Experimentation in life science is necessary for academia, industry and education, but many access barriers exist due to professional training, operation cost, maintenance, and safety requirements. High-throughput experimental equipment combined with cloud computing infrastructure has the potential to alleviate these access barriers through abstraction and time-sharing. Recently, a few commercial cloudlabs such as Transcriptic and Emerald Cloudlab have emerged, where highly standardized experimentation protocols are outsourced and executed semi-automatically based on the instructions provided by the users ahead of time without any means to interrupt or interact with an ongoing experiment. Therefore, a truly interactive biology cloudlab does not yet exist, whereas interaction is the key to exploration based science.

In this dissertation, we conceptualized an interactive biology cloudlab paradigm. The core of this concep- tualization is a domain specific device, Biotic Processing Unit (BPU) that hosts a biological sample and allows interactive experimentation. Biological systems are particularly difficult to handle as they, unlike physical systems, may exhibit unpredictable natural variabilities. Therefore, to provide a better Quality of Service (QoS), we proposed a general method to automatically monitor an array of backend BPUs to check the underlying biological state. We implemented two different cloudlab architectures to support 1) nonreal-time chemotaxis experimentation with a slime mold, Physarum polycephalum and 2) real-time phototaxis experimentation with a single celled micro-swimmer Euglena gracilis. In the Physarum based cloudlab, users time-shared a set of pre-allocated BPUs for experiments that would last two days. In the Euglena based cloudlab, users were scheduled in a queue using an Automatic Call Distributor (ACD) system, where experiments would last ~1 minute. We compared these two architectures to draw general design rules, and recommended future cloudlab implementations based on the time-scale of the underlying biology.

We iteratively developed our cloudlab by deploying it in various educational settings with different interaction modalities: in graduate level university classes, middle and high school classes, and eventually through an MOOC course with over 300 students. To this end, we also implemented a modeling platform along with other HCI components to facilitate visual and data analytics to enable inquiry-based learning as mandated by the National Research Council. In conclusion, we have paved the way to make complex biology experimentation accessible to a broader audience - including researchers, citizen scientists, and learners alike - at low cost and scale.