Beyond directed evolution: Darwinian selection as a tool for synthetic biology

Synthetic biology is an engineering approach that seeks to design and construct new biological parts, devices and systems, as well as to re-design existing components. However, rationally designed synthetic circuits may not work as expected due to the context-dependence of biological parts. Darwinian selection, the main mechanism through which evolution works, is a major force in creating biodiversity and may be a powerful tool for synthetic biology. This article reviews selection-based techniques and proposes strict Darwinian selection as an alternative approach for the identification and characterization of parts. Additionally, a strategy for fine-tuning of relatively complex circuits by coupling them to a master standard circuit is discussed

Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology

Modular and orthogonal genetic logic gates are essential for building robust biologically based digital devices to customize cell signalling in synthetic biology. Here we constructed an orthogonal AND gate in Escherichia coli using a novel hetero-regulation module from Pseudomonas syringae. The device comprises two co-activating genes hrpR and hrpS controlled by separate promoter inputs, and a σ54-dependent hrpL promoter driving the output. The hrpL promoter is activated only when both genes are expressed, generating digital-like AND integration behaviour. The AND gate is demonstrated to be modular by applying new regulated promoters to the inputs, and connecting the output to a NOT gate module to produce a combinatorial NAND gate. The circuits were assembled using a parts-based engineering approach of quantitative characterization, modelling, followed by construction and testing. The results show that new genetic logic devices can be engineered predictably from novel native orthogonal biological control elements using quantitatively in-context characterized parts

Prediction by Promoter Logic in Bacterial Quorum Sensing

Quorum-sensing systems mediate chemical communication between bacterial cells, coordinating cell-density-dependent processes like biofilm formation and virulence-factor expression. In the proteobacterial LuxI/LuxR quorum sensing paradigm, a signaling molecule generated by an enzyme (LuxI) diffuses between cells and allosterically stimulates a transcriptional regulator (LuxR) to activate its cognate promoter (pR). By expressing either LuxI or LuxR in positive feedback from pR, these versatile systems can generate smooth (monostable) or abrupt (bistable) density-dependent responses to suit the ecological context. Here we combine theory and experiment to demonstrate that the promoter logic of pR – its measured activity as a function of LuxI and LuxR levels – contains all the biochemical information required to quantitatively predict the responses of such feedback loops. The interplay of promoter logic with feedback topology underlies the versatility of the LuxI/LuxR paradigm: LuxR and LuxI positive-feedback systems show dramatically different responses, while a dual positive/negative-feedback system displays synchronized oscillations. These results highlight the dual utility of promoter logic: to probe microscopic parameters and predict macroscopic phenotype

Recent advanceas and opportunities in synthetic logic gates engineering in living cells

Recently, a number of synthetic biologic gates including AND, OR, NOR, NOT, XOR and NAND have been engineered and characterized in a wide range of hosts. The hope in the emerging synthetic biology community is to construct an inventory of well-characterized parts and install distinct gene and circuit behaviours that are externally controllable. Though the field is still growing and major successes are yet to emerge, the payoffs are predicted to be significant. In this review, we highlight specific examples of logic gates engineering with applications towards fundamental understanding of network complexity and generating a novel socially useful applications.close