Journal article Open Access
Marucci, Lucia; Barberis, Matteo; Karr, Jonathan; Ray, Oliver; Race, Paul R.; Andrade, Miguel de Souza; Grierson, Claire; Hoffmann, Stefan Andreas; Landon, Sophie; Rech, Elibio; Rees-Garbutt, Joshua; Seabrook, Richard; Shaw, William; Woods, Christopher
Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silicooffers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.