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Continuous-flow based microfluidic laboratory-on-a-chip (LoC) devices have gained traction in recent years for their ability to automate biological experiments at micro and nano-liter scales. While the design automation algorithms for these devices have been maturing, there has yet to be any work to create a benchmark suite to enable the analysis of algorithmic quality or the exchange of device designs between researchers. We propose ParchMint, a new standard interchange format for continuous-flow based microfluidic LoCs and an associated set of benchmarks.

Signal Temporal Logic (STL) is a formal language for describing a broad range of real-valued, temporal properties in cyber-physical systems. While there has been extensive research on verification and control synthesis from STL requirements, there is no formal framework for comparing two STL formulae. In this paper, we show that under mild assumptions, STL formulae admit a metric space. We propose two metrics over this space based on i) the Pompeiu-Hausdorff distance and ii) the symmetric difference measure, and present algorithms to compute them. Alongside illustrative examples, we present applications of these metrics for two fundamental problems: a) design quality measures: to compare all the temporal behaviors of a designed system, such as a synthetic genetic circuit, with the "desired" specification, and b) loss functions: to quantify errors in Temporal Logic Inference (TLI) as a first step to establish formal performance guarantees of TLI algorithms., This paper has been accepted for presentation at, and publication in the proceedings of, the 2018 IEEE Conference on Decision and Control (CDC), to be held in Fontainebleau, Miami Beach, FL, USA on Dec. 17-19, 2018

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modelling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models has still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoding using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

Micromilling is a proven method for prototyping microfluidic devices; however, high overhead costs, large machine footprints, an esoteric software stack, and nonstandard device bonding protocols may be hampering the widespread adoption of micromilling in the greater microfluidics community. This research exploits a free design-to-device software chain and uses it to explore the applicability of a new class of inexpensive, desktop micromills for fabricating microfluidic devices out of polycarbonate. We present an analysis framework for stratifying micromill’s spatial accuracy and surface quality. Utilizing this we concluded milling geometries directly on the substrate is advantageous to making molds out of the substrate, in terms of accuracy and minimum feature size. Moreover, we proposed a general procedure to calculate feedrate and spindle-speed for any sub-millimeter endmill based on a recommended load percentage. We also established stepover is the major parameter in determining the surface quality rather than spindle-speed and feedrate, showing low-cost mills are able to deliver high-quality surface finishes. Ultimately, we clarified the suitability of low-cost micromills and a cost-efficient assembly method in the field of microfluidics by demonstrating rate- and size-controlled microfluidic droplet generation.

Recent advances in modular DNA assembly techniques have enabled synthetic biologists to test significantly more of the available "design space" represented by "devices" created as combinations of individual genetic components. However, manual assembly of such large numbers of devices is time-intensive, error-prone, and costly. The increasing sophistication and scale of synthetic biology research necessitates an efficient, reproducible way to accommodate large-scale, complex, and high throughput device construction. Here, a DNA assembly protocol using the Type-IIS restriction endonuclease based Modular Cloning (MoClo) technique is automated on two liquid-handling robotic platforms. Automated liquid-handling robots require careful, often times tedious optimization of pipetting parameters for liquids of different viscosities (e.g. enzymes, DNA, water, buffers), as well as explicit programming to ensure correct aspiration and dispensing of DNA parts and reagents. This makes manual script writing for complex assemblies just as problematic as manual DNA assembly, and necessitates a software tool that can automate script generation. To this end, we have developed a web-based software tool, http://mocloassembly.com, for generating combinatorial DNA device libraries from basic DNA parts uploaded as Genbank files. We provide access to the tool, and an export file from our liquid handler software which includes optimized liquid classes, labware parameters, and deck layout. All DNA parts used are available through Addgene, and their digital maps can be accessed via the Boston University BDC ICE Registry. Together, these elements provide a foundation for other organizations to automate modular cloning experiments and similar protocols. The automated DNA assembly workflow presented here enables the repeatable, automated, high-throughput production of DNA devices, and reduces the risk of human error arising from repetitive manual pipetting. Sequencing data show the automated DNA assembly reactions generated from this workflow are ~95% correct and require as little as 4% as much hands-on time, compared to manual reaction preparation.

