AccScience Publishing / IJB / Online First / DOI: 10.36922/ijb.3814
Submit to IJB
Cite this article
66
Download
704
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
RESEARCH ARTICLE

Machine learning-generated compression modulus database for 3D printing of gelatin methacryloyl

Shiue-Luen Chen1,2 ,  Manisha Senadeera3 ,  Kalani Ruberu4 ,  Johnson Chung4 ,  Santu Rana3 ,  Svetha Venkatesh3 ,  Chong-You Chen1,2 ,  Guan-Yu Chen1,2,5,6* ,  Gordon Wallace4*
Show Less
1 Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
2 Department of Electronics and Electrical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
3 Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Victoria, Australia
4 Intelligent Polymer Research Institute, ARC Centre of Excellence for Electromaterial Science, AIIM Facility, Innovation Campus, University of Wollongong, New South Wales, Australia
5 Department of Biological Science and Technology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
6 Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
IJB 2024, 10(5), 3814 https://doi.org/10.36922/ijb.3814
Submitted: 31 May 2024 | Accepted: 19 July 2024 | Published: 20 September 2024
(This article belongs to the Special Issue Bioprinting of in vitro tissue and disease models)
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
Cite
XML
Abstract

3D bioprinting enables the fabrication of printable tissues, including those for neural, cartilage, and skin repair. The mechanical properties, especially stiffness, of 3D-bioprinted scaffolds are crucial for modulating cell adhesion, growth, migration, and differentiation. The stiffness of a scaffold can be adjusted post-printing by modifying the hydrogel concentration, crosslinker concentration, light intensity during photocrosslinking, and duration of crosslinking. The optimization of these conditions to produce the desired scaffold stiffness for a particular cell type or application is a time-consuming and rigorous process. This study developed an innovative approach to predict the compression modulus of 3D-printed gelatin methacryloyl (GelMA) scaffolds using the Bayesian optimization (BO) algorithm. Through just 10 iterations (75 experimental data points), the model was able to predict > 13,000 possible compression modulus values in a search space comprising four independent variables (GelMA concentration, crosslinker concentration, ultraviolet light [UV] distance, and UV exposure time). This approach can be utilized in other photocrosslinkable bioinks for 3D printing that have a myriad of pre- or post-printing parameters that can affect scaffold stiffness.

 

