  Computer-aided design 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, 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. Computer-aided design strategies require quantitative
representations of cells, able to 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, and we discuss several
challenges for the realization of our vision. The possibility to describe and
build in silico whole-cells offers an opportunity to develop increasingly
automatized, precise and accessible computer-aided design tools and strategies
throughout novel interdisciplinary collaborations.