This paper introduces a new algorithm to infer temporal logic properties of a system from data consisting of a set of finite time system traces. We propose an algorithm that generates a Signal Temporal Logic formula by discretizing the entire domain and codomain of the system traces. Unlike many popular inference algorithms which require labeled data that represents whether a trace exhibits a desired behavior (positive) or not (negative), this approach only requires positive traces to infer temporal logic properties. We present two case studies to illustrate the efficiency and accuracy of the proposed algorithm. The first is a biological network consisting of a genetic logic circuit in a bacterial cell. The second is a fault detection problem in automotive powertrain systems. We also compare the performance of the algorithm with an existing inference algorithm.

One goal of synthetic biology is to design and build genetic circuits in living cells for a range of applications. Our incomplete knowledge of the effects of metabolic load and biological “crosstalk” on the host cell make it difficult to construct multilevel genetic logic circuits in a single cell, limiting the scalability of engineered biological systems. Microfluidic technologies provide reliable and scalable construction of synthetic biological systems by allowing compartmentalization of cells encoding simple genetic circuits and the spatiotemporal control of communication among these cells. This control is achieved via valves on the microfluidics chip which restrict fluid flow when activated. We describe a Computer Aided Design (CAD) framework called “Fluigi” for optimizing the layout of genetic circuits on a microfluidic chip, generating the control sequence of the associated signaling fluid valves, and simulating the behavior of the configured biological circuits. We demonstrate the capabilities of Fluigi on a set of Boolean algebraic benchmark circuits found in both synthetic biology and electrical engineering and a set of assay-based benchmark circuits. The integration of microfluidics and synthetic biology has the capability to increase the scale of engineered biological systems for applications in DNA assembly, biosensors, and screening assays for novel orthogonal genetic parts.

Synthetic Biology is an engineering discipline where parts of DNA sequences are composed into novel, complex systems that execute a desired biological function. Functioning and well-behaving biological systems adhere to a certain set of biological “rules”. Data exchange standards and Bio-Design Automation (BDA) tools support the organization of part libraries and the exploration of rule-compliant compositions. In this work, we formally define a design specification language, enabling the integration of biological rules into the Synthetic Biology engineering process. The supported rules are divided into five categories: Counting , Pairing , Positioning , Orientation , and Interactions . We formally define the semantics of each rule, characterize the language's expressive power, and perform a case study in that we iteratively design a genetic Priority Encoder circuit following two alternative paradigms—rule-based and template-driven. Ultimately, we touch a method to approximate the complexity and time to computationally enumerate all rule-compliant designs. Our specification language may or may not be expressive enough to capture all designs that a Synthetic Biologist might want to describe, or the complexity one might find through experiments. However, computational support for the acquisition, specification, management, and application of biological rules is inevitable to understand the functioning of biology.

Acoustic manipulation has emerged as a versatile method for microfluidic separation and concentration of particles and cells. Most recent demonstrations of the technology use piezoelectric actuators to excite resonant modes in silicon or glass microchannels. Here, we focus on acoustic manipulation in disposable, plastic microchannels in order to enable a low-cost processing tool for point-of-care diagnostics. Unfortunately, the performance of resonant acoustofluidic devices in plastic is hampered by a lack of a predictive model. In this paper, we build and test a plastic blood-bacteria separation device informed by a design of experiments approach, parametric rapid prototyping, and screening by image-processing. We demonstrate that the new device geometry can separate bacteria from blood while operating at 275% greater flow rate as well as reduce the power requirement by 82%, while maintaining equivalent separation performance and resolution when compared to the previously published plastic acoustofluidic separation device.

Bio-design automation (BDA) is an emerging field focused on computer-aided design, engineering principles, and automated manufacturing of biological systems. Here we discuss some outstanding challenges for bio-design that can be addressed by developing new tools for combinatorial engineering, equipment interfacing, next-generation sequencing, and workflow integration. These four areas, while not an exhaustive list of those that need to be addressed, could yield advances in bio-design, laboratory automation, and biometrology.

Design automation refers to a category of software tools for designing systems that work together in a workflow for designing, building, testing, and analyzing systems with a target behavior. In synthetic biology, these tools are called bio-design automation (BDA) tools. In this review, we discuss the BDA tools areas-specify, design, build, test, and learn-and introduce the existing software tools designed to solve problems in these areas. We then detail the functionality of some of these tools and show how they can be used together to create the desired behavior of two types of modern synthetic genetic regulatory networks.

