scholarly journals The Fruit Fly Brain Observatory: from structure to function

2016 ◽  
Author(s):  
Nikul H. Ukani ◽  
Chung-Heng Yeh ◽  
Adam Tomkins ◽  
Yiyin Zhou ◽  
Dorian Florescu ◽  
...  
Keyword(s):  
Neural Circuit ◽  
Structural Data ◽  
Fruit Fly ◽  
Biological Data ◽  
Multiple Sources ◽  
Full Data ◽  
Open Platform ◽  
Brain Circuits ◽  
Brain Data ◽  
High Flexibility

SummaryThe Fruit Fly Brain Observatory (FFBO) is a collaborative effort between experimentalists, theorists and computational neuroscientists at Columbia University, National Tsing Hua University and Sheffield University with the goal to (i) create an open platform for the emulation and biological validation of fruit fly brain models in health and disease, (ii) standardize tools and methods for graphical rendering, representation and manipulation of brain circuits, (iii) standardize tools for representation of fruit fly brain data and its abstractions and support for natural language queries, (iv) create a focus for the neuroscience community with interests in the fruit fly brain and encourage the sharing of fruit fly brain structural data and executable code worldwide. NeuroNLP and NeuroGFX, two key FFBO applications, aim to address two major challenges, respectively: i) seamlessly integrate structural and genetic data from multiple sources that can be intuitively queried, effectively visualized and extensively manipulated, ii) devise executable brain circuit models anchored in structural data for understanding and developing novel hypotheses about brain function. NeuroNLP enables researchers to use plain English (or other languages) to probe biological data that are integrated into a novel database system, called NeuroArch, that we developed for integrating biological and abstract data models of the fruit fly brain. With powerful 3D graphical visualization, NeuroNLP presents a highly accessible portal for the fruit fly brain data. NeuroGFX provides users highly intuitive tools to execute neural circuit models with Neurokernel, an open-source platform for emulating the fruit fly brain, with full data support from the NeuroArch database and visualization support from an interactive graphical interface. Brain circuits can be configured with high flexibility and investigated on multiple levels, e.g., whole brain, neuropil, and local circuit levels. The FFBO is publicly available and accessible at http://fruitflybrain.org from any modern web browsers, including those running on smartphones.

scholarly journals NeuroNLP: a natural language portal for aggregated fruit fly brain data

2016 ◽  
Author(s):  
Nikul H. Ukani ◽  
Adam Tomkins ◽  
Chung-Heng Yeh ◽  
Wesley Bruning ◽  
Allison L. Fenichel ◽  
...  
Keyword(s):  
Natural Language ◽  
3D Visualization ◽  
Fruit Fly ◽  
Multiple Sources ◽  
Web Based ◽  
Brain Circuits ◽  
Complex Circuits ◽  
Brain Data ◽  
Biological Circuits ◽  
The Impact

SummaryNeuroNLP, is a key application on the Fruit Fly Brain Observatory platform (FFBO, http://fruitflybrain.org), that provides a modern web-based portal for navigating fruit fly brain circuit data. Increases in the availability and scale of fruit fly connectome data, demand new, scalable and accessible methods to facilitate investigation into the functions of the latest complex circuits being uncovered. NeuroNLP enables in-depth exploration and investigation of the structure of brain circuits, using intuitive natural language queries that are capable of revealing the latent structure and information, obscured due to expansive yet independent data sources. NeuroNLP is built on top of a database system call NeuroArch that codifies knowledge about the fruit fly brain circuits, spanning multiple sources. Users can probe biological circuits in the NeuroArch database with plain English queries, such as “show glutamatergic local neurons in the left antennal lobe” and “show neurons with dendrites in the left mushroom body and axons in the fan-shaped body”. This simple yet powerful interface replaces the usual, cumbersome checkboxes and dropdown menus prevalent in today’s neurobiological databases. Equipped with powerful 3D visualization, NeuroNLP standardizes tools and methods for graphical rendering, representation, and manipulation of brain circuits, while integrating with existing databases such as the FlyCircuit. The userfriendly graphical user interface complements the natural language queries with additional controls for exploring the connectivity of neurons and neural circuits. Designed with an open-source, modular structure, it is highly scalable/flexible/extensible to additional databases or to switch between databases and supports the creation of additional parsers for other languages. By supporting access through a web browser from any modern laptop or smartphone, NeuroNLP significantly increases the accessibility of fruit fly brain data and improves the impact of the data in both scientific and educational exploration.

