Environmental signal integration by a modular AND gate

Designing a Novel Temperature- and Noncanonical Amino Acid-Controlled Biological Logic Gate in Escherichia coli

Genetic code expansion (GCE) is a powerful strategy that expands the genetic code of an organism for incorporating noncanonical amino acids into proteins using engineered tRNAs and aminoacyl-tRNA synthetases (aaRSs). While GCE has opened up new possibilities for synthetic biology, little is known about the potential side effects of exogenous aaRS/tRNA pairs. In this study, we investigated the impact of exogenous aaRS and amber suppressor tRNA on gene expression in Escherichia coli. We discovered that in DH10β ΔcyaA, transformed with the F1RP/F2P two-hybrid system, the high consumption rate of cellular adenosine triphosphate by exogenous aaRS/tRNA at elevated temperatures induces temperature sensitivity in the expression of genes regulated by the cyclic AMP receptor protein (CRP). We harnessed this temperature sensitivity to create a novel biological AND gate in E. coli, responsive to both p-benzoylphenylalanine (BzF) and low temperature, using a BzF-dependent variant of E. coli chorismate mutase and split subunits of Bordetella pertussis adenylate cyclase. Our study provides new insights into the unexpected effects of exogenous aaRS/tRNA pairs and offers a new approach for constructing a biological logic gate.

Multi-layered computational gene networks by engineered tristate logics

So far, biocomputation strictly follows traditional design principles of digital electronics, which could reach their limits when assembling gene circuits of higher complexity. Here, by creating genetic variants of tristate buffers instead of using conventional logic gates as basic signal processing units, we introduce a tristate-based logic synthesis (TriLoS) framework for resource-efficient design of multi-layered gene networks capable of performing complex Boolean calculus within single-cell populations. This sets the stage for simple, modular, and low-interference mapping of various arithmetic logics of interest and an effectively enlarged engineering space within single cells. We not only construct computational gene networks running full adder and full subtractor operations at a cellular level but also describe a treatment paradigm building on programmable cell-based therapeutics, allowing for adjustable and disease-specific drug secretion logics in vivo. This work could foster the evolution of modern biocomputers to progress toward unexplored applications in precision medicine.

Governing principles of transcriptional logic out of equilibrium

To survive, adapt, and develop, cells respond to external and internal stimuli by tightly regulating transcription. Transcriptional regulation involves the combinatorial binding of a repertoire of transcription factors to DNA, which often results in switch-like binary outputs akin to Boolean logic gates. Recent experimental studies have demonstrated that in eukaryotes, transcription factor binding to DNA often involves energy expenditure, thereby driving the system out of equilibrium. The governing principles of transcriptional logic operations out of equilibrium remain unexplored. Here, we employ a simple two-input, single-locus model of transcription that can accommodate both equilibrium and nonequilibrium mechanisms. Using this model, we find that nonequilibrium regimes can give rise to all the logic operations accessible in equilibrium. Strikingly, energy expenditure alters the regulatory function of the two transcription factors in a mutually exclusive manner. This allows for the emergence of new logic operations that are inaccessible in equilibrium. Overall, our results show that energy expenditure can expand the range of cellular decision-making without the need for more complex promoter architectures.

Synthetic microbe-to-plant communication channels

Plants and microbes communicate to collaborate to stop pests, scavenge nutrients, and react to environmental change. Microbiota consisting of thousands of species interact with each other and plants using a large chemical language that is interpreted by complex regulatory networks. In this work, we develop modular interkingdom communication channels, enabling bacteria to convey environmental stimuli to plants. We introduce a "sender device" in Pseudomonas putida and Klebsiella pneumoniae, that produces the small molecule p-coumaroyl-homoserine lactone (pC-HSL) when the output of a sensor or circuit turns on. This molecule triggers a "receiver device" in the plant to activate gene expression. We validate this system in Arabidopsis thaliana and Solanum tuberosum (potato) grown hydroponically and in soil, demonstrating its modularity by swapping bacteria that process different stimuli, including IPTG, aTc and arsenic. Programmable communication channels between bacteria and plants will enable microbial sentinels to transmit information to crops and provide the building blocks for designing artificial consortia.

Engineered live bacteria as disease detection and diagnosis tools

Sensitive and minimally invasive medical diagnostics are essential to the early detection of diseases, monitoring their progression and response to treatment. Engineered bacteria as live sensors are being developed as a new class of biosensors for sensitive, robust, noninvasive, and in situ detection of disease onset at low cost. Akin to microrobotic systems, a combination of simple genetic rules, basic logic gates, and complex synthetic bioengineering principles are used to program bacterial vectors as living machines for detecting biomarkers of diseases, some of which cannot be detected with other sensing technologies. Bacterial whole-cell biosensors (BWCBs) can have wide-ranging functions from detection only, to detection and recording, to closed-loop detection-regulated treatment. In this review article, we first summarize the unique benefits of bacteria as living sensors. We then describe the different bacteria-based diagnosis approaches and provide examples of diagnosing various diseases and disorders. We also discuss the use of bacteria as imaging vectors for disease detection and image-guided surgery. We conclude by highlighting current challenges and opportunities for further exploration toward clinical translation of these bacteria-based systems.

