New combinatorial optimization methods for precursor cell culture and tissue engineering

必须确定生长因子、黏附分子和生物材料的高效组合，以使来自前体(干细胞或祖细胞)培养的组织工程产品能够进行生物制造。然而，多个变量组合的实验研究是一个重大挑战，因为实验的数量随着因素的数量和测试的剂量或水平呈指数级增长。在实践中，只能探索高维参数空间的一小部分，导致所需单元类型的产量不是最优的。我们在这项提案中的目标是开发和验证新的方法，以优化组织工程应用中繁殖前体细胞的条件。我们的方法将基于搜索算法的使用，该算法将考虑到我们目前和拟议的对红血球前体细胞的研究提供的对前体细胞复杂生物学的见解。这些搜索算法将是非常有价值的，因为它们将极大地促进难治性前体细胞培养条件的发现。因此，当这些算法可用时，基于前体的细胞制造工艺的开发将需要更少的试剂(组织、细胞、培养介质、生长因子、生物材料)，需要更少的劳动力，因此成本将更低。同时，电池产量也可以大幅提高。

Highly efficient combinations of growth factors, adhesion molecules and biomaterials must be identified to enable the biomanufacturing of tissue engineering products derived from precursor (stem or progenitor) cell culture. However, the experimental study of multiple combinations of variables presents a major challenge since the number of experiments grows exponentially with the number of factors and doses or levels tested. In practice, only a fraction of the high dimensional parameter space can be explored, resulting in suboptimal yields in the desired cell types. Our goal in this proposal is to develop and validate new approaches to optimize conditions to propagate precursor cells for tissue engineering applications. Our approach will be based on the use of search algorithms that will take into account insights into the complex biology of precursor cells provided by our present and proposed investigations of red blood cell progenitors. These search algorithms will be extremely valuable since they will significantly facilitate the discovery of culture conditions for refractory precursor cells. Therefore, when these algorithms will be available, the development of precursor-based cell manufacturing processes will require less reagents (tissues, cells, culture media, growth factors, biomaterials), require less labor and, therefore, will be less costly. At the same time, the cell yields can be dramatically increased.