How to Optimize Maturation in a Bioreactor for Vascular Tissue Engineering: Focus on a Decision Algorithm for Experimental Planning

摘要

Bioreactors may become essential tools for developing tissue-engineered organs from cells, with or without scaffolds. Cells seeded in these devices are expected to grow and form a tissue. Up to now, one of the main challenges to successfully develop functional organs is the structural organization of the cells into the scaffold during maturation in the bioreactor. The maturation step is affected by a set of highly interlinked dynamical variables (flow, stress, pH, temperature, and growth factors) in such a way that fixing the optimal environmental conditions becomes very complex. This work focuses on how the experimental parameters in the bioreactor can be optimized through numerical modeling to maximize tissue growth. Genetic programming (GP) and Markov decision processes (MDPs) were used in synergy to generate and take full advantage of a model of the vascular construct growth. The approach consists in formulating a model through GP to explain the growth of the construct and using MDPs to come up with a strategy to yield the best results in the experimental runs. Construct growth was improved, and the regeneration process was better understood in numerical simulations that relied on this control system. Therefore, an advanced numerical controller of this type could become an effective and inexpensive tool for planning experimental work in tissue engineering.