LsrR quorum sensing "switch" is revealed by a bottom-up approach.

Quorum sensing (QS) enables bacterial multicellularity and selective advantage for communicating populations. While genetic "switching" phenomena are a common feature, their mechanistic underpinnings have remained elusive. The interplay between circuit components and their regulation are intertwined and embedded. Observable phenotypes are complex and context dependent. We employed a combination of experimental work and mathematical models to decipher network connectivity and signal transduction in the autoinducer-2 (AI-2) quorum sensing system of E. coli. Negative and positive feedback mechanisms were examined by separating the network architecture into sub-networks. A new unreported negative feedback interaction was hypothesized and tested via a simple mathematical model. Also, the importance of the LsrR regulator and its determinant role in the E. coli QS "switch", normally masked by interfering regulatory loops, were revealed. Our simple model allowed mechanistic understanding of the interplay among regulatory sub-structures and their contributions to the overall native functioning network. This "bottom up" approach in understanding gene regulation will serve to unravel complex QS network architectures and lead to the directed coordination of emergent behaviors.

Autonomous induction of recombinant proteins by minimally rewiring native quorum sensing regulon of E. coli.

Quorum sensing (QS) enables an individual bacterium's metabolic state to be communicated to and ultimately control the phenotype of an emerging population. Harnessing the hierarchical nature of this signal transduction process may enable the exploitation of individual cell characteristics to direct or "program" entire populations of cells. We re-engineered the native QS regulon so that individual cell signals (autoinducers) are used to guide high level expression of recombinant proteins in E. coli populations. Specifically, the autoinducer-2 (AI-2) QS signal initiates and guides the overexpression of green fluorescent protein (GFP), chloramphenicol acetyl transferase (CAT) and beta-galactosidase (LacZ). The new process requires no supervision or input (e.g., sampling for optical density measurement, inducer addition, or medium exchange) and represents a low-cost, high-yield platform for recombinant protein production. Moreover, rewiring a native signal transduction circuit exemplifies an emerging class of metabolic engineering approaches that target regulatory functions.

From unicellular properties to multicellular behavior: bacteria quorum sensing circuitry and applications.

Cell-cell communication and coordinated population-based behavior among single cell organisms have gained considerable attention in the recent years. The ability to send, receive, and process information allows unicellular organisms to act as multicellular entities and increases their chances of survival in complex environments. Quorum sensing (QS), a density-dependent cell-signaling mechanism, is one way by which bacteria 'talk' to one another. QS is commonly associated with adverse health effects such as biofilm formation, bacteria pathogenicity, and virulence. But there exists great potential to harness QS circuitry and its properties for other applications, enabling even wider societal impact. Interesting avenues are envisioned for the detection of chemicals and pathogens, the navigation of interspecies communication, the synchronization and control of cell phenotype, and the creation of novel materials based on synthetic biology. In this review, we first highlight the recent discoveries of the molecular underpinnings of QS function, with emphasis on the formation of biofilms. We then discuss how researchers have used QS circuitry to their advantage to build synthetic networks, rewire native metabolic pathways, and engineer cells for a variety of applications.

The effect of negative feedback on noise propagation in transcriptional gene networks.

This paper analyzes how the delay and repression strength of negative feedback in single-gene and multigene transcriptional networks influences intrinsic noise propagation and oscillatory behavior. We simulate a variety of transcriptional networks using a stochastic model and report two main findings. First, intrinsic noise is not attenuated by the addition of negative or positive feedback to transcriptional cascades. Second, for multigene negative feedback networks, synchrony in oscillations among a cell population can be improved by increasing network depth and tightening the regulation at one of the repression stages. Our long term goal is to understand how the noise characteristics of complex networks can be derived from the properties of modules that are used to compose these networks.

Ultrasensitivity and noise propagation in a synthetic transcriptional cascade.

The precise nature of information flow through a biological network, which is governed by factors such as response sensitivities and noise propagation, greatly affects the operation of biological systems. Quantitative analysis of these properties is often difficult in naturally occurring systems but can be greatly facilitated by studying simple synthetic networks. Here, we report the construction of synthetic transcriptional cascades comprising one, two, and three repression stages. These model systems enable us to analyze sensitivity and noise propagation as a function of network complexity. We demonstrate experimentally steady-state switching behavior that becomes sharper with longer cascades. The regulatory mechanisms that confer this ultrasensitive response both attenuate and amplify phenotypical variations depending on the system's input conditions. Although noise attenuation allows the cascade to act as a low-pass filter by rejecting short-lived perturbations in input conditions, noise amplification results in loss of synchrony among a cell population. The experimental results demonstrating the above network properties correlate well with simulations of a simple mathematical model of the system.

Optimizing genetic circuits by global sensitivity analysis.

Artificial genetic circuits are becoming important tools for controlling cellular behavior and studying molecular biosystems. To genetically optimize the properties of complex circuits in a practically feasible fashion, it is necessary to identify the best genes and/or their regulatory components as mutation targets to avoid the mutation experiments being wasted on ineffective regions, but this goal is generally not achievable by current methods. The Random Sampling-High Dimensional Model Representation (RS-HDMR) algorithm is employed in this work as a global sensitivity analysis technique to estimate the sensitivities of the circuit properties with respect to the circuit model parameters, such as rate constants, without knowing the precise parameter values. The sensitivity information can then guide the selection of the optimal mutation targets and thereby reduce the laboratory effort. As a proof of principle, the in vivo effects of 16 pairwise mutations on the properties of a genetic inverter were compared against the RS-HDMR predictions, and the algorithm not only showed good consistency with laboratory results but also revealed useful information, such as different optimal mutation targets for optimizing different circuit properties, not available from previous experiments and modeling.