A syntactic model to design and verify synthetic genetic constructs derived from standard biological parts

Designing Biological Circuits: From Principles to Applications

Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.

Securing the Exchange of Synthetic Genetic Constructs Using Digital Signatures

The field of synthetic biology relies on an ever-growing supply chain of synthetic genetic material. Technologies to secure the exchange of this material are still in their infancy. Solutions proposed thus far have focused on watermarks, a dated security approach that can be used to claim authorship, but is subject to counterfeit, and does not provide any information about the integrity of the genetic material itself. In this manuscript, we describe how data encryption and digital signature algorithms can be used to ensure the integrity and authenticity of synthetic genetic constructs. Using a pilot software that generates digital signatures and other encrypted data for plasmids, we demonstrate that we can predictably extract information about the author, the identity, the integrity of plasmid sequences, and even annotations from sequencing data alone without a reference sequence, all without compromising the function of the plasmids. Encoding a digital signature into a DNA molecule provides an avenue for genetic designers to claim authorship of DNA molecules. This technology could help compliance with material transfer agreements and other licensing agreements.

Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads

Genetic circuits have many applications, from guiding living therapeutics to ordering process in a bioreactor, but to be useful they have to be genetically stable and not hinder the host. Encoding circuits in the genome reduces burden, but this decreases performance and can interfere with native transcription. We have designed genomic landing pads in Escherichia coli at high-expression sites, flanked by ultrastrong double terminators. DNA payloads >8 kb are targeted to the landing pads using phage integrases. One landing pad is dedicated to carrying a sensor array, and two are used to carry genetic circuits. NOT/NOR gates based on repressors are optimized for the genome and characterized in the landing pads. These data are used, in conjunction with design automation software (Cello 2.0), to design circuits that perform quantitatively as predicted. These circuits require fourfold less RNA polymerase than when carried on a plasmid and are stable for weeks in a recA+ strain without selection. This approach enables the design of synthetic regulatory networks to guide cells in environments or for applications where plasmid use is infeasible.

Computer-aided re-engineering of nonribosomal peptide and polyketide biosynthetic assembly lines

Covering: 2014 to 2019Nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) have been the subject of engineering efforts for multiple decades. Their modular assembly line architecture potentially allows unlocking vast chemical space for biosynthesis. However, attempts thus far are often met with mixed success, due to limited molecular compatibility of the parts used for engineering. Now, new engineering strategies, increases in genomic data, and improved computational tools provide more opportunities for major progress. In this review we highlight some of the challenges and progressive strategies for the re-design of NRPSs & type I PKSs and survey useful computational tools and approaches to attain the ultimate goal of semi-automated and design-based engineering of novel peptide and polyketide products.

A Genetic Circuit Compiler: Generating Combinatorial Genetic Circuits with Web Semantics and Inference

A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice, and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating κ-language simulations from semantic descriptions of genetic circuits.

Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology

Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues-either compositional, host or environmental-will be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs.

Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest

Background

It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs – a motif grammar – located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model.

Results

We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar.

Conclusions

Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4340-z) contains supplementary material, which is available to authorized users.

Optimizing DNA assembly based on statistical language modelling

By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selected. Based on statistical language model, which is a probability distribution P(s) over strings S that attempts to reflect how frequently a string S occurs as a sentence, the most commonly used parts will be selected. Then, a dynamic programming algorithm was designed to figure out the solution of maximum probability. The algorithm optimizes the results of a genetic design based on a grammatical model and finds an optimal solution. In this way, redundant operations can be reduced and the time and cost required for conducting biological experiments can be minimized.

Genetic Design via Combinatorial Constraint Specification

We present a formal language for specifying via constraints a "design space" of DNA constructs composed of genetic parts, and an algorithm for automatically and correctly creating a novel representation of the space of satisfying designs. The language is simple, captures a large class of design spaces, and possesses algorithms for common operations on design spaces. The flexibility of this approach is demonstrated using a 16-gene nitrogen fixation pathway and genetic logic circuits.