Living Material with Temperature-Dependent Light Absorption

Engineered living materials (ELMs) exhibit desirable characteristics of the living component, including growth and repair, and responsiveness to external stimuli. Escherichia coli (E. coli) are a promising constituent of ELMs because they are very tractable to genetic engineering, produce heterologous proteins readily, and grow exponentially. However, seasonal variation in ambient temperature presents a challenge in deploying ELMs outside of a laboratory environment because E. coli growth rate is impaired both below and above 37 °C. Here, a genetic circuit is developed that controls the expression of a light-absorptive chromophore in response to changes in temperature. It is demonstrated that at temperatures below 36 °C, the engineered E. coli increase in pigmentation, causing an increase in sample temperature and growth rate above non-pigmented counterparts in a model planar ELM. On the other hand, at above 36 °C, they decrease in pigmentation, protecting the growth compared to bacteria with temperature-independent high pigmentation. Integrating the temperature-responsive circuit into an ELM has the potential to improve living material performance by optimizing growth and protein production in the face of seasonal temperature changes.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis

Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control, and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be nonfunctional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular "AND gate" logic system based on the B. subtilis two-subunit σ-factor, SigO-RsoA, designed to maximize future use for synthetic biology applications in B. subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potentially wider applicability of the approach to other systems and genetic backgrounds.

Synthetic neuromorphic computing in living cells

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

Designer bacterial cell factories for improved production of commercially valuable non-ribosomal peptides

Non-ribosomal peptides have gained significant attention as secondary metabolites of high commercial importance. This group houses a diverse range of bioactive compounds, ranging from biosurfactants to antimicrobial and cytotoxic agents. However, low yield of synthesis by bacteria and excessive losses during purification hinders the industrial-scale production of non-ribosomal peptides, and subsequently limits their widespread applicability. While isolation of efficient producer strains and optimization of bioprocesses have been extensively used to enhance yield, further improvement can be made by optimization of the microbial strain using the tools and techniques of metabolic engineering, synthetic biology, systems biology, and adaptive laboratory evolution. These techniques, which directly target the genome of producer strains, aim to redirect carbon and nitrogen fluxes of the metabolic network towards the desired product, bypass the feedback inhibition and repression mechanisms that limit the maximum productivity of the strain, and even extend the substrate range of the cell for synthesis of the target product. The present review takes a comprehensive look into the biosynthesis of bacterial NRPs, how the same is regulated by the cell, and dives deep into the strategies that have been undertaken for enhancing the yield of NRPs, while also providing a perspective on other potential strategies that can allow for further yield improvement. Furthermore, this review provides the reader with a holistic perspective on the design of cellular factories of NRP production, starting from general techniques performed in the laboratory to the computational techniques that help a biochemical engineer model and subsequently strategize the architectural plan.

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Engineered microbial consortia: strategies and applications

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

Quantifying the regulatory role of individual transcription factors in Escherichia coli

Gene regulation often results from the action of multiple transcription factors (TFs) acting at a promoter, obscuring the individual regulatory effect of each TF on RNA polymerase (RNAP). Here we measure the fundamental regulatory interactions of TFs in E. coli by designing synthetic target genes that isolate individual TFs' regulatory effects. Using a thermodynamic model, each TF's regulatory interactions are decoupled from TF occupancy and interpreted as acting through (de)stabilization of RNAP and (de)acceleration of transcription initiation. We find that the contribution of each mechanism depends on TF identity and binding location; regulation immediately downstream of the promoter is insensitive to TF identity, but the same TFs regulate by distinct mechanisms upstream of the promoter. These two mechanisms are uncoupled and can act coherently, to reinforce the observed regulatory role (activation/repression), or incoherently, wherein the TF regulates two distinct steps with opposing effects.

Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways

The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.

In-situ monitoring of xenobiotics using genetically engineered whole-cell-based microbial biosensors: recent advances and outlook

Industrialisation, directly or indirectly, exposes humans to various xenobiotics. The increased magnitude of chemical pesticides and toxic heavy metals in the environment, as well as their intrusion into the food chain, seriously threatens human health. Therefore, the surveillance of xenobiotics is crucial for social safety and security. Online investigation by traditional methods is not sufficient for the detection and identification of such compounds because of the high costs and their complexity. Advancement in the field of genetic engineering provides a potential opportunity to use genetically modified microorganisms. In this regard, whole-cell-based microbial biosensors (WCBMB) represent an essential tool that couples genetically engineered organisms with an operator/promoter derived from a heavy metal-resistant operon combined with a regulatory protein in the gene circuit. The plasmid controls the expression of the reporter gene, such as gfp, luc, lux and lacZ, to an inducible gene promoter and has been widely applied to assay toxicity and bioavailability. This review summarises the recent trends in the development and application of microbial biosensors and the use of mobile genes for biomedical and environmental safety concerns.

Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis

A goal of the biotechnology industry is to be able to recognise detrimental cellular states that may lead to suboptimal or anomalous growth in a bacterial population. Our current knowledge of how different environmental treatments modulate gene regulation and bring about physiology adaptations is limited, and hence it is difficult to determine the mechanisms that lead to their effects. Patterns of gene expression, revealed using technologies such as microarrays or RNA-seq, can provide useful biomarkers of different gene regulatory states indicative of a bacterium's physiological status. It is desirable to have only a few key genes as the biomarkers to reduce the costs of determining the transcriptional state by opening the way for methods such as quantitative RT-PCR and amplicon panels. In this paper, we used unsupervised machine learning to construct a transcriptional landscape model from condition-dependent transcriptome data, from which we have identified 10 clusters of samples with differentiated gene expression profiles and linked to different cellular growth states. Using an iterative feature elimination strategy, we identified a minimal panel of 10 biomarker genes that achieved 100% cross-validation accuracy in predicting the cluster assignment. Moreover, we designed and evaluated a variety of data processing strategies to ensure our methods were able to generate meaningful transcriptional landscape models, capturing relevant biological processes. Overall, the computational strategies introduced in this study facilitate the identification of a detailed set of relevant cellular growth states, and how to sense them using a reduced biomarker panel.

Feasibility and reliability of sequential logic with gene regulatory networks

Gene regulatory networks exhibiting Boolean behaviour, e.g. AND, OR or XOR, have been routinely designed for years. However, achieving more sophisticated functions, such as control or computation, usually requires sequential circuits or so-called state machines. For such a circuit, outputs depend both on inputs and the current state of the system. Although it is still possible to design such circuits by analogy with digital electronics, some particularities of biology make the task trickier. The impact of two of them, namely the stochasticity of biological processes and the inhomogeneity in the response of regulation mechanisms, are assessed in this paper. Numerical simulations performed in two use cases point out high risks of malfunctions even for designed GRNs functional from a theoretical point of view. Several solutions to improve reliability of such systems are also discussed.

Application of CRISPR/Cas System in the Metabolic Engineering of Small Molecules

Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated Cas protein technology area is rapidly growing technique for genome editing and modulation of transcription of several microbes. Successful engineering in microbes requires an emphasis on the aspect of efficiency and targeted aiming, which can be employed using CRISPR/Cas system. Hence, this type of system is used to modify the genome of several microbes such as yeast and bacteria. In recent years, CRISPR/Cas systems have been chosen for metabolic engineering in microbes due to their specificity, orthogonality, and efficacy. Therefore, we need to review the scheme which was acquired for the execution of the CRISPR/Cas system for the modification and metabolic engineering in yeast and bacteria. In this review, we highlighted the application of the CRISPR/Cas system which has been used for the production of small molecules in the microbial system that is chemically and biologically important.

The Emerging Trend of Bio-Engineering Approaches for Microbial Nanomaterial Synthesis and Its Applications

Micro-organisms colonized the world before the multi-cellular organisms evolved. With the advent of microscopy, their existence became evident to the mankind and also the vast processes they regulate, that are in direct interest of the human beings. One such process that intrigued the researchers is the ability to grow in presence of toxic metals. The process seemed to be simple with the metal ions being sequestrated into the inclusion bodies or cell surfaces enabling the conversion into nontoxic nanostructures. However, the discovery of genome sequencing techniques highlighted the genetic makeup of these microbes as a quintessential aspect of these phenomena. The findings of metal resistance genes (MRG) in these microbes showed a rather complex regulation of these processes. Since most of these MRGs are plasmid encoded they can be transferred horizontally. With the discovery of nanoparticles and their many applications from polymer chemistry to drug delivery, the demand for innovative techniques of nanoparticle synthesis increased dramatically. It is now established that microbial synthesis of nanoparticles provides numerous advantages over the existing chemical methods. However, it is the explicit use of biotechnology, molecular biology, metabolic engineering, synthetic biology, and genetic engineering tools that revolutionized the world of microbial nanotechnology. Detailed study of the micro and even nanolevel assembly of microbial life also intrigued biologists and engineers to generate molecular motors that mimic bacterial flagellar motor. In this review, we highlight the importance and tremendous hidden potential of bio-engineering tools in exploiting the area of microbial nanoparticle synthesis. We also highlight the application oriented specific modulations that can be done in the stages involved in the synthesis of these nanoparticles. Finally, the role of these nanoparticles in the natural ecosystem is also addressed.

