NY 11772 (United States); Brown, D.A., E-mail: dbrown@bnl.gov [National Nuclear Data Center, Brookhaven National Laboratory, Upton, NY 11973-5000 (United States)

2016-01-15

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

International Nuclear Information System (INIS)

Hirdt, J.A.; Brown, D.A.

2016-01-01

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

Report on the IAEA technical meeting of the International Network of Nuclear Reaction Data Centres

Energy Technology Data Exchange (ETDEWEB)

Schwerer, O; Dunaeva, S [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria); org, S Dunaeva@iaea [eds.

2007-11-15

An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at IAEA Headquarters, Vienna, Austria, from 8 to 10 October 2007. The meeting was attended by 19 participants from 11 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

Report on the IAEA technical meeting of the International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Schwerer, O.; Dunaeva, S.; S.Dunaeva@iaea.org

2007-11-01

An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at IAEA Headquarters, Vienna, Austria, from 8 to 10 October 2007. The meeting was attended by 19 participants from 11 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

Dose controlled low energy electron irradiator for biomolecular films.

Science.gov (United States)

Kumar, S V K; Tare, Satej T; Upalekar, Yogesh V; Tsering, Thupten

2016-03-01

We have developed a multi target, Low Energy Electron (LEE), precise dose controlled irradiator for biomolecular films. Up to seven samples can be irradiated one after another at any preset electron energy and dose under UHV conditions without venting the chamber. In addition, one more sample goes through all the steps except irradiation, which can be used as control for comparison with the irradiated samples. All the samples are protected against stray electron irradiation by biasing them at -20 V during the entire period, except during irradiation. Ethernet based communication electronics hardware, LEE beam control electronics and computer interface were developed in house. The user Graphical User Interface to control the irradiation and dose measurement was developed using National Instruments Lab Windows CVI. The working and reliability of the dose controlled irradiator has been fully tested over the electron energy range of 0.5 to 500 eV by studying LEE induced single strand breaks to ΦX174 RF1 dsDNA.

DNA-assisted swarm control in a biomolecular motor system.

Science.gov (United States)

Keya, Jakia Jannat; Suzuki, Ryuhei; Kabir, Arif Md Rashedul; Inoue, Daisuke; Asanuma, Hiroyuki; Sada, Kazuki; Hess, Henry; Kuzuya, Akinori; Kakugo, Akira

2018-01-31

In nature, swarming behavior has evolved repeatedly among motile organisms because it confers a variety of beneficial emergent properties. These include improved information gathering, protection from predators, and resource utilization. Some organisms, e.g., locusts, switch between solitary and swarm behavior in response to external stimuli. Aspects of swarming behavior have been demonstrated for motile supramolecular systems composed of biomolecular motors and cytoskeletal filaments, where cross-linkers induce large scale organization. The capabilities of such supramolecular systems may be further extended if the swarming behavior can be programmed and controlled. Here, we demonstrate that the swarming of DNA-functionalized microtubules (MTs) propelled by surface-adhered kinesin motors can be programmed and reversibly regulated by DNA signals. Emergent swarm behavior, such as translational and circular motion, can be selected by tuning the MT stiffness. Photoresponsive DNA containing azobenzene groups enables switching between solitary and swarm behavior in response to stimulation with visible or ultraviolet light.

A fast mollified impulse method for biomolecular atomistic simulations

Energy Technology Data Exchange (ETDEWEB)

Fath, L., E-mail: lukas.fath@kit.edu [Institute for App. and Num. Mathematics, Karlsruhe Institute of Technology (Germany); Hochbruck, M., E-mail: marlis.hochbruck@kit.edu [Institute for App. and Num. Mathematics, Karlsruhe Institute of Technology (Germany); Singh, C.V., E-mail: chandraveer.singh@utoronto.ca [Department of Materials Science & Engineering, University of Toronto (Canada)

2017-03-15

Classical integration methods for molecular dynamics are inherently limited due to resonance phenomena occurring at certain time-step sizes. The mollified impulse method can partially avoid this problem by using appropriate filters based on averaging or projection techniques. However, existing filters are computationally expensive and tedious in implementation since they require either analytical Hessians or they need to solve nonlinear systems from constraints. In this work we follow a different approach based on corotation for the construction of a new filter for (flexible) biomolecular simulations. The main advantages of the proposed filter are its excellent stability properties and ease of implementation in standard softwares without Hessians or solving constraint systems. By simulating multiple realistic examples such as peptide, protein, ice equilibrium and ice–ice friction, the new filter is shown to speed up the computations of long-range interactions by approximately 20%. The proposed filtered integrators allow step sizes as large as 10 fs while keeping the energy drift less than 1% on a 50 ps simulation.

Dose controlled low energy electron irradiator for biomolecular films

Energy Technology Data Exchange (ETDEWEB)

Kumar, S. V. K., E-mail: svkk@tifr.res.in; Tare, Satej T.; Upalekar, Yogesh V.; Tsering, Thupten [Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai 400 005 (India)

2016-03-15

We have developed a multi target, Low Energy Electron (LEE), precise dose controlled irradiator for biomolecular films. Up to seven samples can be irradiated one after another at any preset electron energy and dose under UHV conditions without venting the chamber. In addition, one more sample goes through all the steps except irradiation, which can be used as control for comparison with the irradiated samples. All the samples are protected against stray electron irradiation by biasing them at −20 V during the entire period, except during irradiation. Ethernet based communication electronics hardware, LEE beam control electronics and computer interface were developed in house. The user Graphical User Interface to control the irradiation and dose measurement was developed using National Instruments Lab Windows CVI. The working and reliability of the dose controlled irradiator has been fully tested over the electron energy range of 0.5 to 500 eV by studying LEE induced single strand breaks to ΦX174 RF1 dsDNA.

Stable isotope applications in biomolecular structure and mechanisms. A meeting to bring together producers and users of stable-isotope-labeled compounds to assess current and future needs

International Nuclear Information System (INIS)

Trewhella, J.; Cross, T.A.; Unkefer, C.J.

1994-12-01

Knowledge of biomolecular structure is a prerequisite for understanding biomolecular function, and stable isotopes play an increasingly important role in structure determination of biological molecules. The first Conference on Stable Isotope Applications in Biomolecular Structure and Mechanisms was held in Santa Fe, New Mexico, March 27--31, 1994. More than 120 participants from 8 countries and 44 institutions reviewed significant developments, discussed the most promising applications for stable isotopes, and addressed future needs and challenges. Participants focused on applications of stable isotopes for studies of the structure and function of proteins, peptides, RNA, and DNA. Recent advances in NMR techniques neutron scattering, EPR, and vibrational spectroscopy were highlighted in addition to the production and synthesis of labeled compounds. This volume includes invited speaker and poster presentations as well as a set of reports from discussion panels that focused on the needs of the scientific community and the potential roles of private industry, the National Stable Isotope Resource, and the National High Magnetic Field Laboratory in serving those needs. This is the leading abstract. Individual papers are processed separately for the database

Stable isotope applications in biomolecular structure and mechanisms. A meeting to bring together producers and users of stable-isotope-labeled compounds to assess current and future needs

Energy Technology Data Exchange (ETDEWEB)

Trewhella, J.; Cross, T.A.; Unkefer, C.J. [eds.

1994-12-01

Knowledge of biomolecular structure is a prerequisite for understanding biomolecular function, and stable isotopes play an increasingly important role in structure determination of biological molecules. The first Conference on Stable Isotope Applications in Biomolecular Structure and Mechanisms was held in Santa Fe, New Mexico, March 27--31, 1994. More than 120 participants from 8 countries and 44 institutions reviewed significant developments, discussed the most promising applications for stable isotopes, and addressed future needs and challenges. Participants focused on applications of stable isotopes for studies of the structure and function of proteins, peptides, RNA, and DNA. Recent advances in NMR techniques neutron scattering, EPR, and vibrational spectroscopy were highlighted in addition to the production and synthesis of labeled compounds. This volume includes invited speaker and poster presentations as well as a set of reports from discussion panels that focused on the needs of the scientific community and the potential roles of private industry, the National Stable Isotope Resource, and the National High Magnetic Field Laboratory in serving those needs. This is the leading abstract. Individual papers are processed separately for the database.

Report on the IAEA advisory group meeting on network of nuclear reaction data centres

Energy Technology Data Exchange (ETDEWEB)

Pronyaev, V G; Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria)

2000-08-01

This report summarizes the IAEA Advisory Group Meeting (AGM) on Network of Nuclear Reaction Data Centres, hold at the Institute of Physics and Power Engineering, Obninsk, Russia, 15 to 19 May 2000. The meeting was attended by 28 participants from 13 co-operating data centres from seven Member States and two International Organizations. The report contains a meeting summary, the conclusions and actions, progress and status reports of the participating data centres and working papers considered at the meeting. (author)

The use of gold nanoparticle aggregation for DNA computing and logic-based biomolecular detection

International Nuclear Information System (INIS)

Lee, In-Hee; Yang, Kyung-Ae; Zhang, Byoung-Tak; Lee, Ji-Hoon; Park, Ji-Yoon; Chai, Young Gyu; Lee, Jae-Hoon

2008-01-01

The use of DNA molecules as a physical computational material has attracted much interest, especially in the area of DNA computing. DNAs are also useful for logical control and analysis of biological systems if efficient visualization methods are available. Here we present a quick and simple visualization technique that displays the results of the DNA computing process based on a colorimetric change induced by gold nanoparticle aggregation, and we apply it to the logic-based detection of biomolecules. Our results demonstrate its effectiveness in both DNA-based logical computation and logic-based biomolecular detection

Conformation of bovine submaxillary mucin layers on hydrophobic surface as studied by biomolecular probes

DEFF Research Database (Denmark)

Pakkanen, Kirsi I.; Madsen, Jan Busk; Lee, Seunghwan

2015-01-01

In the present study, the conformational changes of bovine submaxillary mucin (BSM) adsorbed on a hydrophobic surface (polystyrene (PS)) as a function of concentration in bulk solution (up to 2mg/mL) have been investigated with biomolecular probe-based approaches, including bicinchoninic acid (BCA),enzyme-linkedimmunosorbentassay(EIA...... solution. Adsorbed masses of BSM onto hydrophobic surface, as probe by BCA, showed a continuously increasing trend up to 2mg/mL. But, the signals from EIA and ELLA, which probe the concentration of available unglycosylatedC-terminals and the central glycosylated regions, respectively, showed complicated...

Sequence co-evolutionary information is a natural partner to minimally-frustrated models of biomolecular dynamics [version 1; referees: 3 approved

Directory of Open Access Journals (Sweden)

Jeffrey K Noel

2016-01-01

Full Text Available Experimentally derived structural constraints have been crucial to the implementation of computational models of biomolecular dynamics. For example, not only does crystallography provide essential starting points for molecular simulations but also high-resolution structures permit for parameterization of simplified models. Since the energy landscapes for proteins and other biomolecules have been shown to be minimally frustrated and therefore funneled, these structure-based models have played a major role in understanding the mechanisms governing folding and many functions of these systems. Structural information, however, may be limited in many interesting cases. Recently, the statistical analysis of residue co-evolution in families of protein sequences has provided a complementary method of discovering residue-residue contact interactions involved in functional configurations. These functional configurations are often transient and difficult to capture experimentally. Thus, co-evolutionary information can be merged with that available for experimentally characterized low free-energy structures, in order to more fully capture the true underlying biomolecular energy landscape.

Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks

Energy Technology Data Exchange (ETDEWEB)

Deng, De-Ming; Chang, Cheng-Hung [Institute of Physics, National Chiao Tung University, Hsinchu 300, Taiwan (China)

2015-05-14

Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks.

Science.gov (United States)

Deng, De-Ming; Chang, Cheng-Hung

2015-05-14

Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks.

Science.gov (United States)

Chen, Yuan-Jyue; Rao, Sundipta D; Seelig, Georg

2015-11-25

DNA nanotechnology requires large amounts of highly pure DNA as an engineering material. Plasmid DNA could meet this need since it is replicated with high fidelity, is readily amplified through bacterial culture and can be stored indefinitely in the form of bacterial glycerol stocks. However, the double-stranded nature of plasmid DNA has so far hindered its efficient use for construction of DNA nanostructures or devices that typically contain single-stranded or branched domains. In recent work, it was found that nicked double stranded DNA (ndsDNA) strand displacement gates could be sourced from plasmid DNA. The following is a protocol that details how these ndsDNA gates can be efficiently encoded in plasmids and can be derived from the plasmids through a small number of enzymatic processing steps. Also given is a protocol for testing ndsDNA gates using fluorescence kinetics measurements. NdsDNA gates can be used to implement arbitrary chemical reaction networks (CRNs) and thus provide a pathway towards the use of the CRN formalism as a prescriptive molecular programming language. To demonstrate this technology, a multi-step reaction cascade with catalytic kinetics is constructed. Further it is shown that plasmid-derived components perform better than identical components assembled from synthetic DNA.

Summary Report on IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otsuka, Naohiko

2011-07-01

This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres, held at the IAEA Headquarters in Vienna, Austria from 23 - 24 May 2011. The meeting was attended by 25 participants from 13 cooperating data centres of nine Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. (author)

Catalyst Initiation in the Oscillatory Carbonylation Reaction

Directory of Open Access Journals (Sweden)

Katarina Novakovic

2011-01-01

Full Text Available Palladium(II iodide is used as a catalyst in the phenylacetylene oxidative carbonylation reaction that has demonstrated oscillatory behaviour in both pH and heat of reaction. In an attempt to extract the reaction network responsible for the oscillatory nature of this reaction, the system was divided into smaller parts and they were studied. This paper focuses on understanding the reaction network responsible for the initial reactions of palladium(II iodide within this oscillatory reaction. The species researched include methanol, palladium(II iodide, potassium iodide, and carbon monoxide. Several chemical reactions were considered and applied in a modelling study. The study revealed the significant role played by traces of water contained in the standard HPLC grade methanol used.

An efficient forward–reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Bayer, Christian

2016-02-20

© 2016 Taylor & Francis Group, LLC. ABSTRACT: In this work, we present an extension of the forward–reverse representation introduced by Bayer and Schoenmakers (Annals of Applied Probability, 24(5):1994–2032, 2014) to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, that is, SRNs conditional on their values in the extremes of given time intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the expectation-maximization algorithm to the phase I output. By selecting a set of overdispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.