EMMA: An Extensible Mammalian Modular Assembly Toolkit for the Rapid Design and Production of Diverse Expression Vectors

Mammalian plasmid expression vectors are critical reagents underpinning many facets of research across biology, biomedical research, and the biotechnology industry. Traditional cloning methods often require laborious manual design and assembly of plasmids using tailored sequential cloning steps. This process can be protracted, complicated, expensive, and error-prone. New tools and strategies that facilitate the efficient design and production of bespoke vectors would help relieve a current bottleneck for researchers. To address this, we have developed an extensible mammalian modular assembly kit (EMMA). This enables rapid and efficient modular assembly of mammalian expression vectors in a one-tube, one-step golden-gate cloning reaction, using a standardized library of compatible genetic parts. The high modularity, flexibility, and extensibility of EMMA provide a simple method for the production of functionally diverse mammalian expression vectors. We demonstrate the value of this toolkit by constructing and validating a range of representative vectors, such as transient and stable expression vectors (transposon based vectors), targeting vectors, inducible systems, polycistronic expression cassettes, fusion proteins, and fluorescent reporters. The method also supports simple assembly combinatorial libraries and hierarchical assembly for production of larger multigenetic cargos. In summary, EMMA is compatible with automated production, and novel genetic parts can be easily incorporated, providing new opportunities for mammalian synthetic biology.

Low escape-rate genome safeguards with minimal molecular perturbation of Saccharomyces cerevisiae

As the use of synthetic biology both in industry and in academia grows, there is an increasing need to ensure biocontainment. There is growing interest in engineering bacterial- and yeast-based safeguard (SG) strains. First-generation SGs were based on metabolic auxotrophy; however, the risk of cross-feeding and the cost of growth-controlling nutrients led researchers to look for other avenues. Recent strategies include bacteria engineered to be dependent on nonnatural amino acids and yeast SG strains that have both transcriptional- and recombinational-based biocontainment. We describe improving yeast Saccharomyces cerevisiae-based transcriptional SG strains, which have near-WT fitness, the lowest possible escape rate, and nanomolar ligands controlling growth. We screened a library of essential genes, as well as the best-performing promoter and terminators, yielding the best SG strains in yeast. The best constructs were fine-tuned, resulting in two tightly controlled inducible systems. In addition, for potential use in the prevention of industrial espionage, we screened an array of possible "decoy molecules" that can be used to mask any proprietary supplement to the SG strain, with minimal effect on strain fitness.

Diseño de casetes de expresión que confieran tolerancia a sequía y a glufosinato en maíz (Zea mays)

Como primera aproximación en la obtención de una línea transgénica de maíz tolerante a sequía y al herbicida glufosinato de amonio, se seleccionaron genes y elementos reguladores para el diseño in silico de casetes de expresión, a través del análisis de literatura científica y bases de datos de genes y patentes. Las secuencias génicas fueron modificadas con base en el criterio de uso codónico del maíz para optimizar su expresión. Los casetes de expresión diseñados con el software DNA 2.0., fueron sintetizados por una empresa especializada. La presencia del transgen y la expresión a nivel de mARN fue demostrada mediante PCR y RT-PCR en la planta modelo Nicotiana benthamiana transformada vía Agrobacterium tumefaciens. Un ensayo preliminar in vitro en condiciones simuladas de sequía en medio MS con PEG (PM 6000)10 % no demostró incremento notorio en la tolerancia de las plántulas transformantes, posiblemente debido a que el uso codónico del diseño no favorece la expresión génica en la planta modelo.

Smart DNA Fabrication Using Sound Waves: Applying Acoustic Dispensing Technologies to Synthetic Biology

Acoustic droplet ejection (ADE) technology uses focused acoustic energy to transfer nanoliter-scale liquid droplets with high precision and accuracy. This noncontact, tipless, low-volume dispensing technology minimizes the possibility of cross-contamination and potentially reduces the costs of reagents and consumables. To date, acoustic dispensers have mainly been used in screening libraries of compounds. In this paper, we describe the first application of this powerful technology to the rapidly developing field of synthetic biology, for DNA synthesis and assembly at the nanoliter scale using a Labcyte Echo 550 acoustic dispenser. We were able to successfully downscale PCRs and the popular one-pot DNA assembly methods, Golden Gate and Gibson assemblies, from the microliter to the nanoliter scale with high assembly efficiency, which effectively cut the reagent cost by 20- to 100-fold. We envision that acoustic dispensing will become an instrumental technology in synthetic biology, in particular in the era of DNA foundries.