Development of insect cell line using CRISPR technology

In this chapter, we delineated the methods of CRISPR technology that has been used for the development of engineered insect cell line. We elaborated on how CRISPR/Cas9 genome editing in Drosophila melanogaster, Bombyx mori, Spodoptera frugiperda (Sf9 and Sf21), and Mosquitoes enabled the use of model or non-model insect system in various biological and medical applications. Also, the application of synthetic baculovirus genome along with CRISPR/Cas9 vector system to enable genome editing of insect cell systems for treatment of communicable and non-communicable diseases.

Engineering calcium signaling of astrocytes for neural-molecular computing logic gates

This paper proposes the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Ca}^{2+}$$\end{document}Ca2+ ion flows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Ca}^{2+}$$\end{document}Ca2+ signals that flow through the population. A reinforced learning platform is also presented in the paper to optimize the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Ca}^{2+}$$\end{document}Ca2+ activated level and time slot of input signals \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_b$$\end{document}Tb into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the effectiveness of the reinforced learning platform, a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Ca}^{2+}$$\end{document}Ca2+ signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Ca}^{2+}$$\end{document}Ca2+ activated level and time slot of input signals \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_b$$\end{document}Tb is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural–Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.

Multiplexed Gene Expression Tuning with Orthogonal Synthetic Gene Circuits

Understanding the individual and joint contribution of multiple protein levels toward a phenotype requires precise and tunable multigene expression control. Here we introduce a pair of mammalian synthetic gene circuits that linearly and orthogonally control the expression of two reporter genes in mammalian cells with low variability in response to chemical inducers introduced into the growth medium. These gene expression systems can be used to simultaneously probe the individual and joint effects of two gene product concentrations on a cellular phenotype in basic research or biomedical applications.

Programming Escherichia coli to function as a digital display

Synthetic genetic circuits offer the potential to wield computational control over biology, but their complexity is limited by the accuracy of mathematical models. Here, we present advances that enable the complete encoding of an electronic chip in the DNA carried by Escherichia coli (E. coli). The chip is a binary-coded digit (BCD) to 7-segment decoder, associated with clocks and calculators, to turn on segments to visualize 0-9. Design automation is used to build seven strains, each of which contains a circuit with up to 12 repressors and two activators (totaling 63 regulators and 76,000 bp DNA). The inputs to each circuit represent the digit to be displayed (encoded in binary by four molecules), and output is the segment state, reported as fluorescence. Implementation requires an advanced gate model that captures dynamics, promoter interference, and a measure of total power usage (RNAP flux). This project is an exemplar of design automation pushing engineering beyond that achievable "by hand", essential for realizing the potential of biology.

Design, Fabrication, and Device Chemistry of a 3-Input-3-Output Synthetic Genetic Combinatorial Logic Circuit with a 3-Input AND Gate in a Single Bacterial Cell

Advancement of in-cell molecular computation requires multi-input-multi-output genetic logic devices. However, increased physical size, a higher number of molecular interactions, cross-talk, and complex systems level device chemistry limited the realization of such multi-input-multi-output devices in a single bacterial cell. Here, by adapting a circuit minimization and conjugated promoter engineering approach, we created the first 3-input-3-output logic function in a single bacterial cell. The circuit integrated three extracellular chemical signals as inputs and produced three different fluorescent proteins as outputs following the truth table of the circuit. First, we created a noncascaded 1-gate-3-input synthetic genetic AND gate in bacteria. We showed that the 3-input AND gate was digital in nature and mathematically predictable, two important characteristics, which were not reported for previous 3-input AND gates in bacteria. Our design consists of a 128 bp DNA scaffold, which conjugated various protein-binding sites in a single piece of DNA and worked as a hybrid promoter. The scaffold was a few times smaller than the similar 3-input synthetic genetic AND gate promoter reported. Integrating this AND gate with a new 2-input-2-output integrated circuit, which was also digital-like and predictive, we created a 3-input-3-output combinatorial logic circuit. This work demonstrated the integration of a 3-input AND gate in a larger circuit and a 3-input-3-output synthetic genetic circuit, both for the first time. The work has significance in molecular computation, biorobotics, DNA nanotechnology, and synthetic biology.

