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Predicting bone formation in mesenchymal stromal cell- seeded hydrogels using experiment-based mathematical modelling

Abstract:
In vitro bone formation by mesenchymal stromal cells encapsulated in type-1 collagen hydrogels is demonstrated after a 28-day in vitro culture period. Analysis of the hydrogels is carried out by X-ray microcomputed tomography, histology, and immunohistochemistry, which collectively demonstrates that bone formation in the hydrogels was quantifiably proportional to the initial collagen concentration, and subsequently the population density of seeded cells. This was established by varying the initial collagen concentration at a constant cell seeding density (3 × 105 cells/0.3 mL hydrogel), and separately varying cell seeding density at a constant collagen concentration (1 mg/mL). Using these data, a mathematical model is presented for the total hydrogel volume and mineralization volume based on the observed linear contraction dynamics of cell-seeded collagen gels. The model parameters are fitted by comparing the predictions of the mathematical model for the hydrogel and mineralized volumes on day 28 with the experimental data. The model is then used to predict the hydrogel and mineralization volumes for a range of hydrogel collagen concentrations and cell seeding densities, providing comprehensive input/output descriptors for generating mineralized hydrogels for bone tissue engineering. It is proposed that this quantitative approach will be a useful tool for generating in vitro manufactured bone tissue, defining input parameters that yield predictable output measures of tissue maturation.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1089/ten.tea.2020.0027

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Division:
MPLS
Department:
Mathematical Institute
Sub department:
Mathematical Institute
Oxford college:
St Anne's College
Role:
Author
ORCID:
0000-0001-9638-7278


Publisher:
Mary Ann Liebert
Journal:
Tissue Engineering: Parts A More from this journal
Volume:
26
Issue:
17-18
Pages:
1014-1023
Publication date:
2020-09-17
Acceptance date:
2020-03-16
DOI:
EISSN:
1937-335X
ISSN:
1937-3368


Language:
English
Keywords:
Pubs id:
1094901
Local pid:
pubs:1094901
Deposit date:
2020-03-18

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