Age and disease often result in the deterioration of tissues and organs in humans. Although changes in lifestyle and administration of drugs can delay or prevent tissue degradation, organ failure is an inevitable outcome for a large section of an aging population. Surgical transplantation can provide relief in some cases but is not scalable due to the scarcity of viable donors, the difficulty in preventing an adverse immune responses, and rising medical costs. An attractive alternative is tissue engineering, a field of research that attempts to create replacements for living tissues and organs. The goal of the tissue engineering community is to recapitulate organs and tissues that contain several components of native tissue or to design cellular systems that closely mimic structures found in vivo.
The field of tissue engineering has made rapid strides in the last few decades. From simple cell-biomaterial systems, the field has evolved towards smart materials, stem cells and sophisticated analytical methods. However, progress in the field is stymied by several factors. The space of experiments can be highly complex and large, since they require optimization of several parameters, such as the type of biomaterials, the numbers of different types of cells, the spatial arrangement of cells with respect to each other, and the biomaterial scaffold. Although engineered tissues may include many different types of cells, we have an incomplete understanding of how different cell types in a tissue or organ communicate with each other in order to optimize phenotypic function. Currently, tissue engineers surmount these challenges through their deep intuitive understanding of cell and biomaterial interactions, empirical evidence, and intensive experimentation. However, the current state of the art does not scale up well and intuitively designed experiments may not always be the most informative. Current approaches to experimentation do not tap into the possibilities laid open by systematic computationally-guided approaches to design of experiments, automated analysis of high-throughput experimental data, and modeling and simulation of cellular control mechanisms.
The goal of this interdisciplinary graduate education program is to define the field of Computational Tissue Engineering (CTE), wherein seamlessly intertwined computational and experimental models will drive the next generation of advances in tissue engineering. Our vision is that predictive computational models will drive novel experimental analyses of engineered tissues, while the demands of tissue engineering will inspire novel analysis frameworks in computational science.
We will train students at the confluence of tissue engineering, molecular and cell biology, and computational science. Our vision is that trainees will emerge as the leaders of the trans-disciplinary field of “Computational Tissue Engineering”. They will be equipped to lead and develop this new field, have the training to span traditional disciplinary boundaries, and to converse in the languages of tissue engineering, molecular and cellular biology, and computational science with ease. These students will be well-equipped to address the current challenges faced by each of these fields.