GenoCAD Plant Grammar to Design Plant Expression Vectors for Promoter Analysis

With the rapid advances in prediction tools for discovery of new promoters and their cis-elements, there is a need to improve plant expression methodologies in order to facilitate a high-throughput functional validation of these promoters in planta. The promoter-reporter analysis is an indispensible approach for characterization of plant promoters. It requires the design of complex plant expression vectors, which can be challenging. Here, we describe the use of a plant grammar implemented in GenoCAD that will allow the users to quickly design constructs for promoter analysis experiments but also for other in planta functional studies. The GenoCAD plant grammar includes a library of plant biological parts organized in structural categories to facilitate their use and management and a set of rules that guides the process of assembling these biological parts into large constructs.

Harnessing QbD, Programming Languages, and Automation for Reproducible Biology

Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.

Rule-Based Design of Plant Expression Vectors Using GenoCAD

Plant synthetic biology requires software tools to assist on the design of complex multi-genic expression plasmids. Here a vector design strategy to express genes in plants is formalized and implemented as a grammar in GenoCAD, a Computer-Aided Design software for synthetic biology. It includes a library of plant biological parts organized in structural categories and a set of rules describing how to assemble these parts into large constructs. Rules developed here are organized and divided into three main subsections according to the aim of the final construct: protein localization studies, promoter analysis and protein-protein interaction experiments. The GenoCAD plant grammar guides the user through the design while allowing users to customize vectors according to their needs. Therefore the plant grammar implemented in GenoCAD will help plant biologists take advantage of methods from synthetic biology to design expression vectors supporting their research projects.

GenoLIB: a database of biological parts derived from a library of common plasmid features

Synthetic biologists rely on databases of biological parts to design genetic devices and systems. The sequences and descriptions of genetic parts are often derived from features of previously described plasmids using ad hoc, error-prone and time-consuming curation processes because existing databases of plasmids and features are loosely organized. These databases often lack consistency in the way they identify and describe sequences. Furthermore, legacy bioinformatics file formats like GenBank do not provide enough information about the purpose of features. We have analyzed the annotations of a library of ∼2000 widely used plasmids to build a non-redundant database of plasmid features. We looked at the variability of plasmid features, their usage statistics and their distributions by feature type. We segmented the plasmid features by expression hosts. We derived a library of biological parts from the database of plasmid features. The library was formatted using the Synthetic Biology Open Language, an emerging standard developed to better organize libraries of genetic parts to facilitate synthetic biology workflows. As proof, the library was converted into GenoCAD grammar files to allow users to import and customize the library based on the needs of their research projects.

A grammar inference approach for predicting kinase specific phosphorylation sites

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.

Intrinsic biocontainment: multiplex genome safeguards combine transcriptional and recombinational control of essential yeast genes

Biocontainment may be required in a wide variety of situations such as work with pathogens, field release applications of engineered organisms, and protection of intellectual properties. Here, we describe the control of growth of the brewer's yeast, Saccharomyces cerevisiae, using both transcriptional and recombinational "safeguard" control of essential gene function. Practical biocontainment strategies dependent on the presence of small molecules require them to be active at very low concentrations, rendering them inexpensive and difficult to detect. Histone genes were controlled by an inducible promoter and controlled by 30 nM estradiol. The stability of the engineered genes was separately regulated by the expression of a site-specific recombinase. The combined frequency of generating viable derivatives when both systems were active was below detection (<10(-10)), consistent with their orthogonal nature and the individual escape frequencies of <10(-6). Evaluation of escaper mutants suggests strategies for reducing their emergence. Transcript profiling and growth test suggest high fitness of safeguarded strains, an important characteristic for wide acceptance.

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.

Rule-based design of synthetic transcription factors in eukaryotes

To design and build living systems, synthetic biologists have at their disposal an increasingly large library of naturally derived and synthetic parts. These parts must be combined together in particular orders, orientations, and spacings to achieve desired functionalities. These structural constraints can be viewed as grammatical rules describing how to assemble parts together into larger functional units. Here, we develop a grammar for the design of synthetic transcription factors (sTFs) in eukaryotic cells and implement it within GenoCAD, a Computer-Aided Design (CAD) software for synthetic biology. Knowledge derived from experimental evidence was captured in this grammar to guide the user to create designer transcription factors that should operate as intended. The grammar can be easily updated and refined as our experience with using sTFs in different contexts increases. In combination with grammars that define other synthetic systems, we anticipate that this work will enable the more reliable, efficient, and automated design of synthetic cells with rich functionalities.