Genetic designs can consist of dozens of genes and hundreds of genetic parts. After evaluating a design, it is desirable to implement changes without the cost and burden of starting the construction process from scratch. Here, we report a two-step process where a large design space is divided into deep pools of composite parts, from which individuals are retrieved and assembled to build a final construct. The pools are built via multiplexed assembly and sequenced using next-generation sequencing. Each pool consists of ∼20 Mb of up to 5000 unique and sequence-verified composite parts that are barcoded for retrieval by PCR. This approach is applied to a 16-gene nitrogen fixation pathway, which is broken into pools containing a total of 55 848 composite parts (71.0 Mb). The pools encompass an enormous design space (1043 possible 23 kb constructs), from which an algorithm-guided 192-member 4.5 Mb library is built. Next, all 1030 possible genetic circuits based on 10 repressors (NOR/NOT gates) are encoded in pools where each repressor is fused to all permutations of input promoters. These demonstrate that multiplexing can be applied to encompass entire design spaces from which individuals can be accessed and evaluated., Institute for Collaborative Biotechnologies (W911NF-09-0001), United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-13-1-0074), United States. Defense Advanced Research Projects Agency. Living Foundries Program (Awards HR0011-12-C-0067, HR0011- 13-1-0001, HR0011-15-C-0084 and HR0011-15-C-0084)

Variation in the DNA sequence upstream of bacterial promoters is known to affect the expression levels of the products they regulate, sometimes dramatically. While neutral synthetic insulator sequences have been found to buffer promoters from upstream DNA context, there are no established methods for designing effective insulator sequences with predictable effects on expression levels. We address this problem with Degenerate Insulation Screening (DIS), a novel method based on a randomized 36-nucleotide insulator library and a simple, high-throughput, flow-cytometry-based screen that randomly samples from a library of 436 potential insulated promoters. The results of this screen can then be compared against a reference uninsulated device to select a set of insulated promoters providing a precise level of expression. We verify this method by insulating the constitutive, inducible, and repressible promotors of a four transcriptional-unit inverter (NOT-gate) circuit, finding both that order dependence is largely eliminated by insulation and that circuit performance is also significantly improved, with a 5.8-fold mean improvement in on/off ratio.

The synthetic biology research community describes a standard language for exchanging designs of biological 'parts'. The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-driven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.

Molecular biologists routinely clone genetic constructs from DNA segments and formulate plans to assemble them. However, manual assembly planning is complex, error prone and not scalable. We address this problem with an algorithm-driven DNA assembly planning software tool suite called Raven (http://www.ravencad.org/) that produces optimized assembly plans and allows users to apply experimental outcomes to redesign assembly plans interactively. We used Raven to calculate assembly plans for thousands of variants of five types of genetic constructs, as well as hundreds of constructs of variable size and complexity from the literature. Finally, we experimentally validated a subset of these assembly plans by reconstructing four recombinase-based 'genetic counter' constructs and two 'repressilator' constructs. We demonstrate that Raven's solutions are significantly better than unoptimized solutions at small and large scales and that Raven's assembly instructions are experimentally valid.

Owl ( www.owlcad.org ) is a biodesign automation tool that generates electronic datasheets for synthetic biological parts using common formatting. Data can be retrieved automatically from existing repositories and modified in the Owl user interface (UI). Owl uses the data to generate an HTML page with standard typesetting that can be saved as a PDF file. Here we present the Owl software tool in its alpha version, its current UI, its description of input data for generating a datasheet, its example datasheets, and the vision of the tool's role in biodesign automation.

Reality-based interfaces (RBIs) such as tabletop and tangible user interfaces draw upon ideas from embodied cognition to offer a more natural, intuitive, and accessible form of interaction that reduces the mental effort required to learn and operate computational systems. However, to date, little research has been devoted to investigating the strengths and limitations of applying reality-based interaction for promoting learning of complex scientific concepts at the college level. We propose that RBIs offer unique opportunities for enhancing college-level science education. This paper presents three main contributions: (1) design considerations and participatory design process for enhancing college-level science education through reality-based interaction, (2) reflections on the design, implementation, and validation of two case studies--RBIs for learning synthetic biology, and (3) discussion of opportunities and challenges for advancing learning of college-level sciences through next-generation interfaces.

