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1. Brain simulation as a cloud service: The Virtual Brain on EBRAINS

Author

Michael Schirner, Lia Domide, Dionysios Perdikis, Paul Triebkorn, Leon Stefanovski, Roopa Pai, Paula Prodan, Bogdan Valean, Jessica Palmer, Chloê Langford, André Blickensdörfer, Michiel van der Vlag, Sandra Diaz-Pier, Alexander Peyser, Wouter Klijn, Dirk Pleiter, Anne Nahm, Oliver Schmid, Marmaduke Woodman, Lyuba Zehl, Jan Fousek, Spase Petkoski, Lionel Kusch, Meysam Hashemi, Daniele Marinazzo, Jean-François Mangin, Agnes Flöel, Simisola Akintoye, Bernd Carsten Stahl, Michael Cepic, Emily Johnson, Gustavo Deco, Anthony R. McIntosh, Claus C. Hilgetag, Marc Morgan, Bernd Schuller, Alex Upton, Colin McMurtrie, Timo Dickscheid, Jan G. Bjaalie, Katrin Amunts, Jochen Mersmann, Viktor Jirsa, and Petra Ritter

Subjects
Brain modelling, Cloud, Connectome, Neuroimaging, Network model, High performance computing, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

Published
2022
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2. ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

Author

Patrick Herbers, Iago Calvo, Sandra Diaz-Pier, Oscar D. Robles, Susana Mata, Pablo Toharia, Luis Pastor, Alexander Peyser, Abigail Morrison, and Wouter Klijn

Subjects
multiscale simulation, large scale simulation, visual language, neural networks, connectivity generation, connectome, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.

Published
2022
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3. On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.

Author

Meysam Hashemi, Anirudh N Vattikonda, Viktor Sip, Sandra Diaz-Pier, Alexander Peyser, Huifang Wang, Maxime Guye, Fabrice Bartolomei, Marmaduke M Woodman, and Viktor K Jirsa

Subjects
Biology (General), QH301-705.5
Abstract

Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.

Published
2021
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5. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Author

