Bounds on topological Abelian string-vortex and string-cigar from information-entropic measure

In this work we obtain bounds on the topological Abelian string-vortex and on the string-cigar, by using a new measure of configurational complexity, known as configurational entropy. In this way, the information-theoretical measure of six-dimensional braneworlds scenarios are capable to probe situations where the parameters responsible for the brane thickness are arbitrary. The so-called configurational entropy (CE) selects the best value of the parameter in the model. This is accomplished by minimizing the CE, namely, by selecting the most appropriate parameters in the model that correspond to the most organized system, based upon the Shannon information theory. This information-theoretical measure of complexity provides a complementary perspective to situations where strictly energy-based arguments are inconclusive. We show that the higher the energy the higher the CE, what shows an important correlation between the energy of the a localized field configuration and its associated entropic measure.Comment: 6 pages, 7 figures, final version to appear in Phys. Lett.

On the dimensional dependence of duality groups for massive p-forms

We study the soldering formalism in the context of abelian p-form theories. We develop further the fusion process of massless antisymmetric tensors of different ranks into a massive p-form and establish its duality properties. To illustrate the formalism we consider two situations. First the soldering mass generation mechanism is compared with the Higgs and Julia-Toulouse mechanisms for mass generation due to condensation of electric and magnetic topological defects. We show that the soldering mechanism interpolates between them for even dimensional spacetimes, in this way confirming the Higgs/Julia-Toulouse duality proposed by Quevedo and Trugenberger \cite{QT} a few years ago. Next, soldering is applied to the study of duality group classification of the massive forms. We show a dichotomy controlled by the parity of the operator defining the symplectic structure of the theory and find their explicit actions.Comment: Reference [8] has been properly place

Secure and trustworthy remote JavaScript execution

Javascript is used more and more as a programming language to develop web applications in order to increase the user experience and application interactivity. Although Javascript is a powerful technology that offers these characteristics, it is also a potential web application attack vector that can be exploited to impact the end-user, since it can be maliciously intercepted and modified. Today, web browsers act as worldwide open windows, executing, on a given user machine (computer, smartphone, tablet or any other), remote code. Therefore, it is important to ensure the trust on the execution of this remote code. This trust should be ensured at the JavaScript remote code producer, during transport and also locally before being executed on the end-user web-browser. In this paper, the authors propose and present a mechanism that allows the secure production and verification of web-applications JavaScript code. The paper also presents a set of tools that were developed to offer JavaScript code protection and ensure its trust at the production stage, but also a proxy-based mechanism that ensures end-users the un-modified nature and source validation of the remote JavaScript code prior to its execution by the end-user browser.info:eu-repo/semantics/acceptedVersio

Ab initio study of electron transport in dry poly(G)-poly(C) A-DNA strands

The bias-dependent transport properties of short poly(G)-poly(C) A-DNA strands attached to Au electrodes are investigated with first principles electronic transport methods. By using the non- equilibrium Green's function approach combined with self-interaction corrected density functional theory, we calculate the fully self-consistent coherent I-V curve of various double-strand polymeric DNA fragments. We show that electronic wave-function localization, induced either by the native electrical dipole and/or by the electrostatic disorder originating from the first few water solvation layers, drastically suppresses the magnitude of the elastic conductance of A-DNA oligonucleotides. We then argue that electron transport through DNA is the result of sequence-specific short-range tunneling across a few bases combined with general diffusive/inelastic processes.Comment: 15 pages, 13 figures, 1 tabl

Modeling formalisms in systems biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.Research supported by grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the development of improved microbial cell factories" (MIT-Pt/BS-BB/0082/2008)

Model transformation of metabolic networks using a Petri net based framework

The different modeling approaches in Systems Biology create models with different levels of detail. The transformation techniques in Petri net theory can provide a solid framework for zooming between these different levels of abstraction and refinement. This work presents a Petri net based approach to Metabolic Engineering that implements model reduction methods to reduce the complexity of large-scale metabolic networks. These methods can be complemented with kinetics inference to build dynamic models with a smaller number of parameters. The central carbon metabolism model of E. coli is used as a test-case to illustrate the application of these concepts. Model transformation is a promising mechanism to facilitate pathway analysis and dynamic modeling at the genome-scale level.(undefined

Bromeliaceae species from coastal restinga habitats, Brazilian states of Rio de Janeiro, Espírito Santo, and Bahia.

