In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 10 ( 2022-10-19), p. e1010587-
Abstract:
Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro . MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where HepaRG single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behaviour of the system under hypoglycemia by simulating this condition in silico , and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010587
DOI:
10.1371/journal.pcbi.1010587.g001
DOI:
10.1371/journal.pcbi.1010587.g002
DOI:
10.1371/journal.pcbi.1010587.g003
DOI:
10.1371/journal.pcbi.1010587.g004
DOI:
10.1371/journal.pcbi.1010587.g005
DOI:
10.1371/journal.pcbi.1010587.g006
DOI:
10.1371/journal.pcbi.1010587.g007
DOI:
10.1371/journal.pcbi.1010587.g008
DOI:
10.1371/journal.pcbi.1010587.t001
DOI:
10.1371/journal.pcbi.1010587.t002
DOI:
10.1371/journal.pcbi.1010587.s001
DOI:
10.1371/journal.pcbi.1010587.s002
DOI:
10.1371/journal.pcbi.1010587.s003
DOI:
10.1371/journal.pcbi.1010587.s004
DOI:
10.1371/journal.pcbi.1010587.s005
DOI:
10.1371/journal.pcbi.1010587.s006
DOI:
10.1371/journal.pcbi.1010587.s007
DOI:
10.1371/journal.pcbi.1010587.s008
DOI:
10.1371/journal.pcbi.1010587.s009
DOI:
10.1371/journal.pcbi.1010587.s010
DOI:
10.1371/journal.pcbi.1010587.s011
DOI:
10.1371/journal.pcbi.1010587.r001
DOI:
10.1371/journal.pcbi.1010587.r002
DOI:
10.1371/journal.pcbi.1010587.r003
DOI:
10.1371/journal.pcbi.1010587.r004
DOI:
10.1371/journal.pcbi.1010587.r005
DOI:
10.1371/journal.pcbi.1010587.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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