scholarly journals The Fruit Fly Brain Observatory: From Structure to Function

2019 ◽  
Author(s):  
Nikul H. Ukani ◽  
Chung-Heng Yeh ◽  
Adam Tomkins ◽  
Yiyin Zhou ◽  
Dorian Florescu ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Research ◽  
Model Organism ◽  
Fruit Fly ◽  
Data Repositories ◽  
Brain Circuits ◽  
Multiple Data ◽  
Computer Scientists ◽  
Brain Data ◽  
The Brain

AbstractThe fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works.To that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.

scholarly journals NeuroGFX: a graphical functional explorer for fruit fly brain circuits

2016 ◽  
Author(s):  
Chung-Heng Yeh ◽  
Yiyin Zhou ◽  
Nikul H. Ukani ◽  
Aurel A. Lazar
Keyword(s):  
Computational Modeling ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Fruit Fly ◽  
Graphical Interface ◽  
Biological Structure ◽  
Levels Of Abstraction ◽  
Whole Brain ◽  
Brain Circuits ◽  
Circuit Structure

SummaryRecently, multiple focused efforts have resulted in substantial increase in the availability of connectome data in the fruit fly brain. Elucidating neural circuit function from such structural data calls for a scalable computational modeling methodology. We propose such a methodology that includes i) a brain emulation engine, with an architecture that can tackle the complexity of whole brain modeling, ii) a database that supports tight integration of biological and modeling data along with support for domain specific queries and circuit transformations, and iii) a graphical interface that allows for total flexibility in configuring neural circuits and visualizing run-time results, both anchored on model abstractions closely reflecting biological structure. Towards the realization of such a methodology, we have developed NeuroGFX and integrated it into the architecture of the Fruit Fly Brain Observatory (http://fruitflybrain.org). The computational infrastructure in NeuroGFX is provided by Neurokernel, an open source platform for the emulation of the fruit fly brain, and NeuroArch, a database for querying and executing fruit fly brain circuits. The integration of the two enables the algorithmic construction/manipulation/revision of executable circuits on multiple levels of abstraction of the same model organism. The power of this computational infrastructure can be leveraged through an intuitive graphical interface that allows visualizing execution results in the context of biological structure. This provides an environment where computational researchers can present configurable, executable neural circuits, and experimental scientists can easily explore circuit structure and function ultimately leading to biological validation. With these capabilities, NeuroGFX enables the exploration of function from circuit structure at whole brain, neuropil, and local circuit level of abstraction. By allowing for independently developed models to be integrated at the architectural level, NeuroGFX provides an open plug and play, collaborative environment for whole brain computational modeling of the fruit fly.

Long-term Memory Upscales Volume of Postsynaptic Densities in the Process that Requires Autophosphorylation of αCaMKII

2019 ◽  
Vol 30 (4) ◽  
pp. 2573-2585 ◽  
Author(s):  
Małgorzata Alicja Śliwińska ◽  
Anna Cały ◽  
Malgorzata Borczyk ◽  
Magdalena Ziółkowska ◽  
Edyta Skonieczna ◽  
...  
Keyword(s):  
Dendritic Spines ◽  
Smooth Endoplasmic Reticulum ◽  
Structural Data ◽  
Long Term Memory ◽  
Brain Circuits ◽  
Hippocampal Area ◽  
And Function ◽  
And Storage ◽  
Old Mice