Leonid L. Rubchinsky, Sungwoo Ahn, Wouter Klijn, Ben Cumming, Stuart Yates, Vasileios Karakasis, Alexander Peyser, Marmaduke Woodman, Sandra Diaz-Pier, James Deraeve, Eliana Vassena, William Alexander, David Beeman, Pawel Kudela, Dana Boatman-Reich, William S. Anderson, Niceto R. Luque, Francisco Naveros, Richard R. Carrillo, Eduardo Ros, Angelo Arleo, Jacob Huth, Koki Ichinose, Jihoon Park, Yuji Kawai, Junichi Suzuki, Hiroki Mori, Minoru Asada, Sorinel A. Oprisan, Austin I. Dave, Tahereh Babaie, Peter Robinson, Alejandro Tabas, Martin Andermann, André Rupp, Emili Balaguer-Ballester, Henrik Lindén, Rasmus K. Christensen, Mari Nakamura, Tania R. Barkat, Zach Tosi, John Beggs, Davide Lonardoni, Fabio Boi, Stefano Di Marco, Alessandro Maccione, Luca Berdondini, Joanna Jędrzejewska-Szmek, Daniel B. Dorman, Kim T. Blackwell, Christoph Bauermeister, Hanna Keren, Jochen Braun, João V. Dornas, Eirini Mavritsaki, Silvio Aldrovandi, Emma Bridger, Sukbin Lim, Nicolas Brunel, Anatoly Buchin, Clifford Charles Kerr, Anton Chizhov, Gilles Huberfeld, Richard Miles, Boris Gutkin, Martin J. Spencer, Hamish Meffin, David B. Grayden, Anthony N. Burkitt, Catherine E. Davey, Liangyu Tao, Vineet Tiruvadi, Rehman Ali, Helen Mayberg, Robert Butera, Cengiz Gunay, Damon Lamb, Ronald L. Calabrese, Anca Doloc-Mihu, Víctor J. López-Madrona, Fernanda S. Matias, Ernesto Pereda, Claudio R. Mirasso, Santiago Canals, Alice Geminiani, Alessandra Pedrocchi, Egidio D’Angelo, Claudia Casellato, Ankur Chauhan, Karthik Soman, V. Srinivasa Chakravarthy, Vignayanandam R. Muddapu, Chao-Chun Chuang, Nan-yow Chen, Mehdi Bayati, Jan Melchior, Laurenz Wiskott, Amir Hossein Azizi, Kamran Diba, Sen Cheng, Elena Y. Smirnova, Elena G. Yakimova, Anton V. Chizhov, Nan-Yow Chen, Chi-Tin Shih, Dorian Florescu, Daniel Coca, Julie Courtiol, Viktor K. Jirsa, Roberto J. M. Covolan, Bartosz Teleńczuk, Richard Kempter, Gabriel Curio, Alain Destexhe, Jessica Parker, Alexander N. Klishko, Boris I. Prilutsky, Gennady Cymbalyuk, Felix Franke, Andreas Hierlemann, Rava Azeredo da Silveira, Stefano Casali, Stefano Masoli, Martina Rizza, Martina Francesca Rizza, Yinming Sun, Willy Wong, Faranak Farzan, Daniel M. Blumberger, Zafiris J. Daskalakis, Svitlana Popovych, Shivakumar Viswanathan, Nils Rosjat, Christian Grefkes, Silvia Daun, Damiano Gentiletti, Piotr Suffczynski, Vadym Gnatkovski, Marco De Curtis, Hyeonsu Lee, Se-Bum Paik, Woochul Choi, Jaeson Jang, Youngjin Park, Jun Ho Song, Min Song, Vicente Pallarés, Matthieu Gilson, Simone Kühn, Andrea Insabato, Gustavo Deco, Katharina Glomb, Adrián Ponce-Alvarez, Petra Ritter, Adria Tauste Campo, Alexander Thiele, Farah Deeba, P. A. Robinson, Sacha J. van Albada, Andrew Rowley, Michael Hopkins, Maximilian Schmidt, Alan B. Stokes, David R. Lester, Steve Furber, Markus Diesmann, Alessandro Barri, Martin T. Wiechert, David A. DiGregorio, Alexander G. Dimitrov, Catalina Vich, Rune W. Berg, Antoni Guillamon, Susanne Ditlevsen, Romain D. Cazé, Benoît Girard, Stéphane Doncieux, Nicolas Doyon, Frank Boahen, Patrick Desrosiers, Edward Laurence, Louis J. Dubé, Russo Eleonora, Daniel Durstewitz, Dominik Schmidt, Tuomo Mäki-Marttunen, Florian Krull, Francesco Bettella, Christoph Metzner, Anna Devor, Srdjan Djurovic, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll, Solveig Næss, Torbjørn V. Ness, Geir Halnes, Eric Halgren, Klas H. Pettersen, Marte J. Sætra, Espen Hagen, Alina Schiffer, Axel Grzymisch, Malte Persike, Udo Ernst, Daniel Harnack, Udo A. Ernst, Nergis Tomen, Stefano Zucca, Valentina Pasquale, Giuseppe Pica, Manuel Molano-Mazón, Michela Chiappalone, Stefano Panzeri, Tommaso Fellin, Kelvin S. Oie, David L. Boothe, Joshua C. Crone, Alfred B. Yu, Melvin A. Felton, Isma Zulfiqar, Michelle Moerel, Peter De Weerd, Elia Formisano, Kelvin Oie, Piotr Franaszczuk, Roland Diggelmann, Michele Fiscella, Domenico