A computational Grid framework for immunological applications

Within-host demographic fluctuations and correlations in early retroviral infection

In this paper we analyze the demographic fluctuations and correlations present in within-host populations of viruses and their target cells during the early stages of infection. In particular, we present an exact treatment of a discrete-population, stochastic, continuous-time master equation description of HIV or similar retroviral infection dynamics, employing Monte Carlo simulations. The results of calculations employing Gillespie's direct method clearly demonstrate the importance of considering the microscopic details of the interactions which constitute the macroscopic dynamics. We then employ the τ-leaping approach to study the statistical characteristics of infections involving realistic absolute numbers of within-host viral and cellular populations, before going on to investigate the effect that initial viral population size plays on these characteristics. Our main conclusion is that cross-correlations between infected cell and virion populations alter dramatically over the course of the infection. We suggest that these statistical correlations offer a novel and robust signature for the acute phase of retroviral infection.

On the usability of grid middleware and security mechanisms

Usability is an all too often neglected aspect of grid computing, although it is one of the principal factors militating against the widespread uptake of distributed computing. Many resource providers on a grid infrastructure deploy a standard middleware stack and expect users to invoke the default client tools for that middleware stack to access their resources. Unfortunately, many of these middleware client tools have been developed as an afterthought, and are widely considered difficult to use. Such tools typically require a user to interact with a machine, to stage data and launch jobs, and to use digital certificates. Our experience of working with grids over many years has led us to propose a new model of grid interaction, which we call the user-application interaction model. Similar considerations have also led us to develop environments that remove digital certificates from the user's experience, replacing them with familiar username and password authentication credentials. In this paper, we investigate the usability of this interaction model and its security system through a series of tests, which compare the usability of our systems with commonly deployed middleware tools using five usability metrics. Our middleware and security solutions are judged to be more usable than the systems in use by most of today's computational grids.

A software framework for building biomedical machine learning classifiers through grid computing resources

This paper describes the BiomedTK software framework, created to perform massive explorations of machine learning classifiers configurations for biomedical data analysis over distributed Grid computing resources. BiomedTK integrates ROC analysis throughout the complete classifier construction process and enables explorations of large parameter sweeps for training third party classifiers such as artificial neural networks and support vector machines, offering the capability to harness the vast amount of computing power serviced by Grid infrastructures. In addition, it includes classifiers modified by the authors for ROC optimization and functionality to build ensemble classifiers and manipulate datasets (import/export, extract and transform data, etc.). BiomedTK was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature. The comprehensive method herewith presented represents an improvement to biomedical data analysis in both methodology and potential reach of machine learning based experimentation.

Predicting interaction sites from the energetics of isolated proteins: a new approach to epitope mapping

An increasing number of functional studies of proteins have shown that sequence and structural similarities alone may not be sufficient for reliable prediction of their interaction properties. This is particularly true for proteins recognizing specific antibodies, where the prediction of antibody-binding sites, called epitopes, has proven challenging. The antibody-binding properties of an antigen depend on its structure and related dynamics. Aiming to predict the antibody-binding regions of a protein, we investigate a new approach based on the integrated analysis of the dynamical and energetic properties of antigens, to identify nonoptimized, low-intensity energetic interaction networks in the protein structure isolated in solution. The method is based on the idea that recognition sites may correspond to localized regions with low-intensity energetic couplings with the rest of the protein, which allows them to undergo conformational changes, to be recognized by a binding partner, and to tolerate mutations with minimal energetic expense. Upon analyzing the results on isolated proteins and benchmarking against antibody complexes, it is found that the method successfully identifies binding sites located on the protein surface that are accessible to putative binding partners. The combination of dynamics and energetics can thus discriminate between epitopes and other substructures based only on physical properties. We discuss implications for vaccine design.

FRED--a framework for T-cell epitope detection

Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods.

AVAILABILITY

FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED.