New combinatorial optimization methods for precursor cell culture and tissue engineering

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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.