A synthetic gene circuit is often engineered by considering the host cell as an invariable "chassis". Circuit activation, however, may modulate host physiology, which in turn can drastically impact circuit behavior. We illustrate this point by a simple circuit consisting of mutant T7 RNA polymerase (T7 RNAP*) that activates its own expression in bacterium Escherichia coli. Although activation by the T7 RNAP* is noncooperative, the circuit caused bistable gene expression. This counterintuitive observation can be explained by growth retardation caused by circuit activation, which resulted in nonlinear dilution of T7 RNAP* in individual bacteria. Predictions made by models accounting for such effects were verified by further experimental measurements. Our results reveal a novel mechanism of generating bistability and underscore the need to account for host physiology modulation when engineering gene circuits.

SummaryThe integration of synthetic and cell-free biology has made tremendous strides towards creating artificial cellular nanosystems using concepts from solution-based chemistry: only the concentrations of reacting species modulate gene expression rates. However, it is known that macromolecular crowding, a key feature of natural cells, can dramatically influence biochemical kinetics by volume exclusion effects that reduce diffusion rates and enhance binding rates of macromolecules. Here, we demonstrate that macromolecular crowding can increase the robustness of gene expression through integrating synthetic cellular components of biological circuits and artificial cellular nanosystems. In addition, we reveal how ubiquitous cellular modules, including genetic components, a negative feedback loop, and the size of crowding molecules, can fine tune gene circuit response to molecular crowding. By bridging a key gap between artificial and living cells, our work has implications for efficient and robust control of both synthetic and natural cellular circuits.

Mammalian cell cycle entry is controlled at the restriction point by a bistable and resettable switch, which is shown to emerge from a minimal gene circuit containing a mutual-inhibition feedback loop between Rb and E2F modules, coupled with a feed-forward loop between Myc and E2F modules.

The efficacy of many antibiotics decreases with increasing bacterial density, a phenomenon called the ‘inoculum effect' (IE). This study reveals that, for ribosome-targeting antibiotics, IE is due to bistable inhibition of bacterial growth, which reduces the efficacy of certain treatment frequencies.

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the singlecell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, opensource software. ' 2009 International Society for Advancement of Cytometry Key terms cell tracking; fluorescent microscopy; hybrid image filters; image masking; image segmentation; single-cell lineage tracking TIME-lapse microscopy technologies now enable detailed data generation on dynamic cellular processes at the single cell level (1,2). Recent studies have highlighted the use and importance of this technology for investigating biological noise in the dynamics of gene regulation, competence pathways in Bacillus subtilis, and aspects of cell growth and proliferation, among other areas (2-7). Mathematical and statistical models are of growing interest in describing and testing hypothesis for such systems (8-10), and rely on extraction of data from time-lapse microscopy imaging techniques that generate sequences of individual, pixel-based image frames.Unlike data generated from flow cytometry, which represent snapshots of cellular states, time-lapse movies with sufficiently high temporal and spatial resolution allow time courses of gene expression dynamics to be established at the single-cell level. These time courses are critically important in studies investigating the dynamics of both natural (11-14) and synthetic (15-18) cellular networks, dynamics that are often masked at the population level. However, recognizing cells as objects in images, and tracking them from one image to the next, remain as central technical challenges (1). These tasks, segmentation and tracking, respectively, can be approached either manually or automatically. Typically, humans are good at these tasks, but automation-essential for this fast-developing technology-is difficult and poses open questions for methodology development.Depending on the cells being segmented and tracked, various existing algorithms are available (19)(20)(21)(22)(23). A universal solution to cell segmentation and tracking, applicable across cell types, has yet to be de...

The integration of synthetic biology and soft robotics can fundamentally advance sensory, diagnostic, and therapeutic functionality of bioinspired machines. However, such integration is currently impeded by the lack of soft-matter architectures that interface synthetic cells with electronics and actuators for controlled stimulation and response during robotic operation. Here, we synthesized a soft gripper that uses engineered bacteria for detecting chemicals in the environment, a flexible light-emitting diode (LED) circuit for converting biological to electronic signals, and soft pneu-net actuators for converting the electronic signals to movement of the gripper. We show that the hybrid bio-LED-actuator module enabled the gripper to detect chemical signals by applying pressure and releasing the contents of a chemical-infused hydrogel. The biohybrid gripper used chemical sensing and feedback to make actionable decisions during a pick-and-place operation. This work opens previously unidentified avenues in soft materials, synthetic biology, and integrated interfacial robotic systems.

Fig. 2. (A)Interaction web of top-down and bottom-up effects in the eelgrass study system. The top predator is the sea otter (E. lutris), the mesopredators are crabs (Cancer spp. and Pugettia producta), the epiphyte mesograzers are primarily an isopod (I. resecata) and a sea slug (P. taylori), and algal epiphyte competitors of eelgrass primarily consist of chain-forming diatoms, and the red alga Smithora naiadum. Solid arrows indicate direct effects, dashed arrows indicate indirect effects, and the plus and minus symbols indicate positive and/or negative effects on trophic guilds and eelgrass condition. C, competitive interaction; T, trophic interaction. (Original artwork by A. C. Hughes.) (B-E) Survey results testing for the effects of sea otter density on eelgrass bed community properties (Tables S2 and S3). Elkhorn Slough (sea otters present and high nutrients) eelgrass beds (n = 4) are coded in red, and the Tomales Bay reference site (no sea otters, low nutrients) beds (n = 4) are coded in blue. (B) Crab biomass and size structure of two species of Cancer crabs; (C) grazer biomass per shoot and large grazer density; (D) algal epiphyte loading; and (E) aboveground and belowground eelgrass biomass. DW, dry weight; FW, fresh weight.

Assembly of recombinant multiprotein systems requires multiple culturing and purification steps that scale linearly with the number of constituent proteins. This problem is particularly pronounced in the preparation of the 34 proteins involved in transcription and translation systems, which are fundamental biochemistry tools for reconstitution of cellular pathways ex vivo. Here, we engineer synthetic microbial consortia consisting of between 15 and 34 Escherichia coli strains to assemble the 34 proteins in a single culturing, lysis, and purification procedure. The expression of these proteins is controlled by synthetic genetic modules to produce the proteins at the correct ratios. We show that the pure multiprotein system is functional and reproducible, and has low protein contaminants. We also demonstrate its application in the screening of synthetic promoters and protease inhibitors. Our work establishes a novel strategy for producing pure translation machinery, which may be extended to the production of other multiprotein systems.