Bromeliaceae is one of the most representative plant families in restinga habitats. We analyzed the speciesrichness and composition of Bromeliaceae in 13 restinga habitats along the Brazilian coast. We found a total of 41species distributed along the restinga habitats studied. The restinga of Praia do Sul, in the state of Rio de Janeiro, hadthe highest number of species (15), whereas the restinga of Abaeté, in the state of Bahia, had the lowest (4). Our dataare suggestive that the Doce River may represent the limit of distribution for some bromeliad species, with some speciesoccurring only south of that river and others occurring only to the north of it. The differences in Bromeliaceae speciescomposition among restinga habitats probably are not only due to differences in local environmental conditions, butalso due to the geographic distribution pattern of each species and to the present degree of disturbance at each restinga

Modelling allosteric regulation for prediction of flux control in the central carbon metabolism of E. coli

Rational strain design is a fundamental step in the development of microbial cell factories. Multiple genetic manipulations are often required in order to redirect the metabolic flux towards a product of industrial interest. Most manipulation targets are focused on central carbon metabolism, which provides the molecular precursors and the energy required for other biochemical pathways. However, the complex regulation of those pathways is still not completely unraveled. Recent studies have shown that central carbon metabolism is mostly regulated at post-transcriptional levels. In this work, we explore the role of allosteric regulation in the control of metabolic fluxes. We begin by expanding a metabolic network reconstruction of the central carbon metabolism of E. coli with allosteric interaction information from relevant databases. This model is used to integrate a multi-omic dataset for this organism. We analyze the coordinated changes in enzyme, metabolite and flux levels between multiple experimental conditions, and observe cases where allosteric regulators have a major contribution in the metabolic flux changes. We then develop a method for systematic prediction of potential cases of allosteric control for given metabolic perturbations. This is a valuable approach for predicting coordinated flux changes that would not be predicted with a purely stoichiometric model representation.BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF. NORTE-07-0124-FEDER-00002

Novel modeling formalisms and simulation tools in computational biosystems

Living organisms are complex systems that emerge from the fundamental building blocks of life. Systems Biology is a recent field of science that studies these complex phenomena at the cellular level (Kitano 2002). Understanding the mechanisms of the cell is essential for research and development in several areas such as drug discovery and biotechnological production. In the latter, metabolic engineering is used for building mutant microbial strains with increased productivity of compounds with industrial interest, such as biofuels (Stephanopoulos 1998). Using computational models of cellular metabolism, it is possible to systematically test and predict the optimal manipulations, such as gene knockouts, that produce the ideal phenotype for a specific application. These models are typically built in an iterative cycle of experiment and refinement, by multidisciplinary research teams that include biologists, engineers and computer scientists. The interconnection between different cellular processes, such as metabolism and genetic regulation, reflects the importance of the holistic approach claimed by the Systems Biology paradigm in replacement of traditional reductionist methods. Although most cellular components have been studied individually, the behavior of the cell emerges from the network-level interaction and requires an integrative analysis. Recent high–throughput methods have generated the so- called omics data (e.g.: genomics, transcriptomics, proteomics, metabolomics, fluxomics) that have allowed the reconstruction of biological networks (Palsson 2006). However, despite the great advances in the area, we are still far from a whole-cell computational model that is able to simulate all the components of a living cell. Due to the enormous size and complexity of intracellular biological networks, computational cell models tend to be partial and focused on the application of interest. Also, due to the multidisciplinarity of the field, these models are based on several different kinds of formalisms. Therefore, it is important to develop a framework with common modeling formalisms, analysis and simulation methods, that is able to accommodate different kinds biological networks, with different types of entities and their interactions, into genome-scale integrated models. Cells are composed by thousands of components that interact in myriad ways. Despite this intricate interconnection it is usual to divide and classify these networks according to biological function. The main types of networks are signaling, gene regulatory and metabolic. Signal transduction is a process for cellular communication where the cell receives and responds to external stimuli through signaling cascades (Gomperts et al. 2009; Albert and Wang 2009). These cascades affect gene regulation, which is the method for controlling gene expression, and consequently several cellular functions (Schlittand and Brazma 2007; Karlebach and Sgamir 2008). Many genes encode enzymes which are responsible for catalyzing biochemical reactions. The complex network of these reactions forms the cellular metabolism that sustains the cell’s growth and energy requirements (Steuer and Junker 2009; Palsson 2006). The objectives of this work, in the context of a PhD thesis, consist in re-search and selection of an appropriate modeling formalism to develop a framework for integration of different biological networks, with focus on regulatory and metabolic networks, and the implementation of suitable analysis, simulation and optimization methods. To achieve these goals, it is necessary to resolve many modeling issues, such as the integration of discrete and continuous events, representation of network topology, support for different levels of abstraction, lack of parameters and model complexity. This framework will be used for the implementation of an integrated model of E. coli, a widely used organism for industrial application

Modeling the contribution of allosteric regulation for flux control in the central carbon metabolism of E. coli

Redesign of microbial metabolism is a critical step in biotechnology for the production of industrially relevant compounds. Central carbon metabolism provides the energy and building blocks required for cellular growth and synthesis of the desired byproducts and, consequently, it is the main target for intervention in most rational strain design approaches. However, the complexity of central carbon metabolism is still not completely understood. Recent studies in different organisms show that flux control in central carbon metabolism is predominantly regulated by non-transcriptional mechanisms, leaving post-translational modifications, allosteric regulation, and thermodynamics as main candidates. In this work, we extend a model of central carbon metabolism of E.coli with allosteric interactions in order to reveal a hidden topology in metabolic networks. We use this model to integrate a multi-omic dataset containing transcript, protein, flux and metabolite levels to further dissect and analyze the contribution of allosteric regulation for metabolic flux control