Transmission of the Subthalamic Nucleus Oscillatory Activity to the Cortex: A Computational Approach

Parkinsonian tremor is most likely due to oscillatory neuronal activities of central oscillators such as the subthalamic nucleus (STN)-external segment of the globus pallidus (GPe) pacemaker within the basal ganglia (BG). Activity from the central oscillator is proposed to be transmitted via transcortical pathways to the periphery. A computational model of the BG is proposed for simulating the transmission of the STN oscillatory activity to the cortex, based closely on known anatomy and physiology of the BG. According to the results of the simulation, for transmission of the STN oscillatory activity to the cortex, the STN oscillatory activity has to be transmitted simultaneously to the thalamus via STN-internal segment of the globus pallidus (GPi)-thalamus and STN-GPe-GPi-thalamus pathways. This transmission is controlled by the various factors such as the phase between the STN and GPe oscillatory activities, the STN oscillatory activity frequency, the low-threshold calcium spike bursts of the thalamus and the GPi spontaneous activity.     

Conformational preferences in diglycosyl disulfides: NMR and molecular modeling studies

The conformations of several β1→β1′ diglycosyl disulfides were investigated by NMR and computational methods. Experimental data, such as NOEs, proton–proton and proton–carbon-13 coupling constants, measured for solutions in DMSO, are in good agreement with values obtained by MD simulations in explicit DMSO. The disulfide torsion angles (C1–S–S–C1′) preferentially sample values close to either +90° or −90° (+g or −g) and appear as the main metric that determines the conformational behavior of these glycomimetics. There is more conformational freedom around the C1–S and C1′–S′ bonds (Φ and Ω torsions, respectively) and population cluster analysis allowed to identify up to four allowed conformational regions for each of the +g or −g forms. Population analysis of the hydroxylic group rotamers, based on proton–proton and proton–carbon-13 couplings as well as on calculated hydrogen bonding statistics, did not reveal any significant intramolecular hydrogen bonds in DMSO solution.     

Computational methods and evaluation of RNA stabilization reagents for genome-wide expression studies

Gene expression studies require high quality messenger RNA (mRNA) in addition to other factors such as efficient primers and labeling reagents. To prevent RNA degradation and to improve the quality of gene array expression data, several commercial reagents have become available. We examined a conventional hot-phenol lysis method and RNA stabilization reagents, and generated comparative gene expression profiles from Escherichia coli cells grown on minimal medium. Our data indicate that certain RNA stabilization reagents induce stress responses and proper caution must be exercised during their use. We observed that the laboratory reagent (phenol/EtOH, 5:95, v/v) worked efficiently in isolating high quality mRNA and reproducibility was such that reliable gene expression profiles were generated. To assist in the analysis of gene expression data, we wrote a number of macros that use the most recent gene annotation and process data in accordance with gene function. Scripts were also written to examine the occurrence of artifacts, based on GC content, length of the individual open reading frame (ORF), its distribution on plus and minus DNA strands, and the distance from the replication origin.     

真核生物基因启动子的计算机预测及其软件资源

真核生物基因启动子一直是现代分子生物学的研究热点。随着近年来生物技术和计算机技术的高速发展,应用生物信息学技术对真核生物启动子的计算机预测及相关软件开发取得了较大进展。文章将主要对真核生物启动子Ⅱ的预测研究进展和软件资源作一简单介绍。     

Bayesian methods in bioinformatics and computational systems biology

Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.     

Heteronuclear coupling constants of hydroxyl protons in a water solution of oligosaccharides: trehalose and sucrose

Relatively few details are known about the conformational preferences of hydroxyl groups in carbohydrates in water solution, though these would be informative about solvation and H-bonding. We show that highly concentrated solutions of sucrose and trehalose exhibit surprisingly well-resolved ¹H NMR spectra in a deuterium oxide–water solvent mixture at subzero temperatures. Measurement conditions are suitable to extract nearly all homonuclear and, for the first time, heteronuclear coupling constants of OH groups of carbohydrates in their natural abundance. For ^2,3J_HO,C coupling constants new, powerful variants of HETLOC and HECADE techniques were applied. The present data do not support the presence of persistent H-bonds in these two cryogenic disaccharides.     

影响因子:计算方法、用途及局限性

美国科技信息研究所 （Institute for ScientificInformation,ISI）的期刊引证报告（Journal CitationReports,JCR）近年来已逐渐成为国内评价期刊及其刊载文章质量的主要量化指标。但是各人对它的应用价值有不同的看法。     

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and—maybe most importantly—discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.     

