Computational Engineering for 3D Bioprinting
Models, Methods, and Emerging Technologies
Vidyapati Kumar
Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India
Search for more papers by this authorAnkita Mistri
Department of Mechanical Engineering, Indian Institute of Technology, Dhanbad, Jharkhand, India
Search for more papers by this authorVarnit Jain
Department of Metallurgy and Materials Engineering, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India
Search for more papers by this authorManojit Ghosh
Department of Metallurgy and Materials Engineering, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India
Search for more papers by this authorVidyapati Kumar
Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India
Search for more papers by this authorAnkita Mistri
Department of Mechanical Engineering, Indian Institute of Technology, Dhanbad, Jharkhand, India
Search for more papers by this authorVarnit Jain
Department of Metallurgy and Materials Engineering, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India
Search for more papers by this authorManojit Ghosh
Department of Metallurgy and Materials Engineering, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India
Search for more papers by this authorManojit Ghosh
Indian Institute of Engineering Science and Technology (IIEST), Howrah, India
Search for more papers by this authorSummary
Bioprinting is an emerging technology that enables the precise fabrication of complex biological structures by depositing cells, biomaterials, and biomolecules in a layered fashion. Realizing the immense potential of bioprinting requires deep integrative knowledge spanning engineering, materials science, biology, and computing. This chapter provides a comprehensive overview of the computational engineering approaches being developed to understand, optimize, and advance bioprinting methodologies. The fundamental techniques of extrusion, jetting, laser-assisted, and stereolithographic bioprinting are first introduced, along with their respective capabilities and limitations. It then delves into the physics-based modeling methods being employed to simulate key aspects of the bioprinting process, including finite element analysis (FEA), computational fluid dynamics (CFD), agent-based modeling (ABM), lattice Boltzmann techniques, and molecular dynamics (MD) simulations. These computational models enable the design and enhancement of bioprinting systems, materials, and processes by providing insights into factors, such as scaffold mechanics, fluid rheology, cell interactions, and material behavior. The emerging integration of data-driven artificial intelligence methods, like machine learning for design, optimization, and modeling applications is also discussed. This chapter provides a comprehensive guide to the computational engineering approaches and technologies driving innovation in bioprinting. It will be an essential reference for the modeling principles and tools needed to advance bioprinting research and translate findings into impactful clinical and commercial solutions.
References
- Osuchowski , M.F. , Remick , D.G. , Lederer , J.A. et al. ( 2014 ). Abandon the mouse research ship? Not just yet! Shock 41 ( 6 ): 463 – 475 . https://doi.org/10.1097/SHK.0000000000000153 .
-
Pound , P.
and
Bracken , M.B.
(
2014
).
Is animal research sufficiently evidence based to be a cornerstone of biomedical research?
BMJ
348
:
https://doi.org/10.1136/bmj.g3387
.
10.1136/bmj.g3387 Google Scholar
- Perrin , S. ( 2014 ). Preclinical research: make mouse studies work . Nature 507 ( 7493 ): 423 – 425 : https://doi.org/10.1038/507423a .
- Couzin-Frankel , J. ( 2013 ). When mice mislead . Science 342 ( 6161 ): 925 – 923 : https://doi.org/10.1126/science.342.6161.922 .
-
Huh , J.T.
,
Yoo , J.J.
,
Atala , A.
, and
Lee , S.J.
(
2020
).
Three-dimensional bioprinting for tissue engineering
. In:
Principles of Tissue Engineering
,
1391
–
1415
.
Academic Press
.
10.1016/B978-0-12-818422-6.00076-9 Google Scholar
- Weinhart , M. , Hocke , A. , Hippenstiel , S. et al. ( 2019 ). 3D organ models – revolution in pharmacological research? Pharmacol. Res. 139 : 446 – 451 : https://doi.org/10.1016/j.phrs.2018.11.002 .
- Billiet , T. , Gevaert , E. , De Schryver , T. et al. ( 2014 ). The 3D printing of gelatin methacrylamide cell-laden tissue-engineered constructs with high cell viability . Biomaterials 35 ( 1 ): 49 – 62 : https://doi.org/10.1016/j.biomaterials.2013.09.078 .