Parts & pools: a framework for modular design of synthetic gene circuits

Published in 2008, Parts & Pools represents one of the first attempts to conceptualize the modular design of bacterial synthetic gene circuits with Standard Biological Parts (DNA segments) and Pools of molecules referred to as common signal carriers (e.g., RNA polymerases and ribosomes). The original framework for modeling bacterial components and designing prokaryotic circuits evolved over the last years and brought, first, to the development of an algorithm for the automatic design of Boolean gene circuits. This is a remarkable achievement since gene digital circuits have a broad range of applications that goes from biosensors for health and environment care to computational devices. More recently, Parts & Pools was enabled to give a proper formal description of eukaryotic biological circuit components. This was possible by employing a rule-based modeling approach, a technique that permits a faithful calculation of all the species and reactions involved in complex systems such as eukaryotic cells and compartments. In this way, Parts & Pools is currently suitable for the visual and modular design of synthetic gene circuits in yeast and mammalian cells too.

A methodology to annotate systems biology markup language models with the synthetic biology open language

Recently, we have begun to witness the potential of synthetic biology, noted here in the form of bacteria and yeast that have been genetically engineered to produce biofuels, manufacture drug precursors, and even invade tumor cells. The success of these projects, however, has often failed in translation and application to new projects, a problem exacerbated by a lack of engineering standards that combine descriptions of the structure and function of DNA. To address this need, this paper describes a methodology to connect the systems biology markup language (SBML) to the synthetic biology open language (SBOL), existing standards that describe biochemical models and DNA components, respectively. Our methodology involves first annotating SBML model elements such as species and reactions with SBOL DNA components. A graph is then constructed from the model, with vertices corresponding to elements within the model and edges corresponding to the cause-and-effect relationships between these elements. Lastly, the graph is traversed to assemble the annotating DNA components into a composite DNA component, which is used to annotate the model itself and can be referenced by other composite models and DNA components. In this way, our methodology can be used to build up a hierarchical library of models annotated with DNA components. Such a library is a useful input to any future genetic technology mapping algorithm that would automate the process of composing DNA components to satisfy a behavioral specification. Our methodology for SBML-to-SBOL annotation is implemented in the latest version of our genetic design automation (GDA) software tool, iBioSim.

Development of a domain-specific genetic language to design Chlamydomonas reinhardtii expression vectors

MOTIVATION

Expression vectors used in different biotechnology applications are designed with domain-specific rules. For instance, promoters, origins of replication or homologous recombination sites are host-specific. Similarly, chromosomal integration or viral delivery of an expression cassette imposes specific structural constraints. As de novo gene synthesis and synthetic biology methods permeate many biotechnology specialties, the design of application-specific expression vectors becomes the new norm. In this context, it is desirable to formalize vector design strategies applicable in different domains.

RESULTS

Using the design of constructs to express genes in the chloroplast of Chlamydomonas reinhardtii as an example, we show that a vector design strategy can be formalized as a domain-specific language. We have developed a graphical editor of context-free grammars usable by biologists without prior exposure to language theory. This environment makes it possible for biologists to iteratively improve their design strategies throughout the course of a project. It is also possible to ensure that vectors designed with early iterations of the language are consistent with the latest iteration of the language.

AVAILABILITY AND IMPLEMENTATION

The context-free grammar editor is part of the GenoCAD application. A public instance of GenoCAD is available at http://www.genocad.org. GenoCAD source code is available from SourceForge and licensed under the Apache v2.0 open source license.

Microbial evolution in vivo and in silico: methods and applications

Microbial evolution has been extensively studied in the past fifty years, which has lead to seminal discoveries that have shaped our understanding of evolutionary forces and dynamics. It is only recently however, that transformative technologies and computational advances have enabled a larger in-scale and in-depth investigation of the genetic basis and mechanistic underpinnings of evolutionary adaptation. In this review we focus on the strengths and limitations of in vivo and in silico techniques for studying microbial evolution in the laboratory, and we discuss how these complementary approaches can be integrated in a unifying framework for elucidating microbial evolution.

Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits

BACKGROUND

The modular design of synthetic gene circuits via composable parts (DNA segments) and pools of signal carriers (molecules such as RNA polymerases and ribosomes) has been successfully applied to bacterial systems. However, eukaryotic cells are becoming a preferential host for new synthetic biology applications. Therefore, an accurate description of the intricate network of reactions that take place inside eukaryotic parts and pools is necessary. Rule-based modeling approaches are increasingly used to obtain compact representations of reaction networks in biological systems. However, this approach is intrinsically non-modular and not suitable per se for the description of composable genetic modules. In contrast, the Model Description Language (MDL) adopted by the modeling tool ProMoT is highly modular and it enables a faithful representation of biological parts and pools.

