Towards a lightweight generic computational grid framework for biological research

Background: An increasing number of scientific research projects require access to large-scale computational resources. This is particularly true in the biological field, whether to facilitate the analysis of large high-throughput data sets, or to perform large numbers of complex simulations – a characteristic of the emerging field of systems biology. Results: In this paper we present a lightweight generic framework for combining disparate computational resources at multiple sites (ranging from local computers and clusters to established national Grid services). A detailed guide describing how to set up the framework is available from the following URL: http://igrid-ext.cryst.bbk.ac.uk/portal_guide/. Conclusion: This approach is particularly (but not exclusively) appropriate for large-scale biology projects with multiple collaborators working at different national or international sites. The framework is relatively easy to set up, hides the complexity of Grid middleware from the user, and provides access to resources through a single, uniform interface. It has been developed as part of the European ImmunoGrid project

canSAR: an integrated cancer public translational research and drug discovery resource

canSAR is a fully integrated cancer research and drug discovery resource developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface at http://cansar.icr.ac.uk provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data

GPCRTree: online hierarchical classification of GPCR function

Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree

PB.23: Effect of detector type on cancer detection in digital mammography

This work measured the effect that image quality associated with different detectors has on cancer detection in mammography using a novel method for changing the appearance of images.\ud \ud A set of 270 mammography cases (one view, both breasts) was acquired using five Hologic Selenias and two Hologic Dimensions X-ray units: 80 normal, 80 with simulated inserted subtle calcification clusters, 80 with subtle real noncalcification malignant lesions and 30 with benign lesions (biopsy proven). These 270 cases (Arm 1) were converted to appear as if they had been acquired on two other imaging systems: needle image plate computed radiography (CR) (Arm 2) and powder phosphor CR (Arm 3). Three experienced mammography readers marked the location of suspected cancers in the images and classified whether each lesion would require further investigation and the confidence in that decision. Performance was calculated as the area under curve (AUC) of the alternative free-response receiver operating characteristic curv

TIMMA-R : an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples

Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logic-based network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drug-target interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications.Peer reviewe

EpiToolKit—a web server for computational immunomics

Predicting the T-cell-mediated immune response is an important task in vaccine design and thus one of the key problems in computational immunomics. Various methods have been developed during the last decade and are available online. We present EpiToolKit, a web server that has been specifically designed to offer a problem-solving environment for computational immunomics. EpiToolKit offers a variety of different prediction methods for major histocompatibility complex class I and II ligands as well as minor histocompatibility antigens. These predictions are embedded in a user-friendly interface allowing refining, editing and constraining the searches conveniently. We illustrate the value of the approach with a set of novel tumor-associated peptides. EpiToolKit is available online at www.epitoolkit.org

Magnetic hydrophobic-charge induction adsorbents for the recovery of immunoglobulins from antiserum feedstocks by high-gradient magnetic fishing

BACKGROUND The extraction of biopharmaceuticals from plasma and serum often employs overly complicated antiquated procedures, that can inflict serious damage on especially prone protein targets and which afford low purification power and overall yields. Here we describe systematic development of a high-gradient magnetic fishing process for recovery of immunoglobulins from unclarified antiserum.RESULTSNon-porous superparamagnetic particles were transformed into hydrophobic-charge induction adsorbents and then used to recover immunoglobulins from rabbit antiserum feedstocks. Comprehensive characterisation tests conducted with variously diluted clarified antiserum on a magnetic rack revealed that immunoglobulin binding was rapid (equilibrium reached in 72% of the immunoglobulin present in an unclarified antiserum feed was recovered in 0.5 h in >3-fold purified form.CONCLUSIONSFast magnetic particle based capture of antibodies from an unclarified high-titre feed has been demonstrated. Efficient product recovery from ultra-high titre bioprocess liquors by high-gradient magnetic fishing requires that improved magnetic adsorbents displaying high selectivity, ultra-high capacity and operational robustness are used with 'state-of-the-art’ rotor-stator magnetic separators

