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 Neural and non-neural contributions to sexual dimorphism of mid-day sleep in Drosophila: A pilot study

2015 ◽  
Author(s):  
M. Khericha ◽  
J.B. Kolenchery ◽  
E. Tauber
Keyword(s):  
Sexual Dimorphism ◽  
Fat Body ◽  
Hormone Secretion ◽  
Model Organism ◽  
Fruit Fly ◽  
Mushroom Bodies ◽  
Sexually Dimorphic ◽  
Neural Tissues ◽  
Circadian Clock Output ◽  
The Brain

AbstractMany of the characteristics associated with mammalian sleep are also observed in Drosophila, making the fruit-fly a powerful model organism for studying the genetics of this important process. Among these similarities is the presence of sexual dimorphic sleep patterns, which in flies, is manifested as increased mid-day sleep (‘siesta’) in males, compared to females. Here, we have used targeted miss-expression of the gene transformer (tra) and tra2 to either feminise or masculinise specific neural and non-neural tissues in the fly. Feminization of males using three different GAL4 drivers which are expressed in the mushroom bodies induced a female-like reduced siesta, while the masculinisation of females using these drivers triggered the male-like increased siesta. We also observed a similar reversal of sex-specific sleep by miss-expressing tra in the fat body, a key tissue in energy metabolism and hormone secretion. In addition, the daily expression levels of takeout, an important circadian clock output gene, were sexually dimorphic. Taken together, our experiments suggest that sleep-sexual dimorphism in Drosophila is driven by multiple neural and non-neural circuits, within and outside the brain.

scholarly journals FlyBrainLab: Accelerating the Discovery of the Functional Logic of the Drosophila Brain in the Connectomic/Synaptomic Era

2020 ◽  
Author(s):  
Aurel A. Lazar ◽  
Tingkai Liu ◽  
Mehmet Kerem Turkcan ◽  
Yiyin Zhou
Keyword(s):  
Computational Models ◽  
Neural Circuits ◽  
Developmental Stages ◽  
Fruit Fly ◽  
Central Complex ◽  
Neuron Activity ◽  
Activity Data ◽  
Entry Points ◽  
Functional Logic ◽  
Brain Data

AbstractIn recent years, a wealth of Drosophila neuroscience data have become available. These include cell type, connectome and synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab.FlyBrainLab is uniquely positioned to accelerate the discovery of the functional logic of the Drosophila brain. Its interactive open source architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic and electrophysiological data, changing the organization of neuroscientific fly brain data from a group of seemingly disparate databases, arrays and tables, to a well structured data and executable circuit repository.The FlyBrainLab User Interface supports a highly intuitive and automated work-flow that streamlines the 3D exploration and visualization of fly brain circuits, and the interactive exploration of the functional logic of executable circuits created directly from the explored and visualized fly brain data. Furthermore, efficient comparisons of circuit models are supported, across models developed by different researchers, across different developmental stages of the fruit fly and across different datasets.The FlyBrainLab Utility Libraries help untangle the graph structure of neural circuits from raw connectome and synaptome data. The Circuit Libraries facilitate the exploration of neural circuits of the neuropils of the central complex and, the development and implementation of models of the adult and larva fruit fly early olfactory systems.Seeking to transcend the limitations of the connectome, FlyBrainLab provides additional libraries for molecular transduction arising in sensory coding in vision and olfaction. Together with sensory neuron activity data, these libraries serve as entry points for discovering circuit function in the sensory systems of the fruit fly brain. They also enable the biological validation of developed executable circuits within the same platform.

scholarly journals The neuropeptide allatostatin C from clock-associated DN1p neurons generates the circadian rhythm for oogenesis

2021 ◽  
Vol 118 (4) ◽  
pp. e2016878118
Author(s):  
Chen Zhang ◽  
Ivana Daubnerova ◽  
Yong-Hoon Jang ◽  
Shu Kondo ◽  
Dušan Žitňan ◽  
...  
Keyword(s):  
Clock Genes ◽  
Biological Clock ◽  
Model Organism ◽  
Fruit Fly ◽  
Neural Substrates ◽  
Pacemaker Neurons ◽  
Insulin Producing Cells ◽  
Gonadotrophin Secretion ◽  
Show Evidence ◽  
The Brain

