Cellular checkpoint control using programmable sequential logic

Algorithms for Autonomous Formation of Multicellular Shapes from Single Cells

Multicellular organisms originate from a single cell, ultimately giving rise to mature organisms of heterogeneous cell type composition in complex structures. Recent work in the areas of stem cell biology and tissue engineering has laid major groundwork in the ability to convert certain types of cells into other types, but there has been limited progress in the ability to control the morphology of cellular masses as they grow. Contemporary approaches to this problem have included the use of artificial scaffolds, 3D bioprinting, and complex media formulations; however, there are no existing approaches to controlling this process purely through genetics and from a single-cell starting point. Here we describe a computer-aided design approach, called CellArchitect, for designing recombinase-based genetic circuits for controlling the formation of multicellular masses into arbitrary shapes in human cells.

Ten Years of the Synthetic Biology Summer Course at Cold Spring Harbor Laboratory

The Cold Spring Harbor Laboratory (CSHL) Summer Course on Synthetic Biology, established in 2013, has emerged as a premier platform for immersive education and research in this dynamic field. Rooted in CSHL's rich legacy of biological discovery, the course offers a comprehensive exploration of synthetic biology's fundamentals and applications. Led by a consortium of faculty from diverse institutions, the course structure seamlessly integrates practical laboratory sessions, exploratory research rotations, and enriching seminars by leaders in the field. Over the years, the curriculum has evolved to cover essential topics such as cell-free transcription-translation, DNA construction, computational modeling of gene circuits, engineered gene regulation, and CRISPR technologies. In this review, we describe the history, development, and structure of the course, and discuss how elements of the course might inform the development of other short courses in synthetic biology. We also demonstrate the course's impact beyond the lab with a summary of alumni contributions to research, education, and entrepreneurship. Through these efforts, the CSHL Summer Course on Synthetic Biology remains at the forefront of shaping the next generation of synthetic biologists.

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.

Reprogramming cellular metabolism to increase the efficiency of microbial cell factories

Recent studies are increasingly focusing on advanced biotechnological tools, self-adjusting smart microorganisms, and artificial intelligent networks, to engineer microorganisms with various functions. Microbial cell factories are a vital platform for improving the bioproduction of medicines, biofuels, and biomaterials from renewable carbon sources. However, these processes are significantly affected by cellular metabolism, and boosting the efficiency of microbial cell factories remains a challenge. In this review, we present a strategy for reprogramming cellular metabolism to enhance the efficiency of microbial cell factories for chemical biosynthesis, which improves our understanding of microbial physiology and metabolic control. Current methods are mainly focused on synthetic pathways, metabolic resources, and cell performance. This review highlights the potential biotechnological strategy to reprogram cellular metabolism and provide novel guidance for designing more intelligent industrial microbes with broader applications in this growing field.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Escape mutants that alleviate this burden can rapidly evolve and take over cell populations, making genetic engineering less reliable and predictable. Synthetic biologists often use genetic parts encoded on plasmids, but their burden is rarely characterized. We measured how 301 BioBrick plasmids affected Escherichia coli growth and found that 59 (19.6%) were burdensome, primarily because they depleted the limited gene expression resources of host cells. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be unclonable. We made this model available online for education ( https://barricklab.org/burden-model ) and added our burden measurements to the iGEM Registry. Our results establish a fundamental limit on what DNA constructs and genetic modifications can be successfully engineered into cells.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

Biocomputing in plants, from proof of concept to application

In response to the challenges of climate change and the transition toward sustainability, synthetic biology offers innovative solutions. Most current plant synthetic biology applications rely on the constitutive expression of enzymes and regulators. To engineer plant phenotypes tuneable to environmental conditions and plant cellular states, the integration of multiple signals in synthetic circuits is required. While most circuits are developed in model organisms, numerous tools were recently developed to implement biocomputation in plant synthetic circuits. I presented in this review the tools and design methods for logic circuit implementation in plants. I highlighted recent and potential applications of those circuits to understand and engineer plant interaction with the environment, development, and metabolic pathways.