The role of biomarkers in the management of bone-homing malignancies

Bone represents a common site of metastasis from several solid tumours, including breast, prostate and lung malignancies. The onset of bone metastases (BM) is associated not only with serious skeletal complications, but also shortened overall survival, owing to the lack of curative treatment options for late-stage cancer. Despite the diagnostic advances, BM detection often occurs in the symptomatic stage, underlining the need for novel strategies aimed at the early identification of high-risk patients. To this purpose, both bone turnover and tumour-derived markers are being investigated for their potential diagnostic, prognostic and predictive roles. In this review, we summarize the pathogenesis of BM in breast, prostate and lung tumours, while exploring the current research focused on the identification and clinical validation of BM biomarkers

Modeling the competition between lung metastases and the immune system using agents

<p>Abstract</p> <p>Background</p> <p>The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based - ODE model for the simulation of the vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations.</p> <p>Results</p> <p>The model shows that it is possible to obtain "in silico" a 45% reduction in the number of vaccinations. The analysis of the results further suggests that any optimal protocol for preventing lung metastases formation should be composed by an initial massive vaccine dosage followed by few vaccine recalls.</p> <p>Conclusions</p> <p>Such a reduction may represent an important result from the point of view of translational medicine to humans, since a downsizing of the number of vaccinations is usually advisable in order to minimize undesirable effects. The suggested vaccination strategy also represents a notable outcome. Even if this strategy is commonly used for many infectious diseases such as tetanus and hepatitis-B, it can be in fact considered as a relevant result in the field of cancer-vaccines immunotherapy. These results can be then used and verified in future "in vivo" experiments, and their outcome can be used to further improve and refine the model.</p

Evaluating the effectiveness of abbreviated breast MRI (abMRI) interpretation training for mammogram readers: a multi-centre study assessing diagnostic performance, using an enriched dataset

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The dataset generated and analysed during the current study is not yet publicly available because it is currently being developed into a publicly shareable format. Instead, it is available from the corresponding author on reasonable request.BACKGROUND: Abbreviated breast MRI (abMRI) is being introduced in breast screening trials and clinical practice, particularly for women with dense breasts. Upscaling abMRI provision requires the workforce of mammogram readers to learn to effectively interpret abMRI. The purpose of this study was to examine the diagnostic accuracy of mammogram readers to interpret abMRI after a single day of standardised small-group training and to compare diagnostic performance of mammogram readers experienced in full-protocol breast MRI (fpMRI) interpretation (Group 1) with that of those without fpMRI interpretation experience (Group 2). METHODS: Mammogram readers were recruited from six NHS Breast Screening Programme sites. Small-group hands-on workstation training was provided, with subsequent prospective, independent, blinded interpretation of an enriched dataset with known outcome. A simplified form of abMRI (first post-contrast subtracted images (FAST MRI), displayed as maximum-intensity projection (MIP) and subtracted slice stack) was used. Per-breast and per-lesion diagnostic accuracy analysis was undertaken, with comparison across groups, and double-reading simulation of a consecutive screening subset. RESULTS: 37 readers (Group 1: 17, Group 2: 20) completed the reading task of 125 scans (250 breasts) (total = 9250 reads). Overall sensitivity was 86% (95% confidence interval (CI) 84-87%; 1776/2072) and specificity 86% (95%CI 85-86%; 6140/7178). Group 1 showed significantly higher sensitivity (843/952; 89%; 95%CI 86-91%) and higher specificity (2957/3298; 90%; 95%CI 89-91%) than Group 2 (sensitivity = 83%; 95%CI 81-85% (933/1120) p < 0.0001; specificity = 82%; 95%CI 81-83% (3183/3880) p < 0.0001). Inter-reader agreement was higher for Group 1 (kappa = 0.73; 95%CI 0.68-0.79) than for Group 2 (kappa = 0.51; 95%CI 0.45-0.56). Specificity improved for Group 2, from the first 55 cases (81%) to the remaining 70 (83%) (p = 0.02) but not for Group 1 (90-89% p = 0.44), whereas sensitivity remained consistent for both Group 1 (88-89%) and Group 2 (83-84%). CONCLUSIONS: Single-day abMRI interpretation training for mammogram readers achieved an overall diagnostic performance within benchmarks published for fpMRI but was insufficient for diagnostic accuracy of mammogram readers new to breast MRI to match that of experienced fpMRI readers. Novice MRI reader performance improved during the reading task, suggesting that additional training could further narrow this performance gap.National Institute for Health Research (NIHR