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Abdellah M, Foni A, Cantero JJG, Guerrero NR, Boci E, Fleury A, Coggan JS, Keller D, Planas J, Courcol JD, Khazen G. Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling. Brief Bioinform 2024; 25:bbae393. [PMID: 39129363 DOI: 10.1093/bib/bbae393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/06/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024] Open
Abstract
Understanding the intracellular dynamics of brain cells entails performing three-dimensional molecular simulations incorporating ultrastructural models that can capture cellular membrane geometries at nanometer scales. While there is an abundance of neuronal morphologies available online, e.g. from NeuroMorpho.Org, converting those fairly abstract point-and-diameter representations into geometrically realistic and simulation-ready, i.e. watertight, manifolds is challenging. Many neuronal mesh reconstruction methods have been proposed; however, their resulting meshes are either biologically unplausible or non-watertight. We present an effective and unconditionally robust method capable of generating geometrically realistic and watertight surface manifolds of spiny cortical neurons from their morphological descriptions. The robustness of our method is assessed based on a mixed dataset of cortical neurons with a wide variety of morphological classes. The implementation is seamlessly extended and applied to synthetic astrocytic morphologies that are also plausibly biological in detail. Resulting meshes are ultimately used to create volumetric meshes with tetrahedral domains to perform scalable in silico reaction-diffusion simulations for revealing cellular structure-function relationships. Availability and implementation: Our method is implemented in NeuroMorphoVis, a neuroscience-specific open source Blender add-on, making it freely accessible for neuroscience researchers.
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Affiliation(s)
- Marwan Abdellah
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Alessandro Foni
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Juan José García Cantero
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Nadir Román Guerrero
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Elvis Boci
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Adrien Fleury
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Jay S Coggan
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Daniel Keller
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Judit Planas
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
| | - Georges Khazen
- Blue Brain Project, École Polytecnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, Genève 1202, Switzerland
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Abdellah M, Cantero JJG, Guerrero NR, Foni A, Coggan JS, Calì C, Agus M, Zisis E, Keller D, Hadwiger M, Magistretti PJ, Markram H, Schürmann F. Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. Brief Bioinform 2022; 24:6847753. [PMID: 36434788 PMCID: PMC9851302 DOI: 10.1093/bib/bbac491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/27/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022] Open
Abstract
Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations.
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Affiliation(s)
- Marwan Abdellah
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
| | | | - Nadir Román Guerrero
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Alessandro Foni
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Corrado Calì
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,Neuroscience Institute Cavalieri Ottolenghi (NICO) Orbassano, Italy,Department of Neuroscience, University of Torino Torino, Italy
| | - Marco Agus
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,College of Science and Engineering Hamad Bin Khalifa University Doha, Qatar
| | - Eleftherios Zisis
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Markus Hadwiger
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Henry Markram
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Felix Schürmann
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
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McDougal RA, Conte C, Eggleston L, Newton AJH, Galijasevic H. Efficient Simulation of 3D Reaction-Diffusion in Models of Neurons and Networks. Front Neuroinform 2022; 16:847108. [PMID: 35655652 PMCID: PMC9152282 DOI: 10.3389/fninf.2022.847108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 04/20/2022] [Indexed: 12/20/2022] Open
Abstract
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 NEURON's 1D mechanisms for ion channels, synapses, pumps, and so forth. 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.