Genetic Parts and Enabling Tools for Biocircuit Design

Synthetic biology aims to engineer biological systems for customized tasks through the bottom-up assembly of fundamental building blocks, which requires high-quality libraries of reliable, modular, and standardized genetic parts. To establish sets of parts that work well together, synthetic biologists created standardized part libraries in which every component is analyzed in the same metrics and context. Here we present a state-of-the-art review of the currently available part libraries for designing biocircuits and their gene expression regulation paradigms at transcriptional, translational, and post-translational levels in Escherichia coli. We discuss the necessary facets to integrate these parts into complex devices and systems along with the current efforts to catalogue and standardize measurement data. To better display the range of available parts and to facilitate part selection in synthetic biology workflows, we established biopartsDB, a curated database of well-characterized and useful genetic part and device libraries with detailed quantitative data validated by the published literature.

Logic "AND Gate Circuit"-Based Gasdermin Protein Expressing Nanoplatform Induces Tumor-Specific Pyroptosis to Enhance Cancer Immunotherapy

Pyroptosis mediated by gasdermin protein has shown great potential in cancer immunotherapies. However, the low expression of gasdermin proteins and the systemic toxicity of nonspecific pyroptosis limit its clinical application. Here, we designed a synthetic biology strategy to construct a tumor-specific pyroptosis-inducing nanoplatform M-CNP/Mn@pPHS, in which a pyroptosis-inducing plasmid (pPHS) was loaded onto a manganese (Mn)-doped calcium carbonate nanoparticle and wrapped in a tumor-derived cell membrane. M-CNP/Mn@pPHS showed an efficient tumor targeting ability. After its internalization by tumor cells, the degradation of M-CNP/Mn@pPHS in the acidic endosomal environment allowed the efficient endosomal escape of plasmid pPHS. To trigger tumor-specific pyroptosis, pPHS was designed according to the logic "AND gate circuit" strategy, with Hif-1α and Sox4 as two input signals and gasdermin D induced pyroptosis as output signal. Only in cells with high expression of Hif-1α and Sox4 simultaneously will the output signal gasdermin D be expressed. Since Hif-1α and Sox4 are both specifically expressed in tumor cells, M-CNP/Mn@pPHS induces the tumor-specific expression of gasdermin D and thus pyroptosis, triggering an efficient immune response with little systemic toxicity. The Mn2+ released from the nanoplatform further enhanced the antitumor immune response by stimulating the cGAS-STING pathway. Thus, M-CNP/Mn@pPHS efficiently inhibited tumor growth with 79.8% tumor regression in vivo. We demonstrate that this logic "AND gate circuit"-based gasdermin nanoplatform is a promising strategy for inducing tumor-specific pyroptosis with little systemic toxicity.

Synthetic biological neural networks: From current implementations to future perspectives

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape

Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

Genetic Circuits for Feedback Control of Gamma-Aminobutyric Acid Biosynthesis in Probiotic Escherichia coli Nissle 1917

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate the production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Customizing cellular signal processing by synthetic multi-level regulatory circuits

As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. Towards this mission, novel regulatory mechanisms and genetic circuits with unprecedented complexity have been implemented over the past decade. These regulatory mechanisms, such as transcription and translation control, could be integrated into hybrid circuits termed "multi-level circuits". The multi-level circuit design will tremendously benefit the current genetic circuit design paradigm, from modifying basic circuit dynamics to facilitating real-world applications, unleashing our capabilities to customize cellular signal processing and address global challenges through synthetic biology.