-
Chiesa , I.
,
Ligorio , C.
,
Bonatti , A.F.
et al. (
2020
).
Modeling the three-dimensional bioprinting process of β-sheet self-assembling peptide hydrogel scaffolds
.
Front. Med. Technol.
2
(
October
):
1
–
16
:
https://doi.org/10.3389/fmedt.2020.571626
.
10.3389/fmedt.2020.571626 Google Scholar
-
Gómez Blanco , J.C.
,
Mancha Sánchez , E.
,
Torrejón Martín , D.
et al. (
2020
).
Nozzle pressure analysis of several hydrogel on extrusion-based bioprinting using computational fluid dynamics
.
Jornadas de Automática
737
–
741
:
https://doi.org/10.17979/spudc.9788497497565.0737
.
10.17979/spudc.9788497497565.0737 Google Scholar
-
Ingelsten , S.
,
Göhl , J.
,
Mark , A.
, and
Edelvik , F.
(
2018
).
A virtual framework for simulation of complex viscoelastic flows
.
Procedia CIRP
72
:
392
–
397
:
https://doi.org/10.1016/j.procir.2018.03.226
.
10.1016/j.procir.2018.03.226 Google Scholar
- Leppiniemi , J. , Lahtinen , P. , Paajanen , A. et al. ( 2017 ). 3D-printable bioactivated nanocellulose-alginate hydrogels . ACS Appl. Mater. Interfaces 9 ( 26 ): 21959 – 21970 : https://doi.org/10.1021/acsami.7b02756 .
- Ning , L. , Betancourt , N. , Schreyer , D.J. , and Chen , X. ( 2018 ). Characterization of cell damage and proliferative ability during and after bioprinting . ACS Biomater. Sci. Eng. 4 ( 11 ): 3906 – 3918 : https://doi.org/10.1021/acsbiomaterials.8b00714 .
- Song , K. , Zhang , D. , Yin , J. , and Huang , Y. ( 2021 ). Computational study of extrusion bioprinting with jammed gelatin microgel-based composite ink . Addit. Manuf. 41 : https://doi.org/10.1016/j.addma.2021.101963 .
- Williams , H. , McPhail , M. , Mondal , S. , and Münch , A. ( 2018 ). Modeling gel fiber formation in an emerging coaxial flow from a nozzle . J. Fluids Eng. Trans. ASME 141 ( 1 ): https://doi.org/10.1115/1.4040833 .
-
Ramírez López , D.V.
,
Peña-Reyes , C.
, and
Rojas , Á.J.
(
2019
).
Agent-based modeling of mesenchymal stem cells on a 3D-printed bio-device for the regenerative treatment of the infarcted myocardium
.
Proceedings – 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
, pp. 2033–2040 (3–6 December 2018),
Madrid
.
https://doi.org/10.1109/BIBM.2018.8621314
.
10.1109/BIBM.2018.8621314 Google Scholar
- Keshavarzian , M. , Meyer , C.A. , and Hayenga , H.N. ( 2019 ). In silico tissue engineering: a coupled agent-based finite element approach . Tissue Eng. – Part C Methods 25 ( 11 ): 641 – 654 : https://doi.org/10.1089/ten.tec.2019.0103 .
-
Nolan , D.
and
Lally , C.
(
2018
).
Coupled finite element-agent-based models for the simulation of vascular growth and remodeling
. In:
Numerical Methods and Advanced Simulation in Biomechanics and Biological Processes
(ed.
M. Cerrolaza
,
S.J. Shefelbine
, and
D. Garzón-Alvarado
),
283
–
300
.
Academic Press
.
10.1016/B978-0-12-811718-7.00016-2 Google Scholar
- Cristea , A. and Neagu , A. ( 2016 ). Shape changes of bioprinted tissue constructs simulated by the Lattice Boltzmann method . Comput. Biol. Med. 70 : 80 – 87 : https://doi.org/10.1016/j.compbiomed.2015.12.020 .
- Cristea , A. , Horhat , R. , and Neagu , A. ( 2021 ). Lattice Boltzmann simulations of morphogenesis in artificial tissues . Rom. J. Physiol. 66 ( 3–4 ): 185 .