Dihydroxamic acid complexes of iron (III): ligand pKa and coordinated water hydrolysis constants

A series of dihydroxamic acid ligands of the formula [RN(OH)C(O)]₂(CH₂)_n, (n = 2, 4, 6, 7, 8; R = CH₃, H) has been studied in 2.0 M aqueous sodium perchlorate at 25.0 °C. These ligands may be considered as synthetic analogs to the siderophore rhodotorulic acid. Acid dissociation constants (pK_a) have been determined for the ligands and for N-methylacetohydroxamic acid (NMHA). The pK_a1 and pK_a2 values are: n = 2, R = CH₃ (8.72, 9.37); N = 4, R = CH₃ (8.79, 9.37); N = 6, R = CH₃; N = 7, R = CH₃ (8.95, 9.47); N = 8, R = CH₃ (8.93, 9.45); N = 8, R = H (9.05, 9.58). Equilibrium constants for the hydrolysis of coordinated water (log K) have been estimated for the 1:1 feeric complexes of the ligands n = 2, 4, 8; R = CH₃. The N = 8 ligand forms a monomeric complex with Fe(III) while the n = 2 and 4 ligands form dimeric complexes. For hydrolysis of the n = 8 monomeric complex, log K₁ = −6.36 and log K₂ = −9.84. Analysis of the spectrophotometric data for the dimeric complexes indicates deprotonation of all four coordinated waters. The successive hydrolysis constants, log K_1–4, for the dimeric complexes are as follows: n = 2 (−6.37, −5.77, −10.73, −11.8); n = 4 (−5.54, −5.07, −11.57, −10.17). The log K₂ values for the dimers are unexpectedly high, higher in fact than log K₁, inconsistent with the formation of simple ternary hydroxo complexes. A scheme is proposed for the hydrolysis of the ferric dihydroxamate dimers, which includes the possible formation of μ-hydroxo and μ-oxo bridges.     

Computational models of cells and tissues: machines, agents and fungal infection

Computational models have been of interest in biology for many years and have represented a particular approach to trying to understand biological processes and phenomena from a systems point of view. Much of the early work was rather abstract and high level and probably seemed to many to be of more philosophical than practical value. There have, however, been some advances in the development of more realistic models and the current state of computer science research provides us with new opportunities through both the emergence of models that can model seriously complex systems and also the support that modern software can give to the modelling process. This paper describes a few of the early simple models and then goes on to look at some new ideas in the area with a particular application drawn from the world of mycology. Some general principles relating to how new and emerging computational techniques can help to represent and understand extremely complex models conclude the paper.     

Computational studies of molecular machines: the ribosome

The past decade has produced an avalanche of experimental data on the structure and dynamics of the ribosome. Groundbreaking studies in structural biology and kinetics have placed important constraints on ribosome structural dynamics. However, a gulf remains between static structures and time dependent data. In particular, X-ray crystallography and cryo-EM studies produce static models of the ribosome in various states, but lack dynamic information. Single molecule studies produce information on the rates of transitions between these states but do not have high-resolution spatial information. Computational studies have aided in bridging this gap by providing atomic resolution simulations of structural fluctuations and transitions between configurations.     

Computational prediction of proteotypic peptides

Evaluation of: Mallick P, Schirle M, Chen SS et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25(1), 125–131 (2007).

Mass spectrometry, the driving analytical force behind proteomics, is primarily used to identify and quantify as many proteins in a complex biological mixture as possible. While there are many ways to prepare samples, one aspect that is common to a vast majority of bottom-up proteomic studies is the digestion of proteins into tryptic peptides prior to their analysis by mass spectrometry. As correctly highlighted by Mallick and colleagues, only a few peptides are repeatedly and consistently identified for any given protein within a complex mixture. While the existence of these proteotypic peptides (to borrow the authors’ terminology) is well known in the proteomics community, there has never been an empirical method to recognize which peptides may be proteotypic for a given protein. In this study, the investigators discovered over 16,000 proteotypic peptides from a collection of over 600,000 peptide identifications obtained from four different analytical platforms. The study examined a number of physicochemical parameters of these peptides to determine which properties were most relevant in defining a proteotypic peptide. These characteristic properties were then used to develop computational tools to predict proteotypic peptides for any given protein within an organism.     

Computational analysis and prediction for exons of PAC579 genomic sequence

To isolate the novel genes related to human hepatocellular carcinoma (HCC), we sequenced P1-derived artificial chromosome PAC579 (D17S926 locus) mapped in the minimum LOH (loss of heterozygosity) deletion region of chromosome 17p13.3 in HCC. Four novel genes mapped in this genomic sequence area were isolated and cloned by wet-lab experiments, and the exons of these genes were located. 0–60 kb of this genomic sequence including the genes of interest was scanned with five different computational exon prediction programs as well as four splice site recognition programs. After analyzing and comparing the computationally predicted results with the wet-lab experiment results, some potential exons were predicted in the genomic sequence by using these programs.     

Computational methods for exon detection

Computer methods for the complete and accurate detection of genes in vertebrate genomic sequences are still a long way to perfection. The intermediate task of identifying the coding moiety of genes (coding exons) is now reasonably well achieved using a combination of methods. After reviewing the intrinsic difficulties in interpreting vertebrate genomic sequences, this article presents the state-of-the-art, with an emphasis on similarity search methods and the resources available through Internet.