Programming cells: towards an automated 'Genetic Compiler'

Engineered live bacteria as disease detection and diagnosis tools

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

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.

Quantitative and Predictive Genetic Parts for Plant Synthetic Biology

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

Synthetic Biology Goes Cell-Free

Cell-free systems (CFS) have recently evolved into key platforms for synthetic biology applications. Many synthetic biology tools have traditionally relied on cell-based systems, and while their adoption has shown great progress, the constraints inherent to the use of cellular hosts have limited their reach and scope. Cell-free systems, which can be thought of as programmable liquids, have removed many of these complexities and have brought about exciting opportunities for rational design and manipulation of biological systems. Here we review how these simple and accessible enzymatic systems are poised to accelerate the rate of advancement in synthetic biology and, more broadly, biotechnology.

Engineering a New Class of Anti-LacI Transcription Factors with Alternate DNA Recognition

The lactose repressor, LacI (I⁺_YQR), is an archetypal transcription factor that has been a workhorse in many synthetic genetic networks. LacI represses gene expression (apo ligand) and is induced upon binding of the ligand isopropyl β-d-1-thiogalactopyranoside (IPTG). Recently, laboratory evolution was used to confer inverted function in the native LacI topology resulting in anti-LacI (antilac) function (I^A_YQR), where IPTG binding results in gene suppression. Here we engineered 46 antilacs with alternate DNA binding function (I^A_ADR). Phenotypically, I^A_ADR transcription factors are the inverse of wild-type I⁺_YQR function and possess alternate DNA recognition (ADR). This collection of bespoke I^A_ADR bind orthogonally to disparate non-natural operator DNA sequences and suppress gene expression in the presence of IPTG. This new class of I^A_ADR gene regulators were designed modularly via the systematic pairing of nine alternate allosteric regulatory cores with six alternate DNA binding domains that interact with complementary synthetic operator DNA sequences. The 46 I^A_ADR identified in this study are also orthogonal to the naturally occurring operator O¹. Finally, a demonstration of full orthogonality was achieved via the construction of synthetic genetic toggle switches composed of two nonsynonymous unit pair operations that control two distinct fluorescent outputs. This new class of I^A_ADR transcription factors will facilitate the expansion of the computational capacity of engineered gene circuits, via the scalable increase in the control over the number of gene outputs by way of the expansion of the number of unique transcription factors (or systems of transcription factors) that can simultaneously regulate one or more promoter(s).

Biomolecular Assemblies: Moving from Observation to Predictive Design

Biomolecular assembly is a key driving force in nearly all life processes, providing structure, information storage, and communication within cells and at the whole organism level. These assembly processes rely on precise interactions between functional groups on nucleic acids, proteins, carbohydrates, and small molecules, and can be fine-tuned to span a range of time, length, and complexity scales. Recognizing the power of these motifs, researchers have sought to emulate and engineer biomolecular assemblies in the laboratory, with goals ranging from modulating cellular function to the creation of new polymeric materials. In most cases, engineering efforts are inspired or informed by understanding the structure and properties of naturally occurring assemblies, which has in turn fueled the development of predictive models that enable computational design of novel assemblies. This Review will focus on selected examples of protein assemblies, highlighting the story arc from initial discovery of an assembly, through initial engineering attempts, toward the ultimate goal of predictive design. The aim of this Review is to highlight areas where significant progress has been made, as well as to outline remaining challenges, as solving these challenges will be the key that unlocks the full power of biomolecules for advances in technology and medicine.

Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways

Detection of chemical signals is critical for cells in nature as well as in synthetic biology, where they serve as inputs for designer circuits. Important progress has been made in the design of signal processing circuits triggering complex biological behaviors, but the range of small molecules recognized by sensors as inputs is limited. The ability to detect new molecules will increase the number of synthetic biology applications, but direct engineering of tailor-made sensors takes time. Here we describe a way to immediately expand the range of biologically detectable molecules by systematically designing metabolic pathways that transform nondetectable molecules into molecules for which sensors already exist. We leveraged computer-aided design to predict such sensing-enabling metabolic pathways, and we built several new whole-cell biosensors for molecules such as cocaine, parathion, hippuric acid, and nitroglycerin.

