Automated design, build, test, learn workflows to engineer synthetic genetic networks

Abstract

Synthetic biology is an interdisciplinary field that pursues the engineering of biological systems. The design, build, test, learn (DBTL) cycle is at the core of engineering disciplines and is iterated until a desired goal is achieved. Synthetic biology is still defining abstractions, standards and developing a software ecosystem to iterate the DBTL cycle. The aim is to work in a similar way as other engineering disciplines, making designs with a computational aided design (CAD) tool that can simulate the expected behaviour of the designed biological system, and that can communicate to build tools to create a physical implementation of the biological system. After the biological system is built, it is tested by taking measurements of its behaviour. The test has to be automated, calibrated and standardised to get high quantity and quality data that can inform the learn stage properly. Given the diversity of synthetic biology and its applications the DBTL cycle could have different needs when the researcher needs to engineer a genetic network, a metabolic pathways, a strain or a protein, among others. The focus of this work is in creating DBTL cycle workflows for engineering synthetic genetic network dynamics, because it allows to control the logic of a system and how that logic state is reached and maintained over time with direct applications in biochemical production, drug dosage, and the study of pattern formation and developmental biology. Existing tools for engineering genetic network dynamics do not cover the whole DBTL cycle and lack connections, leaving several gaps. Most tools do not use standardised inputs and outputs hindering the connectivity between tools and slowing the research process. To iterate faster through the DBTL cycle it has to be closed and automated by leveraging software tools and liquid handling robots. Software tools have to be compatible with standards to make them useful and accessible for the community, promoting the use of best practices. The workflow has to be flexible to accommodate different needs and resources, to be used for researchers without a wetlab, with non-automated wetlab and with lab automation. Here I have created a set of software tools tackling different DBTL cycle stages that are modular and leverage standards to connect and automate the DBTL cycle for genetic network engineering. The workflows developed in this work provides novel teaching and research tools available for different needs.