Guarino, Jan Antolík, Andrew P. Davison, Yves Frègnac, Benjamin Xavier Etienne, Flavio Frohlich, Jérémie Lefebvre, Encarni Marcos, Maurizio Mattia, Aldo Genovesio, Leonid A. Fedorov, Tjeerd M.H. Dijkstra, Louisa Sting, Howard Hock, Martin A. Giese, Laure Buhry, Clément Langlet, Francesco Giovannini, Christophe Verbist, Stefano Salvadé, Michele Giugliano, James A. Henderson, Hendrik Wernecke, Bulcsú Sándor, Claudius Gros, Nicole Voges, Paulina Dabrovska, Alexa Riehle, Thomas Brochier, Sonja Grün, Yifan Gu, Pulin Gong, Grégory Dumont, Nikita A. Novikov, Boris S. Gutkin, Parul Tewatia, Olivia Eriksson, Andrei Kramer, Joao Santos, Alexandra Jauhiainen, Jeanette H. Kotaleski, Jovana J. Belić, Arvind Kumar, Jeanette Hellgren Kotaleski, Masanori Shimono, Naomichi Hatano, Subutai Ahmad, Yuwei Cui, Jeff Hawkins, Johanna Senk, Karolína Korvasová, Tom Tetzlaff, Moritz Helias, Tobias Kühn, Michael Denker, PierGianLuca Mana, David Dahmen, Jannis Schuecker, Sven Goedeke, Christian Keup, Katja Heuer, Rembrandt Bakker, Paul Tiesinga, Roberto Toro, Wei Qin, Alex Hadjinicolaou, Michael R. Ibbotson, Tatiana Kameneva, William W. Lytton, Lealem Mulugeta, Andrew Drach, Jerry G. Myers, Marc Horner, Rajanikanth Vadigepalli, Tina Morrison, Marlei Walton, Martin Steele, C. Anthony Hunt, Nicoladie Tam, Rodrigo Amaducci, Carlos Muñiz, Manuel Reyes-Sánchez, Francisco B. Rodríguez, Pablo Varona, Joseph T. Cronin, Matthias H. Hennig, Elisabetta Iavarone, Jane Yi, Ying Shi, Bas-Jan Zandt, Werner Van Geit, Christian Rössert, Henry Markram, Sean Hill, Christian O’Reilly, Rodrigo Perin, Huanxiang Lu, Alexander Bryson, Michal Hadrava, Jaroslav Hlinka, Ryosuke Hosaka, Mark Olenik, Conor Houghton, Nicolangelo Iannella, Thomas Launey, Rebecca Kotsakidis, Jaymar Soriano, Takatomi Kubo, Takao Inoue, Hiroyuki Kida, Toshitaka Yamakawa, Michiyasu Suzuki, Kazushi Ikeda, Samira Abbasi, Amber E. Hudson, Detlef H. Heck, Dieter Jaeger, Joel Lee, Skirmantas Janušonis, Maria Luisa Saggio, Andreas Spiegler, William C. Stacey, Christophe Bernard, Davide Lillo, Spase Petkoski, Mark Drakesmith, Derek K. Jones, Ali Sadegh Zadeh, Chandra Kambhampati, Jan Karbowski, Zeynep Gokcen Kaya, Yair Lakretz, Alessandro Treves, Lily W. Li, Joseph Lizier, Cliff C. Kerr, Timothée Masquelier, Saeed Reza Kheradpisheh, Hojeong Kim, Chang Sub Kim, Julia A. Marakshina, Alexander V. Vartanov, Anastasia A. Neklyudova, Stanislav A. Kozlovskiy, Andrey A. Kiselnikov, Kanako Taniguchi, Katsunori Kitano, Oliver Schmitt, Felix Lessmann, Sebastian Schwanke, Peter Eipert, Jennifer Meinhardt, Julia Beier, Kanar Kadir, Adrian Karnitzki, Linda Sellner, Ann-Christin Klünker, Lena Kuch, Frauke Ruß, Jörg Jenssen, Andreas Wree, Paula Sanz-Leon, Stuart A. Knock, Shih-Cheng Chien, Burkhard Maess, Thomas R. Knösche, Charles C. Cohen, Marko A. Popovic, Jan Klooster, Maarten H.P. Kole, Erik A. Roberts, Nancy J. Kopell, Daniel Kepple, Hamza Giaffar, Dima Rinberg, Alex Koulakov, Caroline Garcia Forlim, Leonie Klock, Johanna Bächle, Laura Stoll, Patrick Giemsa, Marie Fuchs, Nikola Schoofs, Christiane Montag, Jürgen Gallinat, Ray X. Lee, Greg J. Stephens, Bernd Kuhn, Luiz Tauffer, Philippe Isope, Katsuma Inoue, Yoshiyuki Ohmura, Shogo Yonekura, Yasuo Kuniyoshi, Hyun Jae Jang, Jeehyun Kwag, Marc de Kamps, Yi Ming Lai, Filipa dos Santos, K. P. Lam, Peter Andras, Julia Imperatore, Jessica Helms, Tamas Tompa, Antonieta Lavin, Felicity H. Inkpen, Michael C. Ashby, Nathan F. Lepora, Aaron R. Shifman, John E. Lewis, Zhong Zhang, Yeqian Feng, Christian Tetzlaff, Tomas Kulvicius, Yinyun Li, Rodrigo F. O. Pena, Davide Bernardi, Antonio C. Roque, Benjamin Lindner, Sebastian Vellmer, Ausra Saudargiene, Tiina Maninen, Riikka Havela, Marja-Leena Linne, Arthur Powanwe, Andre Longtin, Jesús A. Garrido, Joe W. Graham, Salvador Dura-Bernal, Sergio L. Angulo, Samuel A. Neymotin, and Srdjan D. Antic

