Biology subjects

Wang, C.-T.

Publications and source records attributed to Wang, C.-T..

6 recordsLinked to original sources

bioRxiv2025

Ca2+ Plateau Potentials Reflect Cross-Theta Cortico-Hippocampal Input Dynamics and Acetylcholine for Rapid Formation of Efficient Place-Cell Code

A central tenet of Systems Neuroscience lies in an understanding of memory and behavior through learning rules, but synaptic plasticity has rarely been shown to create functional single-neuron code in a causal and biophysically rooted manner. Behavioral Time-Scale Synaptic Plasticity (BTSP), identified in vivo, holds a great potential for explaining instantaneous hippocampal selectivity emergence by long-term potentiation (LTP), yet the cellular and endogenous mechanisms are unknown, impeding broader conceptualization of this novel rule for its algorithmic, systems-level and theoretical implications. Here, we addressed this gap by in-vivo, ex-vivo, in-silico and computational approaches to seek neurophysiologically inspired protocols for synaptically evoking Ca2+ plateau potentials and inducing potentiation in the CA1. We found induction of BTSP-LTP is best explained by a theta-oscillation-paced, gradually developed cellular state being supported with precisely timed weak ramping inputs. Remarkably, the previously presumed one-shot LTP for in-vivo place-field formation is possible under the influence of muscarinic activation. Through modeling, the notion of acetylcholine-gated BTSP gave rise to a computational advantage for low-interference continual learning. We further demonstrated that biophysics of Transient Receptor Potential (TRPM) and NMDA receptor (NMDAR) channels powerfully shapes the cross-theta dynamics underlying BTSP. These results which cover pre-, post-synaptic and neuromodulatory factors and their timing suggest fundamental principles for graded plateau potentials and hippocampal LTP induction. Overall, our work dissects cellular mechanisms potentially important for a prominent in-vivo hippocampal plasticity phenomenon, and offers a biological basis for framing BTSP as an input-dynamics-aware, neuromodulation-tuned synaptic algorithm.

neuroscience↗

bioRxiv2022

CRISPR/Cas9 mutants delineate roles of Populus FT and TFL1/CEN/BFT family members in growth, dormancy release and flowering

Vegetative and reproductive phase change and phenology are economically and ecologically important traits. Trees typically require several years of growth before flowering and once mature, seasonal control of the transition to flowering and flower development is necessary to maintain vegetative meristems and for reproductive success. Members of two related gene subfamilies, FLOWERING LOCUST (FT) and TERMINAL FLOWER1 (TFL1)/CENTRORADIALIS (CEN)/BROTHER OF FT AND TFL1 (BFT), have antagonistic roles in flowering in diverse species and roles in vegetative phenology in trees, but many details of their functions in trees have yet to be resolved. Here, we used CRISPR/Cas9 to generate single and double mutants involving the five Populus FT and TFL1/CEN/BFT genes. ft1 mutants exhibited wild-type-like phenotypes in long days and short days, but after chilling to release dormancy showed delayed bud flush and GA3 could compensate for the ft1 mutation. After rooting and generating some phytomers in tissue culture, both cen1 and cen1ft1 mutants produced terminal as well as axillary flowers, indicating that the cen1 flowering phenotype is independent of FT1. Some axillary meristems initially generated phytomers and in potted plants, the timing of flowering in these shoots correlated with upregulation of FT2 in maturing leaves, suggesting that, in long days, CEN1 antagonizes FT2 promotion of flowering but enables FT2 promotion of shoot growth by maintaining indeterminacy of the shoot apical meristem. CEN1 showed distinct circannual expression patterns in vegetative and reproductive tissues and comparison with the expression patterns of FT1 and FT2 suggest that the relative levels of CEN1 compared to FT1 and FT2 regulate multiple phases of vegetative and reproductive seasonal development.

plant biology↗

bioRxiv2021

Discovering Novel Antimicrobial Peptides in Generative Adversarial Network

Due to the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) can become ideal for next-generation antibiotics. This study trained a deep convolutional generative adversarial network (GAN) with known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them named GAN-pep 1[~]8 were chosen to be synthesized for further experiments. Disk diffusion testing and minimum inhibitory concentration (MIC) determination were used to determine the antibacterial effects of the synthesized GAN-designed peptides. Seven out of the eight synthesized GAN-designed peptides showed antibacterial activities. Additionally, GAN-pep 3 and GAN-pep 8 had a broad spectrum of antibacterial effects. Both of them were also effective against antibiotic-resistant bacteria strains such as methicillin-resistant Staphylococcus aureus (S. aureus) and carbapenem-resistant Pseudomonas aeruginosa (P. aeruginosa). GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria.

microbiology↗

bioRxiv2019

The Fruit Fly Brain Observatory: From Structure to Function

The 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.\n\nTo 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.

neuroscience↗

bioRxiv2018

A single-cell level and connectome-derived computational model of the Drosophila brain

Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which 1) identifies the polarity of each neuron arbor, 2) predicts connections between neurons, 3) translates morphology data from the database into physiology parameters for computational modeling, 4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and 5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.

bioinformatics↗

bioRxiv2016

The Fruit Fly Brain Observatory: from structure to function

The 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.

neuroscience↗