A methodology for co-simulation-based optimization of biofabrication protocols

Abstract

Biofabrication processes are complex and often unsatisfactory. Trial-and-error methods are costly and yield only incremental innovation, starting from sub-optimal and poorly represented existing processes. Although computational techniques might support efficient process design to find optimal process configurations, intelligent computational approaches must comprise biological complexity to provide meaningful insights. This paper proposes a novel co-simulation-based optimization methodology for the systematic design of protocols for cell culture and biofabrication. The proposed strategy integrates evolutionary computation and simulation for efficient design space exploration and assessment of candidate protocols. A generic library supports the modular and flexible composition of multiscale and multidomain co-simulation scenarios. The feasibility of the presented approach was demonstrated in the automatic generation of protocols for the biofabrication of an epithelial cell monolayer. The results are twofold. First, the prototype co-simulation library helps build flexible, loosely coupled simulation scenarios. Second, the in-silico experimentation on the use case shows that the proposed approach is a viable first step towards standard and automated design in biofabrication.

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

“…In [132], the authors employ a model of tissue growth inside 3D scaffolds in a perfusion bioreactor and a multi-objective optimization strategy to find the most cost-effective medium refreshment strategy for maximizing tissue growth while minimizing experimental cost. Authors in [133] combine evolutionary computation and simulation of intra-and extracellular processes for efficient DSE of process designs for the biofabrication of human epithelial monolayers.…”

“…A model of tissue growth inside 3D scaffolds in a perfusion bioreactor combines with a multi-objective optimization method to find the most cost-effective medium refreshment strategy for maximizing tissue growth while minimizing experimental costs in [152]. Discrete simulation of intra-and extracellular processes combines with evolutionary computation to perform the DSE of process designs to generate optimal biofabrication protocols to maximize size and control geometry of human epithelial monolayers in silico in [153]. Continuous simulation of tissue dynamics based on vertex models of cells (leveraging on the PalaCell2D simulation framework [154]) combine with a deep Reinforcement Learning (RL) approach to generate optimal protocols for epithelial sheets culture for maximizing the total number of cells produced and optimizing the spatial organization within the cell aggregate in [155].…”