Integrated data-driven modeling and experimental optimization of granular hydrogel matrices
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1-s2.0-S2590238523000115-main.pdf
Size
4.96 MB
Format
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Author(s)
Verheyen, Connor A.
Uzel, Sebastien G.M.
Kurum, Armand
Roche, Ellen T.
Lewis, Jennifer A.
Date Issued
March 2023
Journal
Matter
Publisher
Elsevier BV
Citation
Verheyen, Connor A., Uzel, Sebastien G.M., Kurum, Armand, Roche, Ellen T. and Lewis, Jennifer A. 2023. "Integrated data-driven modeling and experimental optimization of granular hydrogel matrices." Matter, 6 (3).
Version
Final published version
Abstract
Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. However, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create
granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability profiles. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials.
Subjects
General Materials Science
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.matt.2023.01.011