Towards programmable plant genetic circuits

Synthetic biology enables the construction of genetic circuits with predictable gene functions in plants. Detailed quantitative descriptions of the transfer function or input-output function for genetic parts (promoters, 5' and 3' untranslated regions, etc.) are collected. These data are then used in computational simulations to determine their robustness and desired properties, thereby enabling the best components to be selected for experimental testing in plants. In addition, the process forms an iterative workflow which allows vast improvement to validated elements with sub-optimal function. These processes enable computational functions such as digital logic in living plants and follow the pathway of technological advances which took us from vacuum tubes to cell phones.

Nature versus design: synthetic biology or how to build a biological non-machine

We suggest that progress in synthetic biology will be achieved by abandoning the bio-machine paradigm and by using an alliance between engineering and evolution as a guiding tool.

Memory and Combinatorial Logic Based on DNA Inversions: Dynamics and Evolutionary Stability

Genetic memory can be implemented using enzymes that catalyze DNA inversions, where each orientation corresponds to a "bit". Here, we use two DNA invertases (FimE and HbiF) that reorient DNA irreversibly between two states with opposite directionality. First, we construct memory that is set by FimE and reset by HbiF. Next, we build a NOT gate where the input promoter drives FimE and in the absence of signal the reverse state is maintained by the constitutive expression of HbiF. The gate requires ∼3 h to turn on and off. The evolutionary stabilities of these circuits are measured by passaging cells while cycling function. The memory switch is stable over 400 h (17 days, 14 state changes); however, the gate breaks after 54 h (>2 days) due to continuous invertase expression. Genome sequencing reveals that the circuit remains intact, but the host strain evolves to reduce invertase expression. This work highlights the need to evaluate the evolutionary robustness and failure modes of circuit designs, especially as more complex multigate circuits are implemented.

Programming a Pavlovian-like conditioning circuit in Escherichia coli

Synthetic genetic circuits are programmed in living cells to perform predetermined cellular functions. However, designing higher-order genetic circuits for sophisticated cellular activities remains a substantial challenge. Here we program a genetic circuit that executes Pavlovian-like conditioning, an archetypical sequential-logic function, in Escherichia coli. The circuit design is first specified by the subfunctions that are necessary for the single simultaneous conditioning, and is further genetically implemented using four function modules. During this process, quantitative analysis is applied to the optimization of the modules and fine-tuning of the interconnections. Analogous to classical Pavlovian conditioning, the resultant circuit enables the cells to respond to a certain stimulus only after a conditioning process. We show that, although the conditioning is digital in single cells, a dynamically progressive conditioning process emerges at the population level. This circuit, together with its rational design strategy, is a key step towards the implementation of more sophisticated cellular computing.

Putting RNA to work: Translating RNA fundamentals into biotechnological engineering practice

Synthetic biology, in close concert with systems biology, is revolutionizing the field of metabolic engineering by providing novel tools and technologies to rationally, in a standardized way, reroute metabolism with a view to optimally converting renewable resources into a broad range of bio-products, bio-materials and bio-energy. Increasingly, these novel synthetic biology tools are exploiting the extensive programmable nature of RNA, vis-à-vis DNA- and protein-based devices, to rationally design standardized, composable, and orthogonal parts, which can be scaled and tuned promptly and at will. This review gives an extensive overview of the recently developed parts and tools for i) modulating gene expression ii) building genetic circuits iii) detecting molecules, iv) reporting cellular processes and v) building RNA nanostructures. These parts and tools are becoming necessary armamentarium for contemporary metabolic engineering. Furthermore, the design criteria, technological challenges, and recent metabolic engineering success stories of the use of RNA devices are highlighted. Finally, the future trends in transforming metabolism through RNA engineering are critically evaluated and summarized.

Generating Effective Models and Parameters for RNA Genetic Circuits

RNA genetic circuitry is emerging as a powerful tool to control gene expression. However, little work has been done to create a theoretical foundation for RNA circuit design. A prerequisite to this is a quantitative modeling framework that accurately describes the dynamics of RNA circuits. In this work, we develop an ordinary differential equation model of transcriptional RNA genetic circuitry, using an RNA cascade as a test case. We show that parameter sensitivity analysis can be used to design a set of four simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions to determine the 13 parameters of the model. The resulting model accurately recapitulates the dynamic behavior of the cascade, and can be easily extended to predict the function of new cascade variants that utilize new elements with limited additional characterization experiments. Interestingly, we show that inconsistencies between model predictions and experiments led to the model-guided discovery of a previously unknown maturation step required for RNA regulator function. We also determine circuit parameters in two different batches of TX-TL, and show that batch-to-batch variation can be attributed to differences in parameters that are directly related to the concentrations of core gene expression machinery. We anticipate the RNA circuit models developed here will inform the creation of computer aided genetic circuit design tools that can incorporate the growing number of RNA regulators, and that the parametrization method will find use in determining functional parameters of a broad array of natural and synthetic regulatory systems.