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Affiliation(s)
- Robert A McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.,Center for Medical Informatics, Yale University, New Haven, CT, United States.,Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
| | - Cameron Conte
- Center for Medical Informatics, Yale University, New Haven, CT, United States.,Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States.,Department of Statistics, The Ohio State University, Columbus, OH, United States
| | - Lia Eggleston
- Yale College, Yale University, New Haven, CT, United States
| | - Adam J H Newton
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.,Center for Medical Informatics, Yale University, New Haven, CT, United States.,Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, New York, NY, United States
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Abstract
The brain is a complex organ operating on multiple scales. From molecular events that inform electrical and biochemical cellular responses, the brain interconnects processes all the way up to the massive network size of billions of brain cells. This strongly coupled, nonlinear, system has been subject to research that has turned increasingly multidisciplinary. The seminal work of Hodgkin and Huxley in the 1950s made use of experimental data to derive a coherent physical model of electrical signaling in neurons, which can be solved using mathematical and computational methods, thus bringing together neuroscience, physics, mathematics, and computer science. Over the last decades numerous projects have been dedicated to modeling and simulation of specific parts of molecular dynamics, neuronal signaling, and neural network behavior. Simulators have been developed around a specific objective and scale, in order to cope with the underlying computational complexity. Often times a dimension reduction approach allows larger scale simulations, this however has the inherent drawback of losing insight into structure-function interplay at the cellular level. This paper gives an overview of the project NeuroBox that has the objective of integrating multiple brain scales and associated physical models into one unified framework. NeuroBox hosts geometry and anatomical reconstruction methods, such that detailed three-dimensional domains can be integrated into numerical simulations of models based on partial differential equations. The project further focusses on deriving numerical methods for handling complex computational domains, and to couple multiple spatial dimensions. The latter allows the user to specify in which parts of the biological problem high-dimensional representations are necessary and where low-dimensional approximations are acceptable. NeuroBox offers workflow user interfaces that are automatically generated with VRL-Studio and can be controlled by non-experts. The project further uses uG4 as the numerical backend, and therefore accesses highly advanced discretization methods as well as hierarchical and scalable numerical solvers for very large neurobiological problems.
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Mörschel K, Breit M, Queisser G. Generating Neuron Geometries for Detailed Three-Dimensional Simulations Using AnaMorph. Neuroinformatics 2018; 15:247-269. [PMID: 28447297 DOI: 10.1007/s12021-017-9329-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Generating realistic and complex computational domains for numerical simulations is often a challenging task. In neuroscientific research, more and more one-dimensional morphology data is becoming publicly available through databases. This data, however, only contains point and diameter information not suitable for detailed three-dimensional simulations. In this paper, we present a novel framework, AnaMorph, that automatically generates water-tight surface meshes from one-dimensional point-diameter files. These surface triangulations can be used to simulate the electrical and biochemical behavior of the underlying cell. In addition to morphology generation, AnaMorph also performs quality control of the semi-automatically reconstructed cells coming from anatomical reconstructions. This toolset allows an extension from the classical dimension-reduced modeling and simulation of cellular processes to a full three-dimensional and morphology-including method, leading to novel structure-function interplay studies in the medical field. The developed numerical methods can further be employed in other areas where complex geometries are an essential component of numerical simulations.
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Affiliation(s)
- Konstantin Mörschel
- Goethe Center for Scientific Computing, Goethe University Frankfurt, Kettenhofweg 139, 60325, Frankfurt am Main, Germany
| | - Markus Breit
- Goethe Center for Scientific Computing, Goethe University Frankfurt, Kettenhofweg 139, 60325, Frankfurt am Main, Germany
| | - Gillian Queisser
- Department of Mathematics, Temple University, 1805 N Broad Street, Philadelphia, PA, 19122-6094, USA.
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Quantitative Analysis of Hepatitis C NS5A Viral Protein Dynamics on the ER Surface. Viruses 2018; 10:v10010028. [PMID: 29316722 PMCID: PMC5795441 DOI: 10.3390/v10010028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 01/02/2018] [Accepted: 01/04/2018] [Indexed: 02/06/2023] Open
Abstract
Exploring biophysical properties of virus-encoded components and their requirement for virus replication is an exciting new area of interdisciplinary virological research. To date, spatial resolution has only rarely been analyzed in computational/biophysical descriptions of virus replication dynamics. However, it is widely acknowledged that intracellular spatial dependence is a crucial component of virus life cycles. The hepatitis C virus-encoded NS5A protein is an endoplasmatic reticulum (ER)-anchored viral protein and an essential component of the virus replication machinery. Therefore, we simulate NS5A dynamics on realistic reconstructed, curved ER surfaces by means of surface partial differential equations (sPDE) upon unstructured grids. We match the in silico NS5A diffusion constant such that the NS5A sPDE simulation data reproduce experimental NS5A fluorescence recovery after photobleaching (FRAP) time series data. This parameter estimation yields the NS5A diffusion constant. Such parameters are needed for spatial models of HCV dynamics, which we are developing in parallel but remain qualitative at this stage. Thus, our present study likely provides the first quantitative biophysical description of the movement of a viral component. Our spatio-temporal resolved ansatz paves new ways for understanding intricate spatial-defined processes central to specfic aspects of virus life cycles.
