Motivation: Fabrication of synthetic biological systems is greatly enhanced by incorporating engineering design principles and techniques such as computer-aided design. To this end, the ongoing ...standardization of biological parts presents an opportunity to develop libraries of standard virtual parts in the form of mathematical models that can be combined to inform system design. Results: We present an online Repository, populated with a collection of standardized models that can readily be recombined to model different biological systems using the inherent modularity support of the CellML 1.1 model exchange format. The applicability of this approach is demonstrated by modeling gold-medal winning iGEM machines. Availability and Implementation: The Repository is available online as part of http://models.cellml.org. We hope to stimulate the worldwide community to reuse and extend the models therein, and contribute to the Repository of Standard Virtual Parts thus founded. Systems Model architecture information for the Systems Model described here, along with an additional example and a tutorial, is also available as Supplementary information. The example Systems Model from this manuscript can be found at http://models.cellml.org/workspace/bugbuster. The Template models used in the example can be found at http://models.cellml.org/workspace/SVP_Templates200906. Contact: m.cooling@auckland.ac.nz Supplementary information: Supplementary data are available at Bioinformatics online.
Clostridium difficile is a Gram-positive, spore-forming bacterium that continues to present a worldwide problem in healthcare settings. The bacterium causes disease, the symptoms of which include ...diarrhea, fever, nausea, abdominal pain and even death. Despite the prevalence of the disease, the diagnosis of C. difficile infection is still challenging, with a variety of methods available, each varying in their effectiveness. In this work we sought to identify a new biomarker for C. difficile, develop affinity reagents and design a diagnostic assay for C. difficile infection which could be used in a typical two-step testing algorithm. Initially a bioinformatics pipeline was developed that identified a surface associated biomarker “AKDGSTKEDQLVDALA” present in all C. difficile strains sequenced to-date and unique to the C. difficile species. Monoclonal antibodies were subsequently raised against peptides corresponding to the biomarker sequence. During characterization studies, monoclonal antibody 521 (mAb521) was shown to bind all known C. difficile surface layer types, but not closely related strains. Surface plasmon resonance measurements were used to calculate an apparent equilibrium dissociation constant of 36.5 nM between the purified protein target containing the biomarker (surface layer protein A) and mAb521. We demonstrate a limit of detection of 12.4 ng/mL against surface layer protein A and 1.7 × 106 cells/mL in minimally processed C. difficile cultures. The utility of this computational approach to antibody design for diagnostic tests is the ability to produce antibodies that can act as universal species identifiers while mitigating the likelihood of false-positive detection by intelligently screening potential biomarkers against RefSeq data for other nontarget bacteria.
In Saccharomyces cerevisiae, Cdc13 binds telomeric DNA to recruit telomerase and to "cap" chromosome ends. In temperature-sensitive cdc13-1 mutants telomeric DNA is degraded and cell-cycle ...progression is inhibited. To identify novel proteins and pathways that cap telomeres, or that respond to uncapped telomeres, we combined cdc13-1 with the yeast gene deletion collection and used high-throughput spot-test assays to measure growth. We identified 369 gene deletions, in eight different phenotypic classes, that reproducibly demonstrated subtle genetic interactions with the cdc13-1 mutation. As expected, we identified DNA damage checkpoint, nonsense-mediated decay and telomerase components in our screen. However, we also identified genes affecting casein kinase II activity, cell polarity, mRNA degradation, mitochondrial function, phosphate transport, iron transport, protein degradation, and other functions. We also identified a number of genes of previously unknown function that we term RTC, for restriction of telomere capping, or MTC, for maintenance of telomere capping. It seems likely that many of the newly identified pathways/processes that affect growth of budding yeast cdc13-1 mutants will play evolutionarily conserved roles at telomeres. The high-throughput spot-testing approach that we describe is generally applicable and could aid in understanding other aspects of eukaryotic cell biology.
The spread of drug resistance amongst clinically-important bacteria is a serious, and growing, problem 1. However, the analysis of entire genomes requires considerable computational effort, usually ...including the assembly of the genome and subsequent identification of genes known to be important in pathology. An alternative approach is to use computational algorithms to identify genomic differences between pathogenic and non-pathogenic bacteria, even without knowing the biological meaning of those differences. To overcome this problem, a range of techniques for dimensionality reduction have been developed. One such approach is known as latent-variable models 2. In latent-variable models dimensionality reduction is achieved by representing a high-dimensional data by a few hidden or latent variables, which are not directly observed but inferred from the observed variables present in the model. Probabilistic Latent Semantic Indexing (PLSA) is an extention of LSA 3. PLSA is based on a mixture decomposition derived from a latent class model. The main objective of the algorithm, as in LSA, is to represent high-dimensional co-occurrence information in a lower-dimensional way in order to discover the hidden semantic structure of the data using a probabilistic framework.
