The new automated daily mortality surveillance system

The experience reported in an earlier Eurosurveillance issue on a fast method to evaluate the impact of the 2003 heatwave on mortality in Portugal, generated a daily mortality surveillance system (VDM) that has been operating ever since jointly with the Portuguese Heat Health Watch Warning System. This work describes the VDM system and how it evolved to become an automated system operating year-round, and shows briefly its potential using mortality data from January 2006 to June 2009 collected by the system itself. The new system has important advantages such as: rapid information acquisition, completeness (the entire population is included), lightness (very little information is exchanged, date of death, age, sex, place of death registration). It allows rapid detection of impacts (within five days) and allows a quick preliminary quantification of impacts that usually took several years to be done. These characteristics make this system a powerful tool for public health action. The VDM system also represents an example of inter-institutional cooperation, bringing together organisations from two different ministries, Health and Justice, aiming at improving knowledge about the mortality in the population

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

Damages caused by pressure sensitive tapes on paper artworks from the early 20th century

The use of pressure sensitive tapes (PST) on paper documents and artworks represents one of the most complex issues for the conservation and restoration fields. This paper presents and discusses some examples of damages caused by the PST presence on a 20th century drawing collection from Fábrica Constância, nowadays belonging to the National Museum of Azulejo (MNAz). This work constitutes a first step towards a systematic identification and classification of damage caused by PST use on Fábrica Constância drawing’s collection.info:eu-repo/semantics/publishedVersio

Improved Production of Pharmacologically-active Sclerotiorin by Penicillium sclerotiorum

Purpose: The aim of this work was to study the optimum conditions for the production of sclerotiorin, a highly active secondary metabolite of Penicillium sclerotiorum under different cultural media. Methods: A Brazilian strain of P. sclerotiorum was grown under different culture conditions in two liquid media (malt and a dextrose-peptone salt medium supplemented with sodium chloride) and in solid state fermentation in rice. Sclerotiorin production was monitored by high performance liquid chromatography (HPLC). Results: Quantitative analysis of sclerotiorin content by HPLC indicated that sclerotiorin production reached the highest level (up to 313 + 10 mg.L-1) in the dextrose-based medium after 10 days of fermentation. Rice and malt broth showed lower production levels.Conclusion: Enhanced production of P. sclerotiorin for pharmaceutical development can be achieved using dextrose-based cultures.Keywords  : Penicillium sclerotiorum, Sclerotiorin,Yield improvement, HPLC, Pharmaceutical industr

Large scale dynamic model reconstruction for the central carbon metabolism of escherichia coli

The major objective of metabolic engineering is the construction of industrially relevant microbial strains with desired properties. From an engineering perspective, dynamic mathematical modeling to quantitatively assess intracellular metabolism and predict the complex behavior of living cells is one of the most successful tools to achieve that goal. In this work, we present an expansion of the original E. coli dynamic model [1], which links the acetate metabolism and tricarboxylic acid cycle (TCA) with the phosphotransferase systems, the pentose-phosphate pathway and the glycolysis system based on mechanistic enzymatic rate equations. The kinetic information is collected from available database and literature, and is used as an initial guess for the global fitting. The results of the numeric simulations were in good agreement with the experimental results. Thus, the results are sufficiently good to prompt us to seek further experimental data for comparison with the simulations

Dynamic modeling of E. coli central carbon metabolism combining different kinetic rate laws

Detailed dynamic kinetic models at the network reaction level are traditionally constructed using mechanistic enzymatic rate equations and a large number of kinetic parameters have to be determined under nonphysiological conditions in vitro. However, the validity of these parameters under in vivo conditions is doubtful and the rates equations are usually highly complex. Therefore, one of the major obstacles in building accurate kinetic models is the lack of detailed knowledge of the rate laws that describe the reaction mechanism and the absence of their associated parameters. There is an urgent need for alternative modelling approaches to fill this gap. In this study, we analyze four alternative hybrid modeling strategies to the reference large scale mechanistic E. coli central carbon metabolic network model based on the Michaelis-Menten equation only for the bimolecular reactions and the other reactions with different formats of approximative rate kinetics (Generalized Mass-Action, convenience equation, lin-log and power-law). These rate equations help to reduce the number of parameters that have to be estimated. The kinetic parameters optimization was performed through the combination of a global search evolutionary programming method followed by a local optimization method (Hooke and Jeeves) to refine the fitting. Predictions and stability analyses to test the viability of the alternative models were also performed. The good dynamic behaviour and powerful predictive power obtained by the mixed modeling composed on Michaelis-Menten kinetics and the approximate lin-log kinetics indicate that this as a suitable approach to complex large scale models where the exact rate laws are unknown

