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Journal article

Model-driven experimentation: A new approach to understand mechanisms of tertiary lymphoid tissue formation, function, and therapeutic resolution

Abstract:
The molecular and cellular processes driving the formation of secondary lymphoid tissues have been extensively studied using a combination of mouse knockouts, lineage-specific reporter mice, gene expression analysis, immunohistochemistry, and flow cytometry. However, the mechanisms driving the formation and function of tertiary lymphoid tissue (TLT) experimental techniques have proven to be more enigmatic and controversial due to differences between experimental models and human disease pathology. Systems-based approaches including data-driven biological network analysis (gene interaction network, metabolic pathway network, cell-cell signaling, and cascade networks) and mechanistic modeling afford a novel perspective from which to understand TLT formation and identify mechanisms that may lead to the resolution of tissue pathology. In this perspective, we make the case for applying model-driven experimentation using two case studies, which combined simulations with experiments to identify mechanisms driving lymphoid tissue formation and function, and then discuss potential applications of this experimental paradigm to identify novel therapeutic targets for TLT pathology.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3389/fimmu.2016.00658

Authors

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Role:
Author
ORCID:
0000-0003-4411-1776
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Role:
Author
ORCID:
0000-0003-1055-0471
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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Sub department:
Kennedy Institute for Rheumatology
Role:
Author


Publisher:
Frontiers Media
Journal:
Frontiers in Immunology More from this journal
Volume:
7
Issue:
APR
Pages:
658
Publication date:
2017-04-04
Acceptance date:
2016-12-16
DOI:
EISSN:
1664-3224
Pmid:
28421068


Language:
English
Keywords:
Pubs id:
pubs:821758
UUID:
uuid:7c8c2c2d-6cb7-4433-828b-b40e6a40bbca
Local pid:
pubs:821758
Source identifiers:
821758
Deposit date:
2018-05-17
ARK identifier:

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