The link between the biological clock and reproduction is evident in most metazoans. The fruit fly Drosophila melanogaster, a key model organism in the field of chronobiology because of its well-defined networks of molecular clock genes and pacemaker neurons in the brain, shows a pronounced diurnal rhythmicity in oogenesis. Still, it is unclear how the circadian clock generates this reproductive rhythm. A subset of the group of neurons designated “posterior dorsal neuron 1” (DN1p), which are among the ∼150 pacemaker neurons in the fly brain, produces the neuropeptide allatostatin C (AstC-DN1p). Here, we report that six pairs of AstC-DN1p send inhibitory inputs to the brain insulin-producing cells, which express two AstC receptors, star1 and AICR2. Consistent with the roles of insulin/insulin-like signaling in oogenesis, activation of AstC-DN1p suppresses oogenesis through the insulin-producing cells. We show evidence that AstC-DN1p activity plays a role in generating an oogenesis rhythm by regulating juvenile hormone and vitellogenesis indirectly via insulin/insulin-like signaling. AstC is orthologous to the vertebrate neuropeptide somatostatin (SST). Like AstC, SST inhibits gonadotrophin secretion indirectly through gonadotropin-releasing hormone neurons in the hypothalamus. The functional and structural conservation linking the AstC and SST systems suggest an ancient origin for the neural substrates that generate reproductive rhythms.

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 Invertebrate central pattern generator circuits

2010 ◽  
Vol 365 (1551) ◽  
pp. 2329-2345 ◽  
Author(s):  
Allen I. Selverston
Keyword(s):  
Central Pattern Generator ◽  
Computational Models ◽  
Mechanistic Explanation ◽  
Pattern Generator ◽  
Individual Organism ◽  
Multiple Phase ◽  
Brain Circuits ◽  
Sufficient Detail ◽  
Cellular Components ◽  
The Brain

There are now a reasonable number of invertebrate central pattern generator (CPG) circuits described in sufficient detail that a mechanistic explanation of how they work is possible. These small circuits represent the best-understood neural circuits with which to investigate how cell-to-cell synaptic connections and individual channel conductances combine to generate rhythmic and patterned output. In this review, some of the main lessons that have appeared from this analysis are discussed and concrete examples of circuits ranging from single phase to multiple phase patterns are described. While it is clear that the cellular components of any CPG are basically the same, the topology of the circuits have evolved independently to meet the particular motor requirements of each individual organism and only a few general principles of circuit operation have emerged. The principal usefulness of small systems in relation to the brain is to demonstrate in detail how cellular infrastructure can be used to generate rhythmicity and form specialized patterns in a way that may suggest how similar processes might occur in more complex systems. But some of the problems and challenges associated with applying data from invertebrate preparations to the brain are also discussed. Finally, I discuss why it is useful to have well-defined circuits with which to examine various computational models that can be validated experimentally and possibly applied to brain circuits when the details of such circuits become available.

scholarly journals Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic era

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Aurel A Lazar ◽  
Tingkai Liu ◽  
Mehmet Kerem Turkcan ◽  
Yiyin Zhou
Keyword(s):  
Computational Models ◽  
Fruit Fly ◽  
Neuron Activity ◽  
Activity Data ◽  
Computing Platform ◽  
Brain Circuits ◽  
Entry Points ◽  
Functional Logic ◽  
Drosophila Brain ◽  
3D Exploration

In recent years, a wealth of Drosophila neuroscience data have become available including cell type, connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison and evaluation of circuit functions of the fruit fly brain.

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 Quantitative investigation reveals distinct phases in Drosophila sleep

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Xiaochan Xu ◽  
Wei Yang ◽  
Binghui Tian ◽  
Xiuwen Sui ◽  
Weilai Chi ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
General Pattern ◽  
Model Organism ◽  
Infrared Camera ◽  
Fruit Fly ◽  
Genetic Dissection ◽  
Power Law Distribution ◽  
Quantitative Investigation ◽  
Waking Up ◽  
Set Up

AbstractThe fruit fly, Drosophila melanogaster, has been used as a model organism for the molecular and genetic dissection of sleeping behaviors. However, most previous studies were based on qualitative or semi-quantitative characterizations. Here we quantified sleep in flies. We set up an assay to continuously track the activity of flies using infrared camera, which monitored the movement of tens of flies simultaneously with high spatial and temporal resolution. We obtained accurate statistics regarding the rest and sleep patterns of single flies. Analysis of our data has revealed a general pattern of rest and sleep: the rest statistics obeyed a power law distribution and the sleep statistics obeyed an exponential distribution. Thus, a resting fly would start to move again with a probability that decreased with the time it has rested, whereas a sleeping fly would wake up with a probability independent of how long it had slept. Resting transits to sleeping at time scales of minutes. Our method allows quantitative investigations of resting and sleeping behaviors and our results provide insights for mechanisms of falling into and waking up from sleep.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

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