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurophysiology and neuropsychology, QP351-495
Published
2017
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6. Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Author

Inga Blundell, Romain Brette, Thomas A. Cleland, Thomas G. Close, Daniel Coca, Andrew P. Davison, Sandra Diaz-Pier, Carlos Fernandez Musoles, Padraig Gleeson, Dan F. M. Goodman, Michael Hines, Michael W. Hopkins, Pramod Kumbhar, David R. Lester, Bóris Marin, Abigail Morrison, Eric Müller, Thomas Nowotny, Alexander Peyser, Dimitri Plotnikov, Paul Richmond, Andrew Rowley, Bernhard Rumpe, Marcel Stimberg, Alan B. Stokes, Adam Tomkins, Guido Trensch, Marmaduke Woodman, and Jochen Martin Eppler

Subjects
code generation, simulation, neuronal networks, domain specific language, modeling language, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.

Published
2018
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7. Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

Author

Christian Nowke, Sandra Diaz-Pier, Benjamin Weyers, Bernd Hentschel, Abigail Morrison, Torsten W. Kuhlen, and Alexander Peyser

Subjects
simulation and modeling, neural networks, structural plasticity, interactive systems, high performance computing, visualization software, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

Published
2018
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8. Cation Selectivity in Biological Cation Channels Using Experimental Structural Information and Statistical Mechanical Simulation.

Author

Justin John Finnerty, Alexander Peyser, and Paolo Carloni

Subjects
Medicine, Science
Abstract

Cation selective channels constitute the gate for ion currents through the cell membrane. Here we present an improved statistical mechanical model based on atomistic structural information, cation hydration state and without tuned parameters that reproduces the selectivity of biological Na+ and Ca2+ ion channels. The importance of the inclusion of step-wise cation hydration in these results confirms the essential role partial dehydration plays in the bacterial Na+ channels. The model, proven reliable against experimental data, could be straightforwardly used for designing Na+ and Ca2+ selective nanopores.

Published
2015
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12. Brain Modelling as a Service: The Virtual Brain on EBRAINS.

Author

Michael Schirner, Lia Domide, Dionysios Perdikis, Paul Triebkorn, Leon Stefanovski, Roopa Pai, Paula Popa, Bogdan Valean, Jessica Palmer, Chloê Langford, André Blickensdörfer, Michiel van der Vlag, Sandra Diaz-Pier, Alexander Peyser, Wouter Klijn, Dirk Pleiter, Anne Nahm, Oliver Schmid, Michael Marmaduke Woodman, Lyuba Zehl, Jan Fousek, Spase Petkoski, Lionel Kusch, Meysam Hashemi, Daniele Marinazzo, Jean-François Mangin, Agnes Flöel, Simisola Akintoye, Bernd Carsten Stahl, Michael Cepic, Emily Johnson, Anthony R. McIntosh, Claus C. Hilgetag, Marc Morgan, Bernd Schuller, Alex Upton, Colin McMurtrie, Timo Dickscheid, Jan G. Bjaalie, Katrin Amunts, Jochen Mersmann, Viktor K. Jirsa, and Petra Ritter