Synthetic metabolic computation in a bioluminescence-sensing system

Bioluminescence is visible light produced and emitted by living cells using various biological systems (e.g. luxCDABE cassette). Today, this phenomenon is widely exploited in biological research, biotechnology and medical applications as a quantitative technique for the detection of biological signals. However, this technique has mostly been used to detect a single input only. In this work, we re-engineered the complex genetic structure of luxCDABE cassette to build a biological unit that can detect multi-inputs, process the cellular information and report the computation results. We first split the luxCDABE operon into several parts to create a genetic circuit that can compute a soft minimum in living cells. Then, we used the new design to implement an AND logic function with better performance as compared to AND logic functions based on protein-protein interactions. Furthermore, by controlling the reverse reaction of the luxCDABE cassette independently from the forward reaction, we built a comparator with a programmable detection threshold. Finally, we applied the redesigned cassette to build an incoherent feedforward loop that reduced the unwanted crosstalk between stress-responsive promoters (recA, katG). This work demonstrates the construction of genetic circuits that combine regulations of gene expression with metabolic pathways, for sensing and computing in living cells.

Control of ϕC31 integrase-mediated site-specific recombination by protein trans-splicing

Serine integrases are emerging as core tools in synthetic biology and have applications in biotechnology and genome engineering. We have designed a split-intein serine integrase-based system with potential for regulation of site-specific recombination events at the protein level in vivo. The ϕC31 integrase was split into two extein domains, and intein sequences (Npu DnaEN and Ssp DnaEC) were attached to the two termini to be fused. Expression of these two components followed by post-translational protein trans-splicing in Escherichia coli generated a fully functional ϕC31 integrase. We showed that protein splicing is necessary for recombination activity; deletion of intein domains or mutation of key intein residues inactivated recombination. We used an invertible promoter reporter system to demonstrate a potential application of the split intein-regulated site-specific recombination system in building reversible genetic switches. We used the same split inteins to control the reconstitution of a split Integrase-Recombination Directionality Factor fusion (Integrase-RDF) that efficiently catalysed the reverse attR x attL recombination. This demonstrates the potential for split-intein regulation of the forward and reverse reactions using the integrase and the integrase-RDF fusion, respectively. The split-intein integrase is a potentially versatile, regulatable component for building synthetic genetic circuits and devices.

Transcriptional programming using engineered systems of transcription factors and genetic architectures

The control of gene expression is an important tool for metabolic engineering, the design of synthetic gene networks, and protein manufacturing. The most successful approaches to date are based on modulating mRNA synthesis via an inducible coupling to transcriptional effectors. Here we present a biological programming structure that leverages a system of engineered transcription factors and complementary genetic architectures. We use a modular design strategy to create 27 non-natural and non-synonymous transcription factors using the lactose repressor topology as a guide. To direct systems of engineered transcription factors we employ parallel and series genetic (DNA) architectures and confer fundamental and combinatorial logical control over gene expression. Here we achieve AND, OR, NOT, and NOR logical controls in addition to two non-canonical half-AND operations. The basic logical operations and corresponding parallel and series genetic architectures represent the building blocks for subsequent combinatorial programs, which display both digital and analog performance.

Successful approaches for controlling gene expression modulate mRNA synthesis by coupling it to inducible transcription effectors. Here the authors design 27 non-natural and non-synonymous transcription factors.

Circuit-Host Coupling Induces Multifaceted Behavioral Modulations of a Gene Switch

Quantitative modeling of gene circuits is fundamentally important to synthetic biology, as it offers the potential to transform circuit engineering from trial-and-error construction to rational design and, hence, facilitates the advance of the field. Currently, typical models regard gene circuits as isolated entities and focus only on the biochemical processes within the circuits. However, such a standard paradigm is getting challenged by increasing experimental evidence suggesting that circuits and their host are intimately connected, and their interactions can potentially impact circuit behaviors. Here we systematically examined the roles of circuit-host coupling in shaping circuit dynamics by using a self-activating gene switch as a model circuit. Through a combination of deterministic modeling, stochastic simulation, and Fokker-Planck equation formalism, we found that circuit-host coupling alters switch behaviors across multiple scales. At the single-cell level, it slows the switch dynamics in the high protein production regime and enlarges the difference between stable steady-state values. At the population level, it favors cells with low protein production through differential growth amplification. Together, the two-level coupling effects induce both quantitative and qualitative modulations of the switch, with the primary component of the effects determined by the circuit's architectural parameters. This study illustrates the complexity and importance of circuit-host coupling in modulating circuit behaviors, demonstrating the need for a new paradigm-integrated modeling of the circuit-host system-for quantitative understanding of engineered gene networks.

Intracellular Imaging with Genetically Encoded RNA-based Molecular Sensors

Genetically encodable sensors have been widely used in the detection of intracellular molecules ranging from metal ions and metabolites to nucleic acids and proteins. These biosensors are capable of monitoring in real-time the cellular levels, locations, and cell-to-cell variations of the target compounds in living systems. Traditionally, the majority of these sensors have been developed based on fluorescent proteins. As an exciting alternative, genetically encoded RNA-based molecular sensors (GERMS) have emerged over the past few years for the intracellular imaging and detection of various biological targets. In view of their ability for the general detection of a wide range of target analytes, and the modular and simple design principle, GERMS are becoming a popular choice for intracellular analysis. In this review, we summarize different design principles of GERMS based on various RNA recognition modules, transducer modules, and reporting systems. Some recent advances in the application of GERMS for intracellular imaging are also discussed. With further improvement in biostability, sensitivity, and robustness, GERMS can potentially be widely used in cell biology and biotechnology.