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

Redesign of an Escherichia coli Nissle treatment for phenylketonuria using insulated genomic landing pads and genetic circuits to reduce burden

To build therapeutic strains, Escherichia coli Nissle (EcN) have been engineered to express antibiotics, toxin-degrading enzymes, immunoregulators, and anti-cancer chemotherapies. For efficacy, the recombinant genes need to be highly expressed, but this imposes a burden on the cell, and plasmids are difficult to maintain in the body. To address these problems, we have developed landing pads in the EcN genome and genetic circuits to control therapeutic gene expression. These tools were applied to EcN SYNB1618, undergoing clinical trials as a phenylketonuria treatment. The pathway for converting phenylalanine to trans-cinnamic acid was moved to a landing pad under the control of a circuit that keeps the pathway off during storage. The resulting strain (EcN SYN8784) achieved higher activity than EcN SYNB1618, reaching levels near when the pathway is carried on a plasmid. This work demonstrates a simple system for engineering EcN that aids quantitative strain design for therapeutics.

Genetic circuits for feedback control of gamma-aminobutyric acid biosynthesis in probiotic Escherichia coli Nissle 1917

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase (GadB) from E. coli and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically-encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

A blueprint for a synthetic genetic feedback optimizer

Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.

Inducible plasmid copy number control for synthetic biology in commonly used E. coli strains

The ability to externally control gene expression has been paradigm shifting for all areas of biological research, especially for synthetic biology. Such control typically occurs at the transcriptional and translational level, while technologies enabling control at the DNA copy level are limited by either (i) relying on a handful of plasmids with fixed and arbitrary copy numbers; or (ii) require multiple plasmids for replication control; or (iii) are restricted to specialized strains. To overcome these limitations, we present TULIP (TUnable Ligand Inducible Plasmid): a self-contained plasmid with inducible copy number control, designed for portability across various Escherichia coli strains commonly used for cloning, protein expression, and metabolic engineering. Using TULIP, we demonstrate through multiple application examples that flexible plasmid copy number control accelerates the design and optimization of gene circuits, enables efficient probing of metabolic burden, and facilitates the prototyping and recycling of modules in different genetic contexts.

A computational design of a programmable biological processor

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.

A unicellular walker controlled by a microtubule-based finite-state machine

Cells are complex biochemical systems whose behaviors emerge from interactions among myriad molecular components. Computation is often invoked as a general framework for navigating this cellular complexity. However, it is unclear how cells might embody computational processes such that the theories of computation, including finite-state machine models, could be productively applied. Here, we demonstrate finite-state-machine-like processing embodied in cells using the walking behavior of Euplotes eurystomus, a ciliate that walks across surfaces using fourteen motile appendages (cirri). We found that cellular walking entails regulated transitions among a discrete set of gait states. The set of observed transitions decomposes into a small group of high-probability, temporally irreversible transitions and a large group of low-probability, time-symmetric transitions, thus revealing stereotypy in the sequential patterns of state transitions. Simulations and experiments suggest that the sequential logic of the gait is functionally important. Taken together, these findings implicate a finite-state-machine-like process. Cirri are connected by microtubule bundles (fibers), and we found that the dynamics of cirri involved in different state transitions are associated with the structure of the fiber system. Perturbative experiments revealed that the fibers mediate gait coordination, suggesting a mechanical basis of gait control.

Hardware, Software, and Wetware Codesign Environment for Synthetic Biology

Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.

Surveying the Genetic Design Space for Transcription Factor-Based Metabolite Biosensors: Synthetic Gamma-Aminobutyric Acid and Propionate Biosensors in E. coli Nissle 1917

