Bogdan Kirillov1,2*, Katherine Vilinski-Mazur1 and Dmitry Kolomenskiy1
1 Center of Material Technologies, Skolkovo Institute of Science and Technology, Moscow, Russia
2 Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia
Bogdan.Kirillov [at] skoltech.ru
Abstract
Three-dimensional bioprinting serves as a foundation for a number of modern tissue engineering technologies, for example organ-on-a-chip models and nerve conduits. The ability of 3D bioprinting to create functional tissue structures depends on the properties of the used bioink – the material that consists of cells and supporting structure. Most frequently used supporting component of a bioink for extrusion-based 3D bioprinting is a temperature-activated hydrogel (e.g. kappa carrageenan or sodium alginate), a hydrogel that solidifies when it reaches a specific activation temperature. Use of composite hydrogels that consist of different components (e.g. a combination of hydroxyapatite nanorods and gelatin) allows for precise control of bioprinted construct properties. Knowledge of the hydrogel’s activation temperature and the associated thermophysical properties is essential for optimizing the bioprinting process since they influence the settings that one needs to select in order to maximize the probability of successful experiment – printhead temperature, printing speed and extrusion multiplier. Additionally, thermophysical properties have an effect on mechanical properties of the final printed model and also affect the viability of the cells within it. Thermophysical properties, as well as mechanical properties, can be controlled by changing the composition of hydrogel. This study provides a design of experiment for determining thermophysical properties of a composite hydrogel bioink sample using temperature sensors of a 3D bioprinter and a Physics-Informed Machine Learning algorithm. The algorithm combines temperature sensor data, physical simulation of the heat exchange process within a sample and accompanying Machine Learning model that selects the most promising combination of hydrogel components for experimental testing. The experiment is based on a hardware solution of custom open design – the experimental setup can be reproduced using a consumer-grade 3D printer and electronic components readily available on the market. We demonstrate that the combination of open source hardware, artificial intelligence control system and physical simulation allows for accurate assessment of the bioink properties thus making 3D bioprinting more reproducible, robust and accessible.
Keywords: biophysics, 3d bioprinting, thermophysical properties, machine learning, bioinformatics