Designing Spatially Distributed Gene Regulatory Networks To Elicit Contrasting Patterns

Pattern formation and differential interactions are important for microbial consortia to divide labor and perform complex functions. To obtain further insight into such interactions, we present a computational method for simulating physically separated microbial colonies, each implementing different gene regulatory networks. We validate our theory by experimentally demonstrating control over gene expression patterns in a diffusion-mediated lateral inhibition circuit. We highlight the importance of spatial arrangement as a control knob for modulating system behavior. Our systematic approach provides a foundation for future applications that require understanding and engineering of multistrain microbial communities for sophisticated, synergistic functions.

Dynamic control of endogenous metabolism with combinatorial logic circuits

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.

Reprogramming the brain with synthetic neurobiology

The mammalian brain is among the most complex organs known in biology. Historically, neuroscience techniques have consisted primarily of low-throughput microscopy and electrophysiological approaches. While these methods will continue to serve the community, the emerging field of synthetic neurobiology may be better equipped to scale with systems neuroscience. By using genetic techniques to achieve cell-type specificity, a map of the connectome, neural activation and recording, and ultimately to program neural development itself, we can begin to build a better framework with which to understand the brain's mechanisms.

Multiscale effects of heating and cooling on genes and gene networks

Fluctuating environments such as changes in ambient temperature represent a fundamental challenge to life. Cells must protect gene networks that protect them from such stresses, making it difficult to understand how temperature affects gene network function in general. Here, we focus on single genes and small synthetic network modules to reveal four key effects of nonoptimal temperatures at different biological scales: (i) a cell fate choice between arrest and resistance, (ii) slower growth rates, (iii) Arrhenius reaction rates, and (iv) protein structure changes. We develop a multiscale computational modeling framework that captures and predicts all of these effects. These findings promote our understanding of how temperature affects living systems and enables more robust cellular engineering for real-world applications.

Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits in Saccharomyces cerevisiae. We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.

Scaling up genetic circuit design for cellular computing: advances and prospects

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.

Molecular Communications Pulse-Based Jamming Model for Bacterial Biofilm Suppression

Studies have recently shown that the bacteria survivability within biofilms is responsible for the emergence of superbugs. The combat of bacterial infections, without enhancing its resistance to antibiotics, includes the use of nanoparticles to quench the quorum sensing of these biofilm-forming bacteria. Several sequential and parallel multi-stage communication processes are involved in the formation of biofilms. In this paper, we use proteomic data from a wet lab experiment to identify the communication channels that are vital to these processes. We also identified the main proteins from each channel and propose the use of jamming signals from synthetically engineered bacteria to suppress the production of those proteins. This biocompatible technique is based on synthetic biology and enables the inhibition of biofilm formation. We analyze the communications performance of the jamming process by evaluating the path loss for a number of conditions that include different engineered bacterial population sizes, distances between the populations, and molecular signal power. Our results show that sufficient molecular pulse-based jamming signals are able to prevent the biofilm formation by creating lossy communications channels (almost -3 dB for certain scenarios). From these results, we define the main design parameters to develop a fully operational bacteria-based jamming system.

Engineering a living biomaterial via bacterial surface capture of environmental molecules

Synthetic biology holds significant potential in biomaterials science as synthetically engineered cells can produce new biomaterials, or alternately, can function as living components of new biomaterials. Here, we describe the creation of a new biomaterial that incorporates living bacterial constituents that interact with their environment using engineered surface display. We first developed a gene construct that enabled simultaneous expression of cytosolic mCherry and a surface-displayed, catalytically active enzyme capable of covalently bonding with benzylguanine (BG) groups. We then created a functional living material within a microfluidic channel using these genetically engineered cells. The material forms when engineered cells covalently bond to ambient BG-modified molecules upon induction. Given the wide range of materials amenable to functionalization with BG-groups, our system provides a proof-of-concept for the sequestration and assembly of BG-functionalized molecules on a fluid-swept, living biomaterial surface.

Synthesis of control unit for future biocomputer

Background

Synthesis of a variety of biological circuits for specific functional purposes has made a tremendous progress in recent years. The ultimate goal of combining molecular biology and engineering is to realize a functional biocomputer. To address this challenge, all previous efforts work toward building up the bio-computer as the ultimate goal. To this aim, there should be a key module, named control unit (CU), to direct a serious of logic or arithmetic operations within the processor.

Methods

This research task develops a bio-CU to work with a bio-ALU, which is realized from the combination of previously developed genetic logic gates to fulfill the kernel function of CPU as those done in the silicon computer.

