Song, Z., Zhou, Y., Feng, J. et al. (1 more author) (2021) Multiscale 'whole-cell' models to study neural information processing – new insights from fly photoreceptor studies. Journal of Neuroscience Methods, 357. 109156. ISSN 0165-0270
Abstract
Understanding a neuron’s input-output relationship is a longstanding challenge. Arguably, these signalling dynamics can be better understood if studied at three levels of analysis: computational, algorithmic and implementational (Marr, 1982). But it is difficult to integrate such analyses into a single platform that can realistically simulate neural information processing. Multiscale dynamical “whole-cell” modelling, a recent systems biology approach, makes this possible. Dynamical “whole-cell” models are computational models that aim to account for the integrated function of numerous genes or molecules to behave like virtual cells in silico. However, because constructing such models is laborious, only a couple of examples have emerged since the first one, built for Mycoplasma genitalium bacterium, was reported in 2012. Here, we review dynamic “whole-cell” neuron models for fly photoreceptors and how these have been used to study neural information processing. Specifically, we review how the models have helped uncover the mechanisms and evolutionary rules of quantal light information sampling and integration, which underlie light adaptation and further improve our understanding of insect vision.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Published by Elsevier. This is an author produced version of a paper subsequently published in Journal of Neuroscience Methods. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | "whole-cell" modelling; neural information processing; insect vision; fly photoreceptor |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) > Department of Biomedical Science (Sheffield) |
Funding Information: | Funder Grant number BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL BB/H013849/1 BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL BB/M009564/1 BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL BB/F012071/1 BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL BB/D001900/1 JANE & AATOS ERKKO FOUNDATION NONE LEVERHULME TRUST (THE) RPG-2012-567 JANE & AATOS ERKKO FOUNDATION NONE ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/P006094/1 Engineering and Physical Sciences Research Council 2289533 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Apr 2021 13:45 |
Last Modified: | 26 Mar 2022 01:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.jneumeth.2021.109156 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172780 |