An adaptable <i>in silico</i> ensemble model of the arachidonic acid cascade-Reference-Cited by-同舟云学术

An adaptable in silico ensemble model of the arachidonic acid cascade

Published:2024 Issue:7 Volume:20 Page:453-468
ISSN:2515-4184
Container-title:Molecular Omics
language:en
Short-container-title:Mol. Omics

Author:

Uttley Megan¹,Horne Grace¹,Tsigkinopoulou Areti²,Del Carratore Francesco²³,Hawari Aliah²,Kiezel-Tsugunova Magdalena¹,Kendall Alexandra C.¹^ORCID,Jones Janette⁴,Messenger David⁵,Bhogal Ranjit Kaur⁵,Breitling Rainer²^ORCID,Nicolaou Anna¹⁶^ORCID

Affiliation:

1. Laboratory for Lipidomics and Lipid Biology, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK

2. Manchester Institute of Biotechnology, Faculty of Science and Engineering, The University of Manchester, Manchester, UK

3. Department of Biochemistry, Cell and Systems Biology, Institute of Integrative, Systems and Molecular Biology, University of Liverpool, Liverpool, UK

4. Unilever R&D, Quarry Road East, Bebington, Wirral, CH63 3JW, UK

5. Unilever R&D, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK

6. Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK

Abstract

Ensemble modelling approaches, which account for the uncertainty surrounding model parameters, were applied to the arachidonic acid cascade for the first time. The adaptable, tuneable model was tailored to represent different cell types and stimuli.

Funder

Engineering and Physical Sciences Research Council

Biotechnology and Biological Sciences Research Council

Unilever

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/MO/D3MO00187C

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