Daniel Gamermann

Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

Differences Between Young and Old University Students on a Lexical Decision Task: Evidence Through an Ex-Gaussian Approach

ABSTRACT This work compared two common variants of a lexical decision task (LDT) through two different analysis procedures: first, the classical ANOVA method, and second, by fitting the data to an ex-Gaussian distribution function. Two groups of participants (old and young university students) had to perform, blocks of go/no-go and yes/no tasks. Reaction times and error rates were much lower in the go/no-go task than in the yes/no task. Changes in the ex-Gaussian parameter related to attention were found with word frequency but not with the type of LDT tasks. These findings suggest that word frequency shows an attentional cost that is independent of age.

Automation on the Generation of Genome-Scale Metabolic Models

Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. This lengthy process usually requires a full year of one persons work in order to satisfactory collect, analyze, and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic, and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. This work presents the automation of a methodology for the reconstruction of genome-scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome-scale metabolic model of a photosynthetic organism, Synechocystis sp. PCC6803. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models, like connectivity and average shortest mean path of the different models, have been compared and analyzed.

Synechocystis sp. PCC6803 metabolic models for the enhanced production of hydrogen

Abstract In the present economy, difficulties to access energy sources are real drawbacks to maintain our current lifestyle. In fact, increasing interests have been gathered around efficient strategies to use energy sources that do not generate high CO2 titers. Thus, science-funding agencies have invested more resources into research on hydrogen among other biofuels as interesting energy vectors. This article reviews present energy challenges and frames it into the present fuel usage landscape. Different strategies for hydrogen production are explained and evaluated. Focus is on biological hydrogen production; fermentation and photon-fuelled hydrogen production are compared. Mathematical models in biology can be used to assess, explore and design production strategies for industrially relevant metabolites, such as biofuels. We assess the diverse construction and uses of genome-scale metabolic models of cyanobacterium Synechocystis sp. PCC6803 to efficiently obtain biofuels. This organism has been studied as a potential photon-fuelled production platform for its ability to grow from carbon dioxide, water and photons, on simple culture media. Finally, we review studies that propose production strategies to weigh this organism’s viability as a biofuel production platform. Overall, the work presented in this review unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean biofuel production platform.

Age slowing down in detection and visual discrimination under varying presentation times

The reaction time has been described as a measure of perception, decision making, and other cognitive processes. The aim of this work is to examine age-related changes in executive functions in terms of demand load under varying presentation times. Two tasks were employed where a signal detection and a discrimination task were performed by young and older university students. Furthermore, a characterization of the response time distribution by an ex-Gaussian fit was carried out. The results indicated that the older participants were slower than the younger ones in signal detection and discrimination. Moreover, the differences between both processes for the older participants were higher, and they also showed a higher distribution average except for the lower and higher presentation time. The results suggest a general slowdown in both tasks for age under different presentation times, except for the cases where presentation times were lower and higher. Moreover, if these parameters are understood to be a reflection of executive functions, these findings are consistent with the common view that age-related cognitive deficits show a decline in this function.

The Effect of Corrective Feedback on Performance in Basic Cognitive Tasks: An Analysis of RT Components

The current work examines the effect of trial-by-trial feedback about correct and error responding on performance in two basic cognitive tasks: a classic Stroop task (n = 40) and a color-word matching task (n = 30). Standard measures of both RT and accuracy were examined in addition to measures obtained from fitting the ex-Gaussian distributional model to the correct RTs. For both tasks, RTs were faster in blocks of trials with feedback than in blocks without feedback, but this difference was not significant. On the other hand, with respect to the distributional analyses, providing feedback served to significantly reduce the size of the tails of the RT distributions. Such results suggest that, for conditions in which accuracy is fairly high, the effect of corrective feedback might either be to reduce the tendency to double-check before responding or to decrease the amount of attentional lapsing.

New Approach for Phylogenetic Tree Recovery Based on Genome-Scale Metabolic Networks

A wide range of applications and research has been done with genome-scale metabolic models. In this work, we describe an innovative methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in order to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.

Statistical analysis of Brazilian electoral campaigns via Benford’s law

The principle of democracy is that the people govern through elected representatives. Therefore, a democracy is healthy as long as the elected politicians do represent the people. We have analyzed data from the Brazilian electoral court (Tribunal Superior Eleitoral, TSE) concerning money donations for the electoral campaigns and the election results. Our work points to two disturbing conclusions: money is a determining factor on whether a candidate is elected or not (as opposed to representativeness); secondly, the use of Benford’s Law to analyze the declared donations received by the parties and electoral campaigns shows evidence of fraud in the declarations. A better term to define Brazil’s government system is what we define as chrimatocracy (govern by money). keywords: Benford’s Law, Logistic regression, Electoral campaign, Politics, Fraud

Master equation for the degree distribution of a Duplication and Divergence network

Abstract Network growth as described by the Duplication–Divergence model proposes a simple general idea for the evolution dynamics of natural networks. In particular it is an alternative to the well known Barabasi–Albert model when applied to protein–protein interaction networks. In this work we derive a master equation for the node degree distribution of networks growing via Duplication and Divergence and we obtain an expression for the total number of links and for the degree distribution as a function of the number of nodes. Using algebra tools we investigate the degree distribution asymptotic behavior. Analytic results show that the network nodes average degree converges if the total mutation rate is greater than 0.5 and diverges otherwise. Treating original and duplicated node mutation rates as independent parameters has no effect on this result. However, difference in these parameters results in a slower rate of convergence and in different degree distributions. The more different these parameters are, the denser the tail of the distribution. We compare the solutions obtained with simulated networks. These results are in good agreement with the expected values from the derived expressions. The method developed is a robust tool to investigate other models for network growing dynamics.

Just Google It: An Approach on Word Frequencies Based on Online Search Result

ABSTRACT Word frequency is one of the most robust factors in the literature on word processing, based on the lexical corpus of a language. However, different sources might be used in order to determine the actual frequency of each word. Recent research has determined frequencies based on movie subtitles, Twitter, blog posts, or newspapers. In this paper, we examine a determination of these frequencies based on the World Wide Web. For this purpose, a Python script was developed to obtain frequencies of a word through online search results. These frequencies were employed to estimate lexical decision times in comparison to the traditional frequencies in a lexical decision task. It was found that the Google frequencies predict reaction times comparably to the traditional frequencies. Still, the explained variance was higher for the traditional database.