Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Author:

Eriksson Olivia1ORCID,Bhalla Upinder Singh2ORCID,Blackwell Kim T3ORCID,Crook Sharon M4ORCID,Keller Daniel5ORCID,Kramer Andrei16ORCID,Linne Marja-Leena7ORCID,Saudargienė Ausra89ORCID,Wade Rebecca C101112ORCID,Hellgren Kotaleski Jeanette16ORCID

Affiliation:

1. Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology

2. National Center for Biological Sciences, Tata Institute of Fundamental Research

3. Department of Bioengineering, Volgenau School of Engineering, George Mason University

4. School of Mathematical and Statistical Sciences, Arizona State University

5. Blue Brain Project, École Polytechnique Fédérale de Lausanne

6. Department of Neuroscience, Karolinska Institute

7. Faculty of Medicine and Health Technology, Tampere University

8. Neuroscience Institute, Lithuanian University of Health Sciences

9. Department of Informatics, Vytautas Magnus University

10. Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS)

11. Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of Heidelberg

12. Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University

Abstract

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Funder

Horizon 2020 Framework Programme

Swedish Research Council

Swedish e-Science Research Centre

Digital Futures

Department of Atomic Energy, Government of India

J.C. Bose Fellowship

National Institute on Alcohol Abuse and Alcoholism

National Institute on Drug Abuse

National Institute of Biomedical Imaging and Bioengineering

Board of the Swiss Federal Institutes of Technology

Academy of Finland

Klaus Tschira Foundation

Research Council of Lithuania

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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