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Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed ( pta or poxB ) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation., (© 2018 The Authors. Published under the terms of the CC BY 4.0 license.)

Microorganisms use genetic circuits to integrate environmental information. We have constructed a synthetic AND gate in the bacterium Escherichia coli that integrates information from two promoters as inputs and activates a promoter output only when both input promoters are transcriptionally active. The integration occurs via an interaction between an mRNA and tRNA. The first promoter controls the transcription of a T7 RNA polymerase gene with two internal amber stop codons blocking translation. The second promoter controls the amber suppressor tRNA supD. When both components are transcribed, T7 RNA polymerase is synthesized and this in turn activates a T7 promoter. Because inputs and outputs are promoters, the design is modular; that is, it can be reconnected to integrate different input signals and the output can be used to drive different cellular responses. We demonstrate this modularity by wiring the gate to integrate natural promoters (responding to Mg(2+) and AI-1) and using it to implement a phenotypic output (invasion of mammalian cells). A mathematical model of the transfer function is derived and parameterized using experimental data.

Motivation Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. Results The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R 2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis , using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. Availability and implementation The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics%5ftools. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The article presents a study on the characterization of combinatorial patterns generated by multiple two-component sensors in Escherichia coli (E.coli) that respond to many stimuli. It examines the responses of three two-component systems in E. coli which have been reported to respond to many environmental stimuli. Results show that the data demonstrate one mechanism by which bacteria are able to use a limited set of sensors to identify a diverse set of compounds and environmental conditions.

Abstract: Many applications require cells to switch between discrete phenotypic states. Here, we harness the FimBE inversion switch to flip a promoter, allowing expression to be toggled between two genes oriented in opposite directions. The response characteristics of the switch are characterized using two-color cytometry. This switch is used to toggle between orthogonal chemosensory pathways by controlling the expression of CheW and CheW*, which interact with the Tar (aspartate) and Tsr* (serine) chemoreceptors, respectively. CheW* and Tsr* each contain a mutation at their protein–protein interface such that they interact with each other. The complete genetic program containing an arabinose-inducible FimE controlling CheW/CheW* (and constitutively expressed tar/tsr*) is transformed into an Escherichia coli strain lacking all native chemoreceptors. This program enables bacteria to swim toward serine or aspartate in the absence or in the presence of arabinose, respectively. Thus, the program functions as a multiplexer with arabinose as the selector. This demonstrates the ability of synthetic genetic circuits to connect to a natural signaling network to switch between phenotypes. [Copyright &y& Elsevier]

Abstract: Two-component systems are a class of sensors that enable bacteria to respond to environmental and cell-state signals. The canonical system consists of a membrane-bound sensor histidine kinase that autophosphorylates in response to a signal and transfers the phosphate to an intracellular response regulator. Bacteria typically have dozens of two-component systems. The key questions are whether these systems are linear and, if they are, how cross talk between systems is buffered. In this work, we studied the EnvZ/OmpR and CpxA/CpxR systems from Escherichia coli, which have been shown previously to exhibit slow cross talk in vitro. Using in vitro radiolabeling and a rapid quenched-flow apparatus, we experimentally measured 10 biochemical parameters capturing the cognate and non-cognate phosphotransfer reactions between the systems. These data were used to parameterize a mathematical model that was used to predict how cross talk is affected as different genes are knocked out. It was predicted that significant cross talk between EnvZ and CpxR only occurs for the triple mutant ΔompR ΔcpxA ΔactA-pta. All seven combinations of these knockouts were made to test this prediction and only the triple mutant demonstrated significant cross talk, where the cpxP promoter was induced 280-fold upon the activation of EnvZ. Furthermore, the behavior of the other knockouts agrees with the model predictions. These results support a kinetic model of buffering where both the cognate bifunctional phosphatase activity and the competition between regulator proteins for phosphate prevent cross talk in vivo. [Copyright &y& Elsevier]