Engineered probiotic bacteria have been proposed as a next-generation strategy for noninvasively detecting biomarkers in the gastrointestinal tract and interrogating the gut-brain axis. A major challenge impeding the implementation of this strategy has been the difficulty to engineer the necessary whole-cell biosensors. Creation of transcription factor-based biosensors in a clinically-relevant strain often requires significant tuning of the genetic parts and gene expression to achieve the dynamic range and sensitivity required. Here, we propose an approach to efficiently engineer transcription-factor based metabolite biosensors that uses a design prototyping construct to quickly assay the gene expression design space and identify an optimal genetic design. We demonstrate this approach using the probiotic bacterium Escherichia coli Nissle 1917 (EcN) and two neuroactive gut metabolites: the neurotransmitter gamma-aminobutyric acid (GABA) and the short-chain fatty acid propionate. The EcN propionate sensor, utilizing the PrpR transcriptional activator from E. coli, has a large 59-fold dynamic range and >500-fold increased sensitivity that matches biologically-relevant concentrations. Our EcN GABA biosensor uses the GabR transcriptional repressor from Bacillus subtilis and a synthetic GabR-regulated promoter created in this study. This work reports the first known synthetic microbial whole-cell biosensor for GABA, which has an observed 138-fold activation in EcN at biologically-relevant concentrations. Using this rapid design prototyping approach, we engineer highly functional biosensors for specified in vivo metabolite concentrations that achieve a large dynamic range and high output promoter activity upon activation. This strategy may be broadly useful for accelerating the engineering of metabolite biosensors for living diagnostics and therapeutics.

Synthetic nonlinear computation for genetic circuit design

Computation frameworks have been studied in synthetic biology to achieve biosignals integration and processing, for biosensing and therapeutics applications. Biological systems exhibit nonlinearity across scales from the molecular level, to biochemical network and intercellular systems. At the molecular level, cooperative bindings contribute to nonlinear molecular signal processing in a way similar to weight variables. At the intracellular network level, feedback and feedforward regulations result in cell behaviors such as multistability and adaptation. When biochemical networks are distributed in different cell groups, intercelluar networks can generate population dynamics. Here, we review works that highlight nonlinear computations in synthetic biology. We group the works according to the scale of implementations, from the cis-transcription level, to biochemical circuit level and cellular networks.

Synthetic Genetic Reversible Feynman Gate in a Single E. coli Cell and Its Application in Bacterial to Mammalian Cell Information Transfer

Reversible computing is a nonconventional form of computing where the inputs and outputs are mapped in a unique one-to-one fashion. Reversible logic gates in single living cells have not been demonstrated. Here, we constructed a synthetic genetic reversible Feynman gate in single E. coli cells, and the input-output relations were measured in a clonal population. The inputs were extracellular chemicals, isopropyl β-d-1-thiogalactopyranoside (IPTG), and anhydrotetracycline (aTc), and the outputs were two fluorescence proteins. We developed a simple mathematical model and simulation to capture the essential features of the circuit and experimentally demonstrated that the behavior of the circuit was ultrasensitive and predictive. We showed an application by creating an intercellular Feynman gate, where input information from bacteria was computed and transferred to HeLa cells through shRNAs delivery and the output signals were observed as silencing of native AKT1 and CTNNB1 genes. The introduction of reversible logics in synthetic biology is new, and given that one-to-one input-output mapping, such reversible genetic systems might have applications in sensing, diagnostics, cellular computing, and synthetic biology.

CRISPR signal conductor 2.0 for redirecting cellular information flow

A key challenge in designing intelligent artificial gene circuits is generating flexible connections between arbitrary components and directly coupling them with endogenous signaling pathways. The CRISPR signal conductor based on conditionally inducible artificial transcriptional regulators can link classic cellular protein signals with targeted gene expression, but there are still problems with multiple signal processing and gene delivery. With the discovery and characterization of new Cas systems and long noncoding RNA (lncRNA) functional motifs, and because of the compatibility of guide RNA with noncoding RNA elements at multiple sites, it is increasingly possible to solve these problems. In this study, we developed CRISPR signal conductor version 2.0 by integrating various lncRNA functional motifs into different parts of the crRNA in the CRISPR-dCasΦ system. This system can directly regulate the expression of target genes by recruiting cellular endogenous transcription factors and efficiently sense a variety of protein signals that are not detected by a classical synthetic system. The new system solved the problems of background leakage and insensitive signaling responses and enabled the construction of logic gates with as many as six input signals, which can be used to specifically target cancer cells. By rewiring endogenous signaling networks, we further demonstrated the effectiveness and biosafety of this system for in vivo cancer gene therapy.