- Wei , Q. , Yang , R. , Sun , D. et al. ( 2022 ). Design and evaluation of sodium alginate/polyvinyl alcohol blend hydrogel for 3D bioprinting cartilage scaffold: molecular dynamics simulation and experimental method . J. Mater. Res. Technol. 17 : 66 – 78 : https://doi.org/10.1016/j.jmrt.2021.12.130 .
-
Brunner , D.
,
Khawaja , H.
,
Moatamedi , M.
, and
Boiger , G.
(
2018
).
CFD modelling of pressure and shear rate in torsionally vibrating structures using ANSYS CFX and COMSOL multiphysics
.
Int. J. Multiphys.
12
(
4
):
349
–
358
:
https://doi.org/10.21152/1750-9548.12.4.349
.
10.21152/1750?9548.12.4.349 Google Scholar
- Schipani , R. , Nolan , D.R. , Lally , C. , and Kelly , D.J. ( 2020 ). Integrating finite element modelling and 3D printing to engineer biomimetic polymeric scaffolds for tissue engineering . Connect. Tissue Res. 61 ( 2 ): 174 – 189 : https://doi.org/10.1080/03008207.2019.1656720 .
-
Babbar , A.
,
Jain , V.
,
Gupta , D.
et al. (
2022
).
Additive manufacturing for the development of biological implants, scaffolds, and prosthetics
.
Addit. Manuf. Process. Biomed. Eng.
27
–
46
:
https://doi.org/10.1201/9781003217961-2
.
10.1201/9781003217961-2 Google Scholar
-
Hjorth , A.
,
Head , B.
,
Head , B.
, and
Wilensky , U.
(
2020
).
Levelspace: a netlogo extension for multi-level agent-based modeling
.
JASSS
23
(
1
):
https://doi.org/10.18564/jasss.4130
.
10.18564/jasss.4130 Google Scholar
- Größler , A. , Stotz , M. , and Schieritz , N. ( 2003 ). A software interface between system dynamics and agent-based simulations – linking vensim and repast . Proc. 21st Int. Conf. Syst. Dyn. Soc. 8 : 20 – 24 .
- Latt , J. , Malaspinas , O. , Kontaxakis , D. et al. ( 2021 ). Palabos: parallel lattice Boltzmann solver . Comput. Math. Appl. 81 : 334 – 350 : https://doi.org/10.1016/j.camwa.2020.03.022 .
- Krause , M.J. , Kummerländer , A. , Avis , S.J. et al. ( 2021 ). OpenLB – open source lattice Boltzmann code . Comput. Math. Appl. 81 : 258 – 288 : https://doi.org/10.1016/j.camwa.2020.04.033 .
-
Kumar , V.
,
Prakash , C.
,
Babbar , A.
et al. (
2022
).
Additive manufacturing in biomedical engineering
.
Addit. Manuf. Process. Biomed. Eng.
143
–
164
:
https://doi.org/10.1201/9781003217961-8
.
10.1201/9781003217961-8 Google Scholar
-
Kumar , V.
,
Babbar , A.
,
Sharma , A.
et al. (
2022
).
Polymer 3D bioprinting for bionics and tissue engineering applications
. In:
Additive Manufacturing of Polymers for Tissue Engineering
(ed.
A. Babbar
,
R. Kumar
,
V. Dhawan
, et al.),
17
–
39
.
Boca Raton
:
CRC Press
.
10.1201/9781003266464-2 Google Scholar
-
Babbar , A.
,
Tian , Y.
,
Kumar , V.
, and
Sharma , A.
(
2022
).
3D bioprinting in biomedical applications
. In:
Additive Manufacturing of Polymers for Tissue Engineering
(ed.
A. Babbar
,
R. Kumar
,
V. Dhawan
, et al.),
1
–
16
.
Boca Raton
:
CRC Press
.
10.1201/9781003266464-1 Google Scholar
- Páll , S. , Zhmurov , A. , Bauer , P. et al. ( 2020 ). Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS . J. Chem. Phys. 153 ( 13 ): https://doi.org/10.1063/5.0018516 .
