Quantitative Analysis of the Effective Functional Structure in Yeast Glycolysis

Yeast glycolysis is considered the prototype of dissipative biochemical oscillators. In cellular conditions, under sinusoidal source of glucose, the activity of glycolytic enzymes can display either periodic, quasiperiodic or chaotic behavior. In order to quantify the functional connectivity for the glycolytic enzymes in dissipative conditions we have analyzed different catalytic patterns using the non-linear statistical tool of Transfer Entropy. The data were obtained by means of a yeast glycolytic model formed by three delay differential equations where the enzymatic speed functions of the irreversible stages have been explicitly considered. These enzymatic activity functions were previously modeled and tested experimentally by other different groups. In agreement with experimental conditions, the studied time series corresponded to a quasi-periodic route to chaos. The results of the analysis are three-fold: first, in addition to the classical topological structure characterized by the specific location of enzymes, substrates, products and feedback regulatory metabolites, an effective functional structure emerges in the modeled glycolytic system, which is dynamical and characterized by notable variations of the functional interactions. Second, the dynamical structure exhibits a metabolic invariant which constrains the functional attributes of the enzymes. Finally, in accordance with the classical biochemical studies, our numerical analysis reveals in a quantitative manner that the enzyme phosphofructokinase is the key-core of the metabolic system, behaving for all conditions as the main source of the effective causal flows in yeast glycolysis.Comment: Biologically improve

Identification and characterization of Rhipicephalus (Boophilus) microplus candidate protective antigens for the control of cattle tick infestations

The cattle ticks, Rhipicephalus (Boophilus) spp., affect cattle production in tropical and subtropical regions of the world. Tick vaccines constitute a cost-effective and environmentally friendly alternative to tick control. The recombinant Rhipicephalus microplus Bm86 antigen has been shown to protect cattle against tick infestations. However, variable efficacy of Bm86-based vaccines against geographic tick strains has encouraged the research for additional tick-protective antigens. Herein, we describe the analysis of R. microplus glutathione-S transferase, ubiquitin (UBQ), selenoprotein W, elongation factor-1 alpha, and subolesin (SUB) complementary DNAs (cDNAs) by RNA interference (RNAi) in R. microplus and Rhipicephalus annulatus. Candidate protective antigens were selected for vaccination experiments based on the effect of gene knockdown on tick mortality, feeding, and fertility. Two cDNA clones encoding for UBQ and SUB were used for cattle vaccination and infestation with R. microplus and R. annulatus. Control groups were immunized with recombinant Bm86 or adjuvant/saline. The highest vaccine efficacy for the control of tick infestations was obtained for Bm86. Although with low immunogenic response, the results with the SUB vaccine encourage further investigations on the use of recombinant subolesin alone or in combination with other antigens for the control of cattle tick infestations. The UBQ peptide showed low immunogenicity, and the results of the vaccination trial were inconclusive to assess the protective efficacy of this antigen. These experiments showed that RNAi could be used for the selection of candidate tick-protective antigens. However, vaccination trials are necessary to evaluate the effect of recombinant antigens in the control of tick infestations, a process that requires efficient recombinant protein production and formulation systems

idmTPreg: Regression Model for Progressive Illness Death Data

The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the introduced estimators obtained using a direct binomial regression approach. Also, variance estimates and confidence bands are implemented in the package. This article presents guidelines for the use of the package.BERC 2014-2017 SEV-2013-0323 MTM2016-76969-P FP7/2011: Marie Curie Initial Training Network MEDIASRE