Genetic circuit design automation with Cello 2.0

Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates' structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello's capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users' expertise, jobs may take minutes or hours to complete.

Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (Nielsen, A., et al., Science, 2016, 352 (6281), DOI: 10.1126/science.aac7341). Across this set of functions, 32 of them can be improved by simply considering structural variants yielding performance gains of up to 7.9-fold, whereas 22 of them can be improved with gains up to 26-fold when selecting circuits according to the novel robustness score. We furthermore report on the synergistic combination of the two proposed improvements.

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

A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

Here, we adapted the basic concept of artificial neural networks (ANNs) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created cellular devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.

Principles of synthetic biology

In synthetic biology, biological cells and processes are dismantled and reassembled to make novel systems that do useful things. Designs are encoded by deoxyribonucleic acid (DNA); DNA makes biological (bio-)parts; bioparts are combined to make devices; devices are built into biological systems. Computers are used at all stages of the Design-Build-Test-Learn cycle, from mathematical modelling through to the use of robots for the automation of assembly and experimentation. Synthetic biology applies engineering principles of standardisation, modularity, and abstraction, enabling fast prototyping and the ready exchange of designs between synthetic biologists working around the world. Like toy building blocks, compatible modular designs enable bioparts to be combined and optimised easily; biopart specifications are shared in open registries. Synthetic biology is made possible due to major advances in DNA sequencing and synthesis technologies, and through knowledge gleaned in the field of systems biology. Systems biology aims to understand biology across scales, from the molecular and cellular, up to tissues and organisms, and describes cells as complex information-processing systems. By contrast, synthetic biology seeks to design and build its own systems. Applications of synthetic biology are wide-ranging but include impacting healthcare to improve diagnosis and make better treatments for disease; it seeks to improve the environment by finding novel ways to clean up pollution, make industrial processes for chemical synthesis sustainable, and remove the need for damaging farming practices by making better fertilisers. Synthetic biology has the potential to change the way we live and help us to protect the future of our planet.

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.

Distributed Computing with Engineered Bacteria and Its Application in Solving Chemically Generated 2 × 2 Maze Problems

This work presented an application of genetic distributed computing, where an abstract computational problem was mapped on a complex truth table and solved using simple genetic circuits distributed among various cell populations. Maze generating and solving are challenging problems in mathematics and computing. Here, we mapped all the input-output matrices of a 2 × 2 mathematical maze on a 4-input-4-output truth table. The logic values of four chemical inputs determined the 16 different 2 × 2 maze problems on a chemical space. We created six multi-input synthetic genetic AND gates, which distributed among six cell populations and organized in a single layer. Those cell populations in a mixed culture worked as a computational solver, which solved the chemically generated maze problems by expressing or not expressing four different fluorescent proteins. The three available "solutions" were visualized by glowing bacteria, and for the 13 "no solution" cases, no bacteria glowed. Thus, our system not only solved the maze problems but also showed the number of solvable and unsolvable problems. This work may have significance in cellular computation and synthetic biology.

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.

Rational design of complex phenotype via network models

We demonstrate a modeling and computational framework that allows for rapid screening of thousands of potential network designs for particular dynamic behavior. To illustrate this capability we consider the problem of hysteresis, a prerequisite for construction of robust bistable switches and hence a cornerstone for construction of more complex synthetic circuits. We evaluate and rank most three node networks according to their ability to robustly exhibit hysteresis where robustness is measured with respect to parameters over multiple dynamic phenotypes. Focusing on the highest ranked networks, we demonstrate how additional robustness and design constraints can be applied. We compare our results to more traditional methods based on specific parameterization of ordinary differential equation models and demonstrate a strong qualitative match at a small fraction of the computational cost.

A major challenge in the domains of systems and synthetic biology is an inability to efficiently predict function(s) of complex networks. This work demonstrates a modeling and computational framework that allows for a mathematically justifiable rigorous screening of thousands of potential network designs for a wide variety of dynamical behavior. We screen all 3-node genetic networks and rank them based on their ability to act as an inducible bistable switch. Our results are summarized in a searchable database that can be used to construct robust switches. The ability to quickly screen thousands of designs significantly reduces the set of viable designs and allows synthetic biologists to focus their experimental and more traditional modeling tools to this much smaller set.

