1
|
Birgiolas J, Haynes V, Gleeson P, Gerkin RC, Dietrich SW, Crook S. NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML. PLoS Comput Biol 2023; 19:e1010941. [PMID: 36867658 PMCID: PMC10016719 DOI: 10.1371/journal.pcbi.1010941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 03/15/2023] [Accepted: 02/12/2023] [Indexed: 03/04/2023] Open
Abstract
As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.
Collapse
Affiliation(s)
- Justas Birgiolas
- Ronin Institute, Montclair, New Jersey, United States of America
| | - Vergil Haynes
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, United States of America
- College of Health Solutions, Arizona State University, Phoenix, Arizona, United States of America
| | - Padraig Gleeson
- Department of Neuroscience, Physiology, and Pharmacology, University College London, London, United Kingdom
| | - Richard C. Gerkin
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Suzanne W. Dietrich
- School of Mathematical and Natural Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, United States of America
- * E-mail:
| |
Collapse
|
2
|
Appukuttan S, Bologna LL, Schürmann F, Migliore M, Davison AP. EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience. Neuroinformatics 2023; 21:101-113. [PMID: 35986836 PMCID: PMC9931781 DOI: 10.1007/s12021-022-09598-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/04/2022] [Indexed: 01/23/2023]
Abstract
We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability and easy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, and enabling tight integration with tools and services offered under the EBRAINS ecosystem.
Collapse
Affiliation(s)
- Shailesh Appukuttan
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, Saclay, 91400 France
| | - Luca L. Bologna
- Institute of Biophysics, National Research Council, Palermo, 90143 Italy
| | - Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, 1202 Switzerland
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, 90143 Italy
| | - Andrew P. Davison
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, Saclay, 91400 France
| |
Collapse
|
3
|
Maraver P, Armañanzas R, Gillette TA, Ascoli GA. PaperBot: open-source web-based search and metadata organization of scientific literature. BMC Bioinformatics 2019; 20:50. [PMID: 30678631 PMCID: PMC6345070 DOI: 10.1186/s12859-019-2613-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 01/07/2019] [Indexed: 12/16/2022] Open
Abstract
Background The biomedical literature is expanding at ever-increasing rates, and it has become extremely challenging for researchers to keep abreast of new data and discoveries even in their own domains of expertise. We introduce PaperBot, a configurable, modular, open-source crawler to automatically find and efficiently index peer-reviewed publications based on periodic full-text searches across publisher web portals. Results PaperBot may operate stand-alone or it can be easily integrated with other software platforms and knowledge bases. Without user interactions, PaperBot retrieves and stores the bibliographic information (full reference, corresponding email contact, and full-text keyword hits) based on pre-set search logic from a wide range of sources including Elsevier, Wiley, Springer, PubMed/PubMedCentral, Nature, and Google Scholar. Although different publishing sites require different search configurations, the common interface of PaperBot unifies the process from the user perspective. Once saved, all information becomes web accessible allowing efficient triage of articles based on their actual relevance and seamless annotation of suitable metadata content. The platform allows the agile reconfiguration of all key details, such as the selection of search portals, keywords, and metadata dimensions. The tool also provides a one-click option for adding articles manually via digital object identifier or PubMed ID. The microservice architecture of PaperBot implements these capabilities as a loosely coupled collection of distinct modules devised to work separately, as a whole, or to be integrated with or replaced by additional software. All metadata is stored in a schema-less NoSQL database designed to scale efficiently in clusters by minimizing the impedance mismatch between relational model and in-memory data structures. Conclusions As a testbed, we deployed PaperBot to help identify and manage peer-reviewed articles pertaining to digital reconstructions of neuronal morphology in support of the NeuroMorpho.Org data repository. PaperBot enabled the custom definition of both general and neuroscience-specific metadata dimensions, such as animal species, brain region, neuron type, and digital tracing system. Since deployment, PaperBot helped NeuroMorpho.Org more than quintuple the yearly volume of processed information while maintaining a stable personnel workforce.
Collapse
Affiliation(s)
- Patricia Maraver
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA.,Bioengineering Department; George Mason University, Fairfax, USA
| | - Rubén Armañanzas
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA.,Bioengineering Department; George Mason University, Fairfax, USA
| | - Todd A Gillette
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA. .,Bioengineering Department; George Mason University, Fairfax, USA.
| |
Collapse
|
4
|
Mulugeta L, Drach A, Erdemir A, Hunt CA, Horner M, Ku JP, Myers JG, Vadigepalli R, Lytton WW. Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience. Front Neuroinform 2018; 12:18. [PMID: 29713272 PMCID: PMC5911506 DOI: 10.3389/fninf.2018.00018] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 03/29/2018] [Indexed: 12/27/2022] Open
Abstract
Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.
