Three-factor models versus time series models:quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant.

Molecular biology and biochemistry of brain tumours

ßElucidating some molecular mechanisms and biochemistry of brain tumours is an important step towards the development of adjuvant medical therapies. The present study concentrates on cholecystokinin (CCK), a gut-brain peptide that has been described to be able to induce mitosis of rat gliomas as well as hormone secretion by the anterior pituitary, via the CCK-B receptor. The significance of a polymorphism in the growth hormone releasing hormone (GHRH) receptor (GHRH-R) gene was also determined. Finally, defects in the ß-catenin gene, an important component of the developmental pathway, in a sub-set of craniopharyngiomas were investigated. Reverse transcription-polymerase chain reaction (RT-PCR), restriction digestion analysis and direct sequencing demonstrated expression of CCK peptide itself and its A and B receptors by human gliomas, meningiomas and pituitary tumours. CCK peptides stimulated growth of cultured gliomas and meningiomas as well as in vitro hormone secretion [growth hormone (GH), luteinizing hormone (LH) and follicle stimulating hormone (FSH)] by human pituitary tumours. These biological effects were reduced or abolished by CCK antagonists. In addition, an antibody to CCK reduced mitosis by gliomas and meningiomas, and the same antibody inhibited hormone secretion by cultured human pituitary tumours. CCK peptides stimulated phosphatidylinositol (PI) hydrolysis, indicating coupling of the CCK receptors to phopsholipase C. Cyclic AMP was unaffected. In addition, caspase-3 activity was significantly and markedly increased, whilst proteasome activity was decreased. Taken together, these results may indicate an autocrine/paracrine role of CCK in the control of growth and/or functioning of gliomas, meningiomas and pituitary tumours. Primer induced restriction analysis (PIRA) of a rarer and alternative polymorphism in the GHRH-R receptor, in which Thr replaces Ala at codon 57, in human GH-secreting pituitary tumours was investigated. Whilst the rarer form correlated with an increased response of the pituitary cells to GHRH in vitro, allele distribution studies revealed that it is unlikely that the polymorphism contributes to increased risk of developing GH-secreting tumours and therefore acromegaly. Further findings of this study, using PCR and direct sequencing, were the demonstration of an association between b-catenin gene alterations and craniopharyngiomas of the adamantinomatous type. Since this gene product is involved with development, these results suggest that p-catenin mutations may contribute to the initiation and subsequent growth of congenital adamantinomatous craniopharyngiomas.

Investigation of a binding model for anti-cancer cyclohexadienones by computational chemistry and crystallography

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Ultra-endurance exercise:unanswered questions in redox biology and immunology

Ultra-endurance races are extreme exercise events that can take place over large parts of a day, several consecutive days or over weeks and months interspersed by periods of rest and recovery. Since the first ultraendurance races in the late 1970s, around 1000 races are now held worldwide each year, and more than 100000 people take part. Although these athletes appear to be fit and healthy, there have been occasional reports of severe complications following ultra-endurance exercise. Thus there is concern that repeated extreme exercise events could have deleterious effects on health, which might be brought about by the high levels of ROS (reactive oxygen species) produced during exercise. Studies that have examined biomarkers of oxidative damage following ultra-endurance exercise have found measurements to be elevated for several days, which has usually been interpreted to reflect increased ROS production. Levels of the antioxidant molecule GSH (reduced glutathione) are depleted for 1 month or longer following ultra-endurance exercise, suggesting an impaired capacity to copewith ROS. The present paper summarizes studies that have examined the oxidative footprint of ultra-endurance exercise in light of current thinking in redox biology and the possible health implications of such extreme exercise. © The Authors Journal compilation © 2014 Biochemical Society.

Hydrogen production by steam reforming of DME in a large scale CFB reactor. Part I:computational model and predictions

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). The DME-SR reactions scheme and kinetics in the presence of a bifunctional catalyst of CuO/ZnO/Al2O3+ZSM-5 were incorporated in the model using in-house developed user-defined function. The model was validated by comparing the predictions with experimental data from the literature. The results revealed for the first time detailed CFB reactor hydrodynamics, gas residence time, temperature distribution and product gas composition at a selected operating condition of 300 °C and steam to DME mass ratio of 3 (molar ratio of 7.62). The spatial variation in the gas species concentrations suggests the existence of three distinct reaction zones but limited temperature variations. The DME conversion and hydrogen yield were found to be 87% and 59% respectively, resulting in a product gas consisting of 72 mol% hydrogen. In part II of this study, the model presented here will be used to optimize the reactor design and study the effect of operating conditions on the reactor performance and products.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Novel visualization methods for protein data

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).