Computational neuroscience in China

The ultimate goal of Computational Neuroscience(CNS) is to use and develop mathematical models and approaches to elucidate brain functions.CNS is a young and highly multidisciplinary field.It heavily interacts with experimental neuroscience and such other research areas as artificial intelligence,robotics,computer vision,information science and machine learning.This paper reviews the history of CNS in China,its current status and the prospects for its future development.Examples of CNS research in China are...     

Cell-derived microparticles: a new challenge in neuroscience

Microparticles (MPs) are membrane fragments shed by cells activated by a variety of stimuli including serine proteases, inflammatory cytokines, growth factors, and stress inducers. MPs originating from platelets, leukocytes, endothelial cells, and erythrocytes are found in circulating blood at relative concentrations determined by the pathophysiological context. The procoagulant activity of MPs is their most characterized property as a determinant of thrombosis in various vascular and systemic diseases including myocardial infarction and diabetes. An increase in circulating MPs has also been associated with ischemic cerebrovascular accidents, transient ischemic attacks, multiple sclerosis, and cerebral malaria. Recent data indicate that besides their procoagulant components and identity antigens, MPs bear a number of bioactive effectors that can be disseminated, exchanged, and transferred via MPs cell interactions. Furthermore, as activated parenchymal cells may also shed MPs carrying identity antigens and biomolecules, MPs are now emerging as new messengers/biomarkers from a specific tissue undergoing activation or damage. Thus, detection of MPs of neurovascular origin in biological fluids such as CSF or tears, and even in circulating blood in case of blood–brain barrier leakage, would not only improve our comprehension of neurovascular pathophysiology, but may also constitute a powerful tool as a biomarker in disease prediction, diagnosis, prognosis, and follow-up.     

Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway

Background  

The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway.     

Computational neuroscience and neuroinformatics: Recent progress and resources

The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 ± 8.1 billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. ‘Computational neuroscience’ which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by ‘neuroinformatics’ approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-of-the-art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain–computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.     

Metabolomics shows the way to new discoveries

A report on the First Annual Meeting of the Metabolomics Society, Tsuruoka, Japan, 20-23 June 2005.     

Computational modelling of motion detectors: responses to two-frame displays

Schemes for motion detection fall into two classes. Reichardt correlators compare spatial luminance patterns at two locations at different times; gradient detectors compare spatial and temporal luminance gradients. Both are candidate operators for biological and machine vision systems. A large body of perceptual data exists, defining the properties of motion detectors used by human observers, which can form a basis for determining which class of detector is appropriate for the human visual system. Plausible versions of each detector were implemented, and their responses to a variety of two-frame stimuli were computed. Results indicated that both detectors can predict most of the data, but on balance gradient detectors offer the best working hypothesis for motion detection by human observers. This conclusion is necessarily limited to the type of stimuli used, and may require modification in the light of responses to continuously moving stimuli.     

Computational structural modelling of coronary stent deployment: a review

The finite element (FE) method is a powerful investigative tool in the field of biomedical engineering, particularly in the analysis of medical devices such as coronary stents whose performance is extremely difficult to evaluate in vivo. In recent years, a number of FE studies have been carried out to simulate the deployment of coronary stents, and the results of these studies have been utilised to assess and optimise the performance of these devices. The aim of this paper is to provide a thorough review of the state-of-the-art research in this area, discussing the aims, methods and conclusions drawn from a number of significant studies. It is intended that this paper will provide a valuable reference for future research in this area.     

Membrane trafficking in plants: new discoveries and approaches

The general organization and function of the endomembrane system is highly conserved in eukaryotic cells. In addition, increasing numbers of studies demonstrate that normal plant growth and development are dependent on specialized tissue and subcellular-specific components of the plant membrane trafficking machinery. New approaches, including chemical genomics and proteomics, will likely accelerate our understanding of the diverse functions of the plant endomembrane system.     

Dickkopf1--a new player in modelling the Wnt pathway

The Wnt signaling pathway transducing the stabilization of β-catenin is essential for metazoan embryo development and is misregulated in many diseases such as cancers. In recent years models have been proposed for the Wnt signaling pathway during the segmentation process in developing embryos. Many of these include negative feedback loops where Axin2 plays a key role. However, Axin2 null mice show no segmentation phenotype. We therefore propose a new model where the negative feedback involves Dkk1 rather than Axin2. We show that this model can exhibit the same type of oscillations as the previous models with Axin2 and as observed in experiments. We show that a spatial Wnt gradient can consistently convert this temporal periodicity into the spatial periodicity of somites, provided the oscillations in new cells arising in the presomitic mesoderm are synchronized with the oscillations of older cells. We further investigate the hypothesis that a change in the Wnt level in the tail bud during the later stages of somitogenesis can lengthen the time period of the oscillations and hence the size and separation of the later somites.     

SenseLab: new developments in disseminating neuroscience information

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.     

From white to beige: a new hypothalamic pathway

Understanding how brown and beige adipocytes can be differentially controlled and activated by neuronal circuits is a fundamental prerequisite to fully comprehend the metabolic role that fat tissue plays in energy homeostasis. In this issue of EMBO reports, Wang et al 1 identify a new hypothalamic route that drives the exclusive recruitment of beige fat via the selective control of sympathetic nervous system (SNS) outflow to subcutaneous white adipose tissue. Since the data strongly suggest that the APPL2–AMPK signaling axis is crucial for this activation, this finding sheds a new light on the cross talk between peripheral homeostatic signals and neurons that are part of hypothalamic energy homeostasis regulatory pathways in the ventromedial hypothalamus (VHM) proposing a new defending mechanism to cold and obesity.     

介导心肌肥大的一条新的信号通路--Calcineurin通路

心肌肥大是心肌细胞对外界刺激,如工作负荷、神经体液因子及内在心肌蛋白遗传突变一种基本应答。已知胞内Ｃａ＾２＋浓度升高在各种刺激诱导心肌肥大的信号传递中起重要作用,但对Ｃａ＾２＋信号下游的传递机制一直不甚清楚。新近研究证实,由Ｃａ＾２＋活化的钙调神经磷酸酶（ＣａＮ）在心肌肥大的信号传递中起重要作用,基可能是Ｃａ＾２＋信号致肥大基因活化的偶联环节。抑制ＣａＮ活性可阻滞各种因素诱导的心肌肥大发生与发展,     

A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models

Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics primarily uses landscape resistance surfaces (RSs) and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of RSs is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix RSs were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between explanatory variables and genetic distances was then assessed following a mixed modelling approach to account for the nonindependence of values in distance matrices. Subsequently, the best fitting model was selected using the statistic. We applied LCTA and the mixed modelling approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis. We showed that LCTA is an objective approach that identifies both the most probable migration habitat and landscape elements that either inhibit or facilitate gene flow. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.     

Peptide-mediated interactions in biological systems: new discoveries and applications

Peptide-mediated interactions play very important roles in cellular processes. Recent years have seen much activity in the discovery of new bioactive peptides, and interactions mediated by protein-peptide binding events. At the same time, computational approaches continue to be developed that allow protein-peptide interactions to be discovered with great accuracy. There are also a growing number of chemicals that can target these interactions with various applications in disease. Both new discoveries and predictions suggest that these protein-peptide interactions play greater roles in cellular processes than previously thought. We propose that projects to uncover the protein-peptide repertoire used in Nature in a systematic way will have numerous applications in molecular biology and medicine.