Despite a booming community and notable successes of Synthetic Biology, the basic task of assembling a predictable gene network from biomolecular parts is still a key challenge: this is a major roadblock in the engineering of microbes for industrial biotechnology and even more so in mammalian synthetic biology where it often takes many months to years before the desired network is produced. This translates into high development costs of “genetic devices” that have so far significantly limited the pace at which Synthetic Biology progresses towards applications.
We aim at lifting such roadblock and finally unleash the potential of mammalian Synthetic Biology. To do so we will apply rational engineering principles to the construction of gene circuits in mammalian cells. While rational design, the ability to effectively create new systems/products based on a quantitative understanding of their mechanisms, has been extensively used in engineering, it only found limited applicability in Synthetic Biology as it heavily relies on mathematical models. Unfortunately, obtaining such models has so far proven particularly expensive in biology: reliable models require large amounts of data/information, i.e. many and/or long experiments often involving expensive reagents. With this project we aim at addressing this problem by combining principles from Control Engineering, namely Optimal Experimental Design  and Computer Science (Bayesian filtering and GPGPU computing) with in-vitro experiments carried out using microfluidics  and microscopy to automatically infer mathematical models of individual parts of synthetic circuits with the minimum number of experiments (i.e. with minimal use of reagents). We will then exploit this approach to (a) extensively characterise a set of transcription factor/promoter pairs (e.g. CMV-Tet, dCas9-sgRNA) integrated in specific genomic loci  and (b) use the models obtained in (a) to streamline the assembly of a stable genetic oscillator, a missing fundamental building block in mammalian synthetic biology.
 Menolascina, F. et al. Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering. BMC Bioinformatics 10 Suppl 1, S4 (2009).
 Kolnik, M., Tsimring, L. S. & Hasty, J. Vacuum-assisted cell loading enables shear-free mammalian microfluidic culture. Lab Chip 12, 4732 (2012).
 Duportet, X. et al. A platform for rapid prototyping of synthetic gene networks in mammalian cells. Nucleic Acids Res. 42, 13440–13451 (2014).
This project is part of a collaboration with Imperial College of London (Dr. Velia Siciliano) and University of California at San Diego (Prof. Jeff Jasty).
Dr Filippo Menolascina
Prof Susan Rosser
Applications from students with a background in Biology/Biotechnology, Mathematics, Physics and Engineering are encouraged.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Subject to competition.