title: Building synthetic systems to learn nature's design principles.
abstract: Evolution undoubtedly shapes the architecture of biological systems, yet it is unclear which features of regulatory, metabolic, and signalling circuits have adaptive significance and how the architecture of these circuits constrains or promotes evolutionary processes, such as adaptation to new environments. Experimentally rewiring circuits using genetic engineering and constructing novel circuits in living cells allows direct testing and validation of hypotheses in evolutionary systems biology. Building synthetic genetic systems enables researchers to explore regions of the genotype-phenotype and fitness landscapes that may be inaccessible to more traditional analysis. Here, we review the strategies that allow synthetic systems to be constructed and how evolutionary design principles have advanced these technologies. We also describe how building small genetic regulatory systems can provide insight on the trade-offs that constrain adaptation and can shape the structure of biological networks. In the future, the possibility of building biology de novo at the genome scale means that increasingly sophisticated models of the evolutionary dynamics of networks can be proposed and validated, and will allow us to recreate ancestral systems in the lab. This interplay between evolutionary systems theory and engineering design may illuminate the fundamental limits of performance, robustness, and evolvability of living systems.
authors: Centre for Synthetic Biology and Innovation, Imperial College London, London, UK. e.davidson@imperial.ac.uk
authors: Eric A
authors: EA
authors: Davidson
authors: Oliver P F
authors: OP
authors: Windram
authors: Travis S
authors: TS
authors: Bayer
mesh_terms: D000222:Adaptation, Physiological; D000818:Animals; D001419:Bacteria; D005075:Biological Evolution; D002450:Cell Communication; D002633:Chemotaxis; D004331:Drosophila melanogaster; D053263:Gene Regulatory Networks; D005818:Genetic Engineering; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*; D014780:Viruses