Application of Computational Method in Designing a Unit Cell of Bone Tissue Engineering Scaffold: A Review
Nur Syahirah Mustafa1, Nor Hasrul Akhmal1, Sudin Izman1
1Faculty of Engineering, School of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia.
Polymers
|June 2, 2021
Summary
Computational methods are essential for designing effective bone tissue engineering scaffolds. This study reviews computational approaches, including parametric and non-parametric designs, to optimize scaffold properties for cell viability and growth.
Area of Science:
- Biomaterials Science
- Computational Biology
- Tissue Engineering
Background:
- Scaffold design is critical for bone tissue engineering, impacting cell viability and growth.
- Predictive modeling and simulation are necessary to create ideal scaffolds.
- Computational methods offer significant potential for scaffold design and implementation.
Purpose of the Study:
- To provide an overview of computational methods in designing bone tissue engineering scaffolds.
- To explore the application of computational techniques in optimizing scaffold properties.
- To discuss various design categories and their characteristics for bone scaffolds.
Main Methods:
- Review of computational approaches for designing bone tissue engineering scaffolds.
- Discussion of non-parametric and parametric design categories.
- Categorization of designs based on characteristics like circular and Triply Periodic Minimal Surface (TPMS) structures.
Main Results:
- Identified parametric and non-parametric designs as key categories for scaffold unit cells.
- Detailed advantages and disadvantages of different structural types, including circular and TPMS.
- Highlighted relevant software for scaffold simulation and design.
Conclusions:
- Computational methods are crucial for advancing bone tissue engineering scaffold design.
- Parametric and non-parametric approaches, along with specific structures like TPMS, offer viable design pathways.
- Further research should address current challenges and explore future recommendations for computational scaffold design.