An efficient forward-reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Vilanova, Pedro

2016-01-07

In this work, we present an extension of the forward-reverse representation introduced in Simulation of forward-reverse stochastic representations for conditional diffusions , a 2014 paper by Bayer and Schoenmakers to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the Expectation-Maximization algorithm to the phase I output. By selecting a set of over-dispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular networks

NARCIS (Netherlands)

R. Colak; F. Moser; J. Shu; A. Schönhuth (Alexander); N. Chen; M. Ester

2010-01-01

htmlabstractBackground Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not

Aprotic solvent systems provide mechanistic windows for biomolecular reactions: nucleic acid proton exchange

International Nuclear Information System (INIS)

McConnell, B.; Tan, A.

1986-01-01

Detection of general acid-base catalysis of proton transfer reactions in aqueous cytidine (or adenosine) is completely obscured by the highly reactive endocyclic protonated species of the nucleobase, whose amino proton lifetime is much shorter than that of the neutral form. In aqueous solution, protonation of the nucleobase always accompanies protonation of the buffer catalyzing exchange. However, in DMSO/water mixtures this is not the case; aqueous protonated acetate or chloroacetate can be added to cytidine in DMSO solutions without further dissociation of the buffer or significant protonation of cytidine N-3. Under these conditions general acid catalysis is observed, which involves an H-bonded complex between cytidine (N-3) and the buffer acid. Increased amino proton exchange in response to H-bond donation to C(N-3) is further suggested by increased 1 H NMR saturation-recovery rates with the formation of G-C base-pairs in DMSO and by the inverse dependence of amino proton exchange on nucleoside concentration

Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement

Science.gov (United States)

Srinivas, Niranjan

Over the last century, the silicon revolution has enabled us to build faster, smaller and more sophisticated computers. Today, these computers control phones, cars, satellites, assembly lines, and other electromechanical devices. Just as electrical wiring controls electromechanical devices, living organisms employ "chemical wiring" to make decisions about their environment and control physical processes. Currently, the big difference between these two substrates is that while we have the abstractions, design principles, verification and fabrication techniques in place for programming with silicon, we have no comparable understanding or expertise for programming chemistry. In this thesis we take a small step towards the goal of learning how to systematically engineer prescribed non-equilibrium dynamical behaviors in chemical systems. We use the formalism of chemical reaction networks (CRNs), combined with mass-action kinetics, as our programming language for specifying dynamical behaviors. Leveraging the tools of nucleic acid nanotechnology (introduced in Chapter 1), we employ synthetic DNA molecules as our molecular architecture and toehold-mediated DNA strand displacement as our reaction primitive. Abstraction, modular design and systematic fabrication can work only with well-understood and quantitatively characterized tools. Therefore, we embark on a detailed study of the "device physics" of DNA strand displacement (Chapter 2). We present a unified view of strand displacement biophysics and kinetics by studying the process at multiple levels of detail, using an intuitive model of a random walk on a 1-dimensional energy landscape, a secondary structure kinetics model with single base-pair steps, and a coarse-grained molecular model that incorporates three-dimensional geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Our findings are consistent with previously measured or inferred rates for

LiHe{sup +} IN THE EARLY UNIVERSE: A FULL ASSESSMENT OF ITS REACTION NETWORK AND FINAL ABUNDANCES

Energy Technology Data Exchange (ETDEWEB)

Bovino, Stefano; Tacconi, Mario; Gianturco, Francesco A. [Department of Chemistry, Universita degli Studi di Roma ' La Sapienza' , Piazzale A. Moro 5, 00185 Roma (Italy); Curik, Roman [J. Heyrovsky Institute of Physical Chemistry, Dolejskova 3, Prague (Czech Republic); Galli, Daniele, E-mail: fa.gianturco@caspur.it [INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze (Italy)

2012-06-10

We present the results of quantum calculations based on entirely ab initio methods for a variety of molecular processes and chemical reactions involving the LiHe{sup +} ionic polar molecule. With the aid of these calculations, we derive accurate reaction rates and fitting expressions valid over a range of gas temperatures representative of the typical conditions of the pregalactic gas. With the help of a full chemical network, we then compute the evolution of the abundance of LiHe{sup +} as function of redshift in the early universe. Finally, we compare the relative abundance of LiHe{sup +} with that of other polar cations formed in the same redshift interval.

Biomolecular ions in superfluid helium nanodroplets

Energy Technology Data Exchange (ETDEWEB)

Gonzalez Florez, Ana Isabel

2016-07-01

The function of a biological molecule is closely related to its structure. As a result, understanding and predicting biomolecular structure has become the focus of an extensive field of research. However, the investigation of molecular structure can be hampered by two main difficulties: the inherent complications that may arise from studying biological molecules in their native environment, and the potential congestion of the experimental results as a consequence of the large number of degrees of freedom present in these molecules. In this work, a new experimental setup has been developed and established in order to overcome the afore mentioned limitations combining structure-sensitive gas-phase methods with superfluid helium droplets. First, biological molecules are ionised and brought into the gas phase, often referred to as a clean-room environment, where the species of interest are isolated from their surroundings and, thus, intermolecular interactions are absent. The mass-to-charge selected biomolecules are then embedded inside clusters of superfluid helium with an equilibrium temperature of ∝0.37 K. As a result, the internal energy of the molecules is lowered, thereby reducing the number of populated quantum states. Finally, the local hydrogen bonding patterns of the molecules are investigated by probing specific vibrational modes using the Fritz Haber Institute's free electron laser as a source of infrared radiation. Although the structure of a wide variety of molecules has been studied making use of the sub-Kelvin environment provided by superfluid helium droplets, the suitability of this method for the investigation of biological molecular ions was still unclear. However, the experimental results presented in this thesis demonstrate the applicability of this experimental approach in order to study the structure of intact, large biomolecular ions and the first vibrational spectrum of the protonated pentapeptide leu-enkephalin embedded in helium

Biomolecular ions in superfluid helium nanodroplets

International Nuclear Information System (INIS)

Gonzalez Florez, Ana Isabel

2016-01-01

The function of a biological molecule is closely related to its structure. As a result, understanding and predicting biomolecular structure has become the focus of an extensive field of research. However, the investigation of molecular structure can be hampered by two main difficulties: the inherent complications that may arise from studying biological molecules in their native environment, and the potential congestion of the experimental results as a consequence of the large number of degrees of freedom present in these molecules. In this work, a new experimental setup has been developed and established in order to overcome the afore mentioned limitations combining structure-sensitive gas-phase methods with superfluid helium droplets. First, biological molecules are ionised and brought into the gas phase, often referred to as a clean-room environment, where the species of interest are isolated from their surroundings and, thus, intermolecular interactions are absent. The mass-to-charge selected biomolecules are then embedded inside clusters of superfluid helium with an equilibrium temperature of ∝0.37 K. As a result, the internal energy of the molecules is lowered, thereby reducing the number of populated quantum states. Finally, the local hydrogen bonding patterns of the molecules are investigated by probing specific vibrational modes using the Fritz Haber Institute's free electron laser as a source of infrared radiation. Although the structure of a wide variety of molecules has been studied making use of the sub-Kelvin environment provided by superfluid helium droplets, the suitability of this method for the investigation of biological molecular ions was still unclear. However, the experimental results presented in this thesis demonstrate the applicability of this experimental approach in order to study the structure of intact, large biomolecular ions and the first vibrational spectrum of the protonated pentapeptide leu-enkephalin embedded in helium

Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otuka, Naohiko; Herman, Michal

2016-07-01

This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres held at the China Hall of Science and Technology in Beijing, China from 7 to 10 June 2016. The meeting was attended by 23 participants representing 13 cooperative Centres from 8 Member States (China, Hungary, India, Japan, Korea, Russia, Ukraine and USA) and 2 International Organisations (NEA, IAEA) as well as two participants from Kazakhstan. A summary of the meeting is given in this report along with the conclusions and actions. (author)

A hydrogel-based versatile screening platform for specific biomolecular recognition in a well plate format.

Science.gov (United States)

Beer, Meike V; Rech, Claudia; Diederichs, Sylvia; Hahn, Kathrin; Bruellhoff, Kristina; Möller, Martin; Elling, Lothar; Groll, Jürgen

2012-04-01

Precise determination of biomolecular interactions in high throughput crucially depends on a surface coating technique that allows immobilization of a variety of interaction partners in a non-interacting environment. We present a one-step hydrogel coating system based on isocyanate functional six-arm poly(ethylene oxide)-based star polymers for commercially available 96-well microtiter plates that combines a straightforward and robust coating application with versatile bio-functionalization. This system generates resistance to unspecific protein adsorption and cell adhesion, as demonstrated with fluorescently labeled bovine serum albumin and primary human dermal fibroblasts (HDF), and high specificity for the assessment of biomolecular recognition processes when ligands are immobilized on this surface. One particular advantage is the wide range of biomolecules that can be immobilized and convert the per se inert coating into a specifically interacting surface. We here demonstrate the immobilization and quantification of a broad range of biochemically important ligands, such as peptide sequences GRGDS and GRGDSK-biotin, the broadly applicable coupler molecule biocytin, the protein fibronectin, and the carbohydrates N-acetylglucosamine and N-acetyllactosamine. A simplified protocol for an enzyme-linked immunosorbent assay was established for the detection and quantification of ligands on the coating surface. Cell adhesion on the peptide and protein-modified surfaces was assessed using HDF. All coatings were applied using a one-step preparation technique, including bioactivation, which makes the system suitable for high-throughput screening in a format that is compatible with the most routinely used testing systems.

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

Science.gov (United States)

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

2014-04-06

In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the

Evolução biomolecular homoquiral: a origem e a amplificação da quiralidade nas moléculas da vida Homochiral biomolecular evolution: the origin and the amplification of chirality in life molecules

Directory of Open Access Journals (Sweden)

José Augusto R. Rodrigues

2010-01-01

Full Text Available The fact that biologically relevant molecules exist only as one of the two enantiomers is a fascinating example of complete symmetry breaking of chirality and has long intrigued our curiosity. The origin of this selective chirality has remained a fundamental enigma with regard to the origin of life since the time of Pasteur, 160 years ago. The symmetry breaking processes, which include autocatalytic crystallization, asymmetric autocatalysis, spontaneous crystallization, adsorption and polymerization of amino acids on mineral surfaces, provide new insights into the origin of biomolecular homochirality.

A palladium-doped ceria@carbon core-sheath nanowire network: a promising catalyst support for alcohol electrooxidation reactions

Science.gov (United States)

Tan, Qiang; Du, Chunyu; Sun, Yongrong; Du, Lei; Yin, Geping; Gao, Yunzhi

2015-08-01

A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique interconnected one-dimensional core-sheath structure is revealed to facilitate immobilization of the metal catalysts, leading to the improved durability. This core-sheath nanowire network opens up a new strategy for catalyst performance optimization for next-generation fuel cells.A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique

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

Science.gov (United States)

Zhang, Jiajun; Nie, Qing; Zhou, Tianshou

2016-05-21

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

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

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jiajun [School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China); Nie, Qing [Department of Mathematics, University of California at Irvine, Irvine, California 92697 (United States); Zhou, Tianshou, E-mail: mcszhtsh@mail.sysu.edu.cn [School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China); Guangdong Province Key Laboratory of Computational Science and School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China)

2016-05-21

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

Biomolecular Structure Information from High-Speed Quantum Mechanical Electronic Spectra Calculation.

Science.gov (United States)

Seibert, Jakob; Bannwarth, Christoph; Grimme, Stefan

2017-08-30

A fully quantum mechanical (QM) treatment to calculate electronic absorption (UV-vis) and circular dichroism (CD) spectra of typical biomolecules with thousands of atoms is presented. With our highly efficient sTDA-xTB method, spectra averaged along structures from molecular dynamics (MD) simulations can be computed in a reasonable time frame on standard desktop computers. This way, nonequilibrium structure and conformational, as well as purely quantum mechanical effects like charge-transfer or exciton-coupling, are included. Different from other contemporary approaches, the entire system is treated quantum mechanically and neither fragmentation nor system-specific adjustment is necessary. Among the systems considered are a large DNA fragment, oligopeptides, and even entire proteins in an implicit solvent. We propose the method in tandem with experimental spectroscopy or X-ray studies for the elucidation of complex (bio)molecular structures including metallo-proteins like myoglobin.

Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.

Science.gov (United States)

Arampatzis, Georgios; Katsoulakis, Markos A; Pantazis, Yannis

2015-01-01

Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the

Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.

Directory of Open Access Journals (Sweden)

Georgios Arampatzis

Full Text Available Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of

Nuclear Reaction Data File for Astrophysics (NRDF/A) in Hokkaido University Nuclear Reaction Data Center

International Nuclear Information System (INIS)

Kato, Kiyoshi; Kimura, Masaaki; Furutachi, Naoya; Makinaga, Ayano; Togashi, Tomoaki; Otuka, Naohiko

2010-01-01

The activities of the Japan Nuclear Reaction Data Centre is explained. The main task of the centre is data compilation of Japanese nuclear reaction data in collaboration of the International Network of Nuclear Reaction Data Centres. As one of recent activities, preparation of a new database (NRDF/A) and evaluation of astronuclear reaction data are reported. Collaboration in the nuclear data activities among Asian countries is proposed.

Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface

Directory of Open Access Journals (Sweden)

Bushell Michael E

2011-05-01

Full Text Available Abstract Background Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. Results Here, we present Acorn, an open source (GNU GPL grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a

Theoretical restrictions on longest implicit time scales in Markov state models of biomolecular dynamics

Science.gov (United States)

Sinitskiy, Anton V.; Pande, Vijay S.

2018-01-01

Markov state models (MSMs) have been widely used to analyze computer simulations of various biomolecular systems. They can capture conformational transitions much slower than an average or maximal length of a single molecular dynamics (MD) trajectory from the set of trajectories used to build the MSM. A rule of thumb claiming that the slowest implicit time scale captured by an MSM should be comparable by the order of magnitude to the aggregate duration of all MD trajectories used to build this MSM has been known in the field. However, this rule has never been formally proved. In this work, we present analytical results for the slowest time scale in several types of MSMs, supporting the above rule. We conclude that the slowest implicit time scale equals the product of the aggregate sampling and four factors that quantify: (1) how much statistics on the conformational transitions corresponding to the longest implicit time scale is available, (2) how good the sampling of the destination Markov state is, (3) the gain in statistics from using a sliding window for counting transitions between Markov states, and (4) a bias in the estimate of the implicit time scale arising from finite sampling of the conformational transitions. We demonstrate that in many practically important cases all these four factors are on the order of unity, and we analyze possible scenarios that could lead to their significant deviation from unity. Overall, we provide for the first time analytical results on the slowest time scales captured by MSMs. These results can guide further practical applications of MSMs to biomolecular dynamics and allow for higher computational efficiency of simulations.

Urban networks and Arctic outlands: Craft specialists and reindeer antler in Viking towns

DEFF Research Database (Denmark)

Ashby, Steven P.; Coutu, Ashley N.; Sindbæk, Søren Michael

2015-01-01

This paper presents the results of the use of a minimally destructive biomolecular technique to explore the resource networks behind one of the first specialized urban crafts in early mediaeval northern Europe: the manufacture of composite combs of deer antler. The research incorporates the largest...... the 780s ad at the latest, presenting the earliest unambiguous evidence for exchange-links between urban markets in the southern North Sea region and the Scandinavian Peninsula. The results demonstrate that the common conceptual distinction between urban hinterlands and long-distance trade conceals...... a vital continuity. Long-range networks were vital to urban activities from the first appearance of towns in this part of the world, preceding the historically documented maritime expansion of the Viking Age. We consequently suggest that urbanism is more appropriately defined and researched in terms...

Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

Science.gov (United States)

Maguire, Jack B; Boyken, Scott E; Baker, David; Kuhlman, Brian

2018-05-08

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

Cancer genetics meets biomolecular mechanism-bridging an age-old gulf.

Science.gov (United States)

González-Sánchez, Juan Carlos; Raimondi, Francesco; Russell, Robert B

2018-02-01

Increasingly available genomic sequencing data are exploited to identify genes and variants contributing to diseases, particularly cancer. Traditionally, methods to find such variants have relied heavily on allele frequency and/or familial history, often neglecting to consider any mechanistic understanding of their functional consequences. Thus, while the set of known cancer-related genes has increased, for many, their mechanistic role in the disease is not completely understood. This issue highlights a wide gap between the disciplines of genetics, which largely aims to correlate genetic events with phenotype, and molecular biology, which ultimately aims at a mechanistic understanding of biological processes. Fortunately, new methods and several systematic studies have proved illuminating for many disease genes and variants by integrating sequencing with mechanistic data, including biomolecular structures and interactions. These have provided new interpretations for known mutations and suggested new disease-relevant variants and genes. Here, we review these approaches and discuss particular examples where these have had a profound impact on the understanding of human cancers. © 2018 Federation of European Biochemical Societies.

Overcoming the solubility limit with solubility-enhancement tags: successful applications in biomolecular NMR studies

International Nuclear Information System (INIS)

Zhou Pei; Wagner, Gerhard

2010-01-01

Although the rapid progress of NMR technology has significantly expanded the range of NMR-trackable systems, preparation of NMR-suitable samples that are highly soluble and stable remains a bottleneck for studies of many biological systems. The application of solubility-enhancement tags (SETs) has been highly effective in overcoming solubility and sample stability issues and has enabled structural studies of important biological systems previously deemed unapproachable by solution NMR techniques. In this review, we provide a brief survey of the development and successful applications of the SET strategy in biomolecular NMR. We also comment on the criteria for choosing optimal SETs, such as for differently charged target proteins, and recent new developments on NMR-invisible SETs.

Solving the 0/1 Knapsack Problem by a Biomolecular DNA Computer

Directory of Open Access Journals (Sweden)

Hassan Taghipour

2013-01-01

Full Text Available Solving some mathematical problems such as NP-complete problems by conventional silicon-based computers is problematic and takes so long time. DNA computing is an alternative method of computing which uses DNA molecules for computing purposes. DNA computers have massive degrees of parallel processing capability. The massive parallel processing characteristic of DNA computers is of particular interest in solving NP-complete and hard combinatorial problems. NP-complete problems such as knapsack problem and other hard combinatorial problems can be easily solved by DNA computers in a very short period of time comparing to conventional silicon-based computers. Sticker-based DNA computing is one of the methods of DNA computing. In this paper, the sticker based DNA computing was used for solving the 0/1 knapsack problem. At first, a biomolecular solution space was constructed by using appropriate DNA memory complexes. Then, by the application of a sticker-based parallel algorithm using biological operations, knapsack problem was resolved in polynomial time.

Piezoelectric tuning fork biosensors for the quantitative measurement of biomolecular interactions

International Nuclear Information System (INIS)

Gonzalez, Laura; Maria Benito, Angel; Puig-Vidal, Manel; Otero, Jorge; Rodrigues, Mafalda; Pérez-García, Lluïsa

2015-01-01

The quantitative measurement of biomolecular interactions is of great interest in molecular biology. Atomic force microscopy (AFM) has proved its capacity to act as a biosensor and determine the affinity between biomolecules of interest. Nevertheless, the detection scheme presents certain limitations when it comes to developing a compact biosensor. Recently, piezoelectric quartz tuning forks (QTFs) have been used as laser-free detection sensors for AFM. However, only a few studies along these lines have considered soft biological samples, and even fewer constitute quantified molecular recognition experiments. Here, we demonstrate the capacity of QTF probes to perform specific interaction measurements between biotin–streptavidin complexes in buffer solution. We propose in this paper a variant of dynamic force spectroscopy based on representing adhesion energies E (aJ) against pulling rates v (nm s"–"1). Our results are compared with conventional AFM measurements and show the great potential of these sensors in molecular interaction studies. (paper)

XML-based approaches for the integration of heterogeneous bio-molecular data.

Science.gov (United States)

Mesiti, Marco; Jiménez-Ruiz, Ernesto; Sanz, Ismael; Berlanga-Llavori, Rafael; Perlasca, Paolo; Valentini, Giorgio; Manset, David

2009-10-15

The today's public database infrastructure spans a very large collection of heterogeneous biological data, opening new opportunities for molecular biology, bio-medical and bioinformatics research, but raising also new problems for their integration and computational processing. In this paper we survey the most interesting and novel approaches for the representation, integration and management of different kinds of biological data by exploiting XML and the related recommendations and approaches. Moreover, we present new and interesting cutting edge approaches for the appropriate management of heterogeneous biological data represented through XML. XML has succeeded in the integration of heterogeneous biomolecular information, and has established itself as the syntactic glue for biological data sources. Nevertheless, a large variety of XML-based data formats have been proposed, thus resulting in a difficult effective integration of bioinformatics data schemes. The adoption of a few semantic-rich standard formats is urgent to achieve a seamless integration of the current biological resources.

H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations.

Science.gov (United States)

Anandakrishnan, Ramu; Aguilar, Boris; Onufriev, Alexey V

2012-07-01

The accuracy of atomistic biomolecular modeling and simulation studies depend on the accuracy of the input structures. Preparing these structures for an atomistic modeling task, such as molecular dynamics (MD) simulation, can involve the use of a variety of different tools for: correcting errors, adding missing atoms, filling valences with hydrogens, predicting pK values for titratable amino acids, assigning predefined partial charges and radii to all atoms, and generating force field parameter/topology files for MD. Identifying, installing and effectively using the appropriate tools for each of these tasks can be difficult for novice and time-consuming for experienced users. H++ (http://biophysics.cs.vt.edu/) is a free open-source web server that automates the above key steps in the preparation of biomolecular structures for molecular modeling and simulations. H++ also performs extensive error and consistency checking, providing error/warning messages together with the suggested corrections. In addition to numerous minor improvements, the latest version of H++ includes several new capabilities and options: fix erroneous (flipped) side chain conformations for HIS, GLN and ASN, include a ligand in the input structure, process nucleic acid structures and generate a solvent box with specified number of common ions for explicit solvent MD.

A reaction-based paradigm to model reactive chemical transport in groundwater with general kinetic and equilibrium reactions

International Nuclear Information System (INIS)

Zhang, Fan; Yeh, Gour-Tsyh; Parker, Jack C.; Brooks, Scott C; Pace, Molly; Kim, Young Jin; Jardine, Philip M.; Watson, David B.

2007-01-01

This paper presents a reaction-based water quality transport model in subsurface flow systems. Transport of chemical species with a variety of chemical and physical processes is mathematically described by M. partial differential equations (PDEs). Decomposition via Gauss-Jordan column reduction of the reaction network transforms M. species reactive transport equations into two sets of equations: a set of thermodynamic equilibrium equations representing NE equilibrium reactions and a set of reactive transport equations of M-NE kinetic-variables involving no equilibrium reactions (a kinetic-variable is a linear combination of species). The elimination of equilibrium reactions from reactive transport equations allows robust and efficient numerical integration. The model solves the PDEs of kinetic-variables rather than individual chemical species, which reduces the number of reactive transport equations and simplifies the reaction terms in the equations. A variety of numerical methods are investigated for solving the coupled transport and reaction equations. Simulation comparisons with exact solutions were performed to verify numerical accuracy and assess the effectiveness of various numerical strategies to deal with different application circumstances. Two validation examples involving simulations of uranium transport in soil columns are presented to evaluate the ability of the model to simulate reactive transport with complex reaction networks involving both kinetic and equilibrium reactions

A reaction-based paradigm to model reactive chemical transport in groundwater with general kinetic and equilibrium reactions.

Science.gov (United States)

Zhang, Fan; Yeh, Gour-Tsyh; Parker, Jack C; Brooks, Scott C; Pace, Molly N; Kim, Young-Jin; Jardine, Philip M; Watson, David B

2007-06-16

This paper presents a reaction-based water quality transport model in subsurface flow systems. Transport of chemical species with a variety of chemical and physical processes is mathematically described by M partial differential equations (PDEs). Decomposition via Gauss-Jordan column reduction of the reaction network transforms M species reactive transport equations into two sets of equations: a set of thermodynamic equilibrium equations representing N(E) equilibrium reactions and a set of reactive transport equations of M-N(E) kinetic-variables involving no equilibrium reactions (a kinetic-variable is a linear combination of species). The elimination of equilibrium reactions from reactive transport equations allows robust and efficient numerical integration. The model solves the PDEs of kinetic-variables rather than individual chemical species, which reduces the number of reactive transport equations and simplifies the reaction terms in the equations. A variety of numerical methods are investigated for solving the coupled transport and reaction equations. Simulation comparisons with exact solutions were performed to verify numerical accuracy and assess the effectiveness of various numerical strategies to deal with different application circumstances. Two validation examples involving simulations of uranium transport in soil columns are presented to evaluate the ability of the model to simulate reactive transport with complex reaction networks involving both kinetic and equilibrium reactions.

Network features and pathway analyses of a signal transduction cascade

Directory of Open Access Journals (Sweden)

Ryoji Yanashima

2009-05-01

Full Text Available The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.

Microfluidic Devices for Studying Biomolecular Interactions

Science.gov (United States)

Wilson, Wilbur W.; Garcia, Carlos d.; Henry, Charles S.

2006-01-01

Microfluidic devices for monitoring biomolecular interactions have been invented. These devices are basically highly miniaturized liquid-chromatography columns. They are intended to be prototypes of miniature analytical devices of the laboratory on a chip type that could be fabricated rapidly and inexpensively and that, because of their small sizes, would yield analytical results from very small amounts of expensive analytes (typically, proteins). Other advantages to be gained by this scaling down of liquid-chromatography columns may include increases in resolution and speed, decreases in the consumption of reagents, and the possibility of performing multiple simultaneous and highly integrated analyses by use of multiple devices of this type, each possibly containing multiple parallel analytical microchannels. The principle of operation is the same as that of a macroscopic liquid-chromatography column: The column is a channel packed with particles, upon which are immobilized molecules of the protein of interest (or one of the proteins of interest if there are more than one). Starting at a known time, a solution or suspension containing molecules of the protein or other substance of interest is pumped into the channel at its inlet. The liquid emerging from the outlet of the channel is monitored to detect the molecules of the dissolved or suspended substance(s). The time that it takes these molecules to flow from the inlet to the outlet is a measure of the degree of interaction between the immobilized and the dissolved or suspended molecules. Depending on the precise natures of the molecules, this measure can be used for diverse purposes: examples include screening for solution conditions that favor crystallization of proteins, screening for interactions between drugs and proteins, and determining the functions of biomolecules.

Surface tectonics of nanoporous networks of melamine-capped molecular building blocks formed through interface Schiff-base reactions.

Science.gov (United States)

Liu, Xuan-He; Wang, Dong; Wan, Li-Jun

2013-10-01

Control over the assembly of molecules on a surface is of great importance for the fabrication of molecule-based miniature devices. Melamine (MA) and molecules with terminal MA units are promising candidates for supramolecular interfacial packing patterning, owing to their multiple hydrogen-bonding sites. Herein, we report the formation of self-assembled structures of MA-capped molecules through a simple on-surface synthetic route. MA terminal groups were successfully fabricated onto rigid molecular cores with 2-fold and 3-fold symmetry through interfacial Schiff-base reactions between MA and aldehyde groups. Sub-molecular scanning tunneling microscopy (STM) imaging of the resultant adlayer revealed the formation of nanoporous networks. Detailed structural analysis indicated that strong hydrogen-bonding interactions between the MA groups persistently drove the formation of nanoporous networks. Herein, we demonstrate that functional groups with strong hydrogen-bond-formation ability are promising building blocks for the guided assembly of nanoporous networks and other hierarchical 2D assemblies. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multicomponent Reactions in Ligation and Bioconjugation Chemistry.

Science.gov (United States)

Reguera, Leslie; Méndez, Yanira; Humpierre, Ana R; Valdés, Oscar; Rivera, Daniel G

2018-05-25

Multicomponent reactions (MCRs) encompass an exciting class of chemical transformations that have proven success in almost all fields of synthetic organic chemistry. These convergent procedures incorporate three or more reactants into a final product in one pot, thus combining high levels of complexity and diversity generation with low synthetic cost. Striking applications of these processes are found in heterocycle, peptidomimetic, and natural product syntheses. However, their potential in the preparation of large macro- and biomolecular constructs has been realized just recently. This Account describes the most relevant results of our group in the utilization of MCRs for ligation/conjugation of biomolecules along with significant contributions from other laboratories that validate the utility of this special class of bioconjugation process. Thus, MCRs have proven to be efficient in the ligation of lipids to peptides and oligosaccharides as well as the ligation of steroids, carbohydrates, and fluorescent and affinity tags to peptides and proteins. In the field of glycolipids, we highlight the power of isocyanide-based MCRs with the one-pot double lipidation of glycan fragments functionalized as either the carboxylic acid or amine. In peptide chemistry, the versatility of the multicomponent ligation strategy is demonstrated in both solution-phase lipidation protocols and solid-phase procedures enabling the simultaneous lipidation and biotinylation of peptides. In addition, we show that MCRs are powerful methods for synchronized lipidation/labeling and macrocyclization of peptides, thus accomplishing in one step what usually requires long sequences. In the realm of protein bioconjugation, MCRs have also proven to be effective in labeling, site-selective modification, immobilization, and glycoconjugation processes. For example, we illustrate a successful application of multicomponent polysaccharide-protein conjugation with the preparation of multivalent

Enzyme-Based Logic Gates and Networks with Output Signals Analyzed by Various Methods.