Retroactivity in the Context of Modularly Structured Biomolecular Systems

Synthetic biology has intensively promoted the technical implementation of modular strategies in the fabrication of biological devices. Modules are considered as networks of reactions. The behavior displayed by biomolecular systems results from the information processes carried out by the interconnection of the involved modules. However, in natural systems, module wiring is not a free-of-charge process; as a consequence of interconnection, a reactive phenomenon called retroactivity emerges. This phenomenon is characterized by signals that propagate from downstream modules (the modules that receive the incoming signals upon interconnection) to upstream ones (the modules that send the signals upon interconnection). Such retroactivity signals, depending of their strength, may change and sometimes even disrupt the behavior of modular biomolecular systems. Thus, analysis of retroactivity effects in natural biological and biosynthetic systems is crucial to achieve a deeper understanding of how this interconnection between functionally characterized modules takes place and how it impacts the overall behavior of the involved cell. By discussing the modules interconnection in natural and synthetic biomolecular systems, we propose that such systems should be considered as quasi-modular.

Programming languages for circuit design

This chapter provides an overview of a programming language for Genetic Engineering of Cells (GEC). A GEC program specifies a genetic circuit at a high level of abstraction through constraints on otherwise unspecified DNA parts. The GEC compiler then selects parts which satisfy the constraints from a given parts database. GEC further provides more conventional programming language constructs for abstraction, e.g., through modularity. The GEC language and compiler is available through a Web tool which also provides functionality, e.g., for simulation of designed circuits.

DNA Assembly Tools and Strategies for the Generation of Plasmids

Since the discovery of restriction enzymes and the generation of the first recombinant DNA molecule over 40 years ago, molecular biology has evolved into a multidisciplinary field that has democratized the conversion of a digitized DNA sequence stored in a computer into its biological counterpart, usually as a plasmid, stored in a living cell. In this article, we summarize the most relevant tools that allow the swift assembly of DNA sequences into useful plasmids for biotechnological purposes. We cover the main components and stages in a typical DNA assembly workflow, namely in silico design, de novo gene synthesis, and in vitro and in vivo sequence assembly methodologies.

Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.

Convergence in parameters and predictions using computational experimental design

Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor–nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.

Design of orthogonal genetic switches based on a crosstalk map of σs, anti-σs, and promoters

Cells react to their environment through gene regulatory networks. Network integrity requires minimization of undesired crosstalk between their biomolecules. Similar constraints also limit the use of regulators when building synthetic circuits for engineering applications. Here, we mapped the promoter specificities of extracytoplasmic function (ECF) σs as well as the specificity of their interaction with anti-σs. DNA synthesis was used to build 86 ECF σs (two from every subgroup), their promoters, and 62 anti-σs identified from the genomes of diverse bacteria. A subset of 20 σs and promoters were found to be highly orthogonal to each other. This set can be increased by combining the -35 and -10 binding domains from different subgroups to build chimeras that target sequences unrepresented in any subgroup. The orthogonal σs, anti-σs, and promoters were used to build synthetic genetic switches in Escherichia coli. This represents a genome-scale resource of the properties of ECF σs and a resource for synthetic biology, where this set of well-characterized regulatory parts will enable the construction of sophisticated gene expression programs.

Genetic sensor for strong methylating compounds

Methylating chemicals are common in industry and agriculture and are often toxic, partly due to their propensity to methylate DNA. The Escherichia coli Ada protein detects methylating compounds by sensing aberrant methyl adducts on the phosphoester backbone of DNA. We characterize this system as a genetic sensor and engineer it to lower the detection threshold. By overexpressing Ada from a plasmid, we improve the sensor’s dynamic range to 350-fold induction and lower its detection threshold to 40 μM for methyl iodide. In eukaryotes, there is no known sensor of methyl adducts on the phosphoester backbone of DNA. By fusing the N-terminal domain of Ada to the Gal4 transcriptional activation domain, we built a functional sensor for methyl phosphotriester adducts in Saccharomyces cerevisiae. This sensor can be tuned to variable specifications by altering the expression level of the chimeric sensor and changing the number of Ada operators upstream of the Gal4-sensitive reporter promoter. These changes result in a detection threshold of 28 μM and 5.2-fold induction in response to methyl iodide. When the yeast sensor is exposed to different SN1 and SN2 alkylating compounds, its response profile is similar to that observed for the native Ada protein in E. coli, indicating that its native function is retained in yeast. Finally, we demonstrate that the specifications achieved for the yeast sensor are suitable for detecting methylating compounds at relevant concentrations in environmental samples. This work demonstrates the movement of a sensor from a prokaryotic to eukaryotic system and its rational tuning to achieve desired specifications.

