Adaptation is a fundamental and defining feature of biology. In principle, living systems can adapt via two mechanisms: evolution and learning. Learning is a potentially rapid and powerful mode of adaptation. Even the simplest cells can evolve, but can they demonstrate learning in the absence of evolution? If so, what modes of learning can they engage in, and how simple can learning cells or cell-like systems be? In setting out to address these fundamental questions about the Rules of Life, this project will help to define the essential biological nature of learning systems. This project will make important progress towards the bottom-up construction of 'smart' synthetic cell systems, with potential future applications across a wide range of academic, industry, clinical and environmental settings. A multi-disciplinary cohort of graduate students will be recruited and trained in interdisciplinary research, and a set of 'science & society' modules for bioengineering-related courses will be developed. Furthermore, the project will engage a broader public audience by developing hands-on activities related to the goals of the project.
This project aims to create synthetic cell systems capable of associative learning. Specifically, the project will develop a synthetic cell that learns to respond to a light pulse signal by associating it with the addition of molecules detected by olfactory receptors. Success will provide a proof-of-principle that genetically encoded information-processing systems can carry out learning tasks, and will generate a reusable library of learning circuit motifs. Modeling and design of associative learning circuits will inform the development of corresponding genetic regulatory circuit architectures. Multi-input chemical signals will be sensed using a library of olfactory receptor proteins, and the effects of membrane encapsulation on system behavior will be studied. Finally, an integrated Human Practices component will explore the relationship between learning synthetic cells and artificial neural networks/machine learning, from historical, conceptual and ethical perspectives.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