Science.gov (United States)

Katz, Evgeny

2017-07-05

The paper overviews various methods that are used for the analysis of output signals generated by enzyme-based logic systems. The considered methods include optical techniques (optical absorbance, fluorescence spectroscopy, surface plasmon resonance), electrochemical techniques (cyclic voltammetry, potentiometry, impedance spectroscopy, conductivity measurements, use of field effect transistor devices, pH measurements), and various mechanoelectronic methods (using atomic force microscope, quartz crystal microbalance). Although each of the methods is well known for various bioanalytical applications, their use in combination with the biomolecular logic systems is rather new and sometimes not trivial. Many of the discussed methods have been combined with the use of signal-responsive materials to transduce and amplify biomolecular signals generated by the logic operations. Interfacing of biocomputing logic systems with electronics and "smart" signal-responsive materials allows logic operations be extended to actuation functions; for example, stimulating molecular release and switchable features of bioelectronic devices, such as biofuel cells. The purpose of this review article is to emphasize the broad variability of the bioanalytical systems applied for signal transduction in biocomputing processes. All bioanalytical systems discussed in the article are exemplified with specific logic gates and multi-gate networks realized with enzyme-based biocatalytic cascades. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Frequency-scanning MALDI linear ion trap mass spectrometer for large biomolecular ion detection.

Science.gov (United States)

Lu, I-Chung; Lin, Jung Lee; Lai, Szu-Hsueh; Chen, Chung-Hsuan

2011-11-01

This study presents the first report on the development of a matrix-assisted laser desorption ionization (MALDI) linear ion trap mass spectrometer for large biomolecular ion detection by frequency scan. We designed, installed, and tested this radio frequency (RF) scan linear ion trap mass spectrometer and its associated electronics to dramatically extend the mass region to be detected. The RF circuit can be adjusted from 300 to 10 kHz with a set of operation amplifiers. To trap the ions produced by MALDI, a high pressure of helium buffer gas was employed to quench extra kinetic energy of the heavy ions produced by MALDI. The successful detection of the singly charged secretory immunoglobulin A ions indicates that the detectable mass-to-charge ratio (m/z) of this system can reach ~385 000 or beyond.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Science.gov (United States)

Chylek, Lily A.; Harris, Leonard A.; Tung, Chang-Shung; Faeder, James R.; Lopez, Carlos F.

2013-01-01

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and post-translational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation). PMID:24123887

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems.

Science.gov (United States)

Chylek, Lily A; Harris, Leonard A; Tung, Chang-Shung; Faeder, James R; Lopez, Carlos F; Hlavacek, William S

2014-01-01

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation). © 2013 Wiley Periodicals, Inc.

A framework to find the logic backbone of a biological network.

Science.gov (United States)

Maheshwari, Parul; Albert, Réka

2017-12-06

Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network. In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures. The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

Cholesterol photo-oxidation: A chemical reaction network for kinetic modeling.

Science.gov (United States)

Barnaba, Carlo; Rodríguez-Estrada, Maria Teresa; Lercker, Giovanni; García, Hugo Sergio; Medina-Meza, Ilce Gabriela

2016-12-01

In this work we studied the effect of polyunsaturated fatty acids (PUFAs) methyl esters on cholesterol photo-induced oxidation. The oxidative routes were modeled with a chemical reaction network (CRN), which represents the first application of CRN to the oxidative degradation of a food-related lipid matrix. Docosahexaenoic acid (DHA, T-I), eicosapentaenoic acid (EPA, T-II) and a mixture of both (T-III) were added to cholesterol using hematoporphyrin as sensitizer, and were exposed to a fluorescent lamp for 48h. High amounts of Type I cholesterol oxidation products (COPs) were recovered (epimers 7α- and 7β-OH, 7-keto and 25-OH), as well as 5β,6β-epoxy. Fitting the experimental data with the CRN allowed characterizing the associated kinetics. DHA and EPA exerted different effects on the oxidative process. DHA showed a protective effect to 7-hydroxy derivatives, whereas EPA enhanced side-chain oxidation and 7β-OH kinetic rates. The mixture of PUFAs increased the kinetic rates several fold, particularly for 25-OH. With respect to the control, the formation of β-epoxy was reduced, suggesting potential inhibition in the presence of PUFAs. Copyright © 2016 Elsevier Inc. All rights reserved.

Slave nodes and the controllability of metabolic networks

International Nuclear Information System (INIS)

Kim, Dong-Hee; Motter, Adilson E

2009-01-01

Recent work on synthetic rescues has shown that the targeted deletion of specific metabolic genes can often be used to rescue otherwise non-viable mutants. This raises a fundamental biophysical question: to what extent can the whole-cell behavior of a large metabolic network be controlled by constraining the flux of one or more reactions in the network? This touches upon the issue of the number of degrees of freedom contained by one such network. Using the metabolic network of Escherichia coli as a model system, here we address this question theoretically by exploring not only reaction deletions, but also a continuum of all possible reaction expression levels. We show that the behavior of the metabolic network can be largely manipulated by the pinned expression of a single reaction. In particular, a relevant fraction of the metabolic reactions exhibits canalizing interactions, in that the specification of one reaction flux determines cellular growth as well as the fluxes of most other reactions in optimal steady states. The activity of individual reactions can thus be used as surrogates to monitor and possibly control cellular growth and other whole-cell behaviors. In addition to its implications for the study of control processes, our methodology provides a new approach to study how the integrated dynamics of the entire metabolic network emerges from the coordinated behavior of its component parts.

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

Science.gov (United States)

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

2016-01-01

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

An effective hierarchical model for the biomolecular covalent bond: an approach integrating artificial chemistry and an actual terrestrial life system.

Science.gov (United States)

Oohashi, Tsutomu; Ueno, Osamu; Maekawa, Tadao; Kawai, Norie; Nishina, Emi; Honda, Manabu

2009-01-01

Under the AChem paradigm and the programmed self-decomposition (PSD) model, we propose a hierarchical model for the biomolecular covalent bond (HBCB model). This model assumes that terrestrial organisms arrange their biomolecules in a hierarchical structure according to the energy strength of their covalent bonds. It also assumes that they have evolutionarily selected the PSD mechanism of turning biological polymers (BPs) into biological monomers (BMs) as an efficient biomolecular recycling strategy We have examined the validity and effectiveness of the HBCB model by coordinating two complementary approaches: biological experiments using existent terrestrial life, and simulation experiments using an AChem system. Biological experiments have shown that terrestrial life possesses a PSD mechanism as an endergonic, genetically regulated process and that hydrolysis, which decomposes a BP into BMs, is one of the main processes of such a mechanism. In simulation experiments, we compared different virtual self-decomposition processes. The virtual species in which the self-decomposition process mainly involved covalent bond cleavage from a BP to BMs showed evolutionary superiority over other species in which the self-decomposition process involved cleavage from BP to classes lower than BM. These converging findings strongly support the existence of PSD and the validity and effectiveness of the HBCB model.

The Efficiency of a Small-World Functional Brain Network

Institute of Scientific and Technical Information of China (English)

ZHAO Qing-Bai; ZHANG Xiao-Fei; SUI Dan-Ni; ZHOU Zhi-Jin; CHEN Qi-Cai; TANG Yi-Yuan

2012-01-01

We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task.Functional brain networks are constructed by multichannel eventrelated potential data,in which the electrodes are the nodes and the functional connectivities between them are the edges.The results show that the correlation between small-world measures and reaction time is task-specific,such that in global imagery,there is a positive correlation between the clustering coefficient and reaction time,while in local imagery the average path length is positively correlated with the reaction time.This suggests that the efficiency of a functional brain network is task-dependent.%We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task. Functional brain networks are constructed by multichannel event-related potential data, in which the electrodes are the nodes and the functional connectivities between them are the edges. The results show that the correlation between small-world measures and reaction time is task-specific, such that in global imagery, there is a positive correlation between the clustering coefficient and reaction time, while in local imagery the average path length is positively correlated with the reaction time. This suggests that the efficiency of a functional brain network is task-dependent.

ECOMICS: a web-based toolkit for investigating the biomolecular web in ecosystems using a trans-omics approach.

Directory of Open Access Journals (Sweden)

Yoshiyuki Ogata

Full Text Available Ecosystems can be conceptually thought of as interconnected environmental and metabolic systems, in which small molecules to macro-molecules interact through diverse networks. State-of-the-art technologies in post-genomic science offer ways to inspect and analyze this biomolecular web using omics-based approaches. Exploring useful genes and enzymes, as well as biomass resources responsible for anabolism and catabolism within ecosystems will contribute to a better understanding of environmental functions and their application to biotechnology. Here we present ECOMICS, a suite of web-based tools for ECosystem trans-OMICS investigation that target metagenomic, metatranscriptomic, and meta-metabolomic systems, including biomacromolecular mixtures derived from biomass. ECOMICS is made of four integrated webtools. E-class allows for the sequence-based taxonomic classification of eukaryotic and prokaryotic ribosomal data and the functional classification of selected enzymes. FT2B allows for the digital processing of NMR spectra for downstream metabolic or chemical phenotyping. Bm-Char allows for statistical assignment of specific compounds found in lignocellulose-based biomass, and HetMap is a data matrix generator and correlation calculator that can be applied to trans-omics datasets as analyzed by these and other web tools. This web suite is unique in that it allows for the monitoring of biomass metabolism in a particular environment, i.e., from macromolecular complexes (FT2DB and Bm-Char to microbial composition and degradation (E-class, and makes possible the understanding of relationships between molecular and microbial elements (HetMap. This website is available to the public domain at: https://database.riken.jp/ecomics/.

Zwitterionic Silane Copolymer for Ultra-Stable and Bright Biomolecular Probes Based on Fluorescent Quantum Dot Nanoclusters.

Science.gov (United States)

Dembele, Fatimata; Tasso, Mariana; Trapiella-Alfonso, Laura; Xu, Xiangzhen; Hanafi, Mohamed; Lequeux, Nicolas; Pons, Thomas

2017-05-31

Fluorescent semiconductor quantum dots (QDs) exhibit several unique properties that make them suitable candidates for biomolecular sensing, including high brightness, photostability, broad excitation, and narrow emission spectra. Assembling these QDs into robust and functionalizable nanosized clusters (QD-NSCs) can provide fluorescent probes that are several orders of magnitude brighter than individual QDs, thus allowing an even greater sensitivity of detection with simplified instrumentation. However, the formation of compact, antifouling, functionalizable, and stable QD-NSCs remains a challenging task, especially for a use at ultralow concentrations for single-molecule detection. Here, we describe the development of fluorescent QD-NSCs envisioned as a tool for fast and sensitive biomolecular recognition. First, QDs were assembled into very compact 100-150 nm diameter spherical aggregates; the final QD-NSCs were obtained by growing a cross-linked silica shell around these aggregates. Hydrolytic stability in several concentration and pH conditions is a key requirement for a potential and efficient single-molecule detection tool. However, the hydrolysis of Si-O-Si bonds leads to desorption of monosilane-based surface groups at very low silica concentrations or in a slightly basic medium. Thus, we designed a novel multidentate copolymer composed of multiple silane as well as zwitterionic monomers. Coating silica beads with this multidentate copolymer provided a robust surface chemistry that was demonstrated to be stable against hydrolysis, even at low concentrations. Copolymer-coated silica beads also showed low fouling properties and high colloidal stability in saline solutions. Furthermore, incorporation of additional azido-monomers enabled easy functionalization of QD-NSCs using copper-free bio-orthogonal cyclooctyne-azide click chemistry, as demonstrated by a biotin-streptavidin affinity test.

Report on the IAEA technical meeting on co-ordination of the network of nuclear reaction data centres

International Nuclear Information System (INIS)

Schwerer, O.

2003-08-01

Results of the IAEA Technical Meeting on the Co-ordination of the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 17 to 19 June 2003, are summarised in this report. The meeting was attended by 14 participants from 9 cooperating data centres of five member states and two International Organizations. A meeting summary, the conclusions and actions, progress and status reports of the participating data centres, and working papers considered at the meeting, are given in the relevant sections. (author)

Exponential growth for self-reproduction in a catalytic reaction network: relevance of a minority molecular species and crowdedness

Science.gov (United States)

Kamimura, Atsushi; Kaneko, Kunihiko

2018-03-01

Explanation of exponential growth in self-reproduction is an important step toward elucidation of the origins of life because optimization of the growth potential across rounds of selection is necessary for Darwinian evolution. To produce another copy with approximately the same composition, the exponential growth rates for all components have to be equal. How such balanced growth is achieved, however, is not a trivial question, because this kind of growth requires orchestrated replication of the components in stochastic and nonlinear catalytic reactions. By considering a mutually catalyzing reaction in two- and three-dimensional lattices, as represented by a cellular automaton model, we show that self-reproduction with exponential growth is possible only when the replication and degradation of one molecular species is much slower than those of the others, i.e., when there is a minority molecule. Here, the synergetic effect of molecular discreteness and crowding is necessary to produce the exponential growth. Otherwise, the growth curves show superexponential growth because of nonlinearity of the catalytic reactions or subexponential growth due to replication inhibition by overcrowding of molecules. Our study emphasizes that the minority molecular species in a catalytic reaction network is necessary for exponential growth at the primitive stage of life.

Rational design of functional and tunable oscillating enzymatic networks

Science.gov (United States)

Semenov, Sergey N.; Wong, Albert S. Y.; van der Made, R. Martijn; Postma, Sjoerd G. J.; Groen, Joost; van Roekel, Hendrik W. H.; de Greef, Tom F. A.; Huck, Wilhelm T. S.

2015-02-01

Life is sustained by complex systems operating far from equilibrium and consisting of a multitude of enzymatic reaction networks. The operating principles of biology's regulatory networks are known, but the in vitro assembly of out-of-equilibrium enzymatic reaction networks has proved challenging, limiting the development of synthetic systems showing autonomous behaviour. Here, we present a strategy for the rational design of programmable functional reaction networks that exhibit dynamic behaviour. We demonstrate that a network built around autoactivation and delayed negative feedback of the enzyme trypsin is capable of producing sustained oscillating concentrations of active trypsin for over 65 h. Other functions, such as amplification, analog-to-digital conversion and periodic control over equilibrium systems, are obtained by linking multiple network modules in microfluidic flow reactors. The methodology developed here provides a general framework to construct dissipative, tunable and robust (bio)chemical reaction networks.

