The constant growth of the population has pushed researchers to find novel protein sources. A possible solution to this problem has been found in cellular agriculture, specifically in the production of cultured meat. In the following review, the key steps for the production of in vitro meat are identified, as well as the most important challenges. The main biological and technical approaches are taken into account and discussed, such as the choice of animal, animal-free alternatives to fetal bovine serum (FBS), cell biomaterial interactions, and the implementation of scalable and sustainable biofabrication and culturing systems. In the light of the findings, as promising as cultured meat production is, most of the discussed challenges are in an initial stage. Hence, research must overcome these challenges to ensure efficient large-scale production.

Bioprinting is a promising family of processes combining 3D printing with life sciences, offering the potential to significantly advance various applications. Despite numerous research efforts aimed at enhancing process modeling, optimizing capabilities, and exploring new conditions, there remains a critical need to enhance process efficiency. Experimental data are paramount for improving models. Nevertheless, it is practically unfeasible to explore a multitude of conditions (e.g. different material formulations, process parameters, machines, setups), especially given the experimental constraints of budget and time. Leveraged by in-situ bioprinting monitoring, this paper explores a set of transfer learning (TL) methods designed for resource-efficient bioprinting modeling, aiming to merge established knowledge with new experimental conditions. TL encompasses machine learning strategies focused on transferring knowledge across distinct, yet similar, domains. TL is applied to an extrusion-based bioprinting case study for printability response modeling. The knowledge acquired from a model trained on one material (the source) is transferred to a new material (the target), under conditions of limited experimental data availability. Eventually, the accuracy of the transferred model is assessed and compared against a reference no-transfer scenario, which is developed from scratch following conventional practices. Furthermore, giving high importance to the experimental effort reduction, a sensitivity analysis altering the number of experimental training points is performed to assess performances and limitations of the method. This method demonstrates the feasibility of knowledge transfer in bioprinting as a catalyst for more sophisticated applications across diverse printing conditions, materials, and technologies to advancing this technology towards achieving its full potential.