Cyber-Physical Systems are integrations of computation and physical processes and as such, will be increasingly relevant to industry and people. The complexity of designing CPS resides in their heterogeneity. Heterogeneity manifest itself in modeling their functionality as well as in the implementation platforms that include a multiplicity of components such as microprocessors, signal processors, peripherals, memories, sensors and actuators often integrated on a single chip or on a small package such as a multi-chip module. We need a methodology, tools and environments where heterogeneity can be dealt with at all levels of abstraction and where different tools can be integrated. We present here Platform-Based Design as the CPS methodology of choice and metro II, a design environment that supports it. We present the metamodeling approach followed in metro II, how to couple the functionality and implementation platforms of CPS, and the simulation technology that supports the analysis of CPS and of their implementation. We also present examples of use and the integration of metro II with another popular design environment developed at Verimag, BIP.

We define a new inversion-based machine called a permuton of n genetic elements, which allows the n elements to be rearranged in any of the n·(n - 1)·(n - 2)···2 = n! distinct orderings. We present two design algorithms for architecting such a machine. We define a notion of a feasible design and use the framework to discuss the feasibility of the permuton architectures. We have implemented our design algorithms in a freely usable web-accessible software for exploration of these machines. Permutation machines could be used as memory elements or state machines and explicitly illustrate a rational approach to designing biological systems.

Genome engineering technologies now enable precise manipulation of organism genotype, but can be limited in scalability by their design requirements. Here we describe Merlin ( http://merlincad.org ), an open-source web-based tool to assist biologists in designing experiments using multiplex automated genome engineering (MAGE). Merlin provides methods to generate pools of single-stranded DNA oligonucleotides (oligos) for MAGE experiments by performing free energy calculation and BLAST scoring on a sliding window spanning the targeted site. These oligos are designed not only to improve recombination efficiency, but also to minimize off-target interactions. The application further assists experiment planning by reporting predicted allelic replacement rates after multiple MAGE cycles, and enables rapid result validation by generating primer sequences for multiplexed allele-specific colony PCR. Here we describe the Merlin oligo and primer design procedures and validate their functionality compared to OptMAGE by eliminating seven AvrII restriction sites from the Escherichia coli genome.

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices. Copyright: © 2016 Beal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Through principled engineering methods, synthetic biology aims to build specialized biological components that can be modularly composed to create complex systems. This article outlines bio-design automation using two complementary design approaches, bottom-up modular construction from biological primitives and pathway-based approaches. The article also highlights future challenges for both.

We sincerely apologize for any omissions of relevant work owing to space and reference limitations. D.M.D. acknowledges support from the Office of Naval Research, the Defense Advanced Research Projects Agency, the National Science Foundation, Boston University, and Agilent Technologies. The authors would also like to acknowledge many conversations with colleagues at UC Berkeley, MIT, Harvard, Boston University, BBN Technologies, Autodesk, Agilent, Life Technologies, TeselaGen Biotechnologies, Desktop Genetics, Ginkgo Bioworks, and Genome Compiler. The authors hold financial interests in Lattice Automation Inc., a company developing bio-design automation solutions.

Generating a defined set of genetic constructs within a large combinatorial space provides a powerful method for engineering novel biological functions. However, the process of assembling more than a few specific DNA sequences can be costly, time consuming and error prone. Even if a correct theoretical construction scheme is developed manually, it is likely to be suboptimal by any number of cost metrics. Modular, robust and formal approaches are needed for exploring these vast design spaces. By automating the design of DNA fabrication schemes using computational algorithms, we can eliminate human error while reducing redundant operations, thus minimizing the time and cost required for conducting biological engineering experiments. Here, we provide algorithms that optimize the simultaneous assembly of a collection of related DNA sequences. We compare our algorithms to an exhaustive search on a small synthetic dataset and our results show that our algorithms can quickly find an optimal solution. Comparison with random search approaches on two real-world datasets show that our algorithms can also quickly find lower-cost solutions for large datasets.

