Biology subjects

Newton, A. J. H.

Publications and source records attributed to Newton, A. J. H..

4 recordsLinked to original sources

bioRxiv2022

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

AO_SCPLOWBSTRACTC_SCPLOWThe need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURONs ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURONs reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

neuroscience↗

bioRxiv2022

Multiscale computer modeling of spreading depolarization in brain slices

Spreading depolarization (SD) is a slow-moving wave of neuronal depolarization accompanied by a breakdown of ion concentration homeostasis, followed by long periods of neuronal silence (spreading depression), and associated with several neurological conditions. We developed multiscale (ions to tissue slice) computer models of SD in brain slices using the NEURON simulator: 36,000 neurons (2 voltage-gated ion channels; 3 leak channels; 3 ion exchangers/pumps) in the extracellular space (ECS) of a slice (1 mm sides, varying thickness) with ion (K+, Cl-, Na+) and O2 diffusion and equilibration with a surrounding bath. Glia and neurons cleared K+ from the ECS via Na+/K+ pumps. SD propagated through the slices at realistic speeds of 2-4 mm/min, which increased by as much as 50% in models incorporating the effects of hypoxia or propionate. In both cases, the speedup was mediated principally by ECS shrinkage. Our model allows us to make testable predictions, including: 1. SD can be inhibited by enlarging ECS volume; 2. SD velocity will be greater in areas with greater neuronal density, total neuronal volume, or larger/more dendrites; 3. SD is all-or-none: initiating K+ bolus properties have little impact on SD speed; 4. Slice thickness influences SD due to relative hypoxia in the slice core, exacerbated by SD in a pathological cycle; 5. SD and high neuronal spike rates will be observed in the core of the slice. Cells in the periphery of the slice near an oxygenated bath will resist SD. SignificanceSpreading depolarization (SD) is a slow moving wave of electrical and ionic imbalances in brain tissue and is a hallmark of several neurological disorders. We developed a multiscale computer model of brain slices with realistic neuronal densities, ions, and oxygenation. Our model shows that SD is exacerbated by and causes hypoxia, resulting in strong SD dependence on slice thickness. Our model also predicts that the velocity of SD propagation is not dependent on its initiation, but instead on tissue properties, including the amount of extracellular space and the total area of neuronal membrane, suggesting faster SD following ischemic stroke or traumatic brain injury.

neuroscience↗

bioRxiv2022

Efficient simulation of 3D reaction-diffusion in models of neurons and networks

Neuronal activity is the result of both the electrophysiology and chemophysiology. A neuron can be well represented for the purposes of electrophysiological simulation as a tree composed of connected cylinders. This representation is also apt for 1D simulations of their chemophysiology, provided the spatial scale is larger than the diameter of the cylinders and there is radial symmetry. Higher dimensional simulation is necessary to accurately capture the dynamics when these criteria are not met, such as with wave curvature, spines, or diffusion near the soma. We have developed a solution to enable efficient finite volume method simulation of reaction-diffusion kinetics in intracellular 3D regions in neuron and network models and provide an implementation within the NEURON simulator. An accelerated version of the CTNG 3D reconstruction algorithm transforms morphologies suitable for ion-channel based simulations into consistent 3D voxelized regions. Kinetics are then solved using a parallel algorithm based on Douglas-Gunn that handles the irregular 3D geometry of a neuron; these kinetics are coupled to NEURONs 1D mechanisms for ion channels, synapses, etc. The 3D domain may cover the entire cell or selected regions of interest. Simulations with dendritic spines and of the soma reveal details of dynamics that would be missed in a pure 1D simulation. We describe and validate the methods and discuss their performance.

neuroscience↗

bioRxiv2021

Pipeline for retrieval of COVID-19 immune signatures

ObjectiveThe accelerating pace of biomedical publication has made retrieving papers and extracting specific comprehensive scientific information a key challenge. A timely example of such a challenge is to retrieve the subset of papers that report on immune signatures (coherent sets of biomarkers) to understand the immune response mechanisms which drive differential SARS-CoV-2 infection outcomes. A systematic and scalable approach is needed to identify and extract COVID-19 immune signatures in a structured and machine-readable format. Materials and MethodsWe used SPECTER embeddings with SVM classifiers to automatically identify papers containing immune signatures. A generic web platform was used to manually screen papers and allow anonymous submission. ResultsWe demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. This demonstrates the efficacy of using a SVM classifier with document embeddings of the abstract and title, to retrieve papers with scientifically salient information, even when that information is rarely present in the abstract. Additionally, classification based on the embeddings identified the type of immune signature (e.g., gene expression vs. other types of profiling) with a positive predictive value of 74%. ConclusionCoupling a classifier based on document embeddings with direct author engagement offers a promising pathway to build a semistructured representation of scientifically relevant information. Through this approach, partially automated literature mining can help rapidly create semistructured knowledge repositories for automatic analysis of emerging health threats.

immunology↗