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Bestel R, Appali R, van Rienen U, Thielemann C. Effect of Morphologic Features of Neurons on the Extracellular Electric Potential: A Simulation Study Using Cable Theory and Electro-Quasi-Static Equations. Neural Comput 2017; 29:2955-2978. [PMID: 28957018 DOI: 10.1162/neco_a_01019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Microelectrode arrays serve as an indispensable tool in electro-physiological research to study the electrical activity of neural cells, enabling measurements of single cell as well as network communication analysis. Recent experimental studies have reported that the neuronal geometry has an influence on electrical signaling and extracellular recordings. However, the corresponding mechanisms are not yet fully understood and require further investigation. Allowing systematic parameter studies, computational modeling provides the opportunity to examine the underlying effects that influence extracellular potentials. In this letter, we present an in silico single cell model to analyze the effect of geometrical variability on the extracellular electric potentials. We describe finite element models of a single neuron with varying geometric complexity in three-dimensional space. The electric potential generation of the neuron is modeled using Hodgkin-Huxley equations. The signal propagation is described with electro-quasi-static equations, and results are compared with corresponding cable equation descriptions. Our results show that both the geometric dimensions and the distribution of ion channels of a neuron are critical factors that significantly influence both the amplitude and shape of extracellular potentials.
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Affiliation(s)
- R Bestel
- BioMEMS Lab, University of Applied Science Aschaffenburg, Aschaffenburg 63743, Germany
| | - R Appali
- Institute of General Electrical Engineering, University of Rostock, Rostock 18059, Germany
| | - U van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock 18059, Germany
| | - C Thielemann
- BioMEMS Lab, University of Applied Science Aschaffenburg, Aschaffenburg 63743, Germany
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Breit M, Stepniewski M, Grein S, Gottmann P, Reinhardt L, Queisser G. Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox. Front Neuroanat 2016; 10:8. [PMID: 26903818 PMCID: PMC4751272 DOI: 10.3389/fnana.2016.00008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 01/25/2016] [Indexed: 12/02/2022] Open
Abstract
The morphology of neurons and networks plays an important role in processing electrical and biochemical signals. Based on neuronal reconstructions, which are becoming abundantly available through databases such as NeuroMorpho.org, numerical simulations of Hodgkin-Huxley-type equations, coupled to biochemical models, can be performed in order to systematically investigate the influence of cellular morphology and the connectivity pattern in networks on the underlying function. Development in the area of synthetic neural network generation and morphology reconstruction from microscopy data has brought forth the software tool NeuGen. Coupling this morphology data (either from databases, synthetic, or reconstruction) to the simulation platform UG 4 (which harbors a neuroscientific portfolio) and VRL-Studio, has brought forth the extendible toolbox NeuroBox. NeuroBox allows users to perform numerical simulations on hybrid-dimensional morphology representations. The code basis is designed in a modular way, such that e.g., new channel or synapse types can be added to the library. Workflows can be specified through scripts or through the VRL-Studio graphical workflow representation. Third-party tools, such as ImageJ, can be added to NeuroBox workflows. In this paper, NeuroBox is used to study the electrical and biochemical effects of synapse loss vs. synchrony in neurons, to investigate large morphology data sets within detailed biophysical simulations, and used to demonstrate the capability of utilizing high-performance computing infrastructure for large scale network simulations. Using new synapse distribution methods and Finite Volume based numerical solvers for compartment-type models, our results demonstrate how an increase in synaptic synchronization can compensate synapse loss at the electrical and calcium level, and how detailed neuronal morphology can be integrated in large-scale network simulations.
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Affiliation(s)
- Markus Breit
- Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany
| | - Martin Stepniewski
- Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany
| | - Stephan Grein
- Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe UniversityFrankfurt am Main, Germany; Department of Mathematics, Temple UniversityPhiladelphia, PA, USA
| | - Pascal Gottmann
- Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany
| | - Lukas Reinhardt
- Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany
| | - Gillian Queisser
- Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe UniversityFrankfurt am Main, Germany; Department of Mathematics, Temple UniversityPhiladelphia, PA, USA
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