- Mermelstein , D.J. , Lin , C. , Nelson , G. et al. ( 2018 ). Fast and flexible gpu accelerated binding free energy calculations within the amber molecular dynamics package . J. Comput. Chem. 39 ( 19 ): 1354 – 1358 : https://doi.org/10.1002/jcc.25187 .
- Emmermacher , J. , Spura , D. , Cziommer , J. et al. ( 2020 ). Engineering considerations on extrusion-based bioprinting: interactions of material behavior, mechanical forces and cells in the printing needle . Biofabrication 12 ( 2 ): https://doi.org/10.1088/1758-5090/ab7553 .
- Göhl , J. , Markstedt , K. , Mark , A. et al. ( 2018 ). Simulations of 3D bioprinting: predicting bioprintability of nanofibrillar inks . Biofabrication 10 ( 3 ): https://doi.org/10.1088/1758-5090/aac872 .
- Paxton , N. , Smolan , W. , Böck , T. et al. ( 2017 ). Proposal to assess printability of bioinks for extrusion-based bioprinting and evaluation of rheological properties governing bioprintability . Biofabrication 9 ( 4 ): https://doi.org/10.1088/1758-5090/aa8dd8 .
- Ozbolat , I.T. and Hospodiuk , M. ( 2016 ). Current advances and future perspectives in extrusion-based bioprinting . Biomaterials 76 : 321 – 343 : https://doi.org/10.1016/j.biomaterials.2015.10.076 .
- Rezende , R.A. , Bartolo , P.J. , Mendes , A. , and Filho , R.M. ( 2009 ). Rheological behavior of alginate solutions for biomanufacturing . J. Appl. Polym. Sci. 113 ( 6 ): 3866 – 3871 : https://doi.org/10.1002/app.30170 .
- Almeida , H.A. and Bártolo , P.J. ( 2013 ). Numerical simulations of bioextruded polymer scaffolds for tissue engineering applications . Polym. Int. 62 ( 11 ): 1544 – 1552 : https://doi.org/10.1002/pi.4585 .
- Bertrand , T. , Peixinho , J. , Mukhopadhyay , S. , and MacMinn , C.W. ( 2016 ). Dynamics of swelling and drying in a spherical gel . Phys. Rev. Appl. 6 ( 6 ): https://doi.org/10.1103/PhysRevApplied.6.064010 .
- Hajikhani , A. , Wriggers , P. , and Marino , M. ( 2021 ). Chemo-mechanical modelling of swelling and crosslinking reaction kinetics in alginate hydrogels: a novel theory and its numerical implementation . J. Mech. Phys. Solids 153 : https://doi.org/10.1016/j.jmps.2021.104476 .
- Feng , L. , Jia , Y. , Li , X. , and An , L. ( 2011 ). Comparison of the multiphasic model and the transport model for the swelling and deformation of polyelectrolyte hydrogels . J. Mech. Behav. Biomed. Mater. 4 ( 7 ): 1328 – 1335 : https://doi.org/10.1016/j.jmbbm.2011.05.001 .
- Naghieh , S. , Karamooz Ravari , M.R. , Badrossamay , M. et al. ( 2016 ). Numerical investigation of the mechanical properties of the additive manufactured bone scaffolds fabricated by FDM: the effect of layer penetration and post-heating . J. Mech. Behav. Biomed. Mater. 59 : 241 – 250 : https://doi.org/10.1016/j.jmbbm.2016.01.031 .
-
Naghieh , S.
,
Sarker , M.D.
,
Karamooz-Ravari , M.R.
et al. (
2018
).
Modeling of the mechanical behavior of 3D bioplotted scaffolds considering the penetration in interlocked strands
.
Appl. Sci.
8
(
9
):
https://doi.org/10.3390/app8091422
.
10.3390/app8091422 Google Scholar
-
Soufivand , A.A.
,
Abolfathi , N.
,
Hashemi , S.A.
, and
Lee , S.J.
(
2019
).
Prediction of mechanical behavior of 3D bioprinted tissue-engineered scaffolds using finite element method (FEM) analysis
.
SSRN Electron. J.
:
https://doi.org/10.2139/ssrn.3431851
.
