Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.National Institutes of Health (U.S.) (Grant # 7R01GM74712-5)United States. Defense Advanced Research Projects Agency (contract HR0011-10-C-0168)National Science Foundation (U.S.) (NSF CAREER award 0968682)BBN Technologie

CloneQC: lightweight sequence verification for synthetic biology

Synthetic biology projects aim to produce physical DNA that matches a designed target sequence. Chemically synthesized oligomers are generally used as the starting point for building larger and larger sequences. Due to the error rate of chemical synthesis, these oligomers can have many differences from the target sequence. As oligomers are joined together to make larger and larger synthetic intermediates, it becomes essential to perform quality control to eliminate intermediates with errors and retain only those DNA molecules that are error free with respect to the target. This step is often performed by transforming bacteria with synthetic DNA and sequencing colonies until a clone with a perfect sequence is identified. Here we present CloneQC, a lightweight software pipeline available as a free web server and as source code that performs quality control on sequenced clones. Input to the server is a list of desired sequences and forward and reverse reads for each clone. The server generates summary statistics (error rates and success rates target-by-target) and a detailed report of perfect clones. This software will be useful to laboratories conducting in-house DNA synthesis and is available at http://cloneqc.thruhere.net/ and as Berkeley Software Distribution (BSD) licensed source

Current status and unanswered questions on the use of Denosumab in giant cell tumor of bone

Denosumab is a monoclonal antibody to RANK ligand approved for use in giant cell tumour (GCT) of bone. Due to its efficacy, Denosumab is recommended as the first option in inoperable or metastatic GCT. Denosumab has also been used pre-operatively to downstage tumours with large soft tissue extension to allow for less morbid surgery. The role of Denosumab for conventional limb GCT of bone is yet to be defined. Further studies are required to determine whether local recurrence rates will be decreased with the adjuvant use of Denosumab along with surgery. The long term use and toxicity of this agent is unknown as is the proportion of patients with primary or secondary resistance. It is advised that complicated cases of GCT requiring Denosumab treatment should be referred and followed up at expert centres. Collaborative studies involving further clinical trials and rigorous data collection are strongly recommended to identify the optimum use of this drug

PATRIC: The VBI PathoSystems Resource Integration Center

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: ‘breadth first’ beginning with whole-genome and proteome curation using standardized protocols, a ‘targeted’ approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website () will continually grow with the addition of data, analysis and functionality over the course of the project

Characterization of Sulfolobus islandicus rod-shaped virus 2 gp19, a single-strand specific endonuclease

The hyperthermophilic Sulfolobus islandicus rod-shaped virus 2 (SIRV2) encodes a 25-kDa protein (SIRV2gp19) annotated as a hypothetical protein with sequence homology to the RecB nuclease superfamily. Even though SIRV2gp19 homologs are conserved throughout the rudivirus family and presumably play a role in the viral life cycle, SIRV2gp19 has not been functionally characterized. To define the minimal requirements for activity, SIRV2gp19 was purified and tested under varying conditions. SIRV2gp19 is a single-strand specific endonuclease that requires Mg2+ for activity and is inactive on double-stranded DNA. A conserved aspartic acid in RecB nuclease superfamily Motif II (D89) is also essential for SIRV2gp19 activity and mutation to alanine (D89A) abolishes activity. Therefore, the SIRV2gp19 cleavage mechanism is similar to previously described RecB nucleases. Finally, SIRV2gp19 single-stranded DNA endonuclease activity could play a role in host chromosome degradation during SIRV2 lytic infection

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

Eugene – A Domain Specific Language for Specifying and Constraining Synthetic Biological Parts, Devices, and Systems

BACKGROUND: Synthetic biological systems are currently created by an ad-hoc, iterative process of specification, design, and assembly. These systems would greatly benefit from a more formalized and rigorous specification of the desired system components as well as constraints on their composition. Therefore, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) that allows for the specification of synthetic biological designs based on biological parts, as well as provides a very expressive constraint system to drive the automatic creation of composite Parts (Devices) from a collection of individual Parts. RESULTS: We illustrate Eugene's capabilities in three different areas: Device specification, design space exploration, and assembly and simulation integration. These results highlight Eugene's ability to create combinatorial design spaces and prune these spaces for simulation or physical assembly. Eugene creates functional designs quickly and cost-effectively. CONCLUSIONS: Eugene is intended for forward engineering of DNA-based devices, and through its data types and execution semantics, reflects the desired abstraction hierarchy in synthetic biology. Eugene provides a powerful constraint system which can be used to drive the creation of new devices at runtime. It accomplishes all of this while being part of a larger tool chain which includes support for design, simulation, and physical device assembly

The farnesoid X receptor regulates transcription of 3 beta-hydroxysteroid dehydrogenase type 2 in human adrenal cells

Recent studies have shown that the adrenal cortex expresses high levels of farnesoid X receptor (FXR), but its function remains not known. Herein, using microarray technology, we tried to identify candidate FXR targeting genes in the adrenal glands, and showed that FXR regulates 3β-hydroxysteroid dehydrogenase type 2 (HSD3B2) expression in human adrenocortical cells. We further demonstrated that FXR stimulated HSD3B2 promoter activity and have defined the cis-element responsible for FXR regulation of HSD3B2 transcription. Transfection of H295R adrenocortical cells with FXR expression vector effectively increased FXR expression levels and additional treatment with chenodeoxycholic acid (CDCA) caused a 25-fold increase in the mRNA for organic solute transporter alpha (OSTα), a known FXR target gene. HSD3B2 mRNA levels also increased following CDCA treatment in a concentration-dependent manner. Cells transfected with a HSD3B2 promoter construct and FXR expression vector responded to CDCA with a 20-fold increase in reporter activity compared to control. Analysis of constructs containing sequential deletions of the HSD3B2 promoter suggested a putative regulatory element between -166 and -101. Mutation of an inverted repeat between -137 and -124 completely blocked CDCA/FXR induced reporter activity. Chromatin immunoprecipitation assays further confirmed the presence of a FXR response element in the HSD3B2 promoter. In view of the emerging role of FXR agonists as therapeutic treatment of diabetes and certain liver diseases, the effects of such agonists on other FXR expressing tissues should be considered. Our findings suggest that in human adrenal cells, FXR increases transcription and expression of HSD3B2. Alterations in this enzyme would influence the capacity of the adrenal gland to produce corticosteroids