Pulse Detecting Genetic Circuit - A New Design Approach

Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression

Cells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly understood. Here, we devise networked optogenetic pathways that achieve dynamic signal processing functions that recapitulate cellular information processing. Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling edge pulse detector and show that this circuit can be employed to demultiplex dynamically encoded signals. We combine this demultiplexer with dCas9-based gene networks to construct pulsatile signal filters and decoders. Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state. Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway. Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.

A synthetic distributed genetic multi-bit counter

A design for genetically encoded counters is proposed via repressor-based circuits. An N-bit counter reads sequences of input pulses and displays the total number of pulses, modulo 2N. The design is based on distributed computation with specialized cell types allocated to specific tasks. This allows scalability and bypasses constraints on the maximal number of circuit genes per cell due to toxicity or failures due to resource limitations. The design starts with a single-bit counter. The N-bit counter is then obtained by interconnecting (using diffusible chemicals) a set of N single-bit counters and connector modules. An optimization framework is used to determine appropriate gate parameters and to compute bounds on admissible pulse widths and relaxation (inter-pulse) times, as well as to guide the construction of novel gates. This work can be viewed as a step toward obtaining circuits that are capable of finite automaton computation in analogy to digital central processing units.

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A single-bit counter is designed for a repressor-based genetic circuit

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A scalable multi-bit counter is enabled by distributing the design across cells

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A computational optimization framework is proposed to guide the design

Synthetic Gene Circuits Enable Systems-Level Biosensor Trigger Discovery at the Host-Microbe Interface

Engineering synthetic circuits into intestinal bacteria to sense, record, and respond to in vivo signals is a promising new approach for the diagnosis, treatment, and prevention of disease. However, because the design of disease-responsive circuits is limited by a relatively small pool of known biosensors, there is a need for expanding the capacity of engineered bacteria to sense and respond to the host environment. Here, we apply a robust genetic memory circuit in Escherichia coli to identify new bacterial biosensor triggers responding in the healthy and diseased mammalian gut, which may be used to construct diagnostic or therapeutic circuits. We developed a pipeline for rapid systems-level library construction and screening, using next-generation sequencing and computational analysis, which demonstrates remarkably reliable identification of responsive biosensor triggers from pooled libraries. By testing libraries of potential triggers-each consisting of a promoter and ribosome binding site (RBS)-and using RBS variation to augment the range of trigger sensitivity, we identify and validate triggers that selectively activate our synthetic memory circuit during transit through the gut. We further identify biosensor triggers with increased response in the inflamed gut through comparative screening of one of our libraries in healthy mice and those with intestinal inflammation. Our results demonstrate the power of systems-level screening for the identification of novel biosensor triggers in the gut and provide a platform for disease-specific screening that is capable of contributing to both the understanding and clinical management of intestinal illness.IMPORTANCE The gut is a largely obscure and inaccessible environment. The use of live, engineered probiotics to detect and respond to disease signals in vivo represents a new frontier in the management of gut diseases. Engineered probiotics have also shown promise as a novel mechanism for drug delivery. However, the design and construction of effective strains that respond to the in vivo environment is hindered by our limited understanding of bacterial behavior in the gut. Our work expands the pool of environmentally responsive synthetic circuits for the healthy and diseased gut, providing insight into host-microbe interactions and enabling future development of increasingly complex biosensors. This method also provides a framework for rapid prototyping of engineered systems and for application across bacterial strains and disease models, representing a practical step toward the construction of clinically useful synthetic tools.

Phage-Based Applications in Synthetic Biology

Bacteriophage research has been instrumental to advancing many fields of biology, such as genetics, molecular biology, and synthetic biology. Many phage-derived technologies have been adapted for building gene circuits to program biological systems. Phages also exhibit significant medical potential as antibacterial agents and bacterial diagnostics due to their extreme specificity for their host, and our growing ability to engineer them further enhances this potential. Phages have also been used as scaffolds for genetically programmable biomaterials that have highly tunable properties. Furthermore, phages are central to powerful directed evolution platforms, which are being leveraged to enhance existing biological functions and even produce new ones. In this review, we discuss recent examples of how phage research is influencing these next-generation biotechnologies.