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Mathematical and computational models in spheroid-based biofabrication.

Stelian Arjoca1, Andreea Robu2, Monica Neagu1

  • 1Center for Modeling Biological Systems and Data Analysis, Department of Functional Sciences, Victor Babes University of Medicine and Pharmacy Timisoara, Piata Eftimie Murgu Nr. 2-4, Timisoara 300041, Romania.

Acta Biomaterialia
|July 19, 2022
PubMed
Summary

This review explores mathematical and computational models for predicting tissue spheroid fusion in 3D bioprinted constructs. Understanding these models aids in optimizing biofabrication processes for tissue engineering applications.

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Area of Science:

  • Biophysics
  • Developmental Biology
  • Tissue Engineering

Background:

  • Tissue fusion is crucial in embryonic development and a key process for building three-dimensional (3D) multicellular constructs using tissue spheroids or organoids.
  • Differential adhesion is the primary morphogenetic mechanism driving structure formation in post-printing bioprinted constructs.

Purpose of the Study:

  • To review mathematical models and computer simulations of tissue spheroid fusion.
  • To analyze the validity and practical application of computational models for multicellular self-assembly in bioprinted constructs.
  • To discuss the future of biofabrication with advancements in machine learning and robotic workstations.

Main Methods:

  • Overview of differential adhesion as a morphogenetic mechanism.
  • Discussion of mathematical models based on continuum hydrodynamics and statistical mechanics.
  • Analysis of computational models for multicellular self-assembly in bioprinted constructs.

Main Results:

  • Cohesive cell clusters exhibit behavior akin to incompressible viscous fluids over hours.
  • Mathematical and computational tools can model post-printing structure formation in bioprinted constructs.
  • These tools assist in achieving desired outcomes by reducing the need for extensive optimization experiments.

Conclusions:

  • Mathematical and computational modeling are essential for predicting and controlling the evolution of 3D bioprinted constructs.
  • The review highlights the practical utility of these models in biofabrication, moving beyond theoretical limitations.
  • Future directions involve integrating machine learning and robotics to enhance biofabrication processes and experimental design.