1,720,989 research outputs found
A novel parameter to estimate the minimum number of bound ligands needed to activate an ion channel
We consider the Hill coefficient obtained from concentration-response curves and we stress the differences between the use of binding and the use of current for the response. We first show that in order to estimate the cooperativity of binding sites one must use the coefficient obtained from the binding curves and not that obtained from the current curves. For what concerns the estimation of the minimum number of bound ligands needed to activate the channel, while the coefficient obtained from the binding is simply not applicable, we show that the coefficient obtained from the current is not appropriate. We finally introduce a novel parameter that predicts the number of ligand molecules required to open the channel from current measurements at very low ligand concentration. The above considerations are exemplified by a few theoretical models. (C) 1999 Academic Press
Empowering precision medicine through high performance computing clusters
The role of High Performance Computing (HPC) in Medicine is greatly increase in these last years,
moving from basic research to the clinics. With the advent of Next Generation Sequencing (NGS)
technologies, diverse areas of human health have been investigated through different omics
techniques. The extensive use of these NGS platforms to high throughput profile human health
issues in a cost-efficient manner, is generating huge amount of sequencing data pushing
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bioinformatic research in the big-data field. Speed, accuracy and reproducibility of massively
sequencing analysis have allowed to transfer molecular biology knowledge into precision medicine.
Furthermore, Molecular Dynamics (MD) earned a great importance in aiding genome research.
Sequencing studies of cancer have allowed to detect and characterize mutated genes that drive
tumorigenesis. As a complementary approach, from a biophysical perspective, MD simulations,
executed on HPC architectures, have permitted to investigate the role played by pathological
mutations on the molecular mechanism of activation
Cronaca del Seminario
Il contributo ripercorre alcune fasi salienti del Seminario svoltosi presso il Dipartimento di giurisprudenza dell'Università di Pisa il 4 marzo 2012, nell'ambito del tema "Diritto e religione". Si è trattato di un incontro che si è articolato in fasi specifiche distribuite secondo orari precisi nel corso della giornata allo scopo di garantire l’applicazione di una metodologia partecipativa, solitamente poco utilizzata nei consessi accademici. Infatti, mentre la prima parte della giornata ha seguito un andamento tradizionale, con i saluti e le relazioni introduttive, l’attività pomeridiana si è strutturata nella forma della divisione in gruppi facilitati. Questo elemento, che ha costituito la novità dell’incontro e la cui previsione nel programma aveva suscitato negli iscritti una certa suspence, ha svolto un ruolo significativo per una serie di ragioni che il contributo mette in rilievo
A novel algorithm for the numerical integration of systems of ordinary differential equations arising in chemical problems.
Impact Factor 0.64
JSTRING: Java Search for Tandem Repeats IN Genomes
http://bioinf.dms.med.uniroma1.it/JSTRING/
http://w3.uniroma1.it/valerio.parisi/JSTRING
Hidden Markov Models in bioinformatics
Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics applications, such as alignment, labeling, and profiling of sequences, protein structure prediction, and pattern recognition. We finally provide a critical appraisal of the use and perspectives of HMMs in bioinformatics. © 2007 Bentham Science Publishers Ltd
Answer to a comment to: “A Brief Survey Of Major Discoveries That Impact On Eukaryotic Cellular Pathway Modelling” by Parisi V. et al. , appeared in BBL, 2011: 4[1].
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