New combinatorial optimization methods for precursor cell culture and tissue engineering

必须确定生长因子、粘附分子和生物材料的高效组合，以使前体（干细胞或祖细胞）细胞培养的组织工程产品的生物制造成为可能。然而，对多种变量组合的实验研究提出了一项重大挑战，因为实验的数量随着被测因素和剂量或水平的数量呈指数增长。在实践中，只有一小部分的高维参数空间可以探索，导致在理想的细胞类型的次优产量。我们在本提案中的目标是开发和验证新的方法来优化条件，以繁殖组织工程应用的前体细胞。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红细胞祖细胞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发现难治性前体细胞的培养条件。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、培养基、生长因子、生物材料），需要更少的劳动力，因此成本更低。与此同时，电池产量可以显著提高。

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.