Results

A possible framework of the bio-CPU has demonstrated how to connect a bio-CU with a bio-ALU to conduct a fetch-decode-execute cycle of a macro instruction. It presents not only capability of 4-bit full adder but coordination of related modules in biocomputer.

Conclusions

We have demonstrated computer simulation for applications of the genetic circuits in biocomputer construction. It’s expected to inspire follow-up study to synthesize potential configurations of the future biocomputer.

Binary addition in a living cell based on riboregulation

Synthetic biology aims at (re-)programming living cells like computers to perform new functions for a variety of applications. Initial work rested on transcription factors, but regulatory RNAs have recently gained much attention due to their high programmability. However, functional circuits mainly implemented with regulatory RNAs are quite limited. Here, we report the engineering of a fundamental arithmetic logic unit based on de novo riboregulation to sum two bits of information encoded in molecular concentrations. Our designer circuit robustly performs the intended computation in a living cell encoding the result as fluorescence amplitudes. The whole system exploits post-transcriptional control to switch on tightly silenced genes with small RNAs, together with allosteric transcription factors to sense the molecular signals. This important result demonstrates that regulatory RNAs can be key players in synthetic biology, and it paves the way for engineering more complex RNA-based biocomputers using this designer circuit as a building block.

An Automated Design Framework for Multicellular Recombinase Logic

Tools to systematically reprogram cellular behavior are crucial to address pressing challenges in manufacturing, environment, or healthcare. Recombinases can very efficiently encode Boolean and history-dependent logic in many species, yet current designs are performed on a case-by-case basis, limiting their scalability and requiring time-consuming optimization. Here we present an automated workflow for designing recombinase logic devices executing Boolean functions. Our theoretical framework uses a reduced library of computational devices distributed into different cellular subpopulations, which are then composed in various manners to implement all desired logic functions at the multicellular level. Our design platform called CALIN (Composable Asynchronous Logic using Integrase Networks) is broadly accessible via a web server, taking truth tables as inputs and providing corresponding DNA designs and sequences as outputs (available at http://synbio.cbs.cnrs.fr/calin). We anticipate that this automated design workflow will streamline the implementation of Boolean functions in many organisms and for various applications.

Genetic programs can be compressed and autonomously decompressed in live cells

Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes 1-13 and genetically encoded circuits in live cells 14-21 . Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity 22 of two bits per nucleotide 23 . In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum 24 . We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit 25 that comprises redundant DNA sequences and is therefore amenable for compression, as are many other complex gene circuits 15,18,26-28 . In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

Cellular Biosensors with Engineered Genetic Circuits

An increasing interest in building novel biological devices with designed cellular functionalities has triggered the search of innovative tools for biocomputation. Utilizing the tools of synthetic biology, numerous genetic circuits have been implemented such as engineered logic operation in analog and digital circuits. Whole cell biosensors are widely used biological devices that employ several biocomputation tools to program cells for desired functions. Up to the present date, a wide range of whole-cell biosensors have been designed and implemented for disease theranostics, biomedical applications, and environmental monitoring. In this review, we investigated the recent developments in biocomputation tools such as analog, digital, and mix circuits, logic gates, switches, and state machines. Additionally, we stated the novel applications of biological devices with computing functionalities for diagnosis and therapy of various diseases such as infections, cancer, or metabolic diseases, as well as the detection of environmental pollutants such as heavy metals or organic toxic compounds. Current whole-cell biosensors are innovative alternatives to classical biosensors; however, there is still a need to advance decision making capabilities by developing novel biocomputing devices.

Negative Regulation Gene Circuits for Efflux Pump Control

Synthetic biologists aim to design biological systems for a variety of industrial and medical applications, ranging from biofuel to drug production. Synthetic gene circuits regulating efflux pump protein expression can achieve this by driving desired substrates such as biofuels, pharmaceuticals, or other chemicals out of the cell in a precisely controlled manner. However, efflux pumps may introduce implicit negative feedback by pumping out intracellular inducer molecules that control gene circuits, which then can alter gene circuit function. Therefore, synthetic gene circuits must be carefully designed and constructed for precise efflux control. Here, we provide protocols for quantitatively modeling and building synthetic gene constructs for efflux pump regulation.

Optimization-based synthesis of stochastic biocircuits with statistical specifications

Model-guided design has become a standard approach to engineering biomolecular circuits in synthetic biology. However, the stochastic nature of biomolecular reactions is often overlooked in the design process. As a result, cell-cell heterogeneity causes unexpected deviation of biocircuit behaviours from model predictions and requires additional iterations of design-build-test cycles. To enhance the design process of stochastic biocircuits, this paper presents a computational framework to systematically specify the level of intrinsic noise using well-defined metrics of statistics and design highly heterogeneous biocircuits based on the specifications. Specifically, we use descriptive statistics of population distributions as an intuitive specification language of stochastic biocircuits and develop an optimization-based computational tool that explores parameter configurations satisfying design requirements. Sensitivity analysis methods are also performed to ensure the robustness of a biocircuit design against extrinsic perturbations. These design tools are formulated with convex optimization programs to enable rigorous and efficient quantification of the statistics. We demonstrate these features by designing a stochastic negative feedback biocircuit that satisfies multiple statistical constraints and perform an in-depth study of noise propagation and regulation in negative feedback pathways.