In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering

Among the main learning methods reviewed in this study and used in synthetic biology and metabolic engineering are supervised learning, reinforcement and active learning, and in vitro or in vivo learning. In the context of biosynthesis, supervised machine learning is being exploited to predict biological sequence activities, predict structures and engineer sequences, and optimize culture conditions. Active and reinforcement learning methods use training sets acquired through an iterative process generally involving experimental measurements. They are applied to design, engineer, and optimize metabolic pathways and bioprocesses. The nascent but promising developments with in vitro and in vivo learning comprise molecular circuits performing simple tasks such as pattern recognition and classification.

On the hierarchical design of biochemical-based digital computations

The understanding of the biochemical processes underpinning various biological systems has significantly increased in recent decades and has even prompted reverse engineering of certain of life's more complex processes. The most prominent example is modern computers designed to mimic neuron activity. This work forms part of growing endeavors to return advances in the theory of computation and electronics to biology. In this context, we present a set of requirements sufficient for the design of biochemical analogs of modern electronics in a hierarchical, modular fashion that mimics the design of modern computational devices. This theoretical approach is based on a simple enzymatic analog of the transistor and supported by numerical simulations of biochemical models of enzymatic networks equivalent to complex, and modular, interconnecting electronic circuitry (including clocks, Flip-Flops, adders, decoders, and multiplexers). Furthermore, the proposed approach has been implemented in the form of a Python library capable of creating and testing models of complex bio-analog digital computations based on the execution of an elementary universal logic gate. In tribute to Claude Shannon, our biochemical network materializes his example of a "password" recognition that moves the language of the modern theory of automata beyond combinatorial logic and towards sequential logic.

An engineered protein-phosphorylation toggle network with implications for endogenous network discovery

Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle's architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.

Cell-Free Characterization of Coherent Feed-Forward Loop-Based Synthetic Genetic Circuits

Regulatory pathways inside living cells employ feed-forward architectures to fulfill essential signal processing functions that aid in the interpretation of various types of inputs through noise-filtering, fold-change detection and adaptation. Although it has been demonstrated computationally that a coherent feed-forward loop (CFFL) can function as noise filter, a property essential to decoding complex temporal signals, this motif has not been extensively characterized experimentally or integrated into larger networks. Here we use post-transcriptional regulation to implement and characterize a synthetic CFFL in an Escherichia coli cell-free transcription-translation system and build larger composite feed-forward architectures. We employ microfluidic flow reactors to probe the response of the CFFL circuit using both persistent and short, noise-like inputs and analyze the influence of different circuit components on the steady-state and dynamics of the output. We demonstrate that our synthetic CFFL implementation can reliably repress background activity compared to a reference circuit, but displays low potential as a temporal filter, and validate these findings using a computational model. Our results offer practical insight into the putative noise-filtering behavior of CFFLs and show that this motif can be used to mitigate leakage and increase the fold-change of the output of synthetic genetic circuits.

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.

Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.

Nanoscale programming of cellular and physiological phenotypes: inorganic meets organic programming

The advent of protein design in recent years has brought us within reach of developing a "nanoscale programing language," in which molecules serve as operands with their conformational states functioning as logic gates. Combining these operands into a set of operations will result in a functional program, which is executed using nanoscale computing agents (NCAs). These agents would respond to any given input and return the desired output signal. The ability to utilize natural evolutionary processes would allow code to "evolve" in the course of computation, thus enabling radically new algorithmic developments. NCAs will revolutionize the studies of biological systems, enable a deeper understanding of human biology and disease, and facilitate the development of in situ precision therapeutics. Since NCAs can be extended to novel reactions and processes not seen in biological systems, the growth of this field will spark the growth of biotechnological applications with wide-ranging impacts, including fields not typically considered relevant to biology. Unlike traditional approaches in synthetic biology that are based on the rewiring of signaling pathways in cells, NCAs are autonomous vehicles based on single-chain proteins. In this perspective, I will introduce and discuss this new field of biological computing, as well as challenges and the future of the NCA. Addressing these challenges will provide a significant leap in technology for programming living cells.