Multipart and modular DNA part libraries and assembly standards have become common tools in synthetic biology since the publication of the Gibson and Golden Gate assembly methods, yet no multipart modular library exists for use in bacterial systems. Building upon the existing MoClo assembly framework, we have developed a publicly available collection of modular DNA parts and enhanced MoClo protocols to enable rapid one-pot, multipart assembly, combinatorial design, and expression tuning in Escherichia coli. The Cross-disciplinary Integration of Design Automation Research lab (CIDAR) MoClo Library is openly available and contains promoters, ribosomal binding sites, coding sequence, terminators, vectors, and a set of fluorescent control plasmids. Optimized protocols reduce reaction time and cost by >80% from that of previously published protocols.

miniEugene provides computational support for solving combinatorial design problems, enabling users to specify and enumerate designs for novel biological systems based on sets of biological constraints. This technical note presents a brief tutorial for biologists and software engineers in the field of synthetic biology on how to use miniEugene. After reading this technical note, users should know which biological constraints are available in miniEugene, understand the syntax and semantics of these constraints, and be able to follow a step-by-step guide to specify the design of a classical synthetic biological system-the genetic toggle switch.1 We also provide links and references to more information on the miniEugene web application and the integration of the miniEugene software library into sophisticated Computer-Aided Design (CAD) tools for synthetic biology ( www.eugenecad.org ).

One goal of synthetic biology is to design and build genetic circuits in living cells for a range of applications. Major challenges in these efforts include increasing the scalability and robustness of engineered biological systems and streamlining and automating the synthetic biology workflow of specification–design–assembly–verification. We present here a summary of the advances in microfluidic technology, particularly microfluidic large scale integration, that can be used to address the challenges facing each step of the synthetic biology workflow. Microfluidic technologies allow precise control over the flow of biological content within microscale devices, and thus may provide more reliable and scalable construction of synthetic biological systems. The integration of microfluidics and synthetic biology has the capability to produce rapid prototyping platforms for characterization of genetic devices, testing of biotherapeutics, and development of biosensors.

RNA replicons are an emerging platform for engineering synthetic biological systems. Replicons self-amplify, can provide persistent high-level expression of proteins even from a small initial dose, and, unlike DNA vectors, pose minimal risk of chromosomal integration. However, no quantitative model sufficient for engineering levels of protein expression from such replicon systems currently exists. Here, we aim to enable the engineering of multigene expression from more than one species of replicon by creating a computational model based on our experimental observations of the expression dynamics in single- and multireplicon systems. To this end, we studied fluorescent protein expression in baby hamster kidney (BHK-21) cells using a replicon derived from Sindbis virus (SINV). We characterized expression dynamics for this platform based on the dose-response of a single species of replicon over 50 h and on a titration of two cotransfected replicons expressing different fluorescent proteins. From this data, we derive a quantitative model of multireplicon expression and validate it by designing a variety of three-replicon systems, with profiles that match desired expression levels. We achieved a mean error of 1.7-fold on a 1000-fold range, thus demonstrating how our model can be applied to precisely control expression levels of each Sindbis replicon species in a system.

Large microbial gene clusters encode useful functions, including energy utilization and natural product biosynthesis, but genetic manipulation of such systems is slow, difficult and complicated by complex regulation. We exploit the modularity of a refactored Klebsiella oxytoca nitrogen fixation (nif) gene cluster (16 genes, 103 parts) to build genetic permutations that could not be achieved by starting from the wild-type cluster. Constraint-based combinatorial design and DNA assembly are used to build libraries of radically different cluster architectures by varying part choice, gene order, gene orientation and operon occupancy. We construct 84 variants of the nifUSVWZM operon, 145 variants of the nifHDKY operon, 155 variants of the nifHDKYENJ operon and 122 variants of the complete 16-gene pathway. The performance and behavior of these variants are characterized by nitrogenase assay and strand-specific RNA sequencing (RNA-seq), and the results are incorporated into subsequent design cycles. We have produced a fully synthetic cluster that recovers 57% of wild-type activity. Our approach allows the performance of genetic parts to be quantified simultaneously in hundreds of genetic contexts. This parallelized design-build-test-learn cycle, which can access previously unattainable regions of genetic space, should provide a useful, fast tool for genetic optimization and hypothesis testing.

