Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

Abstract

Biofabrication is the generation of biologically functional products from living cells and biomaterials through bioprinting and subsequent maturation processes. Technological and scientific domains are underlying biofabrication ranging from biology to automated manufacturing and culture systems. Among its application domains, Tissue Engineering and Regenerative Medicine poses strict quality requirements for biofabrication, requiring fast and disruptive innovation for processes and products to comply. Innovation in biofabrication is slow and incremental, relying on R&D processes that are inefficient (slow, risky, and costly), ineffective (products have sub-optimal quality), and challenging to replicate. While automation and digitalization support process execution, design, and innovation, they often work as tools for empirical, problem-specific, and operator-dependent approaches. On the other hand, computational approaches support biofabrication process modeling, design, and optimization of experimental activity to improve the quality of processes and products. To express their full potential, computational methods must factor in the biological complexity underlying biofabrication. This review analyzes computational approaches to biofabrication process modeling, design, and optimization with a focus on solutions explicitly accounting for biological complexity.

Delivery Systems in Ocular Retinopathies: The Promising Future of Intravitreal Hydrogels as Sustained-Release Scaffolds

“…In recent decades, new tools termed “computational methods” have allowed a change in the paradigm of the established “trial and error” by the “rational design” of HGs [ 174 ]. With regard to trial and error, previously only once the HG had been prepared in the laboratory was it possible to evidence the real release efficacy of a specific therapeutic agent [ 175 ].…”

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

“…Computational methods have a key role in the analysis [7], [8], modeling [9], [10], design [1] and optimization [11], [12], [13] of complex biological processes. In particular, for analyzing and predicting the dynamics of multicellular synthetic systems, computational tools must offer instruments for modeling and simulation, accounting for multiple spatial and temporal scales [14].…”

A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

“…In particular, computational modeling and optimization exploit the power of computation to explore system behavior and to conduct systematic DSE, accounting for the relations between complex parameter configurations and process outputs. To impact the biofabrication domain, computational approaches must rely on white-box models of the physical process to explicitly represent the relations between the generated protocols and the biological mechanisms modeled [17]. Nevertheless, most computational approaches to biofabrication overlook biological complexity and primarily focus on the non-living components of the process.…”

“…They enable systematic DSE, considering complex parameter interactions and process outputs. Effective computational approaches require white-box models that represent biological processes accurately [14]. However, current models often neglect biological complexity, leading to large state spaces and necessitating metaheuristics for feasibility [15, 16].…”