Published
2021

14. Staged deployment of interactive multi-application HPC workflows

Author

Abigail Morrison, Alexander Peyser, Sandra Diaz-Pier, and Wouter Klijn

Subjects
FOS: Computer and information sciences, 0303 health sciences, business.industry, Computer science, Computer Science - Human-Computer Interaction, computer.software_genre, Supercomputer, Human-Computer Interaction (cs.HC), Visualization, 03 medical and health sciences, Task (computing), 0302 clinical medicine, Workflow, Computer Science - Distributed, Parallel, and Cluster Computing, Software deployment, Middleware (distributed applications), Robot, Use case, Distributed, Parallel, and Cluster Computing (cs.DC), Software engineering, business, computer, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Running scientific workflows on a supercomputer can be a daunting task for a scientific domain specialist. Workflow management solutions (WMS) are a standard method for reducing the complexity of application deployment on high performance computing (HPC) infrastructure. We introduce the design for a middleware system that extends and combines the functionality from existing solutions in order to create a high-level, staged user-centric operation/deployment model. This design addresses the requirements of several use cases in the life sciences, with a focus on neuroscience. In this manuscript we focus on two use cases: 1) three coupled neuronal simulators (for three different space/time scales) with in-transit visualization and 2) a closed-loop workflow optimized by machine learning, coupling a robot with a neural network simulation. We provide a detailed overview of the application-integrated monitoring in relationship with the HPC job. We present here a novel usage model for large scale interactive multi-application workflows running on HPC systems which aims at reducing the complexity of deployment and execution, thus enabling new science., Comment: 7 pages, 3 figures, The 2019 International Conference on High Performance Computing & Simulation

Published
2019
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15. High-Performance Scientific Computing : First JARA-HPC Symposium, JHPCS 2016, Aachen, Germany, October 4–5, 2016, Revised Selected Papers

Author

Edoardo Di Napoli, Marc-André Hermanns, Hristo Iliev, Andreas Lintermann, Alexander Peyser, Edoardo Di Napoli, Marc-André Hermanns, Hristo Iliev, Andreas Lintermann, and Alexander Peyser

Subjects
Algorithms, Software engineering, Computer programming, Computer networks, Application software, Computer science
Abstract

This book constitutes the thoroughly refereed post-conference proceedings of the First JARA High-Performance Computing Symposium, JARA-HPC 2016, held in Aachen, Germany, in October 2016.The 21 full papers presented were carefully reviewed and selected from 26 submissions. They cover many diverse topics, such as coupling methods and strategies in Computational Fluid Dynamics (CFD), performance portability and applications in HPC, as well as provenance tracking for large-scale simulations.

Published
2017

16. Domain and Interdomain Energetics Underlying Gating in Shaker -Type K V Channels

Author

Wolfgang Nonner, Dirk Gillespie, Alexander Peyser, and Roland Roth

Subjects
Bistability, Chemistry, Biophysics, Observable, Nanotechnology, Gating, Protein Structure, Tertiary, symbols.namesake, Shaker Superfamily of Potassium Channels, Hardware_INTEGRATEDCIRCUITS, symbols, Animals, Humans, Thermodynamics, Probability distribution, Channels and Transporters, Statistical physics, Shaker, Hamiltonian (quantum mechanics), Ion Channel Gating, AND gate, Protein Binding, Voltage
Abstract

To understand gating events with a time-base many orders-of-magnitude slower than that of atomic motion in voltage-gated ion channels such as the Shaker-type KV channels, a multiscale physical model is constructed from the experimentally well-characterized voltage-sensor (VS) domains coupled to a hydrophobic gate. The four VS domains are described by a continuum electrostatic model under voltage-clamp conditions, the control of ion flow by the gate domain is described by a vapor-lock mechanism, and the simple coupling principle is informed by known experimental results and trial-and-error. The configurational energy computed for each element is used to produce a total Hamiltonian that is a function of applied voltage, VS positions, and gate radius. We compute statistical-mechanical expectation values of macroscopic laboratory observables. This approach stands in contrast with molecular-dynamic models which are challenged by increasing scale, and kinetic models which assume a probability distribution rather than derive it from the underlying physics. This generic model predicts well the Shaker charge/voltage and conductance/voltage relations; the tight constraints underlying these results allow us to quantitatively assess the underlying physical mechanisms. The total electrical work picked up by the VS domains is an order-of-magnitude larger than the work required to actuate the gate itself, suggesting an energetic basis for the evolutionary flexibility of the voltage-gating mechanism. The cooperative slide-and-interlock behavior of the VS domains described by the VS-gate coupling relation leads to the experimentally observed bistable gating. This engineering approach should prove useful in the investigation of various elements underlying gating characteristics and degraded behavior due to mutation.

Published
2014
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18. The sliding-helix voltage sensor: mesoscale views of a robust structure–function relationship

Author

Wolfgang Nonner and Alexander Peyser

Subjects
Chemistry, Amino Acid Motifs, Biophysics, Charge (physics), General Medicine, Dielectric, Mechanics, Molecular Dynamics Simulation, Parameter space, Electrostatics, Article, Protein Structure, Tertiary, Molecular dynamics, Potassium Channels, Voltage-Gated, Polarizability, Boundary value problem, Atomic physics, Ion Channel Gating, Voltage
Abstract

The voltage sensor (VS) domain of voltage-gated ion channels underlies the electrical excitability of living cells. We simulate a mesoscale model of the VS domain to determine the functional consequences of some of its physical elements. Our mesoscale model is based on VS charges, linear dielectrics, and whole-body motion, applied to an S4 "sliding helix." The electrostatics under voltage-clamped boundary conditions are solved consistently using a boundary-element method. Based on electrostatic configurational energy, statistical-mechanical expectations of the experimentally observable relation between displaced charge and membrane voltage are predicted. Consequences of the model are investigated for variations of S4 configuration (α- and 3(10)-helical), countercharge alignment with S4 charges, protein polarizability, geometry of the gating canal, screening of S4 charges by the baths, and fixed charges located at the bath interfaces. The sliding-helix VS domain has an inherent electrostatic stability in the explored parameter space: countercharges present in the region of weak dielectric always retain an equivalent S4 charge in that region but allow sliding movements displacing 3-4 e (0). That movement is sensitive to small energy variations (2 kT) along the path dependent on a number of electrostatic parameters tested in our simulations. These simulations show how the slope of the relation between displaced charge and voltage could be tuned in a channel.

Published
2012
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19. Integrating Visualizations into Modeling NEST Simulations

Author

Daniel Zielasko, Christian Nowke, Alexander Peyser, Torsten Kuhlen, Benjamin Weyers, and Bernd Hentschel

Subjects
Process (engineering), Computer science, Biomedical Engineering, Neuroscience (miscellaneous), Workflow integration, Machine learning, computer.software_genre, lcsh:RC321-571, Human–computer interaction, Methods, Use case, ddc:610, Interactive visualization, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Spiking neural network, coordinated and multiple views, business.industry, Computer Science Applications, Visualization, Workflow, spiking neural network modeling, Problem domain, Artificial intelligence, data management, business, Software architecture, computer, interactive visualization, Neuroscience
Abstract

Modeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks. However, solutions to assist researchers in understanding the simulation's complex emergent behavior by means of visualization are still lacking. While developing tools to partially fill this gap, we encountered the challenge to integrate these tools easily into the neuroscientists' daily workflow. To understand what makes this so challenging, we looked into the workflows of our collaborators and analyzed how they use the visualizations to solve their daily problems. We identified two major issues: first, the analysis process can rapidly change focus which requires to switch the visualization tool that assists in the current problem domain. Second, because of the heterogeneous data that results from simulations, researchers want to relate data to investigate these effectively. Since a monolithic application model, processing and visualizing all data modalities and reflecting all combinations of possible workflows in a holistic way, is most likely impossible to develop and to maintain, a software architecture that offers specialized visualization tools that run simultaneously and can be linked together to reflect the current workflow, is a more feasible approach. To this end, we have developed a software architecture that allows neuroscientists to integrate visualization tools more closely into the modeling tasks. In addition, it forms the basis for semantic linking of different visualizations to reflect the current workflow. In this paper, we present this architecture and substantiate the usefulness of our approach by common use cases we encountered in our collaborative work.

Published
2015
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20. Relating Microscopic Charge Movement to Macroscopic Currents: The Ramo-Shockley Theorem Applied to Ion Channels

Author

Alexander Peyser, Wolfgang Nonner, Dirk Gillespie, and Bob Eisenberg

Subjects
Electromagnetic field, Patch-Clamp Techniques, Current (mathematics), Static Electricity, Biophysics, Biophysical Theory and Modeling, 02 engineering and technology, Gating, Electric Capacitance, Models, Biological, Ion Channels, Membrane Potentials, 03 medical and health sciences, Electromagnetic Fields, Computer Simulation, Ion channel, 030304 developmental biology, 0303 health sciences, Chemistry, Movement (music), Charge (physics), 021001 nanoscience & nanotechnology, Classical mechanics, Models, Chemical, Atomic physics, 0210 nano-technology, Ion Channel Gating, Algorithms, Communication channel
Abstract