Automatic design of synthetic gene networks with specific functions is an emerging field in synthetic biology. Quantitative evaluation of gene network designs is a missing feature of the existing automatic design tools. In this work, we address this issue and present a framework to probabilistically analyze the dynamic behavior of a gene network against specifications given in a rich and high level language. Given a gene network built from primitive DNA parts, and given experimental data for the parts, the tool proposed here allows for the automatic construction of a stochastic model of the gene network and in silico probabilistic verification against a rich specification.

MoClo Planner is an interactive visualization system for collaborative bio-design, utilizing a multi-touch interactive surface. The system integrates the information gathering, design, and specification of complex synthetic biological constructs using the Modular Cloning (MoClo) assembly method. Modular Cloning is a hierarchical DNA construction method that allows for the assembly of multi-part constructs from a library of biological parts in a one-pot reaction. This cutting-edge method facilitates and expedites the assembly of complex biological designs. However, it is an intricate multi-step process, which to date, has not been adequately supported by existing bio-design tools. Novel visual tools are needed in order to make MoClo more tractable and accessible to a broad range of users, to facilitate a less error prone bio-design process, and to improve workflow. MoClo Planner is a result of a participatory and user-centered design process, which included close collaboration with domain experts. Using multi-touch interactions and a rich graphical interface, the system accelerates the MoClo learning process, and reduces design time and errors. In this paper, we present user requirements and describe the design, implementation, and evaluation of MoClo Planner.

Pigeon is a Web-based tool that translates a textual description of a synthetic biology design into an image. It allows programmatic generation of design visualizations, is easy to learn, is easily extensible to new glyphs and notation, and can be connected to other software tools for visualizing their output. We present the Pigeon syntax, its current command set, and some examples of Pigeon programs and their output.

We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.

There is growing demand for robust DNA assembly strategies to quickly and accurately fabricate genetic circuits for synthetic biology. One application of this technology is reconstitution of multi-gene assemblies. Here, we integrate a new software tool chain with 2ab assembly and show that it is robust enough to generate 528 distinct composite parts with an error-free success rate of 96%. Finally, we discuss our findings in the context of its implications for biosafety and biosecurity.

INTRODUCTION Cells respond to their environment, make decisions, build structures, and coordinate tasks. Underlying these processes are computational operations performed by networks of regulatory proteins that integrate signals and control the timing of gene expression. Harnessing this capability is critical for biotechnology projects that require decision-making, control, sensing, or spatial organization. It has been shown that cells can be programmed using synthetic genetic circuits composed of regulators organized to generate a desired operation. However, the construction of even simple circuits is time-intensive and unreliable. RATIONALE Electronic design automation (EDA) was developed to aid engineers in the design of semiconductor-based electronics. In an effort to accelerate genetic circuit design, we applied principles from EDA to enable increased circuit complexity and to simplify the incorporation of synthetic gene regulation into genetic engineering projects. We used the hardware description language Verilog to enable a user to describe a circuit function. The user also specifies the sensors, actuators, and “user constraints file” (UCF), which defines the organism, gate technology, and valid operating conditions. Cello (www.cellocad.org) uses this information to automatically design a DNA sequence encoding the desired circuit. This is done via a set of algorithms that parse the Verilog text, create the circuit diagram, assign gates, balance constraints to build the DNA, and simulate performance. RESULTS Cello designs circuits by drawing upon a library of Boolean logic gates. Here, the gate technology consists of NOT/NOR logic based on repressors. Gate connection is simplified by defining the input and output signals as RNA polymerase (RNAP) fluxes. We found that the gates need to be insulated from their genetic context to function reliably in the context of different circuits. Each gate is isolated using strong terminators to block RNAP leakage, and input interchangeability is improved using ribozymes and promoter spacers. These parts are varied for each gate to avoid breakage due to recombination. Measuring the load of each gate and incorporating this into the optimization algorithms further reduces evolutionary pressure. Cello was applied to the design of 60 circuits for Escherichia coli , where the circuit function was specified using Verilog code and transformed to a DNA sequence. The DNA sequences were built as specified with no additional tuning, requiring 880,000 base pairs of DNA assembly. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts). Across all circuits, 92% of the 412 output states functioned as predicted. CONCLUSION Our work constitutes a hardware description language for programming living cells. This required the co-development of design algorithms with gates that are sufficiently simple and robust to be connected by automated algorithms. We demonstrate that engineering principles can be applied to identify and suppress errors that complicate the compositions of larger systems. This approach leads to highly repetitive and modular genetics, in stark contrast to the encoding of natural regulatory networks. The use of a hardware-independent language and the creation of additional UCFs will allow a single design to be transformed into DNA for different organisms, genetic endpoints, operating conditions, and gate technologies.