Synthetic biological networks

Despite their obvious relationship and overlap, the field of physics is blessed with many insightful laws, while such laws are sadly absent in biology. Here we aim to discuss how the rise of a more recent field known as synthetic biology may allow us to more directly test hypotheses regarding the possible design principles of natural biological networks and systems. In particular, this review focuses on synthetic gene regulatory networks engineered to perform specific functions or exhibit particular dynamic behaviors. Advances in synthetic biology may set the stage to uncover the relationship of potential biological principles to those developed in physics.

Genetic circuit performance under conditions relevant for industrial bioreactors

Synthetic genetic programs promise to enable novel applications in industrial processes. For such applications, the genetic circuits that compose programs will require fidelity in varying and complex environments. In this work, we report the performance of two synthetic circuits in Escherichia coli under industrially relevant conditions, including the selection of media, strain, and growth rate. We test and compare two transcriptional circuits: an AND and a NOR gate. In E. coli DH10B, the AND gate is inactive in minimal media; activity can be rescued by supplementing the media and transferring the gate into the industrial strain E. coli DS68637 where normal function is observed in minimal media. In contrast, the NOR gate is robust to media composition and functions similarly in both strains. The AND gate is evaluated at three stages of early scale-up: 100 mL shake flask experiments, a 1 mL MTP microreactor, and a 10 L bioreactor. A reference plasmid that constitutively produces a GFP reporter is used to make comparisons of circuit performance across conditions. The AND gate function is quantitatively different at each scale. The output deteriorates late in fermentation after the shift from exponential to constant feed rates, which induces rapid resource depletion and changes in growth rate. In addition, one of the output states of the AND gate failed in the bioreactor, effectively making it only responsive to a single input. Finally, cells carrying the AND gate show considerably less accumulation of biomass. Overall, these results highlight challenges and suggest modified strategies for developing and characterizing genetic circuits that function reliably during fermentation.

Genetic programs constructed from layered logic gates in single cells

Genetic programs function to integrate environmental sensors, implement signal processing algorithms and control expression dynamics. These programs consist of integrated genetic circuits that individually implement operations ranging from digital logic to dynamic circuits, and they have been used in various cellular engineering applications, including the implementation of process control in metabolic networks and the coordination of spatial differentiation in artificial tissues. A key limitation is that the circuits are based on biochemical interactions occurring in the confined volume of the cell, so the size of programs has been limited to a few circuits. Here we apply part mining and directed evolution to build a set of transcriptional AND gates in Escherichia coli. Each AND gate integrates two promoter inputs and controls one promoter output. This allows the gates to be layered by having the output promoter of an upstream circuit serve as the input promoter for a downstream circuit. Each gate consists of a transcription factor that requires a second chaperone protein to activate the output promoter. Multiple activator-chaperone pairs are identified from type III secretion pathways in different strains of bacteria. Directed evolution is applied to increase the dynamic range and orthogonality of the circuits. These gates are connected in different permutations to form programs, the largest of which is a 4-input AND gate that consists of 3 circuits that integrate 4 inducible systems, thus requiring 11 regulatory proteins. Measuring the performance of individual gates is sufficient to capture the behaviour of the complete program. Errors in the output due to delays (faults), a common problem for layered circuits, are not observed. This work demonstrates the successful layering of orthogonal logic gates, a design strategy that could enable the construction of large, integrated circuits in single cells.

Ribozyme-based insulator parts buffer synthetic circuits from genetic context

Synthetic genetic programs are built from circuits that integrate sensors and implement temporal control of gene expression. Transcriptional circuits are layered by using promoters to carry the signal between circuits. In other words, the output promoter of one circuit serves as the input promoter to the next. Thus, connecting circuits requires physically connecting a promoter to the next circuit. We show that the sequence at the junction between the input promoter and circuit can affect the input-output response (transfer function) of the circuit. A library of putative sequences that might reduce (or buffer) such context effects, which we refer to as 'insulator parts', is screened in Escherichia coli. We find that ribozymes that cleave the 5' untranslated region (5'-UTR) of the mRNA are effective insulators. They generate quantitatively identical transfer functions, irrespective of the identity of the input promoter. When these insulators are used to join synthetic gene circuits, the behavior of layered circuits can be predicted using a mathematical model. The inclusion of insulators will be critical in reliably permuting circuits to build different programs.

