Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications. We outline the basic features of synthetic biology as a new engineering discipline, covering examples from the latest literature and reflecting on the features that make it unique among all other existing engineering fields. We discuss methods for designing and constructing engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. The discussion brings to light issues at the heart of designing complex living systems and provides a trajectory for future development.

It is generally assumed that antibiotics can promote horizontal gene transfer (HGT). However, because of a variety of confounding factors that complicate the interpretation of previous studies, the mechanisms by which antibiotics modulate HGT remain poorly understood. In particular, it is unclear whether antibiotics directly regulate the efficiency of HGT, serve as a selection force to modulate population dynamics after HGT has occurred, or both. Here, we address this question by quantifying conjugation dynamics in the presence and absence of antibiotic-mediated selection. Surprisingly, we find that sub-lethal concentrations of antibiotics from the most widely used classes do not significantly increase the conjugation efficiency. Instead, our modeling and experimental results demonstrate that conjugation dynamics are dictated by antibiotic-mediated selection, which can both promote and suppress conjugation dynamics. Our findings suggest that the contribution of antibiotics to the promotion of HGT may have been overestimated. These findings have implications for designing effective antibiotic treatment protocols and for assessing the risks of antibiotic use.

Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience. While conceptually appealing, the conditions when DOL is advantageous have not been rigorously established. Here, we have analyzed 24 common architectures of metabolic pathways in which DOL can be implemented. Our analysis reveals general criteria defining the conditions that favor DOL, accounting for the burden or benefit of the pathway activity on the host populations as well as the transport and turnover of enzymes and intermediate metabolites. These criteria can help guide engineering of metabolic pathways and have implications for understanding evolution of natural microbial communities.

Microbial consortia form when multiple species colocalize and communally generate a function that none is capable of alone. Consortia abound in nature, and their cooperative metabolic activities influence everything from biodiversity in the global food chain to human weight gain. Here, we present an engineered consortium in which the microbial members communicate with each other and exhibit a ''consensus'' gene expression response. Two colocalized populations of Escherichia coli converse bidirectionally by exchanging acyl-homoserine lactone signals. The consortium generates the gene-expression response if and only if both populations are present at sufficient cell densities. Because neither population can respond without the other's signal, this consensus function can be considered a logical AND gate in which the inputs are cell populations. The microbial consensus consortium operates in diverse growth modes, including in a biofilm, where it sustains its response for several days.biological engineering ͉ cellular circuits ͉ synthetic biology M ost bacteria live in heterogeneous surface-bound congregations called biofilms, and vast reaches of the earth are coated in these living films. In many cases, the microorganisms composing this ubiquitous coating form complex, interactive communities (1-5). Despite their abundance, these microbial communities are poorly understood. Reflecting this relative ignorance of how bacteria behave in biofilms, efforts to program biofilm functions are still in their infancy. The ability to manipulate these films, however, would enable controlled studies of microbial ecosystem dynamics and microscale environmental manipulation. To begin to explore these possibilities, we have engineered de novo cellular circuits that control Escherichia coli behavior in a stable, robust mixed-population biofilm community. The populations communicate, come to a consensus, and respond to each other's presence with a flexible, combinatory gene-expression output.Engineered circuits have been used to control the behavior of single cells (6-12) and cell populations (8,11,(13)(14)(15) in both time and space. Cell-cell communication is a prerequisite for coordination of cellular circuit dynamics on the population level. Engineered communication, via broadcasting and receiving small-molecule signals, can enable the programming of robust and predictable population dynamics (13). One-way engineered cell-cell communication has been used to coordinate biofilm formation in a single population at a predictable cell density (8) and to engineer pattern formation in a mixed population (14,15). Here, we demonstrate an engineered bidirectional cell-cell communication network that can coordinate gene expression from a mixed population. We have characterized the spatial and temporal behavior of this communication network in liquid, agar, and biofilm growth systems. Results and DiscussionMicrobial Consensus Consortium (MCC) Design and Implementation.

The origin of biological morphology and form is one of the deepest problems in science, underlying our understanding of development and the functioning of living systems. In 1952, Alan Turing showed that chemical morphogenesis could arise from a linear instability of a spatially uniform state, giving rise to periodic pattern formation in reaction-diffusion systems but only those with a rapidly diffusing inhibitor and a slowly diffusing activator. These conditions are disappointingly hard to achieve in nature, and the role of Turing instabilities in biological pattern formation has been called into question. Recently, the theory was extended to include noisy activator-inhibitor birth and death processes. Surprisingly, this stochastic Turing theory predicts the existence of patterns over a wide range of parameters, in particular with no severe requirement on the ratio of activator-inhibitor diffusion coefficients. To explore whether this mechanism is viable in practice, we have genetically engineered a synthetic bacterial population in which the signaling molecules form a stochastic activator-inhibitor system. The synthetic pattern-forming gene circuit destabilizes an initially homogenous lawn of genetically engineered bacteria, producing disordered patterns with tunable features on a spatial scale much larger than that of a single cell. Spatial correlations of the experimental patterns agree quantitatively with the signature predicted by theory. These results show that Turing-type pattern-forming mechanisms, if driven by stochasticity, can potentially underlie a broad range of biological patterns. These findings provide the groundwork for a unified picture of biological morphogenesis, arising from a combination of stochastic gene expression and dynamical instabilities.