Background Biological Computer Aided Design (bioCAD) assists the de novo design and selection of existing genetic components to achieve a desired biological activity, as part of an integrated design-build-test cycle. To meet the emerging needs of Synthetic Biology, bioCAD tools must address the increasing prevalence of combinatorial library design, design rule specification, and scar-less multi-part DNA assembly. Results We report the development and deployment of web-based bioCAD software, DeviceEditor, which provides a graphical design environment that mimics the intuitive visual whiteboard design process practiced in biological laboratories. The key innovations of DeviceEditor include visual combinatorial library design, direct integration with scar-less multi-part DNA assembly design automation, and a graphical user interface for the creation and modification of design specification rules. We demonstrate how biological designs are rendered on the DeviceEditor canvas, and we present effective visualizations of genetic component ordering and combinatorial variations within complex designs. Conclusions DeviceEditor liberates researchers from DNA base-pair manipulation, and enables users to create successful prototypes using standardized, functional, and visual abstractions. Open and documented software interfaces support further integration of DeviceEditor with other bioCAD tools and software platforms. DeviceEditor saves researcher time and institutional resources through correct-by-construction design, the automation of tedious tasks, design reuse, and the minimization of DNA assembly costs.

The primary bottleneck in synthetic biology research today is the construction of physical DNAs, a process that is often expensive, time-consuming, and riddled with cloning difficulties associated with the uniqueness of each DNA sequence. We have developed a series of biological and computational tools that lower existing barriers to automation and scaling to enable affordable, fast, and accurate construction of large DNA sets. Here we provide detailed protocols for high-throughput, automated assembly of BglBrick standard biological parts using iterative 2ab reactions. We have implemented these protocols on a minimal hardware platform consisting of a Biomek 3000 liquid handling robot, a benchtop centrifuge and a plate thermocycler, with additional support from a software tool called AssemblyManager. This methodology enables parallel assembly of several hundred large error-free DNAs with a 96+% success rate.

Synthetic biological systems are currently created by an ad hoc, iterative process of design, simulation, and assembly. These systems would greatly benefit from the introduction of a more formalized and rigorous specification of the desired system components as well as constraints on their composition. In order to do so, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) which allows for both the specification of synthetic biological designs based on biological parts as well as providing a very expressive constraint system to drive the creation of composite devices from collection of parts. This chapter provides an overview of the language primitives as well as instructions on installation and use of Eugene v0.03b.

To design the complex systems that synthetic biologists propose to create, software tools must be developed. Critical to success is the enablement of collaboration across our community such that individual tools that perform specific tasks combine with other tools to provide multiplicative benefits. This will require standardization of the form of the data that exists within the field (Parts, Strains, measurements, etc.), a software environment that enables communication between tools, and a sharing mechanism for distributing the tools. Additionally, this data model must describe the data in a sufficiently rigorous and validated form such that meaningful layers of abstraction can be built upon the base. Herein, we describe a software platform called "Clotho" which provides such a data model, and the plugin and sharing mechanisms needed for a rich tool environment. This document provides a tutorial for users of Clotho and information for software developers who wish to contribute new tools (known as "Apps") to it.