Re-using biological devices: a model-aided analysis of interconnected transcriptional cascades designed from the bottom-up

Background

The study of simplified, ad-hoc constructed model systems can help to elucidate if quantitatively characterized biological parts can be effectively re-used in composite circuits to yield predictable functions. Synthetic systems designed from the bottom-up can enable the building of complex interconnected devices via rational approach, supported by mathematical modelling. However, such process is affected by different, usually non-modelled, unpredictability sources, like cell burden.

Methods

Here, we analyzed a set of synthetic transcriptional cascades in Escherichia coli. We aimed to test the predictive power of a simple Hill function activation/repression model (no-burden model, NBM) and of a recently proposed model, including Hill functions and the modulation of proteins expression by cell load (burden model, BM). To test the bottom-up approach, the circuit collection was divided into training and test sets, used to learn individual component functions and test the predicted output of interconnected circuits, respectively.

Results

Among the constructed configurations, two test set circuits showed unexpected logic behaviour. Both NBM and BM were able to predict the quantitative output of interconnected devices with expected behaviour, but only the BM was also able to predict the output of one circuit with unexpected behaviour. Moreover, considering training and test set data together, the BM captures circuits output with higher accuracy than the NBM, which is unable to capture the experimental output exhibited by some of the circuits even qualitatively. Finally, resource usage parameters, estimated via BM, guided the successful construction of new corrected variants of the two circuits showing unexpected behaviour.

Conclusions

Superior descriptive and predictive capabilities were achieved considering resource limitation modelling, but further efforts are needed to improve the accuracy of models for biological engineering.

Electronic supplementary material

The online version of this article (10.1186/s13036-017-0090-3) contains supplementary material, which is available to authorized users.

Multiplexing Engineered Receptors for Multiparametric Evaluation of Environmental Ligands

Engineered cell-based therapies comprise a promising, emerging biomedical technology. Broad utilization of this strategy will require new approaches for implementing sophisticated functional programs, such as sensing and responding to the environment in a defined fashion. Toward this goal, we investigated whether our self-contained receptor and signal transduction system (MESA) could be multiplexed to evaluate extracellular cues, with a focus on elucidating principles governing the integration of such engineered components. We first developed a set of hybrid promoters that exhibited AND gate activation by two transcription factors. We then evaluated these promoters when paired with two MESA receptors and various ligand combinations. Unexpectedly, although the multiplexed system exhibited distinct responses to ligands applied individually and in combination, the same synergy was not observed as when promoters were characterized with soluble transcription factors. Therefore, we developed a mechanistic computational model leveraging these observations, to both improve our understanding of how the receptors and promoters interface and to guide the design and implementation of future systems. Notably, the model explicitly accounts for the impact of intercellular variation on system characterization and performance. Model analysis identified key factors that affect the current receptors and promoters, and enabled an in silico exploration of potential modifications that inform the design of improved logic gates and their robustness to intercellular variation. Ultimately, this quantitative design-driven approach may guide the use and multiplexing of synthetic receptors for diverse custom biological functions beyond the case study considered here.

Targeted Cell-to-Cell Delivery of Protein Payloads via the Granzyme-Perforin Pathway

There is great potential for engineering cellular therapeutics by repurposing biological systems. Here, we report utilization of the granzyme-perforin pathway of cytotoxic lymphocytes as a cell-to-cell protein delivery module. We designed and constructed granzyme B-derived chaperone molecules fused to a fluorescent protein payload and expressed these constructs in natural killer (NK) cells. Using confocal microscopy and flow cytometry, we investigated the co-localization of the chaperones with lytic granules and the chaperone-mediated transfer of the fluorescent protein payload from NK to target cells in co-culture experiments. A synthetic chaperone consisting of the granzyme B ER signal peptide and a domain encompassing putative N-linked glycosylation sites in granzyme B is insufficient for payload transfer to target cells, whereas full-length granzyme B is sufficient for payload delivery. Combining our functional data with an analysis of the crystal structure of granzyme B suggests that the necessary motifs for granzyme B loading into lytic granules are dispersed throughout the primary amino acid sequence and are only functional when contiguous in the tertiary structure. These results illustrate that by using granzyme B as a molecular chaperone the granzyme-perforin pathway can be exploited as a programmable molecular delivery system for cell-based therapies.