RESULTS

We developed a computational framework for the design of complex (eukaryotic) gene circuits by generating dynamic models of parts and pools via the joint usage of the BioNetGen rule-based modeling approach and MDL. The framework converts the specification of a part (or pool) structure into rules that serve as inputs for BioNetGen to calculate the part's species and reactions. The BioNetGen output is translated into an MDL file that gives a complete description of all the reactions that take place inside the part (or pool) together with a proper interface to connect it to other modules in the circuit. In proof-of-principle applications to eukaryotic Boolean circuits with more than ten genes and more than one thousand reactions, our framework yielded proper representations of the circuits' truth tables.

CONCLUSIONS

For the model-based design of increasingly complex gene circuits, it is critical to achieve exact and systematic representations of the biological processes with minimal effort. Our computational framework provides such a detailed and intuitive way to design new and complex synthetic gene circuits.

Dynamic modeling of cellular populations within iBioSim

As the complexity of synthetic genetic circuits increases, modeling is becoming a necessary first step to inform subsequent experimental efforts. In recent years, the design automation community has developed a wealth of computational tools for assisting experimentalists in designing and analyzing new genetic circuits at several scales. However, existing software primarily caters to either the DNA- or single-cell level, with little support for the multicellular level. To address this need, the iBioSim software package has been enhanced to provide support for modeling, simulating, and visualizing dynamic cellular populations in a two-dimensional space. This capacity is fully integrated into the software, capitalizing on iBioSim's strengths in modeling, simulating, and analyzing single-celled systems.

AutoBioCAD: full biodesign automation of genetic circuits

Synthetic regulatory networks with prescribed functions are engineered by assembling a reduced set of functional elements. We could also assemble them computationally if the mathematical models of those functional elements were predictive enough in different genetic contexts. Only after achieving this will we have libraries of models of biological parts able to provide predictive dynamical behaviors for most circuits constructed with them. We thus need tools that can automatically explore different genetic contexts, in addition to being able to use such libraries to design novel circuits with targeted dynamics. We have implemented a new tool, AutoBioCAD, aimed at the automated design of gene regulatory circuits. AutoBioCAD loads a library of models of genetic elements and implements evolutionary design strategies to produce (i) nucleotide sequences encoding circuits with targeted dynamics that can then be tested experimentally and (ii) circuit models for testing regulation principles in natural systems, providing a new tool for synthetic biology. AutoBioCAD can be used to model and design genetic circuits with dynamic behavior, thanks to the incorporation of stochastic effects, robustness, qualitative dynamics, multiobjective optimization, or degenerate nucleotide sequences, all facilitating the link with biological part/circuit engineering.

A composite method based on formal grammar and DNA structural features in detecting human polymerase II promoter region

An important step in understanding gene regulation is to identify the promoter regions where the transcription factor binding takes place. Predicting a promoter region de novo has been a theoretical goal for many researchers for a long time. There exists a number of in silico methods to predict the promoter region de novo but most of these methods are still suffering from various shortcomings, a major one being the selection of appropriate features of promoter region distinguishing them from non-promoters. In this communication, we have proposed a new composite method that predicts promoter sequences based on the interrelationship between structural profiles of DNA and primary sequence elements of the promoter regions. We have shown that a Context Free Grammar (CFG) can formalize the relationships between different primary sequence features and by utilizing the CFG, we demonstrate that an efficient parser can be constructed for extracting these relationships from DNA sequences to distinguish the true promoter sequences from non-promoter sequences. Along with CFG, we have extracted the structural features of the promoter region to improve upon the efficiency of our prediction system. Extensive experiments performed on different datasets reveals that our method is effective in predicting promoter sequences on a genome-wide scale and performs satisfactorily as compared to other promoter prediction techniques.