Microbial cell engineering to improve cellular synthetic capacity

Rapid technological progress in gene assembly, biosensors, and genetic circuits has led to reinforce the cellular synthetic capacity for chemical production. However, overcoming the current limitations of these techniques in maintaining cellular functions and enhancing the cellular synthetic capacity (e.g., catalytic efficiency, strain performance, and cell-cell communication) remains challenging. In this review, we propose a strategy for microbial cell engineering to improve the cellular synthetic capacity by utilizing biotechnological tools along with system biology methods to regulate cellular functions during chemical production. Current strategies in microbial cell engineering are mainly focused on the organelle, cell, and consortium levels. This review highlights the potential of using biotechnology to further develop the field of microbial cell engineering and provides guidance for utilizing microorganisms as attractive regulation targets.

Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage

To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

Tuning Extracellular Electron Transfer by Shewanella oneidensis Using Transcriptional Logic Gates

Extracellular electron transfer (EET) pathways, such as those in the bacterium Shewanella oneidensis, interface cellular metabolism with a variety of redox-driven applications. However, designer control over EET flux in S. oneidensis has proven challenging because a functional understanding of its EET pathway proteins and their effect on engineering parametrizations (e.g., response curves, dynamic range) is generally lacking. To address this, we systematically altered transcription and translation of single genes encoding parts of the primary EET pathway of S. oneidensis, CymA/MtrCAB, and examined how expression differences affected model-fitted parameters for Fe(III) reduction kinetics. Using a suite of plasmid-based inducible circuits maintained by appropriate S. oneidensis knockout strains, we pinpointed construct/strain pairings that expressed cymA, mtrA, and mtrC with maximal dynamic range of Fe(III) reduction rate. These optimized EET gene constructs were employed to create Buffer and NOT gate architectures that predictably turn on and turn off EET flux, respectively, in response to isopropyl β-D-1-thiogalactopyranoside (IPTG). Furthermore, we found that response functions generated by these logic gates (i.e., EET activity vs inducer concentration) were comparable to those generated by conventional synthetic biology circuits, where fluorescent reporters are the output. Our results provide insight on programming EET activity with transcriptional logic gates and suggest that previously developed transcriptional circuitry can be adapted to predictably control EET flux.

Concerted localization-resets precede YAP-dependent transcription

Yes-associated protein 1 (YAP) is a transcriptional regulator with critical roles in mechanotransduction, organ size control, and regeneration. Here, using advanced tools for real-time visualization of native YAP and target gene transcription dynamics, we show that a cycle of fast exodus of nuclear YAP to the cytoplasm followed by fast reentry to the nucleus ("localization-resets") activates YAP target genes. These "resets" are induced by calcium signaling, modulation of actomyosin contractility, or mitosis. Using nascent-transcription reporter knock-ins of YAP target genes, we show a strict association between these resets and downstream transcription. Oncogenically-transformed cell lines lack localization-resets and instead show dramatically elevated rates of nucleocytoplasmic shuttling of YAP, suggesting an escape from compartmentalization-based control. The single-cell localization and transcription traces suggest that YAP activity is not a simple linear function of nuclear enrichment and point to a model of transcriptional activation based on nucleocytoplasmic exchange properties of YAP.

Distributed Implementation of Boolean Functions by Transcriptional Synthetic Circuits

Starting in the early 2000s, sophisticated technologies have been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, each of which implements a small subcircuit, which communicate among themselves using diffusible small molecules (DSMs). Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions. The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs. For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSM's, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software. A toolbox that uses the proposed algorithm was created and made available at https://github.com/sontaglab/DBC/.