Biology subjects

Gast, R.

Publications and source records attributed to Gast, R..

7 recordsLinked to original sources

bioRxiv2024

Hijacking and Integration of Algal Plastids andMitochondria in a Polar Planktonic Host

In oceanic plankton, various host organisms are capable of engulfing and temporarily integrating microalgae (photosymbiosis) or just their photosynthetic plastids (kleptoplastidy) as a solar-powered energy source. These cellular interactions can be considered to be representative of evolutionary steps in plastid acquisition in eukaryotes, but the underlying cellular mechanisms and dynamics are not fully understood. Here, we studied a kleptoplastidic dinoflagellate (RSD: Ross Sea Dinoflagellate), which is known to steal plastids of the microalga Phaeocystis antarctica. We tracked the morphology and activity of stolen plastids over several months by combining multimodal subcellular imaging and photophysiology. Upon integration inside a host vacuole, the volume of plastids and pyrenoids significantly increased and photosynthetic activity was boosted along with carbon fixation and transfer to the host. This may be supported by the retention of a 50-fold larger algal nucleus for [~]1 week. Once the algal nucleus was lost, there was a decrease in plastid volume and photosynthesis, but plastids were still beneficial for the host after > 2 months. Unlike other kleptoplastidic interactions, we showed that the algal mitochondrion was also stolen and retained for several months, transforming into an extensive network in close proximity with plastids. This highlights a new strategy in plankton along the continuum of plastid symbioses where both the energy-producing plastid and mitochondrion of a microalga are hijacked for several months by a host. This symbiosis that we found to be widely-distributed in polar regions suggests that plastid-mitochondrion interaction may have played a role in the evolution of plastid acquisition.

ecology↗

bioRxiv2023

The International Weed Genomics Consortium: Community Resources for Weed Genomics Research

The International Weed Genomics Consortium is a collaborative group of researchers focused on developing genomic resources for the study of weedy plants. Weeds are attractive systems for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Our goal is to use genomic information to develop sustainable and effective weed control methods and to provide insights about biotic and abiotic stress tolerance to assist crop breeding. Here, we outline resources under development by the consortium and highlight areas of research that will be impacted by these enabling resources.

genomics↗

bioRxiv2021

On the role of arkypallidal and prototypical neurons for phase transitions in the external pallidum

The external pallidum (GPe) plays a central role for basal ganglia functions and dynamics and, consequently, has been included in most computational studies of the basal ganglia. These studies considered the GPe as a homogeneous neural population. However, experimental studies have shown that the GPe contains at least two distinct cell types (prototypical and arkypallidal cells). In this work, we provide in silico insight into how pallidal heterogeneity modulates dynamic regimes inside the GPe and how they affect the GPe response to oscillatory input. We derive a mean-field model of the GPe system from a microscopic spiking neural network of recurrently coupled prototypical and arkypallidal neurons. Using bifurcation analysis, we examine the influence of the intra-pallidal connectivity on the GPe dynamics. We find that under healthy conditions, the inhibitory coupling determines whether the GPe is close to either a bi-stable or an oscillatory regime. Furthermore, we show that oscillatory input to the GPe, arriving from subthalamic nucleus or striatum, leads to characteristic patterns of cross-frequency coupling observed at the GPe. Based on these findings, we propose two different hypotheses of how dopamine depletion at the GPe may lead to phase-amplitude coupling between the parkinsonian beta rhythm and a GPe-intrinsic gamma rhythm. Finally, we show that these findings generalize to realistic spiking neural networks of sparsely coupled type-I excitable GPe neurons. Significant StatementOur work provides (a) insight into the theoretical implications of a dichotomous GPe organization for its macroscopic dynamic regimes, and (b) an exact mean-field model that allows for future investigations of the relationship between GPe spiking activity and local field potential fluctuations. We identify the major phase transitions that the GPe can undergo when subject to static or periodic input and link these phase transitions to the emergence of synchronized oscillations and cross-frequency coupling in the basal ganglia. Due to the close links between our model and experimental findings on the structure and dynamics of prototypical and arkypallidal cells, our results can be used to guide both experimental and computational studies on the role of the GPe for basal ganglia dynamics in health and disease.

neuroscience↗

bioRxiv2019

A Mean-Field Description of Bursting Dynamics in Spiking Neural Networks with Short-Term Adaptation

Bursting plays an important role in neural communication. At the population level, macro-scopic bursting has been identified in populations of neurons that do not express intrinsic bursting mechanisms. For the analysis of such phase transitions, mean-field descriptions of macroscopic bursting behavior pose a valuable tool. In this article, we derive mean-field descriptions of populations of spiking neurons in which collective bursting behavior arises via short-term adaptation mechanisms. Specifically, we consider synaptic depression and spike-frequency adaptation in networks of quadratic integrate-and-fire neurons. We characterize the emerging bursting behavior using bifurcation analysis and validate our mean-field derivations by comparing the microscopic and macroscopic descriptions of the population dynamics. Hence, we provide mechanistic descriptions of phase transitions between bursting and non-bursting population dynamics which play important roles in both healthy neural communication and neurological disorders.

neuroscience↗

bioRxiv2019

PyRates - A Python Framework for Rate-Based Neural Simulations

In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of neural models as a graph. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model graph is translated into a tensorflow-based compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We then show via numerical simulations that the behavior shown by the model implementations in PyRates is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.

neuroscience↗

bioRxiv2019

Probing Neural Networks for Dynamic Switches of Communication Pathways

Dynamic communication and routing play important roles in the human brain to facilitate 2exibility in task solving and thought processes. Here, we present a network perturbation methodology that allows to investigate dynamic switching between different network pathways based on phase offsets between two external oscillatory drivers. We apply this method in a computational model of the human connectome with delay-coupled neural masses. To analyze dynamic switching of pathways, we define four new metrics that measure dynamic network response properties for pairs of stimulated nodes. Evaluating these metrics for all network pathways, we found a broad spectrum of pathways with distinct dynamic properties and switching behaviors. Specifically, we found that 60.1% of node pairs can switch their communication from one pathway to another depending on their phase offsets. This indicates that phase offsets and coupling delays play an important computational role for the dynamic switching between communication pathways in the brain.

neuroscience↗

bioRxiv2017

Encoding And Decoding Dynamic Sensory Signals With Recurrent Neural Networks: An Application Of Conceptors To Birdsongs

In a constantly changing environment the brain has to make sense of dynamic patterns of sensory input. These patterns can refer to stimuli with a complex and hierarchical structure which has to be inferred from the neural activity of sensory areas in the brain. Such areas were found to be locally recurrently structured as well as hierarchically organized within a given sensory domain. While there is a great body of work identifying neural representations of various sensory stimuli at different hierarchical levels, less is known about the nature of these representations. In this work, we propose a model that describes a way to encode and decode sensory stimuli based on the activity patterns of multiple, recurrently connected neural populations with different receptive fields. We demonstrate the ability of our model to learn and recognize complex, dynamic stimuli using birdsongs as exemplary data. These birdsongs can be described by a 2-level hierarchical structure, i.e. as sequences of syllables. Our model matches this hierarchy by learning single syllables on a first level and sequences of these syllables on a top level. Model performance on recognition tasks is investigated for an increasing number of syllables or songs to recognize and compared to state-of-the-art machine learning approaches. Finally, we discuss the implications of our model for the understanding of sensory pattern processing in the brain. We conclude that the employed encoding and decoding mechanisms might capture general computational principles of how the brain extracts relevant information from the activity of recurrently connected neural populations.

neuroscience↗