Design-driven engineering of robust mammalian sense-and-respond functions: from parts to programs

点击翻译按钮获取中文摘要

Project Summary The overarching goal of this project is to enable the use of engineered cell therapies to safely and effectively treat conditions ranging across cancer, autoimmune disease, and regenerative medicine. Engineered cell therapies are an exciting frontier, with early successes in cancer treatment demonstrating the transformative potential of this approach. Customized cell therapies could yield safe and effective treatments for many applications beyond cancer, but realizing this potential is limited by the fact that evaluating a potential therapeutic strategy requires extensive time and resources to implement it. The goal of this project is to flip this paradigm— to enable spending less time building and thus focus on evaluating potentially useful strategies. This project will develop state-of-the-art technologies for cell engineering and enable their application to solve three open, complementary, clinically motivated challenges. The first aim is to develop the technology, understanding, and computational tools required to build genetic programs that employ natural mechanisms for implementing long-lived memory. Natural systems employ genetic memory to drive processes such as differentiation and development by adding and removing stable marks to the genome. Although research has yielded insights into how to drive such changes, bioengineers do not yet have the ability to leverage those insights to build programs that implement these effects for useful purposes. This project will address this need by developing genetic programs that exhibit stable behaviors including inducible and autonomous state switching. The second aim will generate novel candidate cell therapies for treating cancer that leverage foundational advances for engineering cells to evaluate and respond to external cues (e.g., unique markers of the tumor site) to induce desired therapeutic behaviors. This work will develop programs hypothesized to improve both safety and efficacy of these approaches. A key aspect of this work is employing model-guided design to evaluate and refine genetic programs to confer desired behaviors. The third aim will develop a computational framework enabling computer-assisted design of genetic programs. Current design is limited by the imagination of the designer—a human must propose a design which is subsequently evaluated. This aim will make the transformative leap to semi-automated design, establishing workflows and tools that are freely accessible to researchers in a graphics-enabled open software framework.