To investigate the behavioral mechanism of chemotaxis in Caenorhabditis elegans, we recorded the instantaneous position, speed, and turning rate of single worms as a function of time during chemotaxis in gradients of the attractants ammonium chloride or biotin. Analysis of turning rate showed that each worm track could be divided into periods of smooth swimming (runs) and periods of frequent turning (pirouettes). The initiation of pirouettes was correlated with the rate of change of concentration (dC/dt) but not with absolute concentration. Pirouettes were most likely to occur when a worm was heading down the gradient (dC/dt < 0) and least likely to occur when a worm was heading up the gradient (dC/dt > 0). Further analysis revealed that the average direction of movement after a pirouette was up the gradient. These observations suggest that chemotaxis is produced by a series of pirouettes that reorient the animal to the gradient. We tested this idea by imposing the correlation between pirouettes and dC/dt on a stochastic point model of worm motion. The model exhibited chemotaxis behavior in a radial gradient and also in a novel planar gradient. Thus, the pirouette model of C. elegans chemotaxis is sufficient and general.

GABAergic inhibition plays a critical role in shaping neuronal activity in the neocortex. Numerous experimental investigations have examined perisomatic inhibitory synapses, which control action potential output from pyramidal neurons. However, most inhibitory synapses in the neocortex are formed onto pyramidal cell dendrites, where theoretical studies suggest they may focally regulate cellular activity. The precision of GABAergic control over dendritic electrical and biochemical signaling is unknown. Using cell type-specific optical stimulation in combination with 2-photon calcium (Ca(2+)) imaging, we show that somatostatin-expressing interneurons exert compartmentalized control over postsynaptic Ca(2+) signals within individual dendritic spines. This highly focal inhibitory action is mediated by a subset of GABAergic synapses that directly target spine heads. GABAergic inhibition thus participates in localized control of dendritic electrical and biochemical signaling.

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Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

Computational neuroscience as a scientific discipline must provide for the ready testing of published models by others in the field. Unfortunately this has rarely been fulfilled. When exact reproduction of a model simulation is achieved, it is often a long and difficult process. Too often, missing or typographically incorrect equations and parameter values have made it difficult to explore or build upon published models. Compounding this difficulty is the proliferation of platforms and operating systems that are incompatible with the author's original computing environment.Because of these problems, most models are never subjected to the rigorous testing by others in the field that is a hallmark of the scientific method. This not only impedes validation of a model, but also prevents a deeper understanding of its inner workings, especially through modification of the parameters. Furthermore, modular pieces of the model, e.g. ion channels or the morphology of a cell, cannot be reused to build new models and propel research forward.ModelDB (http://senselab.med.yale.edu/modeldb) is intended to address these issues (Peterson et al, 1996;Shepherd et al, 1998). ModelDB is a database of computational models, either classics in the field or published in recent years. It focuses on models for different types of neurons, and presently contains over 60 models for 15 neuron types. In addition to compartmental models, it contains models covering from ion channels and receptors through axons and dendrites through neurons to networks. Models can be accessed by author, model name, neuron type, concept, e.g. synaptic plasticity, pattern recognition, etc, or by simulation environment.ModelDB is a member of a major neuroscience database collection called SenseLab. Each SenseLab database has an easily extensible structure achieved through the EAV/CR (EntityAttribute-Value with Classes and Relationships) data schema , Miller et al 2001. ModelDB is integrated with NeuronDB (Marenco et al 1999), another SenseLab database that stores neuronal properties derived from the neuroscience literature (http:// senselab.med.yale.edu/senselab/NeuronDB). Use of the models is free to all. Contributing to the database is also open to all. Contributions are tested for quality-control purposes before being made public. Here we describe how to find, run, and submit models to ModelDB. Browsing ModelDBThe use of ModelDB typically starts with a computational neuroscientist who wishes to test the results of a simulation by a published model, and use that as a starting point for further research. Instead of recreating the model from scratch, the user goes to the ModelDB home page (Fig.1, top), to find the model by any of the various ways already mentioned. All the information about a model is shown on a single page (Fig.1, bottom), which also contains tools for finding related models in the database.The files required to run a simulation are stored in a compressed archive in zip format and can be browsed and/or downloaded (Fig.1, bottom, lower left column)...

Erythroid Krüppel-like factor (EKLF) plays an important role in erythroid development by stimulating β-globin gene expression. We have examined the details by which the minimal transactivation domain (TAD) of EKLF (EKLFTAD) interacts with several transcriptional regulatory factors. We report that EKLFTAD displays homology to the p53TAD and, like the p53TAD, can be divided into two functional subdomains (EKLFTAD1 and EKLFTAD2). Based on sequence analysis, we found that EKLFTAD2 is conserved in KLF2, KLF4, KLF5, and KLF15. In addition, we demonstrate that EKLFTAD2 binds the amino-terminal PH domain of the Tfb1/p62 subunit of TFIIH (Tfb1PH/p62PH) and four domains of CREB-binding protein/ p300. The solution structure of the EKLFTAD2/Tfb1PH complex indicates that EKLFTAD2 binds Tfb1PH in an extended conformation, which is in contrast to the α-helical conformation seen for p53TAD2 in complex with Tfb1PH. These studies provide detailed mechanistic information into EKLFTAD functions as well as insights into potential interactions of the TADs of other KLF proteins. In addition, they suggest that not only have acidic TADs evolved so that they bind using different conformations on a common target, but that transitioning from a disordered to a more ordered state is not a requirement for their ability to bind multiple partners.hematopoiesis | NMR spectroscopy | transcriptional activators | intrinsically unstructured domain | transcription factor IIE

The integrative properties of cortical pyramidal dendrites are essential to the neural basis of cognitive function, but the impact of amyloid beta protein (aβ) on these properties in early Alzheimer's is poorly understood. In animal models, electrophysiological studies of proximal dendrites have shown that aβ induces hyperexcitability by blocking A-type K+ currents (IA), disrupting signal integration. The present study uses a computational approach to analyze the hyperexcitability induced in distal dendrites beyond the experimental recording sites. The results show that back-propagating action potentials in the dendrites induce hyperexcitability and excessive calcium concentrations not only in the main apical trunk of pyramidal cell dendrites, but also in their oblique dendrites. Evidence is provided that these thin branches are particularly sensitive to local reductions in IA. The results suggest the hypothesis that the oblique branches may be most vulnerable to disruptions of IA by early exposure to aβ, and point the way to further experimental analysis of these actions as factors in the neural basis of the early decline of cognitive function in Alzheimer's.