Technologies for design-build-test-learn automation and computational modelling across the synthetic biology workflow: a review

Abstract

Motivated by the need to parameterize and functionalize dynamic, multiscale simulations, as well as bridge the gap between advancing in silico and laboratory Synthetic Biology practices, this work evaluated and contextualized Synthetic Biology data standards and conversion, modelling and simulation methods, genetic design and optimization, software platforms, machine learning, assembly planning, automated modelling, combinatorial methods, biological circuit design and laboratory automation. This review also discusses technologies related to domain specific languages, libraries and APIs, databases, whole cell models, use of ontologies, datamining, metabolic engineering, parameter estimation/acquisition, robotics, microfluidics and touches on a range of applications. The discussed principles should provide a strong, encompassing foundation for primarily dry laboratory Synthetic Biology automation, reproducibility, interoperability, simulatability, data acquisition, parameterization, functionalization of models, classification, computational efficiency, time efficiency and effective genetic engineering. Applications impact the design-build-test-learn loop, in silico computer assisted design and simulations, hypothesis generation, yield optimization, drug design, synthetic organs, sensors and living therapeutics.