Uma abordagem visual para análise comparativa de redes biomoleculares com apoio de diagramas de Venn

OpenAIRE

Henry Heberle

2014-01-01

Sistemas biológicos podem ser representados por redes que armazenam não apenas informações de conectividade, mas também informações de características de seus nós. No contexto biomolecular, esses nós podem representar proteínas, metabólitos, entre outros tipos de moléculas. Cada molécula possui características anotadas e armazenadas em bases de dados como o Gene Ontology. A comparação visual dessas redes depende de ferramentas que permitam o usuário identificar diferenças e semelhanças entre ...

Versatile single-molecule multi-color excitation and detection fluorescence setup for studying biomolecular dynamics

KAUST Repository

Sobhy, M. A.

2011-11-07

Single-molecule fluorescence imaging is at the forefront of tools applied to study biomolecular dynamics both in vitro and in vivo. The ability of the single-molecule fluorescence microscope to conduct simultaneous multi-color excitation and detection is a key experimental feature that is under continuous development. In this paper, we describe in detail the design and the construction of a sophisticated and versatile multi-color excitation and emission fluorescence instrument for studying biomolecular dynamics at the single-molecule level. The setup is novel, economical and compact, where two inverted microscopes share a laser combiner module with six individual laser sources that extend from 400 to 640 nm. Nonetheless, each microscope can independently and in a flexible manner select the combinations, sequences, and intensities of the excitation wavelengths. This high flexibility is achieved by the replacement of conventional mechanical shutters with acousto-optic tunable filter (AOTF). The use of AOTF provides major advancement by controlling the intensities, duration, and selection of up to eight different wavelengths with microsecond alternation time in a transparent and easy manner for the end user. To our knowledge this is the first time AOTF is applied to wide-field total internal reflection fluorescence (TIRF) microscopy even though it has been commonly used in multi-wavelength confocal microscopy. The laser outputs from the combiner module are coupled to the microscopes by two sets of four single-mode optic fibers in order to allow for the optimization of the TIRF angle for each wavelength independently. The emission is split into two or four spectral channels to allow for the simultaneous detection of up to four different fluorophores of wide selection and using many possible excitation and photoactivation schemes. We demonstrate the performance of this new setup by conducting two-color alternating excitation single-molecule fluorescence resonance energy

Phase sensitive spectral domain interferometry for label free biomolecular interaction analysis and biosensing applications

Science.gov (United States)

Chirvi, Sajal

Biomolecular interaction analysis (BIA) plays vital role in wide variety of fields, which include biomedical research, pharmaceutical industry, medical diagnostics, and biotechnology industry. Study and quantification of interactions between natural biomolecules (proteins, enzymes, DNA) and artificially synthesized molecules (drugs) is routinely done using various labeled and label-free BIA techniques. Labeled BIA (Chemiluminescence, Fluorescence, Radioactive) techniques suffer from steric hindrance of labels on interaction site, difficulty of attaching labels to molecules, higher cost and time of assay development. Label free techniques with real time detection capabilities have demonstrated advantages over traditional labeled techniques. The gold standard for label free BIA is surface Plasmon resonance (SPR) that detects and quantifies the changes in refractive index of the ligand-analyte complex molecule with high sensitivity. Although SPR is a highly sensitive BIA technique, it requires custom-made sensor chips and is not well suited for highly multiplexed BIA required in high throughput applications. Moreover implementation of SPR on various biosensing platforms is limited. In this research work spectral domain phase sensitive interferometry (SD-PSI) has been developed for label-free BIA and biosensing applications to address limitations of SPR and other label free techniques. One distinct advantage of SD-PSI compared to other label-free techniques is that it does not require use of custom fabricated biosensor substrates. Laboratory grade, off-the-shelf glass or plastic substrates of suitable thickness with proper surface functionalization are used as biosensor chips. SD-PSI is tested on four separate BIA and biosensing platforms, which include multi-well plate, flow cell, fiber probe with integrated optics and fiber tip biosensor. Sensitivity of 33 ng/ml for anti-IgG is achieved using multi-well platform. Principle of coherence multiplexing for multi

The reaction index and positivity ratio revisited

DEFF Research Database (Denmark)

Andersen, Klaus Ejner; Andersen, Flemming

2008-01-01

BACKGROUND AND OBJECTIVES: Assessing the quality of patch test preparations continues to be a challenge. 2 parameters, the reaction index (RI) and positivity ratio (PR), have been proposed as quality indicators by the Information Network of Departments of Dermatology (IVDK). The value of these st......BACKGROUND AND OBJECTIVES: Assessing the quality of patch test preparations continues to be a challenge. 2 parameters, the reaction index (RI) and positivity ratio (PR), have been proposed as quality indicators by the Information Network of Departments of Dermatology (IVDK). The value...

Isolated photosystem I reaction centers on a functionalized gated high electron mobility transistor.

Science.gov (United States)

Eliza, Sazia A; Lee, Ida; Tulip, Fahmida S; Mostafa, Salwa; Greenbaum, Elias; Ericson, M Nance; Islam, Syed K

2011-09-01

In oxygenic plants, photons are captured with high quantum efficiency by two specialized reaction centers (RC) called Photosystem I (PS I) and Photosystem II (PS II). The captured photon triggers rapid charge separation and the photon energy is converted into an electrostatic potential across the nanometer-scale (~6 nm) reaction centers. The exogenous photovoltages from a single PS I RC have been previously measured using the technique of Kelvin force probe microscopy (KFM). However, biomolecular photovoltaic applications require two-terminal devices. This paper presents for the first time, a micro-device for detection and characterization of isolated PS I RCs. The device is based on an AlGaN/GaN high electron mobility transistor (HEMT) structure. AlGaN/GaN HEMTs show high current throughputs and greater sensitivity to surface charges compared to other field-effect devices. PS I complexes immobilized on the floating gate of AlGaN/GaN HEMTs resulted in significant changes in the device characteristics under illumination. An analytical model has been developed to estimate the RCs of a major orientation on the functionalized gate surface of the HEMTs. © 2011 IEEE

Isolated Photosystem I Reaction Centers on a Functionalized Gated High Electron Mobility Transistor

Energy Technology Data Exchange (ETDEWEB)

Eliza, Sazia A. [University of Tennessee, Knoxville (UTK); Lee, Ida [ORNL; Tulip, Fahmida S [ORNL; Islam, Syed K [University of Tennessee, Knoxville (UTK); Mostafa, Salwa [University of Tennessee, Knoxville (UTK); Greenbaum, Elias [ORNL; Ericson, Milton Nance [ORNL

2011-01-01

In oxygenic plants, photons are captured with high quantum efficiency by two specialized reaction centers (RC) called Photosystem I (PS I) and Photosystem II (PS II). The captured photon triggers rapid charge separation and the photon energy is converted into an electrostatic potential across the nanometer-scale nm reaction centers. The exogenous photovoltages from a single PS I RC have been previously measured using the technique of Kelvin force probe microscopy (KFM). However, biomolecular photovoltaic applications require two-terminal devices. This paper presents for the first time, a micro-device for detection and characterization of isolated PS I RCs. The device is based on an AlGaN/GaN high electron mobility transistor (HEMT) structure. AlGaN/GaN HEMTs show high current throughputs and greater sensitivity to surface charges compared to other field-effect devices. PS I complexes immobilized on the floating gate of AlGaN/GaN HEMTs resulted in significant changes in the device characteristics under illumination. An analytical model has been developed to estimate the RCs of a major orientation on the functionalized gate surface of the HEMTs.

Global exponential stability and periodicity of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo; Lu Linji

2009-01-01

In this paper, global exponential stability and periodicity of a class of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Secondly, we prove periodicity. Sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium and periodic solution are given. These conditions are easy to verify and our results play an important role in the design and application of globally exponentially stable neural circuits and periodic oscillatory neural circuits.

Biomolecular Nano-Flow-Sensor to Measure Near-Surface Flow

Directory of Open Access Journals (Sweden)

Noji Hiroyuki

2009-01-01

Full Text Available Abstract We have proposed and experimentally demonstrated that the measurement of the near-surface flow at the interface between a liquid and solid using a 10 nm-sized biomolecular motor of F1-ATPase as a nano-flow-sensor. For this purpose, we developed a microfluidic test-bed chip to precisely control the liquid flow acting on the F1-ATPase. In order to visualize the rotation of F1-ATPase, several hundreds nanometer-sized particle was immobilized at the rotational axis of F1-ATPase to enhance the rotation to be detected by optical microscopy. The rotational motion of F1-ATPase, which was immobilized on an inner surface of the test-bed chip, was measured to obtain the correlation between the near-surface flow and the rotation speed of F1-ATPase. As a result, we obtained the relationship that the rotation speed of F1-ATPase was linearly decelerated with increasing flow velocity. The mechanism of the correlation between the rotation speed and the near-surface flow remains unclear, however the concept to use biomolecule as a nano-flow-sensor was proofed successfully. (See supplementary material 1 Electronic supplementary material The online version of this article (doi:10.1007/s11671-009-9479-3 contains supplementary material, which is available to authorized users. Click here for file

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

Science.gov (United States)

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

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Bayarbaatar Amgalan

Full Text Available Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function over the standard simplex (sparsity constraints. We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.

Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics.

Science.gov (United States)

Prinz, Jan-Hendrik; Chodera, John D; Pande, Vijay S; Swope, William C; Smith, Jeremy C; Noé, Frank

2011-06-28

Parallel tempering (PT) molecular dynamics simulations have been extensively investigated as a means of efficient sampling of the configurations of biomolecular systems. Recent work has demonstrated how the short physical trajectories generated in PT simulations of biomolecules can be used to construct the Markov models describing biomolecular dynamics at each simulated temperature. While this approach describes the temperature-dependent kinetics, it does not make optimal use of all available PT data, instead estimating the rates at a given temperature using only data from that temperature. This can be problematic, as some relevant transitions or states may not be sufficiently sampled at the temperature of interest, but might be readily sampled at nearby temperatures. Further, the comparison of temperature-dependent properties can suffer from the false assumption that data collected from different temperatures are uncorrelated. We propose here a strategy in which, by a simple modification of the PT protocol, the harvested trajectories can be reweighted, permitting data from all temperatures to contribute to the estimated kinetic model. The method reduces the statistical uncertainty in the kinetic model relative to the single temperature approach and provides estimates of transition probabilities even for transitions not observed at the temperature of interest. Further, the method allows the kinetics to be estimated at temperatures other than those at which simulations were run. We illustrate this method by applying it to the generation of a Markov model of the conformational dynamics of the solvated terminally blocked alanine peptide.

Imaging and chemical surface analysis of biomolecular functionalization of monolithically integrated on silicon Mach-Zehnder interferometric immunosensors

International Nuclear Information System (INIS)

Gajos, Katarzyna; Angelopoulou, Michailia; Petrou, Panagiota; Awsiuk, Kamil; Kakabakos, Sotirios; Haasnoot, Willem; Bernasik, Andrzej; Rysz, Jakub; Marzec, Mateusz M.; Misiakos, Konstantinos; Raptis, Ioannis; Budkowski, Andrzej

2016-01-01

Highlights: • Optimization of probe immobilization with robotic spotter printing overlapping spots. • In-situ inspection of microstructured surfaces of biosensors integrated on silicon. • Imaging and chemical analysis of immobilization, surface blocking and immunoreaction. • Insight with molecular discrimination into step-by-step sensor surface modifications. • Optimized biofunctionalization improves sensor sensitivity and response repeatability. - Abstract: Time-of-flight secondary ion mass spectrometry (imaging, micro-analysis) has been employed to evaluate biofunctionalization of the sensing arm areas of Mach-Zehnder interferometers monolithically integrated on silicon chips for the immunochemical (competitive) detection of bovine κ-casein in goat milk. Biosensor surfaces are examined after: modification with (3-aminopropyl)triethoxysilane, application of multiple overlapping spots of κ-casein solutions, blocking with 100-times diluted goat milk, and reaction with monoclonal mouse anti-κ-casein antibodies in blocking solution. The areas spotted with κ-casein solutions of different concentrations are examined and optimum concentration providing homogeneous coverage is determined. Coverage of biosensor surfaces with biomolecules after each of the sequential steps employed in immunodetection is also evaluated with TOF-SIMS, supplemented by Atomic force microscopy and X-ray photoelectron spectroscopy. Uniform molecular distributions are observed on the sensing arm areas after spotting with optimum κ-casein concentration, blocking and immunoreaction. The corresponding biomolecular compositions are determined with a Principal Component Analysis that distinguished between protein amino acids and milk glycerides, as well as between amino acids characteristic for Mabs and κ-casein, respectively. Use of the optimum conditions (κ-casein concentration) for functionalization of chips with arrays of ten Mach-Zehnder interferometers provided on-chips assays

Imaging and chemical surface analysis of biomolecular functionalization of monolithically integrated on silicon Mach-Zehnder interferometric immunosensors

Energy Technology Data Exchange (ETDEWEB)

Gajos, Katarzyna, E-mail: kasia.fornal@uj.edu.pl [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland); Angelopoulou, Michailia; Petrou, Panagiota [Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, NCSR Demokritos, P. Grigoriou & Neapoleos St, Aghia Paraksevi 15310, Athens (Greece); Awsiuk, Kamil [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland); Kakabakos, Sotirios [Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, NCSR Demokritos, P. Grigoriou & Neapoleos St, Aghia Paraksevi 15310, Athens (Greece); Haasnoot, Willem [RIKILT Wageningen UR, Akkermaalsbos 2, 6708 WB Wageningen (Netherlands); Bernasik, Andrzej [Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków (Poland); Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków (Poland); Rysz, Jakub [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland); Marzec, Mateusz M. [Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków (Poland); Misiakos, Konstantinos; Raptis, Ioannis [Department of Microelectronics, Institute of Nanoscience and Nanotechnology, NCSR Demokritos, P. Grigoriou & Neapoleos St, Aghia Paraksevi 15310, Athens (Greece); Budkowski, Andrzej [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland)