Abstract It is generally accepted that formation and storage of memory relies on alterations of the structure and function of brain circuits. However, the structural data, which show learning-induced and long-lasting remodeling of synapses, are still very sparse. Here, we reconstruct 1927 dendritic spines and their postsynaptic densities (PSDs), representing a postsynaptic part of the glutamatergic synapse, in the hippocampal area CA1 of the mice that underwent spatial training. We observe that in young adult (5 months), mice volume of PSDs, but not the volume of the spines, is increased 26 h after the training. The training-induced growth of PSDs is specific for the dendritic spines that lack smooth endoplasmic reticulum and spine apparatuses, and requires autophosphorylation of αCaMKII. Interestingly, aging alters training-induced ultrastructural remodeling of dendritic spines. In old mice, both the median volumes of dendritic spines and PSDs shift after training toward bigger values. Overall, our data support the hypothesis that formation of memory leaves long-lasting footprint on the ultrastructure of brain circuits; however, the form of circuit remodeling changes with age.

scholarly journals Neue Möglichkeiten für die Strukturbestimmung von Membranproteinen

BIOspektrum ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 615-617
Author(s):  
Theresa Gewering ◽  
Arne Möller
Keyword(s):  
Membrane Proteins ◽  
Structural Data ◽  
Dynamic Membrane ◽  
A Cell ◽  
High Flexibility

Abstract Membrane proteins establish the connection between the outside and the inside of a cell. Even though 30 percent of proteins in a cell are membrane associated, their structural data is strongly underrepresented due to the high flexibility and low purification yield. The resolution revolution in cryo-EM opened up new opportunities to solve structures of dynamic membrane proteins that can only be purified in small quantities.

scholarly journals Idiosyncratic learning performance in flies generalizes across modalities

2021 ◽  
Author(s):  
Matthew Smith ◽  
Kyle S. Honegger ◽  
Glenn Turner ◽  
Benjamin de Bivort
Keyword(s):  
Individual Differences ◽  
Neural Circuit ◽  
Bitter Taste ◽  
Model Organism ◽  
Fruit Fly ◽  
Learning Performance ◽  
Suitable Model ◽  
Mechanistic Basis ◽  
Innate Behaviors ◽  
Stimulus Modalities

AbstractIndividuals vary in their innate behaviors, even when they have the same genome and have been reared in the same environment. The extent of individuality in plastic behaviors, like learning, is less well characterized. Also unknown is the extent to which intragenotypic differences in learning generalize: if an individual performs well in one assay, will it perform well in other assays? We investigated this using the fruit fly Drosophila melanogaster, an organism long-used to study the mechanistic basis of learning and memory. We found that isogenic flies, reared in identical lab conditions, and subject to classical conditioning that associated odorants with electric shock, exhibit clear individuality in their learning responses. Flies that performed well when an odor was paired with shock tended to perform well when other odors were paired with shock, or when the original odor was paired with bitter taste. Thus, individuality in learning performance appears to be prominent in isogenic animals reared identically, and individual differences in learning performance generalize across stimulus modalities. Establishing these results in flies opens up the possibility of studying the genetic and neural circuit basis of individual differences in learning in a highly suitable model organism.

scholarly journals Multiple neurons encode CrebB dependent appetitive long-term memory in the mushroom body circuit

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Yves F Widmer ◽  
Cornelia Fritsch ◽  
Magali M Jungo ◽  
Silvia Almeida ◽  
Boris Egger ◽  
...  
Keyword(s):  
Mushroom Body ◽  
Neural Circuit ◽  
Fruit Fly ◽  
Conditional Knockout ◽  
Long Term Memory ◽  
Temporal Precision ◽  
Regulatory Changes ◽  
Transcriptional Memory ◽  
Knockout Allele

Lasting changes in gene expression are critical for the formation of long-term memories (LTMs), depending on the conserved CrebB transcriptional activator. While requirement of distinct neurons in defined circuits for different learning and memory phases have been studied in detail, only little is known regarding the gene regulatory changes that occur within these neurons. We here use the fruit fly as powerful model system to study the neural circuits of CrebB-dependent appetitive olfactory LTM. We edited the CrebB locus to create a GFP-tagged CrebB conditional knockout allele, allowing us to generate mutant, post-mitotic neurons with high spatial and temporal precision. Investigating CrebB-dependence within the mushroom body (MB) circuit we show that MB α/β and α’/β’ neurons as well as MBON α3, but not in dopaminergic neurons require CrebB for LTM. Thus, transcriptional memory traces occur in different neurons within the same neural circuit.