Automated design of genetic toggle switches with predetermined bistability

Synthetic biology aims to rationally construct biological devices with required functionalities. Methods that automate the design of genetic devices without post-hoc adjustment are therefore highly desired. Here we provide a method to predictably design genetic toggle switches with predetermined bistability. To accomplish this task, a biophysical model that links ribosome binding site (RBS) DNA sequence to toggle switch bistability was first developed by integrating a stochastic model with RBS design method. Then, to parametrize the model, a library of genetic toggle switch mutants was experimentally built, followed by establishing the equivalence between RBS DNA sequences and switch bistability. To test this equivalence, RBS nucleotide sequences for different specified bistabilities were in silico designed and experimentally verified. Results show that the deciphered equivalence is highly predictive for the toggle switch design with predetermined bistability. This method can be generalized to quantitative design of other probabilistic genetic devices in synthetic biology.

A turning point for natural product discovery--ESF-EMBO research conference: synthetic biology of antibiotic production

Synthetic Biology is in a critical phase of its development: it has finally reached the point where it can move from proof-of-principle studies to real-world applications. Secondary metabolite biosynthesis, especially the discovery and production of antibiotics, is a particularly relevant target area for such applications of synthetic biology. The first international conference to explore this subject was held in Spain in October 2011. In four sessions on General Synthetic Biology, Filamentous Fungal Systems, Actinomyces Systems, and Tools and Host Structures, scientists presented the most recent technological and scientific advances, and a final-day Forward Look Plenary Discussion identified future trends in the field.

Genetic design automation: engineering fantasy or scientific renewal?

The aim of synthetic biology is to make genetic systems more amenable to engineering, which has naturally led to the development of computer-aided design (CAD) tools. Experimentalists still primarily rely on project-specific ad hoc workflows instead of domain-specific tools, which suggests that CAD tools are lagging behind the front line of the field. Here, we discuss the scientific hurdles that have limited the productivity gains anticipated from existing tools. We argue that the real value of efforts to develop CAD tools is the formalization of genetic design rules that determine the complex relationships between genotype and phenotype.

A step-by-step introduction to rule-based design of synthetic genetic constructs using GenoCAD

GenoCAD is an open source web-based system that provides a streamlined, rule-driven process for designing genetic sequences. GenoCAD provides a graphical interface that allows users to design sequences consistent with formalized design strategies specific to a domain, organization, or project. Design strategies include limited sets of user-defined parts and rules indicating how these parts are to be combined in genetic constructs. In addition to reducing design time to minutes, GenoCAD improves the quality and reliability of the finished sequence by ensuring that the designs follow established rules of sequence construction. GenoCAD.org is a publicly available instance of GenoCAD that can be found at www.genocad.org. The source code and latest build are available from SourceForge to allow advanced users to install and customize GenoCAD for their unique needs. This chapter focuses primarily on how the GenoCAD tools can be used to organize genetic parts into customized personal libraries, then how these libraries can be used to design sequences. In addition, GenoCAD's parts management system and search capabilities are described in detail. Instructions are provided for installing a local instance of GenoCAD on a server. Some of the future enhancements of this rapidly evolving suite of applications are briefly described.

Standard biological parts knowledgebase

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate "promoter" parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible.

Programming languages for synthetic biology

In the backdrop of accelerated efforts for creating synthetic organisms, the nature and scope of an ideal programming language for scripting synthetic organism in-silico has been receiving increasing attention. A few programming languages for synthetic biology capable of defining, constructing, networking, editing and delivering genome scale models of cellular processes have been recently attempted. All these represent important points in a spectrum of possibilities. This paper introduces Kera, a state of the art programming language for synthetic biology which is arguably ahead of similar languages or tools such as GEC, Antimony and GenoCAD. Kera is a full-fledged object oriented programming language which is tempered by biopart rule library named Samhita which captures the knowledge regarding the interaction of genome components and catalytic molecules. Prominent feature of the language are demonstrated through a toy example and the road map for the future development of Kera is also presented.