Since the discovery of gating current, electrophysiologists have studied the movement of charged groups within channel proteins by changing potential and measuring the resulting capacitive current. The relation of atomic-scale movements of charged groups to the gating current measured in an external circuit, however, is not obvious. We report here that a general solution to this problem exists in the form of the Ramo-Shockley theorem. For systems with different amounts of atomic detail, we use the theorem to calculate the gating charge produced by movements of protein charges. Even without calculation or simulation, the Ramo-Shockley theorem eliminates a class of interpretations of experimental results. The theorem may also be used at each time step of simulations to compute external current.

Published
2004
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21. Voltage sensing in ion channels: Mesoscale simulations of biological devices

Author

Wolfgang Nonner and Alexander Peyser

Subjects
Models, Molecular, Static Electricity, Molecular Conformation, FOS: Physical sciences, Gating, Dielectric, Quantitative Biology - Quantitative Methods, Molecular physics, symbols.namesake, Electromagnetic Fields, ddc:530, Computer Simulation, Physics - Biological Physics, Boundary value problem, Boundary element method, Quantitative Methods (q-bio.QM), Physics, Electric potential energy, Biomolecules (q-bio.BM), Electrostatics, Classical mechanics, Quantitative Biology - Biomolecules, Models, Chemical, Potassium Channels, Voltage-Gated, Biological Physics (physics.bio-ph), FOS: Biological sciences, symbols, Hamiltonian (quantum mechanics), Ion Channel Gating, Porosity, Voltage
Abstract

Electrical signaling via voltage-gated ion channels depends upon the function of a voltage sensor (VS), identified with the S1-S4 domain in voltage-gated K+ channels. Here we investigate some energetic aspects of the sliding-helix model of the VS using simulations based on VS charges, linear dielectrics and whole-body motion. Model electrostatics in voltage-clamped boundary conditions are solved using a boundary element method. The statistical mechanical consequences of the electrostatic configurational energy are computed to gain insight into the sliding-helix mechanism and to predict experimentally measured ensemble properties such as gating charge displaced by an applied voltage. Those consequences and ensemble properties are investigated for two alternate S4 configurations, \alpha- and 3(10)-helical. Both forms of VS are found to have an inherent electrostatic stability. Maximal charge displacement is limited by geometry, specifically the range of movement where S4 charges and counter-charges overlap in the region of weak dielectric. Charge displacement responds more steeply to voltage in the \alpha-helical than the 3(10)-helical sensor. This difference is due to differences on the order of 0.1 eV in the landscapes of electrostatic energy. As a step toward integrating these VS models into a full-channel model, we include a hypothetical external load in the Hamiltonian of the system and analyze the energetic in/output relation of the VS., Comment: arXiv admin note: substantial text overlap with arXiv:1112.2994

Published
2012
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22. Arbor — A Morphologically-Detailed Neural Network Simulation Library for Contemporary High-Performance Computing Architectures

Author

Ben Cumming, Nora Abi Akar, Alexander Peyser, Vasileios Karakasis, Wouter Klijn, Anne Küsters, and Stuart Yates

Subjects
Multi-core processor, Computer science, 05 social sciences, Parallel computing, Supercomputer, computer.software_genre, Neural Network Simulation, 050105 experimental psychology, Simulation software, 03 medical and health sciences, Software portability, 0302 clinical medicine, Large networks, Extreme scale, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, x86, 0501 psychology and cognitive sciences, Neurons and Cognition (q-bio.NC), computer, 030217 neurology & neurosurgery
Abstract

We introduce Arbor, a performance portable library for simulation of large networks of multi-compartment neurons on HPC systems. Arbor is open source software, developed under the auspices of the HBP. The performance portability is by virtue of back-end specific optimizations for x86 multicore, Intel KNL, and NVIDIA GPUs. When coupled with low memory overheads, these optimizations make Arbor an order of magnitude faster than the most widely-used comparable simulation software. The single-node performance can be scaled out to run very large models at extreme scale with efficient weak scaling. HPC, GPU, neuroscience, neuron, software, Comment: PDP 2019 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing

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