Guaranteed Scalability & Robustness of Cell-Free Circuits through Data-Driven Optimization - 1=Healthcare technologies 2=Biomaterials and Ti

Lead Research Organisation: University of Warwick
Department Name: Sch of Engineering


Vision: Synthetic biology, identified by Warwick as one of its growth directions, is a great revolution in the making that has failed to scale well on programmable complexity and yields due to the lack of fundamental understanding on how reproducible synthesis/test protocols should be designed. For the relevant lab-level implementations, cell-free protein synthesis (CFPS) has emerged as a useful platform but suffers from low production yields. The proposed project aims to obtain critical breakthroughs to overcome these hurdles and thereby help synthetic biology achieve the desired scalability.

Project Aims: The proposed project aims to achieve (A1) an automated streamline pipeline that takes a given dynamical circuit specification as the input and produces an optimised in silico DNA/RNA based circuit as an intermediate output, and (A2) data-driven optimisation of CFPS protocols. These protocols will be validated on a biosensor which is developed, and continually improved, in my wet-lab at Warwick.

Project Tasks: To achieve the aim A1, the student will develop a toolbox for the "Visual DSD" software of Microsoft Research (Cambridge, UK) for an automated design of in silico circuits. Apart from theoretical characterization, scripts in MATLAB and Visual DSD languages will be created; these will be documented for inclusion in the user manuals of Visual DSD. To achieve the aim A2, the student will develop machine learning algorithms trained on lab-level and public-domain datasets to optimise the CFPS protocols for in vitro circuit synthesis of circuits and will test those on the biosensor. The machine learning algorithms will be implemented in R and Python and trained on the database facilitated by Tata Consultancy Services (TCS) and on the data generated by the "Benchling" platform. The baseline CFPS protocols of Arbor Bioscience (Ann Arbor, MI, USA) will be improved using these machine learning algorithms.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513374/1 01/10/2018 30/09/2023
2199294 Studentship EP/R513374/1 15/05/2019 15/11/2022 Yanahan Paramalingam