In this work we applied the PLSA approach to analyse the common genomic features in methicillin resistant Staphylococcus aureus, using tokens derived from amino acid sequences rather than DNA. We characterised genome-scale amino acid sequences in terms of their components, and then investigated the relationships between genomes and tokens and the phenotypes they generated. As a control we used the non-pathogenic model Gram-positive bacterium Bacillus subtilis.
Motivation: In silico experiments in bioinformatics involve the co-ordinated use of computational tools and information repositories. A growing number of these resources are being made available with ...programmatic access in the form of Web services. Bioinformatics scientists will need to orchestrate these Web services in workflows as part of their analyses. Results: The Taverna project has developed a tool for the composition and enactment of bioinformatics workflows for the life sciences community. The tool includes a workbench application which provides a graphical user interface for the composition of workflows. These workflows are written in a new language called the simple conceptual unified flow language (Scufl), where by each step within a workflow represents one atomic task. Two examples are used to illustrate the ease by which in silico experiments can be represented as Scufl workflows using the workbench application. Availability: The Taverna workflow system is available as open source and can be downloaded with example Scufl workflows from http://taverna.sourceforge.net
SARGE is a tool for creating, visualizing and manipulating a putative genetic network from time series microarray data. The tool assigns potential edges through time-lagged correlation, incorporates ...a clustering mechanism, an interactive visual graph representation and employs simulated annealing for network optimization. Availability: The application is available as a .jar file from http://www.bioinformatics.cs.ncl.ac.uk/sarge/index.html
Large amounts of detailed biological data have been generated over the past few decades. Much of these data is freely available in over 1000 online databases; an enticing, but frustrating resource ...for microbiologists interested in a systems-level view of the structure and function of microbial cells. The frustration engendered by the need to trawl manually through hundreds of databases in order to accumulate information about a gene, protein, pathway, or organism of interest can be alleviated by the use of computational data integration to generated network views of the system of interest. Biological networks can be constructed from a single type of data, such as protein-protein binding information, or from data generated by multiple experimental approaches. In an integrated network, nodes usually represent genes or gene products, while edges represent some form of interaction between the nodes. Edges between nodes may be weighted to represent the probability that the edge exists in vivo. Networks may also be enriched with ontological annotations, facilitating both visual browsing and computational analysis via web service interfaces. In this review, we describe the construction, analysis of both single-data source and integrated networks, and their application to the inference of protein function in microbes.
It is well known that microbes have an intricate role in human health and disease. However, targeted strategies for modulating human health through the modification of either human-associated ...microbial communities or associated human-host targets have yet to be realized. New knowledge about the role of microbial communities in the microbiota of the gastrointestinal tract (GIT) and their collective genomes, the GIT microbiome, in chronic diseases opens new opportunities for therapeutic interventions. GIT microbiota participation in drug metabolism is a further pharmaceutical consideration. In this review, we discuss how computational methods could lead to a systems-level understanding of the global physiology of the host-microbiota superorganism in health and disease. Such knowledge will provide a platform for the identification and development of new therapeutic strategies for chronic diseases possibly involving microbial as well as human-host targets that improve upon existing probiotics, prebiotics or antibiotics. In addition, integrative bioinformatics analysis will further our understanding of the microbial biotransformation of exogenous compounds or xenobiotics, which could lead to safer and more efficacious drugs.
The spread of drug resistance amongst clinically-important bacteria is a serious, and growing, problem 1. However, the analysis of entire genomes requires considerable computational effort, usually ...including the assembly of the genome and subsequent identification of genes known to be important in pathology. An alternative approach is to use computational algorithms to identify genomic differences between pathogenic and non-pathogenic bacteria, even without knowing the biological meaning of those differences. To overcome this problem, a range of techniques for dimensionality reduction have been developed. One such approach is known as latent-variable models 2. In latent-variable models dimensionality reduction is achieved by representing a high-dimensional data by a few hidden or latent variables, which are not directly observed but inferred from the observed variables present in the model. Probabilistic Latent Semantic Indexing (PLSA) is an extention of LSA 3. PLSA is based on a mixture decomposition derived from a latent class model. The main objective of the algorithm, as in LSA, is to represent high-dimensional co-occurrence information in a lower-dimensional way in order to discover the hidden semantic structure of the data using a probabilistic framework.
In this work we applied the PLSA approach to analyse the common genomic features in methicillin resistant Staphylococcus aureus, using tokens derived from amino acid sequences rather than DNA. We characterised genome-scale amino acid sequences in terms of their components, and then investigated the relationships between genomes and tokens and the phenotypes they generated. As a control we used the non-pathogenic model Gram-positive bacterium Bacillus subtilis.
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