2016-11-01

Highlights: • Optimization of probe immobilization with robotic spotter printing overlapping spots. • In-situ inspection of microstructured surfaces of biosensors integrated on silicon. • Imaging and chemical analysis of immobilization, surface blocking and immunoreaction. • Insight with molecular discrimination into step-by-step sensor surface modifications. • Optimized biofunctionalization improves sensor sensitivity and response repeatability. - Abstract: Time-of-flight secondary ion mass spectrometry (imaging, micro-analysis) has been employed to evaluate biofunctionalization of the sensing arm areas of Mach-Zehnder interferometers monolithically integrated on silicon chips for the immunochemical (competitive) detection of bovine κ-casein in goat milk. Biosensor surfaces are examined after: modification with (3-aminopropyl)triethoxysilane, application of multiple overlapping spots of κ-casein solutions, blocking with 100-times diluted goat milk, and reaction with monoclonal mouse anti-κ-casein antibodies in blocking solution. The areas spotted with κ-casein solutions of different concentrations are examined and optimum concentration providing homogeneous coverage is determined. Coverage of biosensor surfaces with biomolecules after each of the sequential steps employed in immunodetection is also evaluated with TOF-SIMS, supplemented by Atomic force microscopy and X-ray photoelectron spectroscopy. Uniform molecular distributions are observed on the sensing arm areas after spotting with optimum κ-casein concentration, blocking and immunoreaction. The corresponding biomolecular compositions are determined with a Principal Component Analysis that distinguished between protein amino acids and milk glycerides, as well as between amino acids characteristic for Mabs and κ-casein, respectively. Use of the optimum conditions (κ-casein concentration) for functionalization of chips with arrays of ten Mach-Zehnder interferometers provided on-chips assays

Single-Cell Biomolecular Analysis of Coral Algal Symbionts Reveals Opposing Metabolic Responses to Heat Stress and Expulsion

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Katherina Petrou

2018-03-01

Full Text Available The success of corals in nutrient poor environments is largely attributed to the symbiosis between the cnidarian host and its intracellular alga. Warm water anomalies have been shown to destabilize this symbiosis, yet detailed analysis of the effect of temperature and expulsion on cell-specific carbon and nutrient allocation in the symbiont is limited. Here, we exposed colonies of the hard coral Acropora millepora to heat stress and using synchrotron-based infrared microspectroscopy measured the biomolecular profiles of individual in hospite and expelled symbiont cells at an acute state of bleaching. Our results showed symbiont metabolic profiles to be remarkably distinct with heat stress and expulsion, where the two effectors elicited opposing metabolic adjustments independent of treatment or cell type. Elevated temperature resulted in biomolecular changes reflecting cellular stress, with relative increases in free amino acids and phosphorylation of molecules and a concomitant decline in protein content, suggesting protein modification and degradation. This contrasted with the metabolic profiles of expelled symbionts, which showed relative decreases in free amino acids and phosphorylated molecules, but increases in proteins and lipids, suggesting expulsion lessens the overall effect of heat stress on the metabolic signature of the algal symbionts. Interestingly, the combined effects of expulsion and thermal stress were additive, reducing the overall shifts in all biomolecules, with the notable exception of the significant accumulation of lipids and saturated fatty acids. This first use of a single-cell metabolomics approach on the coral symbiosis provides novel insight into coral bleaching and emphasizes the importance of a single-cell approach to demark the cell-to-cell variability in the physiology of coral cellular populations.

Unravelling the Maillard reaction network by multiresponse kinetic modelling

NARCIS (Netherlands)

Martins, S.I.F.S.

2003-01-01

The Maillard reaction is an important reaction in food industry. It is responsible for the formation of colour and aroma, as well as toxic compounds as the recent discovered acrylamide. The knowledge of kinetic parameters, such as rate constants and activation energy, is necessary to predict its

Environmental versatility promotes modularity in genome-scale metabolic networks.

Science.gov (United States)

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

2011-08-24

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple

Environmental versatility promotes modularity in genome-scale metabolic networks

Directory of Open Access Journals (Sweden)

Wagner Andreas

2011-08-01

Full Text Available Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Results Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Conclusions Our work shows that modularity in metabolic networks can be a by-product of functional

Review of Transducer Principles for Label-Free Biomolecular Interaction Analysis

Directory of Open Access Journals (Sweden)

Janos Vörös

2011-07-01

Full Text Available Label-free biomolecular interaction analysis is an important technique to study the chemical binding between e.g., protein and protein or protein and small molecule in real-time. The parameters obtained with this technique, such as the affinity, are important for drug development. While the surface plasmon resonance (SPR instruments are most widely used, new types of sensors are emerging. These developments are generally driven by the need for higher throughput, lower sample consumption or by the need of complimentary information to the SPR data. This review aims to give an overview about a wide range of sensor transducers, the working principles and the peculiarities of each technology, e.g., concerning the set-up, sensitivity, sensor size or required sample volume. Starting from optical technologies like the SPR and waveguide based sensors, acoustic sensors like the quartz crystal microbalance (QCM and the film bulk acoustic resonator (FBAR, calorimetric and electrochemical sensors are covered. Technologies long established in the market are presented together with those newly commercially available and with technologies in the early development stage. Finally, the commercially available instruments are summarized together with their sensitivity and the number of sensors usable in parallel and an outlook for potential future developments is given.

GKIN: a tool for drawing genetic networks

Directory of Open Access Journals (Sweden)

Jonathan Arnold

2012-03-01

Full Text Available We present GKIN, a simulator and a comprehensive graphical interface where one can draw the model specification of reactions between hypothesized molecular participants in a gene regulatory and biochemical reaction network (or genetic network for short. The solver is written in C++ in a nearly platform independentmanner to simulate large ensembles of models, which can run on PCs, Macintoshes, and UNIX machines, and its graphical user interface is written in Java which can run as a standalone or WebStart application. The drawing capability for rendering a network significantly enhances the ease of use of other reaction network simulators, such as KINSOLVER (Aleman-Meza et al., 2009 and enforces a correct semantic specification of the network. In a usability study with novice users, drawing the network with GKIN was preferred and faster in comparison with entry with a dialog-box guided interface in COPASI (Hoops, et al., 2006 with no difference in error rates between GKIN and COPASI in specifying the network. GKIN is freely available at http://faculty.cs.wit.edu/~ldeligia/PROJECTS/GKIN/.

Reaction kinetics in open reactors and serial transfers between closed reactors

Science.gov (United States)

Blokhuis, Alex; Lacoste, David; Gaspard, Pierre

2018-04-01

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

Pre-Clinical Tests of an Integrated CMOS Biomolecular Sensor for Cardiac Diseases Diagnosis.

Science.gov (United States)

Lee, Jen-Kuang; Wang, I-Shun; Huang, Chi-Hsien; Chen, Yih-Fan; Huang, Nien-Tsu; Lin, Chih-Ting

2017-11-26

Coronary artery disease and its related complications pose great threats to human health. In this work, we aim to clinically evaluate a CMOS field-effect biomolecular sensor for cardiac biomarkers, cardiac-specific troponin-I (cTnI), N -terminal prohormone brain natriuretic peptide (NT-proBNP), and interleukin-6 (IL-6). The CMOS biosensor is implemented via a standard commercialized 0.35 μm CMOS process. To validate the sensing characteristics, in buffer conditions, the developed CMOS biosensor has identified the detection limits of IL-6, cTnI, and NT-proBNP as being 45 pM, 32 pM, and 32 pM, respectively. In clinical serum conditions, furthermore, the developed CMOS biosensor performs a good correlation with an enzyme-linked immuno-sorbent assay (ELISA) obtained from a hospital central laboratory. Based on this work, the CMOS field-effect biosensor poses good potential for accomplishing the needs of a point-of-care testing (POCT) system for heart disease diagnosis.

RPYFMM: Parallel adaptive fast multipole method for Rotne-Prager-Yamakawa tensor in biomolecular hydrodynamics simulations

Science.gov (United States)

Guan, W.; Cheng, X.; Huang, J.; Huber, G.; Li, W.; McCammon, J. A.; Zhang, B.

2018-06-01

RPYFMM is a software package for the efficient evaluation of the potential field governed by the Rotne-Prager-Yamakawa (RPY) tensor interactions in biomolecular hydrodynamics simulations. In our algorithm, the RPY tensor is decomposed as a linear combination of four Laplace interactions, each of which is evaluated using the adaptive fast multipole method (FMM) (Greengard and Rokhlin, 1997) where the exponential expansions are applied to diagonalize the multipole-to-local translation operators. RPYFMM offers a unified execution on both shared and distributed memory computers by leveraging the DASHMM library (DeBuhr et al., 2016, 2018). Preliminary numerical results show that the interactions for a molecular system of 15 million particles (beads) can be computed within one second on a Cray XC30 cluster using 12,288 cores, while achieving approximately 54% strong-scaling efficiency.

MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES.

Science.gov (United States)

Correia, Rion Brattig; Li, Lang; Rocha, Luis M

2016-01-01

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram

Charged particle induced thermonuclear reaction rates: a compilation for astrophysics

International Nuclear Information System (INIS)

Grama, C.

1999-01-01

We report on the results of the European network NACRE (Nuclear Astrophysics Compilation of REaction rates). The principal reason for setting up the NACRE network has been the necessity of building up a well-documented and detailed compilation of rates for charged-particle induced reactions on stable targets up to Si and on unstable nuclei of special significance in astrophysics. This work is meant to supersede the only existing compilation of reaction rates issued by Fowler and collaborators. The main goal of NACRE network was the transparency in the procedure of calculating the rates. More specifically this compilation aims at: 1. updating the experimental and theoretical data; 2. distinctly identifying the sources of the data used in rate calculation; 3. evaluating the uncertainties and errors; 4. providing numerically integrated reaction rates; 5. providing reverse reaction rates and analytical approximations of the adopted rates. The cross section data and/or resonance parameters for a total of 86 charged-particle induced reactions are given and the corresponding reaction rates are calculated and given in tabular form. Uncertainties are analyzed and realistic upper and lower bounds of the rates are determined. The compilation is concerned with the reaction rates that are large enough for the target lifetimes shorter than the age of the Universe, taken equal to 15 x 10 9 y. The reaction rates are provided for temperatures lower than T = 10 10 K. In parallel with the rate compilation a cross section data base has been created and located at the site http://pntpm.ulb.ac.be/nacre..htm. (authors)

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

Science.gov (United States)

Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

2014-01-27

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W

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

Science.gov (United States)

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

2007-04-26

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

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

Directory of Open Access Journals (Sweden)

Gould John

2007-04-01

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

ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

Directory of Open Access Journals (Sweden)

Zixiang Xu

Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

Control of fluxes in metabolic networks

Science.gov (United States)

Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

2016-01-01

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. PMID:27197218

What makes it likeable? A study on the reactions to messages in a digital social network: the case of Facebook in Farsi.

Science.gov (United States)

Sabbar, Shaho; Hyun, Daiwon

2016-01-01

After a piece of information is put into a network, its fate depends on the behaviors of the nodes of the network; nodes that are equipped with the hardware and software of the age of information and are more powerful than any time in the past. This study suggests that a useful research for communication, marketing and advertising would be one that looks for patterns in the reactions of the nodes toward different pieces of information. This study has used Facebook to see how people have reacted to different types of messages in terms of liking, sharing and commenting. Rather than looking for universal, generalizable patterns we have tried to examine the practicality of the proposed method. The practical aspect of the study comes after a short theoretical discussion on the issue of flow of information in a digital world. The results revealed dozens of significant relations between the examined variables. This study, its theoretical discussion and results suggest that it would be practical to study the relations between the characteristics of Facebook messages and the type of reactions (liking, sharing and commenting) that they attract.

FY 2000 report on the results of the R and D of the fusion domain. Volume 3. Biomolecular mechanism and design; 2000 nendo yugo ryoiki kenkyu kaihatsu. 3. Biomolecular mechanism and design

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-03-01

For the purpose of creating cells/tissue assemblies and the molecular machine that substitute for self-organizing and self-repairing functions of a living body outside the body, the basic technology research was conducted, and the FY 2000 results were reported. In the study of 3D cell and tissue module engineering, the following were conducted: study of the surface modification and functional expression of biomaterials, study of the mechanical stress to cartilaginous cells and the response, development of the production method of biodegradable synthetic polymer porous materials, study of organism hard tissue materials/bone remodeling and cultured bone transportation, development of zinc-releasing calcium phosphate ceramics. In the study of biomolecular mechanism and design, 1D unidirectional movement of microtubules by applying microlithography technology, structural study of kinesin-family molecular motor by low temperature microscope, ribozyme and the application to leukemia, basic study on assessment of chemical substances by human cultured cells, study of a low molecule detection system using nucleic acid and peptide. (NEDO)

SUPECA kinetics for scaling redox reactions in networks of mixed substrates and consumers and an example application to aerobic soil respiration

Science.gov (United States)

Tang, Jin-Yun; Riley, William J.

2017-09-01

Several land biogeochemical models used for studying carbon-climate feedbacks have begun explicitly representing microbial dynamics. However, to our knowledge, there has been no theoretical work on how to achieve a consistent scaling of the complex biogeochemical reactions from microbial individuals to populations, communities, and interactions with plants and mineral soils. We focus here on developing a mathematical formulation of the substrate-consumer relationships for consumer-mediated redox reactions of the form A + BE→ products, where products could be, e.g., microbial biomass or bioproducts. Under the quasi-steady-state approximation, these substrate-consumer relationships can be formulated as the computationally difficult full equilibrium chemistry problem or approximated analytically with the dual Monod (DM) or synthesizing unit (SU) kinetics. We find that DM kinetics is scaling inconsistently for reaction networks because (1) substrate limitations are not considered, (2) contradictory assumptions are made regarding the substrate processing rate when transitioning from single- to multi-substrate redox reactions, and (3) the product generation rate cannot be scaled from one to multiple substrates. In contrast, SU kinetics consistently scales the product generation rate from one to multiple substrates but predicts unrealistic results as consumer abundances reach large values with respect to their substrates. We attribute this deficit to SU's failure to incorporate substrate limitation in its derivation. To address these issues, we propose SUPECA (SU plus the equilibrium chemistry approximation - ECA) kinetics, which consistently imposes substrate and consumer mass balance constraints. We show that SUPECA kinetics satisfies the partition principle, i.e., scaling invariance across a network of an arbitrary number of reactions (e.g., as in Newton's law of motion and Dalton's law of partial pressures). We tested SUPECA kinetics with the equilibrium chemistry

EVA reactive blending with Si–H terminated polysiloxane by carbonyl hydrosilylation reaction: From compatibilised blends to crosslinking networks

International Nuclear Information System (INIS)

Bonnet, J.; Bounor-Legaré, V.; Alcouffe, P.; Cassagnau, P.

