Synthetic Biology (SynBio) is an emerging engineering discipline with an ambitious goal: empowering scientists with the ability to programme new functions into cells, just like we would do with computers. Despite a thriving community and notable successes, however, writing "functioning algorithms" for cells remains extremely time-consuming. This is a roadblock towards the engineering of mammalian cells, an area uniquely positioned to develop potentially groundbreaking therapeutic applications. This translates into high development costs that, in turn, are limiting the pace at which Synthetic Biology progresses towards applications. Model-Based System Engineering (MBSE) is the answer the engineering community found to similar problems and is widely used to streamline manufacturing. In this framework, mathematical models are used to screen candidate designs via simulations and bring to testing only the most promising solutions. Despite being an engineering discipline, SynBio lacks a MBSE framework. This is largely due to three connected issues: (a) the scarcity of accurate mathematical models of parts (e.g. promoters) in the first place. Such a shortage (b) makes it difficult to "reverse engineer" the connection between the DNA sequence and the kinetics of the transcribed mRNA (e.g. promoter sequence and leakiness of expresion). This means that (c) the inverse "re-design" problem, i.e. finding the optimal DNA sequence of a part, cannot be solved, let alone automatically. With this fellowship, I aim at filling this gap and develop a "Model-Based Biosystem Engineering" (MBBE) framework to automate the Design-Build-Test-Learn (DBTL) cycle in Synthetic Biology. Given their role in cell and gene therapy, with my team, we will focus on synthetic promoters for mammalian cells. Prompted by the recent successes and challenges of CAR T cells -immune cells engineered to kill cancer cells, we will use the framework to engineer a hypoxia-inducible promoter that optimises a set of criteria we will determine and prioritise with our collaborator Prof. Chen at UCLA. We will first focus on the development of the MBBE framework; to this aim we will tackle the three issues mentioned above by: (a) developing a high-throughput microfluidic device that allows to infer, with minimum experimental efforts (via Optimal Experimental Design), reliable mathematical models of hundreds of variants of a promoter, (b) using these results to automatically learn/predict gene expression dynamics from promoter sequence via machine learning and (c) combining this prediction scheme with computational optimisation to identify and refine promoter sequences so that they satisfy given specifications and maximise pre-determined objectives. To develop a hypoxia-inducible promoter, we will start from an initial pool of 600 sequences -designed to cover a fraction of the design space as big as possible, and we will iterate twice over our automatic DBTL loop to finally obtain promoter(s) that can be used to overcome the current limitations of CAR T cells. Besides automating the DBTL cycle, the approach I propose has three main benefits: it allows to obtain, and publicly share, reliable models (1) faster -as we will use Optimal Experimental Design methods to minimise experimental efforts, (2) cost-effectively -as microfluidics drastically reduces the use of reagents and automation renders human intervention unnecessary; (3) in a reproducible way -as all the data and the steps in the inference are tracked and immediately made publicly available.