scholarly journals Multiplex imaging of quantal glutamate release and presynaptic Ca2+ at multiple synapses in situ

2018 ◽  
Author(s):  
Thomas P. Jensen ◽  
Kaiyu Zheng ◽  
Nicholas Cole ◽  
Jonathan S. Marvin ◽  
Loren L. Looger ◽  
...  
Keyword(s):  
Neural Circuit ◽  
Glutamate Release ◽  
Loose Coupling ◽  
Excitatory Neurotransmitter ◽  
Presynaptic Function ◽  
Brain Circuits ◽  
Presynaptic Boutons ◽  
Central Synapses ◽  
Nanomolar Range

AbstractInformation processing by brain circuits depends on Ca2+-dependent, stochastic release of the excitatory neurotransmitter glutamate. Optical glutamate sensors have enabled detection of evoked and spontaneous synaptic discharges. However, monitoring presynaptic function and its underpinning machinery in situ requires simultaneous readout of quantal glutamate release and nanomolar presynaptic Ca2+. Here, we find that the fluorescence lifetime of the red-shifted Ca2+ indicator Cal-590 is Ca2+-sensitive in the nanomolar range, and employ it in combination with green glutamate sensors to relate quantal neurotransmission to presynaptic Ca2+ kinetics. Imaging of multiple synapses in an identified neural circuit reveals that fluctuations both in spike-evoked Ca2+ transients and in resting presynaptic Ca2+ can affect release efficacy. At the sub-microscopic level within individual presynaptic boutons, we detected no consistent co-localisation of presynaptic Ca2+ entry and glutamate release sites, suggesting loose coupling between the two. The present approach broadens qualitatively our horizon in understanding release machinery of central synapses.

scholarly journals A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

2014 ◽  
Author(s):  
Josef C Uyeda ◽  
Luke J Harmon
Keyword(s):  
Large Scale ◽  
Bayesian Method ◽  
A Priori ◽  
Model Fitting ◽  
Biological Data ◽  
Comparative Data ◽  
Adaptive Landscape ◽  
Multiple Sources ◽  
Adaptive Landscapes ◽  
Selection Parameters

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigneda priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether oura priorimodels adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data--such as microevolutionary estimates of selection parameters and paleontological timeseries--allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

scholarly journals Guiana Dolphin (Sotalia guianensis) in the Maracaibo Lake System, Venezuela: Conservation, Threats, and Population Overview

2021 ◽  
Vol 7 ◽  
Author(s):  
Hector Barrios-Garrido ◽  
Kareen De Turris-Morales ◽  
Ninive Edilia Espinoza-Rodriguez
Keyword(s):  
Habitat Degradation ◽  
Lake Management ◽  
Biological Data ◽  
Multiple Sources ◽  
Traditional Use ◽  
Sotalia Guianensis ◽  
Lake System ◽  
Northern Portion ◽  
Conservation Threats ◽  
Guiana Dolphin

The Guiana dolphin (Sotalia guianensis) home range is located across Central and South American countries, in coastal habitats in the Caribbean and Atlantic Ocean. Its distribution is scattered, with multiple population centers which are under threats that vary based on local realities. We compiled and assessed biological data from multiple sources (published and unpublished data) to improve our understanding regarding the Maracaibo Lake Management Unit, which is an isolated and unique population core of this species. We identified at least two distinguishable population centers throughout the Maracaibo Lake System, one in the northern portion—in the Gulf of Venezuela, and another in the southern portion of the Maracaibo Lake itself. Both centers have differences in some biological aspects (e.g., group size and habitat use), but similarities in the human-induced pressures (e.g., intentional take, habitat degradation, and traditional use). We detailed the uses of Guiana dolphin (consumptive and non-consumptive) by community members, including the use as talismans for indigenous fishers and consumption of its meat as a religious belief (Easter period), and dolphin watching tours carried out by local companies. In one artisanal port, at least 15 animals are intentionally taken annually to be used for local consumption, shark-bait, or trade; however, we acknowledge that this annual take is likely an underestimate. Further research is needed to clarify how and at what magnitude mentioned and other key-threats are impacting over Guiana dolphin MU in the Maracaibo Lake System.

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