Collapse
Affiliation(s)
| | - Andrew Drach
- The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - C A Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | | | - Joy P Ku
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Jerry G Myers
- NASA Glenn Research Center, Cleveland, OH, United States
| | - Rajanikanth Vadigepalli
- Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics and Computational Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - William W Lytton
- Department of Neurology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Physiology and Pharmacology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Neurology, Kings County Hospital Center, New York, NY, United States
| |
Collapse
|
5
|
McDougal RA, Bulanova AS, Lytton WW. Reproducibility in Computational Neuroscience Models and Simulations. IEEE Trans Biomed Eng 2016; 63:2021-35. [PMID: 27046845 PMCID: PMC5016202 DOI: 10.1109/tbme.2016.2539602] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. METHODS Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. RESULTS Building on these standard practices, model-sharing sites and tools have been developed that fit into several categories: 1) standardized neural simulators; 2) shared computational resources; 3) declarative model descriptors, ontologies, and standardized annotations; and 4) model-sharing repositories and sharing standards. CONCLUSION A number of complementary innovations have been proposed to enhance sharing, transparency, and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation, and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. SIGNIFICANCE Model management will become increasingly important as multiscale models become larger, more detailed, and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.
Collapse
|
6
|
Parasuram H, Nair B, D'Angelo E, Hines M, Naldi G, Diwakar S. Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim. Front Comput Neurosci 2016; 10:65. [PMID: 27445781 PMCID: PMC4923190 DOI: 10.3389/fncom.2016.00065] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Accepted: 06/13/2016] [Indexed: 11/22/2022] Open
Abstract
Local Field Potentials (LFPs) are population signals generated by complex spatiotemporal interaction of current sources and dipoles. Mathematical computations of LFPs allow the study of circuit functions and dysfunctions via simulations. This paper introduces LFPsim, a NEURON-based tool for computing population LFP activity and single neuron extracellular potentials. LFPsim was developed to be used on existing cable compartmental neuron and network models. Point source, line source, and RC based filter approximations can be used to compute extracellular activity. As a demonstration of efficient implementation, we showcase LFPs from mathematical models of electrotonically compact cerebellum granule neurons and morphologically complex neurons of the neocortical column. LFPsim reproduced neocortical LFP at 8, 32, and 56 Hz via current injection, in vitro post-synaptic N2a, N2b waves and in vivo T-C waves in cerebellum granular layer. LFPsim also includes a simulation of multi-electrode array of LFPs in network populations to aid computational inference between biophysical activity in neural networks and corresponding multi-unit activity resulting in extracellular and evoked LFP signals.
Collapse
Affiliation(s)
- Harilal Parasuram
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University) Amritapuri, India
| | - Bipin Nair
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University) Amritapuri, India
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
| | - Michael Hines
- Department of Neuroscience, Yale School of Medicine New Haven, CT, USA
| | - Giovanni Naldi
- Department of Mathematics, University of Milan Milan, Italy
| | - Shyam Diwakar
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University) Amritapuri, India
| |
Collapse
|
7
|
Waltemath D, Wolkenhauer O. How Modeling Standards, Software, and Initiatives Support Reproducibility in Systems Biology and Systems Medicine. IEEE Trans Biomed Eng 2016; 63:1999-2006. [PMID: 27295645 DOI: 10.1109/tbme.2016.2555481] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Only reproducible results are of significance to science. The lack of suitable standards and appropriate support of standards in software tools has led to numerous publications with irreproducible results. Our objectives are to identify the key challenges of reproducible research and to highlight existing solutions. RESULTS In this paper, we summarize problems concerning reproducibility in systems biology and systems medicine. We focus on initiatives, standards, and software tools that aim to improve the reproducibility of simulation studies. CONCLUSIONS The long-term success of systems biology and systems medicine depends on trustworthy models and simulations. This requires openness to ensure reusability and transparency to enable reproducibility of results in these fields.
Collapse
|
8
|
Givon LE, Lazar AA. Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain. PLoS One 2016; 11:e0146581. [PMID: 26751378 PMCID: PMC4709234 DOI: 10.1371/journal.pone.0146581] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Accepted: 12/18/2015] [Indexed: 11/23/2022] Open
Abstract
We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules' local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly's entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel's model integration by combining independently developed models of the retina and lamina neuropils in the fly's visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel's ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel's communication performance both over the number of interface ports exposed by an emulation's constituent modules and the total number of modules comprised by an emulation.
Collapse
Affiliation(s)
- Lev E. Givon
- Department of Electrical Engineering, Columbia University, New York, NY 10027, United States of America
| | - Aurel A. Lazar
- Department of Electrical Engineering, Columbia University, New York, NY 10027, United States of America
| |
Collapse
|