2012-01-01

A new and original method based on carbonyl hydrosilylation was developed to prepare ethylene-vinyl acetate (EVA)/polysiloxane polymer blends. This focused on the addition of hydrogenosilane groups (SiH) from polysiloxane to the carbonyl groups of EVA. The influence of the nature of the polysiloxane on blend properties was investigated by rheology and scanning electron microscopy. Mixing of a low viscosity polysiloxane with a high viscosity EVA matrix produced a two-phase morphology. The occurrence of the hydrosilylation reaction at the EVA/polysiloxane interface promoted a homogenisation of the blend depending on the molar ratio SiH/vinyl acetate groups, [SiH]/[VA], and the viscosity ratio of the blend. Two distinct behaviours were observed. The formation of a crosslinked network under shear was obtained for a low viscosity ratio between polysiloxane and EVA (λ polysiloxane/EVA = 4.0 × 10 −6 ) with a high concentration of SiH groups ([SiH]/[VA] = 0.5), while the formation of a compatibilised blend was observed for high molar mass polysiloxanes (Mn > 15,000 g mol −1 ) with a low concentration of SiH ([SiH]/[VA] −3 ). -- Highlights: ► Carbonyl hydrosilylation reaction was found to enhance EVA/polysiloxane immiscible blends. ► EVA crosslinking was obtained with a low molar mass polysiloxane. ► EVA compatibilisation was obtained with a high molar mass polysiloxane. ► Shear rate was found to improve the hydrosilylation reaction at the interface. ► A two-phase morphology of the blends was observed after reaction with fine polysiloxane nodules.

Covalently crosslinked diels-alder polymer networks.

Energy Technology Data Exchange (ETDEWEB)

Bowman, Christopher (University of Colorado, Boulder, CO); Adzima, Brian J. (University of Colorado, Boulder, CO); Anderson, Benjamin John

2011-09-01

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

Toxicity evaluation of PEDOT/biomolecular composites intended for neural communication electrodes

International Nuclear Information System (INIS)

Asplund, M; Thaning, E; Von Holst, H; Lundberg, J; Sandberg-Nordqvist, A C; Kostyszyn, B; Inganaes, O

2009-01-01

Electrodes coated with the conducting polymer poly(3,4-ethylene dioxythiophene) (PEDOT) possess attractive electrochemical properties for stimulation or recording in the nervous system. Biomolecules, added as counter ions in electropolymerization, could further improve the biomaterial properties, eliminating the need for surfactant counter ions in the process. Such PEDOT/biomolecular composites, using heparin or hyaluronic acid, have previously been investigated electrochemically. In the present study, their biocompatibility is evaluated. An agarose overlay assay using L929 fibroblasts, and elution and direct contact tests on human neuroblastoma SH-SY5Y cells are applied to investigate cytotoxicity in vitro. PEDOT:heparin was further evaluated in vivo through polymer-coated implants in rodent cortex. No cytotoxic response was seen to any of the PEDOT materials tested. The examination of cortical tissue exposed to polymer-coated implants showed extensive glial scarring irrespective of implant material (Pt:polymer or Pt). However, quantification of immunological response, through distance measurements from implant site to closest neuron and counting of ED1+ cell density around implant, was comparable to those of platinum controls. These results indicate that PEDOT:heparin surfaces were non-cytotoxic and show no marked difference in immunological response in cortical tissue compared to pure platinum controls.

A Quick-responsive DNA Nanotechnology Device for Bio-molecular Homeostasis Regulation.

Science.gov (United States)

Wu, Songlin; Wang, Pei; Xiao, Chen; Li, Zheng; Yang, Bing; Fu, Jieyang; Chen, Jing; Wan, Neng; Ma, Cong; Li, Maoteng; Yang, Xiangliang; Zhan, Yi

2016-08-10

Physiological processes such as metabolism, cell apoptosis and immune responses, must be strictly regulated to maintain their homeostasis and achieve their normal physiological functions. The speed with which bio-molecular homeostatic regulation occurs directly determines the ability of an organism to adapt to conditional changes. To produce a quick-responsive regulatory system that can be easily utilized for various types of homeostasis, a device called nano-fingers that facilitates the regulation of physiological processes was constructed using DNA origami nanotechnology. This nano-fingers device functioned in linked open and closed phases using two types of DNA tweezers, which were covalently coupled with aptamers that captured specific molecules when the tweezer arms were sufficiently close. Via this specific interaction mechanism, certain physiological processes could be simultaneously regulated from two directions by capturing one biofactor and releasing the other to enhance the regulatory capacity of the device. To validate the universal application of this device, regulation of the homeostasis of the blood coagulant thrombin was attempted using the nano-fingers device. It was successfully demonstrated that this nano-fingers device achieved coagulation buffering upon the input of fuel DNA. This nano-device could also be utilized to regulate the homeostasis of other types of bio-molecules.

Model reduction of multiscale chemical langevin equations: a numerical case study.

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Sotiropoulos, Vassilios; Contou-Carrere, Marie-Nathalie; Daoutidis, Prodromos; Kaznessis, Yiannis N

2009-01-01

Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models. Second, models must take into consideration the disparate spectrum of time scales observed in biological phenomena, such as slow transcription events and fast dimerization reactions. In the last decade, significant efforts have been expended on the development of stochastic chemical kinetics models to capture the dynamics of biomolecular systems, and on the development of robust multiscale algorithms, able to handle stiffness. In this paper, the focus is on the dynamics of reaction sets governed by stiff chemical Langevin equations, i.e., stiff stochastic differential equations. These are particularly challenging systems to model, requiring prohibitively small integration step sizes. We describe and illustrate the application of a semianalytical reduction framework for chemical Langevin equations that results in significant gains in computational cost.

Biomolecular detection using a metal semiconductor field effect transistor

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Estephan, Elias; Saab, Marie-Belle; Buzatu, Petre; Aulombard, Roger; Cuisinier, Frédéric J. G.; Gergely, Csilla; Cloitre, Thierry

2010-04-01

In this work, our attention was drawn towards developing affinity-based electrical biosensors, using a MESFET (Metal Semiconductor Field Effect Transistor). Semiconductor (SC) surfaces must be prepared before the incubations with biomolecules. The peptides route was adapted to exceed and bypass the limits revealed by other types of surface modification due to the unwanted unspecific interactions. As these peptides reveal specific recognition of materials, then controlled functionalization can be achieved. Peptides were produced by phage display technology using a library of M13 bacteriophage. After several rounds of bio-panning, the phages presenting affinities for GaAs SC were isolated; the DNA of these specific phages were sequenced, and the peptide with the highest affinity was synthesized and biotinylated. To explore the possibility of electrical detection, the MESFET fabricated with the GaAs SC were used to detect the streptavidin via the biotinylated peptide in the presence of the bovine Serum Albumin. After each surface modification step, the IDS (current between the drain and the source) of the transistor was measured and a decrease in the intensity was detected. Furthermore, fluorescent microscopy was used in order to prove the specificity of this peptide and the specific localisation of biomolecules. In conclusion, the feasibility of producing an electrical biosensor using a MESFET has been demonstrated. Controlled placement, specific localization and detection of biomolecules on a MESFET transistor were achieved without covering the drain and the source. This method of functionalization and detection can be of great utility for biosensing application opening a new way for developing bioFETs (Biomolecular Field-Effect Transistor).

Optical network control plane for multi-domain networking

DEFF Research Database (Denmark)

Manolova, Anna Vasileva

This thesis focuses on multi-domain routing for traffice engineering and survivability support in optical transport networks under the Generalized Multi-Protocol Label Switching (GMPLS) control framework. First, different extensions to the Border Gateway Protocol for multi-domain Traffic...... process are not enough for efficient TE in mesh multi-domain networks. Enhancing the protocol with multi-path dissemination capability, combined with the employment of an end-to-end TE metric proves to be a highly efficient solution. Simulation results show good performance characteristics of the proposed...... is not as essential for improved network performance as the length of the provided paths. Second, the issue of multi-domain survivability support is analyzed. An AS-disjoint paths is beneficial not only for resilience support, but also for facilitating adequate network reactions to changes in the network, which...

Bistability in self-activating genes regulated by non-coding RNAs

International Nuclear Information System (INIS)

Miro-Bueno, Jesus

2015-01-01

Non-coding RNA molecules are able to regulate gene expression and play an essential role in cells. On the other hand, bistability is an important behaviour of genetic networks. Here, we propose and study an ODE model in order to show how non-coding RNA can produce bistability in a simple way. The model comprises a single gene with positive feedback that is repressed by non-coding RNA molecules. We show how the values of all the reaction rates involved in the model are able to control the transitions between the high and low states. This new model can be interesting to clarify the role of non-coding RNA molecules in genetic networks. As well, these results can be interesting in synthetic biology for developing new genetic memories and biomolecular devices based on non-coding RNAs

Biomolecular transport and separation in nanotubular networks.

Energy Technology Data Exchange (ETDEWEB)

Stachowiak, Jeanne C.; Stevens, Mark Jackson (Sandia National Laboratories, Albuquerque, NM); Robinson, David B.; Branda, Steven S.; Zendejas, Frank; Meagher, Robert J.; Sasaki, Darryl Yoshio; Bachand, George David (Sandia National Laboratories, Albuquerque, NM); Hayden, Carl C.; Sinha, Anupama; Abate, Elisa; Wang, Julia; Carroll-Portillo, Amanda (Sandia National Laboratories, Albuquerque, NM); Liu, Haiqing (Sandia National Laboratories, Albuquerque, NM)

2010-09-01

Cell membranes are dynamic substrates that achieve a diverse array of functions through multi-scale reconfigurations. We explore the morphological changes that occur upon protein interaction to model membrane systems that induce deformation of their planar structure to yield nanotube assemblies. In the two examples shown in this report we will describe the use of membrane adhesion and particle trajectory to form lipid nanotubes via mechanical stretching, and protein adsorption onto domains and the induction of membrane curvature through steric pressure. Through this work the relationship between membrane bending rigidity, protein affinity, and line tension of phase separated structures were examined and their relationship in biological membranes explored.

MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES

Science.gov (United States)

CORREIA, RION BRATTIG; LI, LANG; ROCHA, LUIS M.

2015-01-01

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this “Bibliome”, the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products—including cannabis—which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015. We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that

Poisson-Nernst-Planck Equations for Simulating Biomolecular Diffusion-Reaction Processes II: Size Effects on Ionic Distributions and Diffusion-Reaction Rates

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Lu, Benzhuo; Zhou, Y.C.

2011-01-01

The effects of finite particle size on electrostatics, density profiles, and diffusion have been a long existing topic in the study of ionic solution. The previous size-modified Poisson-Boltzmann and Poisson-Nernst-Planck models are revisited in this article. In contrast to many previous works that can only treat particle species with a single uniform size or two sizes, we generalize the Borukhov model to obtain a size-modified Poisson-Nernst-Planck (SMPNP) model that is able to treat nonuniform particle sizes. The numerical tractability of the model is demonstrated as well. The main contributions of this study are as follows. 1), We show that an (arbitrarily) size-modified PB model is indeed implied by the SMPNP equations under certain boundary/interface conditions, and can be reproduced through numerical solutions of the SMPNP. 2), The size effects in the SMPNP effectively reduce the densities of highly concentrated counterions around the biomolecule. 3), The SMPNP is applied to the diffusion-reaction process for the first time, to our knowledge. In the case of low substrate density near the enzyme reactive site, it is observed that the rate coefficients predicted by SMPNP model are considerably larger than those by the PNP model, suggesting both ions and substrates are subject to finite size effects. 4), An accurate finite element method and a convergent Gummel iteration are developed for the numerical solution of the completely coupled nonlinear system of SMPNP equations. PMID:21575582

Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks

Directory of Open Access Journals (Sweden)

Hamelryck Thomas

2010-03-01

Full Text Available Abstract Background Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs. It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations. Results The program package is freely available under the GNU General Public Licence (GPL from SourceForge http://sourceforge.net/projects/mocapy. The package contains the source for building the Mocapy++ library, several usage examples and the user manual. Conclusions Mocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.

Evaluation of Internet Social Networks using Net scoring Tool: A Case Study in Adverse Drug Reaction Mining.

Science.gov (United States)

Katsahian, Sandrine; Simond Moreau, Erica; Leprovost, Damien; Lardon, Jeremy; Bousquet, Cedric; Kerdelhué, Gaétan; Abdellaoui, Redhouane; Texier, Nathalie; Burgun, Anita; Boussadi, Abdelali; Faviez, Carole

2015-01-01

Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary tool to already existing ADRs signal detection processes. However, several studies have shown that the quality of medical information published online varies drastically whatever the health topic addressed. The aim of this study is to use an existing rating tool on a set of social network web sites in order to assess the capabilities of these tools to guide experts for selecting the most adapted social network web site to mine ADRs. First, we reviewed and rated 132 Internet forums and social networks according to three major criteria: the number of visits, the notoriety of the forum and the number of messages posted in relation with health and drug therapy. Second, the pharmacist reviewed the topic-oriented message boards with a small number of drug names to ensure that they were not off topic. Six experts have been chosen to assess the selected internet forums using a French scoring tool: Net scoring. Three different scores and the agreement between experts according to each set of scores using weighted kappa pooled using mean have been computed. Three internet forums were chosen at the end of the selection step. Some criteria get high score (scores 3-4) no matter the website evaluated like accessibility (45-46) or design (34-36), at the opposite some criteria always have bad scores like quantitative (40-42) and ethical aspect (43-44), hyperlinks actualization (30-33). Kappa were positives but very small which corresponds to a weak agreement between experts. The personal opinion of the expert seems to have a major impact, undermining the relevance of the criterion. Our future work is to collect results given by this evaluation grid and proposes a new scoring tool for Internet social networks assessment.

A brief review of advances in complex networks of nuclear science and technology field

International Nuclear Information System (INIS)

Fang Jinqing

2010-01-01

A brief review of advances in complex networks of nuclear science and technology field at home and is given and summarized. These complex networks include: nuclear energy weapon network, network centric warfare, beam transport networks, continuum percolation evolving network associated with nuclear reactions, global nuclear power station network, (nuclear) chemistry reaction networks, radiological monitoring and anti-nuclear terror networks, and so on. Some challenge issues and development prospects of network science are pointed out finally. (authors)

An autonomous organic reaction search engine for chemical reactivity

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Dragone, Vincenza; Sans, Victor; Henson, Alon B.; Granda, Jaroslaw M.; Cronin, Leroy

2017-06-01

The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible reaction combinations and aims for new reactivity, rather than a predefined set of targets. The robotic system combines chemical handling, in-line spectroscopy and real-time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways, generating a reaction selection index (RSI) without need for separate work-up or purification steps. This allows the automatic navigation of a chemical network, leading to previously unreported molecules while needing only to do a fraction of the total possible reactions without any prior knowledge of the chemistry. We show the RSI correlates with reactivity and is able to search chemical space using the most reactive pathways.