Perspectives on the automatic design of regulatory systems for synthetic biology

Automatic design is based on computational modeling and optimization methods to provide prototype designs to targeted problems in an unsupervised manner. For biological circuits, we need to produce quantitative predictions of cell behavior for a given genotype as consequence of the different molecular interactions. Automatic design techniques aim at solving the inverse problem of finding the sequences of nucleotides that better fit a targeted behavior. In the post-genomic era, our molecular knowledge and modeling capabilities have allowed to start using such methodologies with success. Herein, we describe how the emergence of this new type of tools could enable novel synthetic biology applications. We highlight the essential elements to develop automatic design procedures for synthetic biology pointing out their advantages and bottlenecks. We discuss in detail the experimental difficulties to overcome in the in vivo implementation of designed networks. The use of automatic design to engineer biological networks is starting to emerge as a new technique to perform synthetic biology, which should not be neglected in the future.

In silico implementation of synthetic gene networks

Computational synthetic biology has borrowed methods, concepts, and techniques from systems biology and electrical engineering. Features of tools for the analysis of biochemical networks and the design of electric circuits have been combined to develop new software, where Standard Biological Parts (physically stored at the MIT Registry) have a mathematical description, based on mass action or Hill kinetics, and can be assembled into genetic networks in a visual, "drag & drop" fashion. Recent tools provide the user with databases, simulation environments, formal languages, and even algorithms for circuit automatic design to refine and speed up gene network construction. Moreover, advances in automation of DNA assembly indicate that synthetic biology software soon will drive the wet-lab implementation of DNA sequences.

Assembly of standardized DNA parts using BioBrick ends in E. coli

Synthetic biologists have adopted the engineering principle of standardization of parts and assembly in the construction of a variety of genetic circuits that program living cells to perform useful tasks. In this chapter, we describe the BioBrick standard as a widely used method. We present methods by which new BioBrick parts can be designed and produced, starting with existing clones, naturally occurring DNA, or de novo. We detail the procedures by which BioBrick parts can be assembled into construction intermediates and into biological devices. These protocols are based on our experience in conducting synthetic biology research with undergraduate students in the context of the iGEM competition.

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.

Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors

The engineering of synthetic gene networks has mostly relied on the assembly of few characterized regulatory elements using rational design principles. It is of outmost importance to analyze the scalability and limits of such a design workflow. To analyze the design capabilities of libraries of regulatory elements, we have developed the first automated design approach that combines such elements to search the genotype space associated to a given phenotypic behavior. Herein, we calculated the designability of dynamical functions obtained from circuits assembled with a given genetic library. By designing circuits working as amplitude filters, pulse counters and oscillators, we could infer new mechanisms for such behaviors. We also highlighted the hierarchical design and the optimization of the interface between devices. We dissected the functional diversity of a constrained library and we found that even such libraries can provide a rich variety of behaviors. We also found that intrinsic noise slightly reduces the designability of digital circuits, but it increases the designability of oscillators. Finally, we analyzed the robust design as a strategy to counteract the evolvability and noise in gene expression of the engineered circuits within a cellular background, obtaining mechanisms for robustness through non-linear negative feedback loops.

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.

Programming cells: towards an automated 'Genetic Compiler'

One of the visions of synthetic biology is to be able to program cells using a language that is similar to that used to program computers or robotics. For large genetic programs, keeping track of the DNA on the level of nucleotides becomes tedious and error prone, requiring a new generation of computer-aided design (CAD) software. To push the size of projects, it is important to abstract the designer from the process of part selection and optimization. The vision is to specify genetic programs in a higher-level language, which a genetic compiler could automatically convert into a DNA sequence. Steps towards this goal include: defining the semantics of the higher-level language, algorithms to select and assemble parts, and biophysical methods to link DNA sequence to function. These will be coupled to graphic design interfaces and simulation packages to aid in the prediction of program dynamics, optimize genes, and scan projects for errors.

SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications.

GenoCAD for iGEM: a grammatical approach to the design of standard-compliant constructs

One of the foundations of synthetic biology is the project to develop libraries of standardized genetic parts that could be assembled quickly and cheaply into large systems. The limitations of the initial BioBrick standard have prompted the development of multiple new standards proposing different avenues to overcome these shortcomings. The lack of compatibility between standards, the compliance of parts with only some of the standards or even the type of constructs that each standard supports have significantly increased the complexity of assembling constructs from standardized parts. Here, we describe computer tools to facilitate the rigorous description of part compositions in the context of a rapidly changing landscape of physical construction methods and standards. A context-free grammar has been developed to model the structure of constructs compliant with six popular assembly standards. Its implementation in GenoCAD makes it possible for users to quickly assemble from a rich library of genetic parts, constructs compliant with any of six existing standards.