10.2139/ssrn.3431851 Google Scholar
- Wu , T. and Li , H. ( 2018 ). Phase-field model for liquid–solid phase transition of physical hydrogel in an ionized environment subject to electro–chemo–thermo–mechanical coupled field . Int. J. Solids Struct. 138 : 134 – 143 : https://doi.org/10.1016/j.ijsolstr.2018.01.005 .
- Gu , G.X. , Chen , C.T. , and Buehler , M.J. ( 2018 ). De novo composite design based on machine learning algorithm . Extrem. Mech. Lett. 18 : 19 – 28 : https://doi.org/10.1016/j.eml.2017.10.001 .
- Chaparro , B.M. , Thuillier , S. , Menezes , L.F. et al. ( 2008 ). Material parameters identification: gradient-based, genetic and hybrid optimization algorithms . Comput. Mater. Sci. 44 ( 2 ): 339 – 346 : https://doi.org/10.1016/j.commatsci.2008.03.028 .
- Yao , X. , Moon , S.K. , and Bi , G. ( 2017 ). A hybrid machine learning approach for additive manufacturing design feature recommendation . Rapid Prototyp. J. 23 ( 6 ): 983 – 997 : https://doi.org/10.1108/RPJ-03-2016-0041 .
- Gan , Z. , Li , H. , Wolff , S.J. et al. ( 2019 ). Data-driven microstructure and microhardness design in additive manufacturing using a self-organizing map . Engineering 5 ( 4 ): 730 – 735 : https://doi.org/10.1016/j.eng.2019.03.014 .
-
Williams , G.
,
Meisel , N.A.
,
Simpson , T.W.
, and
McComb , C.
(
2019
).
Design repository effectiveness for 3D convolutional neural networks: application to additive manufacturing
.
J. Mech. Des. Trans. ASME
141
(
11
):
https://doi.org/10.1115/1.4044199
.
10.1115/1.4044199 Google Scholar
- Petrov , A. , Pernot , J.P. , Giannini , F. , and Falcidieno , B. ( 2016 ). Mapping aesthetic properties to 3D free form shapes through the use of a machine learning based framework . IMATI Report .
-
Chowdhury , S.
and
Anand , S.
(
2016
).
Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes
.
ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016
, vol. 3, 27 June–1 July 2016.
Blacksburg, Virginia, USA
.
https://doi.org/10.1115/MSEC20168784
.
10.1115/MSEC20168784 Google Scholar
-
Shi , Y.
,
Zhang , Y.
,
Baek , S.
et al. (
2018
).
Manufacturability analysis for additive manufacturing using a novel feature recognition technique
.
Comput. Aided. Des. Appl.
15
(
6
):
941
–
952
:
https://doi.org/10.1080/16864360.2018.1462574
.
10.1080/16864360.2018.1462574 Google Scholar
-
H.P. Nagarajan
,
H Jafarian
,
A Hamedi
et al
. (
2018
).
Knowledge-based optimization of artificial neural network topology for process modeling of fused deposition modeling
.
Proceedings of the ASME Design Engineering Technical Conference
, vol. 4,
https://doi.org/10.1115/DETC2018-85187
.
10.1115/DETC2018?85187 Google Scholar
-
Koeppe , A.
,
Hernandez Padilla , C.A.
,
Voshage , M.
et al. (
2018
).
Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks
.
Manuf. Lett.
15
:
147
–
150
:
https://doi.org/10.1016/j.mfglet.2018.01.002
.
10.1016/j.mfglet.2018.01.002 Google Scholar
- Khadilkar , A. , Wang , J. , and Rai , R. ( 2019 ). Deep learning–based stress prediction for bottom-up SLA 3D printing process . Int. J. Adv. Manuf. Technol. 102 ( 5–8 ): 2555 – 2569 : https://doi.org/10.1007/s00170-019-03363-4 .
-
Sarlo , R.
and
Tarazaga , P.A.
(
2016
).
A neural network approach to 3D printed surrogate systems
.
Conf. Proc. Soc. Exp. Mech. Ser.
10
:
215
–
222
:
https://doi.org/10.1007/978-3-319-30249-2_18
.
10.1007/978?3?319?30249?2_18 Google Scholar