Spin valve sensor for biomolecular identification: Design, fabrication, and characterization

Science.gov (United States)

Li, Guanxiong

Biomolecular identification, e.g., DNA recognition, has broad applications in biology and medicine such as gene expression analysis, disease diagnosis, and DNA fingerprinting. Therefore, we have been developing a magnetic biodetection technology based on giant magnetoresistive spin valve sensors and magnetic nanoparticle (developed for the magnetic nanoparticle detection, assuming the equivalent average field of magnetic nanoparticles and the coherent rotation of spin valve free layer magnetization. Micromagnetic simulations have also been performed for the spin valve sensors. The analytical model and micromagnetic simulations are found consistent with each other and are in good agreement with experiments. The prototype spin valve sensors have been fabricated at both micron and submicron scales. We demonstrated the detection of a single 2.8-mum magnetic microbead by micron-sized spin valve sensors. Based on polymer-mediated self-assembly and fine lithography, a bilayer lift-off process was developed to deposit magnetic nanoparticles onto the sensor surface in a controlled manner. With the lift-off deposition method, we have successfully demonstrated the room temperature detection of monodisperse 16-nm Fe3O 4 nanoparticles in a quantity from a few tens to several hundreds by submicron spin valve sensors, proving the feasibility of the nanoparticle detection. As desired for quantitative biodetection, a fairly linear dependence of sensor signal on the number of nanoparticles has been confirmed. The initial detection of DNA hybridization events labeled by magnetic nanoparticles further proved the magnetic biodetection concept.

Asian collaboration on nuclear reaction data compilation

International Nuclear Information System (INIS)

Aikawa, Masayuki; Furutachi, Naoya; Kato, Kiyoshi; Makinaga, Ayano; Devi, Vidya; Ichinkhorloo, Dagvadorj; Odsuren, Myagmarjav; Tsubakihara, Kohsuke; Katayama, Toshiyuki; Otuka, Naohiko

2013-01-01

Nuclear reaction data are essential for research and development in nuclear engineering, radiation therapy, nuclear physics and astrophysics. Experimental data must be compiled in a database and be accessible to nuclear data users. One of the nuclear reaction databases is the EXFOR database maintained by the International Network of Nuclear Reaction Data Centres (NRDC) under the auspices of the International Atomic Energy Agency. Recently, collaboration among the Asian NRDC members is being further developed under the support of the Asia-Africa Science Platform Program of the Japan Society for the Promotion of Science. We report the activity for three years to develop the Asian collaboration on nuclear reaction data compilation. (author)

SABIO-RK: A data warehouse for biochemical reactions and their kinetics

Directory of Open Access Journals (Sweden)

Krebs Olga

2007-03-01

Full Text Available Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics, a curated database with information about biochemical reactions and their kinetic properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic use at http://sabio.villa-bosch.de/SABIORK/.

Creation and perturbation of planar networks of chemical oscillators

Science.gov (United States)

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

2015-01-01

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

On Macroscopic Quantum Phenomena in Biomolecules and Cells: From Levinthal to Hopfield

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Dejan Raković

2014-01-01

Full Text Available In the context of the macroscopic quantum phenomena of the second kind, we hereby seek for a solution-in-principle of the long standing problem of the polymer folding, which was considered by Levinthal as (semiclassically intractable. To illuminate it, we applied quantum-chemical and quantum decoherence approaches to conformational transitions. Our analyses imply the existence of novel macroscopic quantum biomolecular phenomena, with biomolecular chain folding in an open environment considered as a subtle interplay between energy and conformation eigenstates of this biomolecule, governed by quantum-chemical and quantum decoherence laws. On the other hand, within an open biological cell, a system of all identical (noninteracting and dynamically noncoupled biomolecular proteins might be considered as corresponding spatial quantum ensemble of these identical biomolecular processors, providing spatially distributed quantum solution to a single corresponding biomolecular chain folding, whose density of conformational states might be represented as Hopfield-like quantum-holographic associative neural network too (providing an equivalent global quantum-informational alternative to standard molecular-biology local biochemical approach in biomolecules and cells and higher hierarchical levels of organism, as well.

Hierarchical analysis of dependency in metabolic networks.

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Gagneur, Julien; Jackson, David B; Casari, Georg

2003-05-22

Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html

Correlated Heterospectral Lipidomics for Biomolecular Profiling of Remyelination in Multiple Sclerosis

Directory of Open Access Journals (Sweden)

Mads S. Bergholt

2017-12-01

Full Text Available Analyzing lipid composition and distribution within the brain is important to study white matter pathologies that present focal demyelination lesions, such as multiple sclerosis. Some lesions can endogenously re-form myelin sheaths. Therapies aim to enhance this repair process in order to reduce neurodegeneration and disability progression in patients. In this context, a lipidomic analysis providing both precise molecular classification and well-defined localization is crucial to detect changes in myelin lipid content. Here we develop a correlated heterospectral lipidomic (HSL approach based on coregistered Raman spectroscopy, desorption electrospray ionization mass spectrometry (DESI-MS, and immunofluorescence imaging. We employ HSL to study the structural and compositional lipid profile of demyelination and remyelination in an induced focal demyelination mouse model and in multiple sclerosis lesions from patients ex vivo. Pixelwise coregistration of Raman spectroscopy and DESI-MS imaging generated a heterospectral map used to interrelate biomolecular structure and composition of myelin. Multivariate regression analysis enabled Raman-based assessment of highly specific lipid subtypes in complex tissue for the first time. This method revealed the temporal dynamics of remyelination and provided the first indication that newly formed myelin has a different lipid composition compared to normal myelin. HSL enables detailed molecular myelin characterization that can substantially improve upon the current understanding of remyelination in multiple sclerosis and provides a strategy to assess remyelination treatments in animal models.

Charge patterns as templates for the assembly of layered biomolecular structures.

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Naujoks, Nicola; Stemmer, Andreas

2006-08-01

Electric fields are used to guide the assembly of biomolecules in predefined geometric patterns on solid substrates. Local surface charges serve as templates to selectively position proteins on thin-film polymeric electret layers, thereby creating a basis for site-directed layered assembly of biomolecular structures. Charge patterns are created using the lithographic capabilities of an atomic force microscope, namely by applying voltage pulses between a conductive tip and the sample. Samples consist of a poly(methyl methacrylate) layer on a p-doped silicon support. Subsequently, the sample is developed in a water-in-oil emulsion, consisting of a dispersed aqueous phase containing biotin-modified immunoglobulinG molecules, and a continuous nonpolar, insulating oil phase. The electrostatic fields cause a net force of (di)electrophoretic nature on the droplet, thereby guiding the proteins to the predefined locations. Due to the functionalization of the immunoglobulinG molecules with biotin-groups, these patterns can now be used to initiate the localized layer-by-layer assembly of biomolecules based on the avidin-biotin mechanism. By binding 40 nm sized biotin-labelled beads to the predefined locations via a streptavidin linker, we verify the functionality of the previously deposited immunoglobulinG-biotin. All assembly steps following the initial deposition of the immunoglobulinG from emulsion can conveniently be conducted in aqueous solutions. Results show that pattern definition is maintained after immersion into aqueous solution.

Metabolite coupling in genome-scale metabolic networks

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Palsson Bernhard Ø

2006-03-01

Full Text Available Abstract Background Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŜŜT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where Ŝ is the binary form of S. Results Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual

Studies of the charge instabilities in the complex nano-objects: clusters and bio-molecular systems

International Nuclear Information System (INIS)

Manil, B.

2007-11-01

For the last 6 years, my main research works focused on i) the Coulomb instabilities and the fragmentation processes of fullerenes and clusters of fullerenes ii) the stability and the reactivity of complex bio-molecular systems. Concerning the clusters of fullerenes, which are van der Waals type clusters, we have shown that the multiply charged species, obtained in collisions with slow highly charged ions, keep their structural properties but become very good electric conductor. In another hand, with the aim to understand the role of the biologic environment at the molecular scale in the irradiation damage of complex biomolecules, we have studied the charge stabilities of clusters of small biomolecules and the dissociation processes of larger nano-hydrated biomolecules. Theses studies have shown that first, specific molecular recognition mechanisms continue to exist in gas phase and secondly, a small and very simple biochemical environment is enough to change the dynamics of instabilities. (author)

Modeling of uncertainties in biochemical reactions.

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Mišković, Ljubiša; Hatzimanikatis, Vassily

2011-02-01

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus, we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three-step enzymatic reaction operating far away from the equilibrium in order to respond to

Advances in quantum and molecular mechanical (QM/MM) simulations for organic and enzymatic reactions.

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Acevedo, Orlando; Jorgensen, William L

2010-01-19

Application of combined quantum and molecular mechanical (QM/MM) methods focuses on predicting activation barriers and the structures of stationary points for organic and enzymatic reactions. Characterization of the factors that stabilize transition structures in solution and in enzyme active sites provides a basis for design and optimization of catalysts. Continued technological advances allowed for expansion from prototypical cases to mechanistic studies featuring detailed enzyme and condensed-phase environments with full integration of the QM calculations and configurational sampling. This required improved algorithms featuring fast QM methods, advances in computing changes in free energies including free-energy perturbation (FEP) calculations, and enhanced configurational sampling. In particular, the present Account highlights development of the PDDG/PM3 semi-empirical QM method, computation of multi-dimensional potentials of mean force (PMF), incorporation of on-the-fly QM in Monte Carlo (MC) simulations, and a polynomial quadrature method for efficient modeling of proton-transfer reactions. The utility of this QM/MM/MC/FEP methodology is illustrated for a variety of organic reactions including substitution, decarboxylation, elimination, and pericyclic reactions. A comparison to experimental kinetic results on medium effects has verified the accuracy of the QM/MM approach in the full range of solvents from hydrocarbons to water to ionic liquids. Corresponding results from ab initio and density functional theory (DFT) methods with continuum-based treatments of solvation reveal deficiencies, particularly for protic solvents. Also summarized in this Account are three specific QM/MM applications to biomolecular systems: (1) a recent study that clarified the mechanism for the reaction of 2-pyrone derivatives catalyzed by macrophomate synthase as a tandem Michael-aldol sequence rather than a Diels-Alder reaction, (2) elucidation of the mechanism of action of fatty

Proceedings of the 2nd international advisory committee on biomolecular dynamics instrument DNA in MLF at J-PARC

International Nuclear Information System (INIS)

Arai, Masatoshi; Aizawa, Kazuya; Nakajima, Kenji; Shibata, Kaoru; Takahashi, Nobuaki

2009-07-01

The 2nd International Advisory Committee on the 'Biomolecular Dynamics Backscattering Spectrometer DNA' was held on November 12th - 13th, 2008 at J-PARC Center, Japan Atomic Energy Agency. This IAC has been organized for aiming to realize an innovative neutron backscattering instrument in the Materials and Life Science Experimental Facility (MLF) at the J-PARC and therefore four leading scientists in the field of neutron backscattering instruments has been selected as the member (Dr. Dan Neumann (Chair); Prof. Ferenc Mezei; Dr. Hannu Mutka; Dr. Philip Tregenna-Piggott), and the 1st IAC had been held on February 27th - 29th, 2008. This report includes the executive summary and materials of the presentations in the 2nd IAC. (author)

Porous Cross-Linked Polyimide-Urea Networks

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Meador, Mary Ann B. (Inventor); Nguyen, Baochau N. (Inventor)

2015-01-01

Porous cross-linked polyimide-urea networks are provided. The networks comprise a subunit comprising two anhydride end-capped polyamic acid oligomers in direct connection via a urea linkage. The oligomers (a) each comprise a repeating unit of a dianhydride and a diamine and a terminal anhydride group and (b) are formulated with 2 to 15 of the repeating units. The subunit was formed by reaction of the diamine and a diisocyanate to form a diamine-urea linkage-diamine group, followed by reaction of the diamine-urea linkage-diamine group with the dianhydride and the diamine to form the subunit. The subunit has been cross-linked via a cross-linking agent, comprising three or more amine groups, at a balanced stoichiometry of the amine groups to the terminal anhydride groups. The subunit has been chemically imidized to yield the porous cross-linked polyimide-urea network. Also provided are wet gels, aerogels, and thin films comprising the networks, and methods of making the networks.

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews.

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Tutubalina, Elena; Nikolenko, Sergey

2017-01-01

Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

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Elena Tutubalina

2017-01-01

Full Text Available Adverse drug reactions (ADRs are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.

N-Doped Carbon Nanofibrous Network Derived from Bacterial Cellulose for the Loading of Pt Nanoparticles for Methanol Oxidation Reaction.

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Yuan, Fanshu; Huang, Yang; Fan, Mengmeng; Chen, Chuntao; Qian, Jieshu; Hao, Qingli; Yang, Jiazhi; Sun, Dongping

2018-02-06

The large-scale, low-cost preparation of Pt-based catalysts with high activity and durability for the methanol oxidation reaction is still challenging. The key to achieving this aim is finding suitable supporting materials. In this paper, N-doped carbon nanofibrous networks are prepared by annealing a gel containing two inexpensive and ecofriendly precursors, that is, bacterial cellulose and urea, for the loading of Pt nanoparticles. An undoped analogue is also prepared for comparison. Meanwhile, the effect of the annealing temperature on the performance of the catalysts is evaluated. The results show that the N doping and higher annealing temperature can improve the electron conductivity of the catalyst and provide more active sites for the loading of ultrafine Pt nanoparticles with a narrow size distribution. The best catalyst exhibits a remarkably high electrocatalytic activity (627 mA mg -1 ), excellent poison tolerance, and high durability. This work demonstrates an ideal Pt supporting material for the methanol oxidation reaction. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Starting Point for Fluorescence-Based Single-Molecule Measurements in Biomolecular Research

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Alexander Gust

2014-09-01

Full Text Available Single-molecule fluorescence techniques are ideally suited to provide information about the structure-function-dynamics relationship of a biomolecule as static and dynamic heterogeneity can be easily detected. However, what type of single-molecule fluorescence technique is suited for which kind of biological question and what are the obstacles on the way to a successful single-molecule microscopy experiment? In this review, we provide practical insights into fluorescence-based single-molecule experiments aiming for scientists who wish to take their experiments to the single-molecule level. We especially focus on fluorescence resonance energy transfer (FRET experiments as these are a widely employed tool for the investigation of biomolecular mechanisms. We will guide the reader through the most critical steps that determine the success and quality of diffusion-based confocal and immobilization-based total internal reflection fluorescence microscopy. We discuss the specific chemical and photophysical requirements that make fluorescent dyes suitable for single-molecule fluorescence experiments. Most importantly, we review recently emerged photoprotection systems as well as passivation and immobilization strategies that enable the observation of fluorescently labeled molecules under biocompatible conditions. Moreover, we discuss how the optical single-molecule toolkit has been extended in recent years to capture the physiological complexity of a cell making it even more relevant for biological research.