Keywords
3D bioprinting
Scaffold stiffness
Compression modulus
Bayesian optimization
Gelatin methacryloyl
Funding
The authors acknowledge funding from the Australian Research Council (ARC) (CE140100012) and (FL170100006), and support from the Australian National Fabrication Facility (ANFF) – Materials Node. G.-Y.C. would like to acknowledge funding from The National Science and Technology Council (NSTC113-2321-B-A49-021, NSTC113-2628-B-A49-008-MY3, NSTC113-2823- 8-A49-003, NSTC112-2321-B-A49-015, NSTC 112-2321- B-A49-016), Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) (113W30305), and the Higher Education Sprout Project of the National Yang Ming Chiao Tung University and MOE, Taiwan (113W020211, 113W020211, 113W020214).
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.
References
  1. Hashizume R, Fujimoto KL, Hong Y, et al. Morphological and mechanical characteristics of the reconstructed rat abdominal wall following use of a wet electrospun biodegradable polyurethane elastomer scaffold. Biomaterials. 2010;31(12):3253-3265. doi: 10.1016/j.biomaterials.2010.01.051
  2. Simmons CA, Alsberg E, Hsiong S, Kim WJ, Mooney DJ. Dual growth factor delivery and controlled scaffold degradation enhance in vivo bone formation by transplanted bone marrow stromal cells. Bone. 2004;35(2): 562-569. doi: 10.1016/j.bone.2004.02.027
  3. Chung JHY, Kade JC, Jeiranikhameneh A, et al. 3D hybrid printing platform for auricular cartilage reconstruction. Biomed Phys Eng Express. 2020;6(3):035003. doi: 10.1088/2057-1976/ab54a7
  4. Daikuara LY, Yue Z, Skropeta D, Wallace GG. In vitro characterisation of 3D printed platelet lysate-based bioink for potential application in skin tissue engineering. Acta Biomater. 2021;123:286-297. doi: 10.1016/j.actbio.2021.01.021
  5. Hospodiuk M, Dey M, Sosnoski D, Ozbolat IT. The bioink: a comprehensive review on bioprintable materials. Biotechnol Adv. 2017;35(2):217-239. doi: 10.1016/j.biotechadv.2016.12.006
  6. Floren M, Bonani W, Dharmarajan A, Motta A, Migliaresi C, Tan W. Human mesenchymal stem cells cultured on silk hydrogels with variable stiffness and growth factor differentiate into mature smooth muscle cell phenotype. Acta Biomater. 2016;31:156-166. doi: 10.1016/j.actbio.2015.11.051
  7. Macri-Pellizzeri L, De-Juan-Pardo EM, Prosper F, Pelacho B. Role of substrate biomechanics in controlling (stem) cell fate: implications in regenerative medicine. J Tissue Eng Regene Med. 2018;12(4):1012-1019. doi: 10.1002/term.2586
  8. Yi B, Xu Q, Liu W. An overview of substrate stiffness guided cellular response and its applications in tissue regeneration. Bioact Mater. 2022;15:82-102. doi: 10.1016/j.bioactmat.2021.12.005
  9. Lv H, Wang H, Zhang Z, et al. Biomaterial stiffness determines stem cell fate. Life Sci. 2017;178:42-48. doi: 10.1016/j.lfs.2017.04.014
  10. Querceto S, Santoro R, Gowran A, et al. The harder the climb the better the view: the impact of substrate stiffness on cardiomyocyte fate. J Mol Cell Cardiol. 2022;166:36-49. doi: 10.1016/j.yjmcc.2022.02.001
  11. Huang Y, Xu K, Liu J, Dai G, Yin J, Wei P. Promotion of adrenal pheochromocytoma (PC-12) cell proliferation and outgrowth using Schwann cell-laden gelatin methacrylate substrate. Gels. 2022;8(2):84. doi: 10.3390/gels8020084
  12. Nichol JW, Koshy ST, Bae H, Hwang CM, Yamanlar S, Khademhosseini A. Cell-laden microengineered gelatin methacrylate hydrogels. Biomaterials. 2010;31(21):5536-5544. doi: 10.1016/j.biomaterials.2010.03.064
  13. Fan Y, Yue Z, Lucarelli E, Wallace GG. Hybrid printing using cellulose nanocrystals reinforced GelMA/HAMA hydrogels for improved structural integration. Adv Healthc Mater. 2020;9(24):2001410. doi: 10.1002/adhm.202001410
  14. Sharifi S, Sharifi H, Akbari A, Chodosh J. Systematic optimization of visible light-induced crosslinking conditions of gelatin methacryloyl (GelMA). Sci Rep. 2021;11(1):23276. doi: 10.1038/s41598-021-02830-x
  15. Wu Y, Xiang Y, Fang J, et al. The influence of the stiffness of GelMA substrate on the outgrowth of PC12 cells. Biosci Rep. 2019;39(1): BSR20181748. doi: 10.1042/BSR20181748
  16. Yin J, Yan M, Wang Y, Fu J, Suo H. 3D bioprinting of low-concentration cell-laden gelatin methacrylate (GelMA) bioinks with a two-step cross-linking strategy. ACS Appl Mater Interfaces. 2018;10(8):6849-6857. doi: 10.1021/acsami.7b16059
  17. Chung JHY, Naficy S, Yue Z, et al. Bio-ink properties and printability for extrusion printing living cells. Biomater Sci. 2013;1(7):763-773. doi: 10.1039/c3bm00012e
  18. Her GJ, Wu H-C, Chen M-H, Chen M-Y, Chang S-C, Wang T-W. Control of three-dimensional substrate stiffness to manipulate mesenchymal stem cell fate toward neuronal or glial lineages. Acta Biomater. 2013;9(2):5170-5180. doi: 10.1016/j.actbio.2012.10.012
  19. O’Connell CD, Zhang B, Onofrillo C, et al. Tailoring the mechanical properties of gelatin methacryloyl hydrogels through manipulation of the photocrosslinking conditions. Soft Matter. 2018;14(11):2142-2151. doi: 10.1039/c7sm02187a
  20. Freeman S, Calabro S, Williams R, Jin S, Ye K. Bioink formulation and machine learning-empowered bioprinting optimization. Front Bioeng Biotechnol. 2022;10:913579. doi: 10.3389/fbioe.2022.913579
  21. Ruberu K, Senadeera M, Rana S, et al. Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing. Appl Mater Today. 2021;22:100914. doi: 10.1016/j.apmt.2020.100914
  22. Shin J, Lee Y, Li Z, Hu J, Park SS, Kim K. Optimized 3D bioprinting technology based on machine learning: a review of recent trends and advances. Micromachines. 2022;13(3):363. doi: 10.3390/mi13030363
  23. Sun J, Yao K, An J, Jing L, Huang K, Huang D. Machine learning and 3D bioprinting. Int J Bioprint. 2023;9(4):717. doi: 10.18063/ijb.717
  24. Yu C, Jiang J. A perspective on using machine learning in 3D bioprinting. Int J Bioprint. 2020;6(1):253. doi: 10.18063/ijb.v6i1.253
  25. An J, Chua CK, Mironov V. Application of machine learning in 3D bioprinting: focus on development of big data and digital twin. Int J Bioprint. 2021;7(1):342. doi: 10.18063/ijb.v7i1.342
  26. Tian S, Stevens R, McInnes BT, Lewinski NA. Machine assisted experimentation of extrusion-based bioprinting systems. Micromachines. 2021;12(7):780. doi: 10.3390/mi12070780
  27. Brochu E, Cora VM, Freitas ND. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. ArXiv. 2010;1012:2599 doi: 10.48550/arXiv.1012.2599
  28. Bishop CM, Nasrabadi NM. Pattern recognition and machine learning. J Electronic Imaging. 2007;16(4): 049901. doi: 10.1117/1.2819119
  29. Engler AJ, Sen S, Sweeney HL, Discher DE. Matrix elasticity directs stem cell lineage specification. Cell. 2006;126(4):677-689. doi: 10.1016/j.cell.2006.06.044
  30. Aregueta-Robles UA, Martens PJ, Poole-Warren LA, Green RA. Tissue engineered hydrogels supporting 3D neural networks. Acta Biomater. 2019;95: 269-284. doi: 10.1016/j.actbio.2018.11.044
  31. Chatterjee K, Lin-Gibson S, Wallace WE, et al. The effect of 3D hydrogel scaffold modulus on osteoblast differentiation and mineralization revealed by combinatorial screening. Biomaterials. 2010;31(19):5051-5062. doi: 10.1016/j.biomaterials.2010.03.024

 

 

 

 

Next article in this issue
Share
Back to top
International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept