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Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.

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

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.

Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62-0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65-0.82), but not for female samples (AUC 0.51, 95% CI 0.36-0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58-0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified.

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.

There now exists a rich set of ontologies that provide detailed semantics for biological entities of interest. However, there is not (nor should there be) a single source ontology that provides all the necessary semantics for describing biological phenomena. In the domain of physiological biosimulation models, researchers use annotations to convey semantics, and many of these annotations require the use of multiple reference ontologies. Therefore, we have developed the idea of composite annotations that access multiple ontologies to capture the physics-based meaning of model variables. These composite annotations provide the semantic expressivity needed to disambiguate the often-complex features of biosimulation models, and can be used to assist with model merging and interoperability. In this paper, we demonstrate the utility of composite annotations for model merging by describing their use within SemGen, our semantics-based model composition software. More broadly, if orthogonal reference ontologies are to meet their full potential, users need tools and methods to connect and link these ontologies. Our composite annotations and the SemGen tool provide one mechanism for leveraging multiple reference ontologies., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Background: Although microarray technology has become the most common method for studying global gene expression, a plethora of technical factors across the experiment contribute to the variable of genome gene expression profiling using peripheral whole blood. A practical platform needs to be established in order to obtain reliable and reproducible data to meet clinical requirements for biomarker study., Methods and Findings: We applied peripheral whole blood samples with globin reduction and performed genome-wide transcriptome analysis using Illumina BeadChips. Real-time PCR was subsequently used to evaluate the quality of array data and elucidate the mode in which hemoglobin interferes in gene expression profiling. We demonstrated that, when applied in the context of standard microarray processing procedures, globin reduction results in a consistent and significant increase in the quality of beadarray data. When compared to their pre-globin reduction counterparts, post-globin reduction samples show improved detection statistics, lowered variance and increased sensitivity. More importantly, gender gene separation is remarkably clearer in post-globin reduction samples than in pre-globin reduction samples. Our study suggests that the poor data obtained from pre-globin reduction samples is the result of the high concentration of hemoglobin derived from red blood cells either interfering with target mRNA binding or giving the pseudo binding background signal., Conclusion: We therefore recommend the combination of performing globin mRNA reduction in peripheral whole blood samples and hybridizing on Illumina BeadChips as the practical approach for biomarker study.

Sapje-like (sap(cl100)) was one of eight potential zebrafish muscle mutants isolated as part of an early-pressure screen of 500 families. This mutant shows a muscle tearing phenotype similar to sapje (dys-/-) and both mutants fail to genetically complement suggesting they have a mutation in the same gene. Protein analysis confirms a lack of dystrophin in developing sapje-like embryos. Sequence analysis of the sapje-like dystrophin mRNA shows that exon 62 is missing in the dystrophin transcript causing exon 63 to be translated out of frame terminating translation at a premature stop codon at the end of exon 63. Sequence analysis of sapje-like genomic DNA identified a mutation in the donor splice junction at the end of dystrophin exon 62. This mutation is similar to splicing mutations associated with human forms of Duchenne Muscular Dystrophy. Sapje-like is the first zebrafish dystrophin splicing mutant identified to date and represents a novel disease model which can be used in future studies to identify therapeutic compounds for treating diseases caused by splicing defects.

Biosimulation models have the potential to improve our understanding of biological function, and ultimately could support disease diagnosis and prioritize treatment options. Here we identify characterizing features of computational models and a subset of considerations that may be taken into account when determining the appropriate simulation platform to support research needs.

Despite the growing understanding of pdgf signaling, studies of pdgf function have encountered two major obstacles: the functional redundancy of PDGFRalpha and PDGFRbeta in vitro and their distinct roles in vivo. Here we used wild-type mouse embryonic fibroblasts (MEF), MEF null for either PDGFRalpha, beta, or both to dissect PDGF-PDGFR signaling pathways. These four PDGFR genetically defined cells provided us a platform to study the relative contributions of the pathways triggered by the two PDGF receptors. They were treated with PDGF-BB and analyzed for differential gene expression, in vitro proliferation and differential response to pharmacological effects. No genes were differentially expressed in the double null cells, suggesting minimal receptor-independent signaling. Protean differentiation and proliferation pathways are commonly regulated by PDGFRalpha, PDGFRbeta and PDGFRalpha/beta while each receptor is also responsible for regulating unique signaling pathways. Furthermore, some signaling is solely modulated through heterodimeric PDGFRalpha/beta.

It is believed that 3.2 billion bp of the human genome harbor approximately 35000 protein-coding genes. On average, one could expect one gene per 300000 nucleotides (nt). Although the distribution of the genes in the human genome is not random,it is rather surprising that a large number of genes overlap in the mammalian genomes. Thousands of overlapping genes were recently identified in the human and mouse genomes. However,the origin and evolution of overlapping genes are still unknown. We identified 1316 pairs of overlapping genes in humans and mice and studied their evolutionary patterns. It appears that these genes do not demonstrate greater than usual conservation. Studies of the gene structure and overlap pattern showed that only a small fraction of analyzed genes preserved exactly the same pattern in both organisms.

Here we report the identification of a novel transcript containing SNF2, PHD-finger, RING-finger, helicase, and linker histone domains mapping to the q24 band region of human chromosome 6. These domains are characteristic of several DNA repair proteins, transcription factors, and helicases. We have cloned both human and mouse homologs of this novel gene using interexon PCR and RACE technologies. The human cDNA, termed SHPRH, is 6018 bp and codes for a putative protein of 1683 amino acids. The mouse cDNA, termed Shprh, is 7225 bp and codes for a putative protein of 1616 amino acids. The deduced amino acid sequences of the two proteins share 86% identity. Both genes are expressed ubiquitously, with a transcript size of approximately 7.5 kb. Mapping of this gene to 6q24, a region reported to contain a tumor suppressor locus, prompted us to evaluate SHPRH by mutation analysis in tumor cell lines. We have identified one truncating and three missense mutations, thus suggesting SHPRH as a possible candidate for the tumor suppressor gene.