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 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 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 Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

scholarly journals Population dynamics of pre-imaginal stages of olive fruit fly Bactrocera oleae Gmel. (Diptera, Tephritidae) in the region of Bar (Montenegro)

2013 ◽  
Vol 28 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Tatjana Perovic ◽  
Snjezana Hrncic
Keyword(s):  
Population Dynamics ◽  
Developmental Stages ◽  
Fruit Fly ◽  
Bactrocera Oleae ◽  
Yield Reduction ◽  
Olive Fruit Fly ◽  
Application Timing ◽  
Olive Fruit ◽  
Fruit Drop

Olive fruit fly is the most harmful pest of olive fruits and important for oil production. Damage involves yield reduction as a consequence of premature fruit drop, but also a reduced quality of olive oil and olive products. There is little available data regarding the biology of Bactrocera oleae in Montenegro. Knowledge of the pest life cycle and development would improve optimization of insecticide application timing and protection of fruits, and reduce adverse effects on the environment. Investigation was conducted on the Zutica variety in an olive grove located in Bar during a three-year period. Population dynamics of the pre-imaginal stages and level of fruit infestation were monitored from mid-July until the end of October. The results of this three-year investigation showed that the beginning of infestation was always at the end of July. It was also found that, depending on environmental conditions, the level of infestation was low until the end of August. In September and October it multiplied, and reached maximum by the end of October. Regarding infestation structure, eggs and first instar larvae were the dominant developmental stages of the pest until the middle of September. From mid-September until mid-October all developmental stages (eggs, larvae, pupae) were equally present in infested fruits. Pupae, cocoons and abandoned galleries prevailed until the harvest.

scholarly journals A neuronal circuit for vector computation builds an allocentric traveling-direction signal in the Drosophila fan-shaped body

2020 ◽  
Author(s):  
Cheng Lyu ◽  
L.F. Abbott ◽  
Gaby Maimon
Keyword(s):  
Computational Models ◽  
Neuronal Population ◽  
The Body ◽  
Central Complex ◽  
Two Dimensional ◽  
Neuronal Circuit ◽  
Head Direction ◽  
Neural Signals ◽  
Shaped Body ◽  
External Cues

AbstractMany behavioral tasks require the manipulation of mathematical vectors, but, outside of computational models1–8, it is not known how brains perform vector operations. Here we show how the Drosophila central complex, a region implicated in goal-directed navigation8–14, performs vector arithmetic. First, we describe neural signals in the fan-shaped body that explicitly track a fly’s allocentric traveling direction, that is, the traveling direction in reference to external cues. Past work has identified neurons in Drosophila12,15–17 and mammals18,19 that track allocentric heading (e.g., head-direction cells), but these new signals illuminate how the sense of space is properly updated when traveling and heading angles differ. We then characterize a neuronal circuit that rotates, scales, and adds four vectors related to the fly’s egocentric traveling direction–– the traveling angle referenced to the body axis––to compute the allocentric traveling direction. Each two-dimensional vector is explicitly represented by a sinusoidal activity pattern across a distinct neuronal population, with the sinusoid’s amplitude representing the vector’s length and its phase representing the vector’s angle. The principles of this circuit, which performs an egocentric-to-allocentric coordinate transformation, may generalize to other brains and to domains beyond navigation where vector operations or reference-frame transformations are required.

Genes and neural circuits for sleep of the fruit fly

2017 ◽  
Vol 118 ◽  
pp. 82-91 ◽  
Author(s):  
Jun Tomita ◽  
Gosuke Ban ◽  
Kazuhiko Kume
Keyword(s):  
Neural Circuits ◽  
Fruit Fly

scholarly journals Identification and Expression Analysis of Snf2 Family Proteins in Tomato (Solanum lycopersicum)

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Dongdong Zhang ◽  
Sujuan Gao ◽  
Ping Yang ◽  
Jie Yang ◽  
Songguang Yang ◽  
...  
Keyword(s):  
Amino Acid ◽  
Solanum Lycopersicum ◽  
Developmental Stages ◽  
Profile Analysis ◽  
Nucleosome Position ◽  
Fruit Fly ◽  
Amino Acid Residues ◽  
Expression Profile Analysis ◽  
Wide Range ◽  
Chromatin Remodeling Complexes

As part of chromatin-remodeling complexes (CRCs), sucrose nonfermenting 2 (Snf2) family proteins alter chromatin structure and nucleosome position by utilizing the energy of ATP, which allows other regulatory proteins to access DNA. Plant genomes encode a large number of Snf2 proteins, and some of them have been shown to be the key regulators at different developmental stages in Arabidopsis. Yet, little is known about the functions of Snf2 proteins in tomato (Solanum lycopersicum). In this study, 45 Snf2s were identified by the homologous search using representative sequences from yeast (S. cerevisiae), fruit fly (D. melanogaster), and Arabidopsis (A. thaliana) against the tomato genome annotation dataset. Tomato Snf2 proteins (also named SlCHRs) could be clustered into 6 groups and distributed on 11 chromosomes. All SlCHRs contained a helicase-C domain with about 80 amino acid residues and a SNF2-N domain with more variable amino acid residues. In addition, other conserved motifs were also identified in SlCHRs by using the MEME program. Expression profile analysis indicated that tomato Snf2 family genes displayed a wide range of expressions in different tissues and some of them were regulated by the environmental stimuli such as salicylic acid, abscisic acid, salt, and cold. Taken together, these results provide insights into the functions of SlCHRs in tomato.

scholarly journals It Takes Two: Dimerization Is Essential for the Broad-Spectrum Predatory and Defensive Activities of the Venom Peptide Mp1a from the Jack Jumper Ant Myrmecia pilosula

Biomedicines ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 185 ◽  
Author(s):  
Samantha A. Nixon ◽  
Zoltan Dekan ◽  
Samuel D. Robinson ◽  
Shaodong Guo ◽  
Irina Vetter ◽  
...  
Keyword(s):  
Broad Spectrum ◽  
Haemonchus Contortus ◽  
Parasitic Nematode ◽  
Biological Activities ◽  
Fruit Fly ◽  
Cell Types ◽  
Activity Data ◽  
Chemical Complexity ◽  
Rational Engineering ◽  
Structure Activity

Ant venoms have recently attracted increased attention due to their chemical complexity, novel molecular frameworks, and diverse biological activities. The heterodimeric peptide ∆-myrtoxin-Mp1a (Mp1a) from the venom of the Australian jack jumper ant, Myrmecia pilosula, exhibits antimicrobial, membrane-disrupting, and pain-inducing activities. In the present study, we examined the activity of Mp1a and a panel of synthetic analogues against the gastrointestinal parasitic nematode Haemonchus contortus, the fruit fly Drosophila melanogaster, and for their ability to stimulate pain-sensing neurons. Mp1a was found to be both insecticidal and anthelmintic, and it robustly activated mammalian sensory neurons at concentrations similar to those reported to elicit antimicrobial and cytotoxic activity. The native antiparallel Mp1a heterodimer was more potent than heterodimers with alternative disulfide connectivity, as well as monomeric analogues. We conclude that the membrane-disrupting effects of Mp1a confer broad-spectrum biological activities that facilitate both predation and defense for the ant. Our structure–activity data also provide a foundation for the rational engineering of analogues with selectivity for particular cell types.

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