De Novo metabolic engineering and the promise of synthetic DNA

The uncertain price and tight supply of crude oil and the ever-increasing demand for clean energy have prompted heightened attention to the development of sustainable fuel technologies that ensure continued economic development while maintaining stewardship of the environment. In the face of these enormous challenges, biomass has emerged as a viable alternative to petroleum for the production of energy, chemicals, and materials owing to its abundance, inexpensiveness, and carbon-neutrality. Moreover, the immense ease and efficiency of biological systems at converting biomass-derived feedstocks into fuels, chemicals, and materials has generated renewed interest in biotechnology as a replacement for traditional chemical processes. Aided by the ever-expanding repertoire of microbial genetics and plant biotechnology, improved understanding of gene regulation and cellular metabolism, and incessantly accumulating gene and protein data, scientists are now contemplating engineering microbial cell factories to produce fuels, chemical feedstocks, polymers and pharmaceuticals in an economically and environmentally sustainable way. This goal resonates with that of metabolic engineering - the improvement of cellular properties through the intelligent design, rational modification, or directed evolution of biochemical pathways, and arguably, metabolic engineering seems best positioned to achieve the concomittant goals of environmental stewardship and economic prolificity.Improving a host organism's cellular traits and the potential design of new phenotypes is strongly dependent on the ability to effectively control the organism's genetic machinery. In fact, finely-tuned gene expression is imperative for achieving an optimal balance between pathway expression and cell viability, while avoiding cytotoxicity due to accumulation of certain gene products or metabolites. Early attempts to engineer a cell's metabolism almost exclusively relied on merely deleting or over-expressing single or multiple genes using recombinant DNA, and intervention targets were predominantly selected based on knowledge of the stoichiometry, kinetics, and regulation of the pathway of interest. However, the distributive nature of metabolic control, as opposed to the existence of a single rate-limiting step, predicates the controlled expression of multiple enzymes in several coordinated pathways to achieve the desired flux, and, as such, simple strategies involving either deleting or over-expressing genes are greatly limited in this context. On the other hand, the use of synthetic or modified promoters, riboswitches, tunable intergenic regions, and translation modulators such as internal ribosome entry sequences, upstream open reading frames, optimized mRNA secondary structures, and RNA silencing have been shown to be enormously conducive to achieving the fine-tuning of gene expression. These modifications to the genetic machinery of the host organism can be best achieved via the use of synthetic DNA technology, and the constant improvement in the affordability and quality of oligonucleotide synthesis suggests that these might well become the mainstay of the metabolic engineering toolbox in the years to come. The possibilities that arise with the use of synthetic oligonucleotides will be delineated herein.

Modeling structure-function relationships in synthetic DNA sequences using attribute grammars

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology.

Computational design tools for synthetic biology

Computer-aided design, pervasive in other engineering disciplines, is currently developing in synthetic biology. Concepts for standardization and hierarchies of parts, devices and systems provide a basis for efficient engineering in biology. Recently developed computational tools, for instance, enable rational (and graphical) composition of genetic circuits from standard parts, and subsequent simulation for testing the predicted functions in silico. The computational design of DNA and proteins with predetermined quantitative functions has made similar advances. The biggest challenge, however, is the integration of tools and methods into powerful and intuitively usable workflows-and the field is only starting to address it.

Chemical synthesis using synthetic biology

An immense array of naturally occurring biological systems have evolved that convert simple substrates into the products that cells need for growth and persistence. Through the careful application of metabolic engineering and synthetic biology, this biotransformation potential can be harnessed to produce chemicals that address unmet clinical and industrial needs. Developing the capacity to utilize biology to perform chemistry is a matter of increasing control over both the function of synthetic biological systems and the engineering of those systems. Recent efforts have improved general techniques and yielded successes in the use of synthetic biology for the production of drugs, bulk chemicals, and fuels in microbial platform hosts. Synthetic promoter systems and novel RNA-based, or riboregulator, mechanisms give more control over gene expression. Improved methods for isolating, engineering, and evolving enzymes give more control over substrate and product specificity and better catalysis inside the cell. New computational tools and methods for high-throughput system assembly and analysis may lead to more rapid forward engineering. We highlight research that reduces reliance upon natural biological components and point to future work that may enable more rational design and assembly of synthetic biological systems for synthetic chemistry.