Integration of proteomic data for predicting dynamic behaviour in an E. coli central carbon network after genetic perturbations

One of the great challenges in the post‐genomic era is to understand the dynamic behaviour of a living cell. For that purpose, quantitative models describing metabolic network dynamics are a powerful tool as “dry lab” platforms to simulate experiments before they are performed in vivo. Kinetic models and stoichiometric genome scale models of the microbial metabolism are usually the two large‐scale modelling approaches most used. So far, few large scale kinetic models have been successfully constructed. The main reasons for this are not only the associated mathematical complexity, but also the large number of unknown kinetic parameters required in the rate equations to define the system. In contrast to kinetic models, the genome scale modelling approach bypasses these difficulties by using basically only stoichiometric information with certain physicochemical constraints to limit the space of a network without large fitted parameters sets. Although these constraint‐based models are highly relevant to predict a feasible set of steady‐state fluxes under a diverse range of genetic conditions, the steady‐state assumption may oversimplify cellular behaviour and cannot offer information about time dependent changes. To overcome these problems, combining these two approaches appears a reasonable alternative to modelling large‐scale metabolic networks. In this work, we used a large‐scale central carbon metabolic network of E. coli [1] to investigate whether including high throughput enzyme concentrations data into a model allows an improved prediction of the response to different single‐knockouts perturbations. For this purpose, a model based on the flux balance analysis (FBA) approach and linlog kinetics was constructed. As a first validation, we applied it to predict steady‐state changes in fluxes and metabolite concentrations, as well as dynamic responses to perturbations in the central E. coli metabolism. Then, the approach was evaluated by comparison with various sets of published in vivo measurements [2]. Our results indicate that integration of the quantitative enzyme levels into the kinetic models, in general, can be used to predict dynamic behavior changes

Reconstruction of dynamic metabolic networks : challenges, limitations and alternative solutions

Fundação para a Ciência e a Tecnologia (FCT)MIT-Portuga

Critical perspective on the consequences of the limited availability of kinetic data in metabolic dynamic modeling

Detailed kinetic models at the network reaction level are usually constructed using enzymatic mechanistic rate equations and the associated kinetic parameters. However, during the cellular life cycle thousands of different reactions occur, which makes it very difficult to build a detailed large-scale ldnetic model. In this work, we provide a critical overview of specific limitations found during the reconstruction of the central carbon metabolism dynamic model from E. coli (based on kinetic data available). In addition, we provide clues that will hopefully allow the systems biology community to more accurately construct metabolic dynamic models in the future. The difficulties faced during the construction of dynamic models are due not only to the lack of kinetic information but also to the fact that some data are still not curated. We hope that in the future, with the standardization of the in vitro enzyme protocols the approximation of in vitro conditions to the in vivo ones, it will be possible to integrate the available kinetic data into a complete large scale model. We also expect that collaborative projects between modellers and biologists will provide valuable kinetic data and permit the exchange of important information to solve most of these issues.Rafael S. Costa would like to thank Fundacao para a Ciencia e Tecnologia for providing the grant SFRH/BD/25506/2005. The authors also acknowledge the MIT-Portugal project 'Bridging Systems and Synthetic Biology for the development of improved microbial cell factories' MIT-Pt/BS-BB/0082/2008

Structure-function correlations in Retinitis Pigmentosa patients with partially preserved vision: a voxel-based morphometry study

Retinitis Pigmentosa is a group of hereditary retinal dystrophy disorders associated with progressive peripheral visual field loss. The impact of this retinal loss in cortical gray matter volume has not been addressed before in Retinitis Pigmentosa patients with low vision. Voxel-based morphometry was applied to study whole brain gray matter volume changes in 27 Retinitis Pigmentosa patients with partially preserved vision and 38 age- and gender-matched normally sighted controls to determine whether peripheral visual loss can lead to changes in gray matter volume. We found significant reductions in gray matter volume that were restricted to the occipital cortex of patients. The anteromedial pattern of reduced gray matter volume in visual primary and association cortices was significantly correlated with the extent of the peripheral visual field deficit in this cohort. Moreover, this pattern was found to be associated with the extent of visual field loss. In summary, we found specific visual cortical gray matter loss in Retinitis Pigmentosa patients associated with their visual function profile. The spatial pattern of gray matter loss is consistent with disuse-driven neuronal atrophy which may have clinical implications for disease management, including prosthetic restoration strategies