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While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we 'un-box' our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.

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.

Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells., (Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A distinct advantage of nanosensor arrays is their ability to achieve ultralow detection limits in solution by proximity placement to an analyte. Here, we demonstrate label-free detection of individual proteins from Escherichia coli (bacteria) and Pichia pastoris (yeast) immobilized in a microfluidic chamber, measuring protein efflux from single organisms in real time. The array is fabricated using non-covalent conjugation of an aptamer-anchor polynucleotide sequence to near-infrared emissive single-walled carbon nanotubes, using a variable chemical spacer shown to optimize sensor response. Unlabelled RAP1 GTPase and HIV integrase proteins were selectively detected from various cell lines, via large near-infrared fluorescent turn-on responses. We show that the process of E. coli induction, protein synthesis and protein export is highly stochastic, yielding variability in protein secretion, with E. coli cells undergoing division under starved conditions producing 66% fewer secreted protein products than their non-dividing counterparts. We further demonstrate the detection of a unique protein product resulting from T7 bacteriophage infection of E. coli, illustrating that nanosensor arrays can enable real-time, single-cell analysis of a broad range of protein products from various cell types.

The ability to detect disease early and deliver precision therapy would be transformative for the treatment of human illnesses. To achieve these goals, biosensors that can pinpoint when and where diseases emerge are needed. Rapid advances in synthetic biology are enabling us to exploit the information-processing abilities of living cells to diagnose disease and then treat it in a controlled fashion. For example, living sensors could be designed to precisely sense disease biomarkers, such as by-products of inflammation, and to respond by delivering targeted therapeutics in situ. Here, we provide an overview of ongoing efforts in microbial biosensor design, highlight translational opportunities, and discuss challenges for enabling sense-and-respond precision medicines. [ABSTRACT FROM AUTHOR]

New therapeutic strategies are needed to treat infections caused by drug-resistant bacteria, which constitute a major growing threat to human health. Here, we use a high-throughput technology to identify combinatorial genetic perturbations that can enhance the killing of drug-resistant bacteria with antibiotic treatment. This strategy. Combinatorial Genetics En Masse (CombiGEM), enables the rapid generation of high-order barcoded combinations of genetic elements for high-throughput multiplexed characterization based on next-generation sequencing. We created ~34,000 pairwise combinations of Escherichia coli transcription factor (TF) overexpression constructs. Using lllumina sequencing, we identified diverse perturbations in antibiotic-resistance phenotypes against carbapenem-resistant Enterobacteriaceae. Specifically, we found multiple TF combinations that potentiated antibiotic killing by up to 106-fold and delivered these combinations via phagemids to increase the killing of highly drug-resistant E. coli harboring New Delhi metallo-beta-lactamase-1. Moreover, we constructed libraries of three-wise combinations of transcription factors with >4 million unique members and demonstrated that these could be tracked via next-generation sequencing. We envision that CombiGEM could be extended to other model organisms, disease models, and phenotypes, where it could accelerate massively parallel combinatorial genetics studies for a broad range of biomedical and biotechnology applications, including the treatment of antibiotic-resistant infections. [ABSTRACT FROM AUTHOR]

A central goal of synthetic biology is to achieve multi-signal integration and processing in living cells for diagnostic, therapeutic and biotechnology applications. Digital logic has been used to build small-scale circuits, but other frameworks may be needed for efficient computation in the resource-limited environments of cells. Here we demonstrate that synthetic analog gene circuits can be engineered to execute sophisticated computational functions in living cells using just three transcription factors. Such synthetic analog gene circuits exploit feedback to implement logarithmically linear sensing, addition, ratiometric and power-law computations. The circuits exhibit Weber's law behaviour as in natural biological systems, operate over a wide dynamic range of up to four orders of magnitude and can be designed to have tunable transfer functions. Our circuits can be composed to implement higher-order functions that are well described by both intricate biochemical models and simple mathematical functions. By exploiting analog building-block functions that are already naturally present in cells, this approach efficiently implements arithmetic operations and complex functions in the logarithmic domain. Such circuits may lead to new applications for synthetic biology and biotechnology that require complex computations with limited parts, need wide-dynamic-range biosensing or would benefit from the fine control of gene expression. [ABSTRACT FROM AUTHOR]

Logic and memory are essential functions of circuits that generate complex, state-dependent responses. Here we describe a strategy for efficiently assembling synthetic genetic circuits that use recombinases to implement Boolean logic functions with stable DNA-encoded memory of events. Application of this strategy allowed us to create all 16 two-input Boolean logic functions in living Escherichia coli cells without requiring cascades comprising multiple logic gates. We demonstrate long-term maintenance of memory for at least 90 cell generations and the ability to interrogate the states of these synthetic devices with fluorescent reporters and PCR. Using this approach we created two-bit digital-to-analog converters, which should be useful in biotechnology applications for encoding multiple stable gene expression outputs using transient inputs of inducers. We envision that this integrated logic and memory system will enable the implementation of complex cellular state machines, behaviors and pathways for therapeutic, diagnostic and basic science applications. [ABSTRACT FROM AUTHOR]