Computer-aided design (CAD) for synthetic biology has been proposed to parallel similar efforts in other engineering disciplines, such as electrical engineering or mechanical engineering. However, there is an important distinction between the fields, which is that the mechanisms by which biological systems function are not currently fully understood in sufficient detail to make completely predictive tools. Computational models of biological systems provide, at best, a qualitative understanding of the system under investigation. Quantitative models are limited by the large number of unknown parameters in any given biological system as well the lack of understanding of the detailed mechanisms. It is difficult to determine how much detail is required for predictable design of biological systems. Even assembling individual DNA sequences has shown to be unpredictable due to secondary DNA structures. As a result, the phrase ‘computer-aided design’ takes a very different meaning in synthetic biology: designing biological systems is as much an exploratory process as it is a rational design process. Through design and experimentation, the science of engineering biology is furthered, and that knowledge must be explicitly fed back into the design process itself. Due to its complexity, the challenge of predictably designing biological systems has become a community effort rather than a competitive effort. Consequently, several software developers in synthetic biology have recognized that supporting a community is a necessary component in synthetic biology design applications. Existing software tools in synthetic biology can be categorized into a three broad categories. First, there are software tools for mathematical analysis of biological systems. This category also includes tools from the field of systems biology. Secondly, there are software tools for assembling DNA sequences and analyzing the structure of the resulting composition. This category builds on concepts from genetic engineering for manipulating DNA sequences. The third category of tools are for database access. Synthetic biologists need a catalog of biological components, or ‘parts’, from which systems can be built; therefore, databases, whether local or distributed, are integral for synthetic biology research. This chapter will cover these categories of tools and how they contribute to synthetic biology. We also consider design by combinatorial optimization, which may work well in biological engineering due to properties of DNA replication.

Synthetic biological systems are currently created by an ad hoc, iterative process of design, simulation, and assembly. These systems would greatly benefit from the introduction of a more formalized and rigorous specification of the desired system components as well as constraints on their composition. In order to do so, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) which allows for both the specification of synthetic biological designs based on biological parts as well as providing a very expressive constraint system to drive the creation of composite devices from collection of parts. This chapter provides an overview of the language primitives as well as instructions on installation and use of Eugene v0.03b.

To design the complex systems that synthetic biologists propose to create, software tools must be developed. Critical to success is the enablement of collaboration across our community such that individual tools that perform specific tasks combine with other tools to provide multiplicative benefits. This will require standardization of the form of the data that exists within the field (Parts, Strains, measurements, etc.), a software environment that enables communication between tools, and a sharing mechanism for distributing the tools. Additionally, this data model must describe the data in a sufficiently rigorous and validated form such that meaningful layers of abstraction can be built upon the base. Herein, we describe a software platform called “Clotho” which provides such a data model, and the plugin and sharing mechanisms needed for a rich tool environment. This document provides a tutorial for users of Clotho and information for software developers who wish to contribute new tools (known as “Apps”) to it.

The primary bottleneck in synthetic biology research today is the construction of physical DNAs, a process that is often expensive, time-consuming, and riddled with cloning difficulties associated with the uniqueness of each DNA sequence. We have developed a series of biological and computational tools that lower existing barriers to automation and scaling to enable affordable, fast, and accurate construction of large DNA sets. Here we provide detailed protocols for high-throughput, automated assembly of BglBrick standard biological parts using iterative 2ab reactions. We have implemented these protocols on a minimal hardware platform consisting of a Biomek 3000 liquid handling robot, a benchtop centrifuge and a plate thermocycler, with additional support from a software tool called AssemblyManager. This methodology enables parallel assembly of several hundred large error-free DNAs with a 96+% success rate.

In electronic computing, high-level languages hide much of the details, allowing non-experts and sometimes even children to program and create systems. High level languages for biomolecular systems aim to achieve a similar level of abstraction, so that a system might be designed on the basis of the behaviors that are desired, rather than the particulars of the genetic code that will be used to implement these behaviors. The drawback to this sort of high-level approach is that it generally means giving up control over some aspects of the system and having decreased efficiency relative to hand-tuned designs. Different languages make different tradeoffs in which aspects of design they emphasize and which they automate, so we expect that for biology, there will be no single ‘right language’, just as there is not for electronic computing. Because synthetic biology is a new area, no mature languages have yet emerged. In this chapter, we present an in-depth survey of four representative languages currently in development – GenoCAD, Eugene, GEC, and Proto – as well as a brief overview of other related high-level design tools.

The construction of composite genetic devices from primitive parts is a key activity in synthetic biology. Currently there does not exist a formal method to specify constraints on the construction of these devices. These constraints would help enable an automated design flow from device specification to physical assembly. This paper examines the laboratory creation of variations of a particular genetic device called a phagemid. We illustrate how lessons learned empirically from the non-functional designs can be captured formally as constraints in a newly created domain specific language called “Eugene”. These constraints will prevent many faulty constructions automatically in the future saving time and money while increasing design abstraction and productivity.