Computational immunology: The coming of age

The explosive growth in biotechnology combined with major advancesin information technology has the potential to radically transformimmunology in the postgenomics era. Not only do we now have readyaccess to vast quantities of existing data, but new data with relevanceto immunology are being accumulated at an exponential rate. Resourcesfor computational immunology include biological databases and methodsfor data extraction, comparison, analysis and interpretation. Publiclyaccessible biological databases of relevance to immunologists numberin the hundreds and are growing daily. The ability to efficientlyextract and analyse information from these databases is vital forefficient immunology research. Most importantly, a new generationof computational immunology tools enables modelling of peptide transportby the transporter associated with antigen processing (TAP), modellingof antibody binding sites, identification of allergenic motifs andmodelling of T-cell receptor serial triggering.

Identification of a Golgi-localised GRIP domain protein from Arabidopsis thaliana

A family of Golgi-localised molecules was recently described in animals and fungi possessing extensive coiled regions and a short (similar to40 residues) conserved C-terminal domain, called the GRIP domain, which is responsible for their location to this organelle. Using the model plant Arabidopsis thaliana, we identified a gene (AtGRIP) encoding a putative GRIP protein. We demonstrated that the C-terminal domain from AtGRIP functions as a Golgi-targeting sequence in plant cells. Localisation studies in living cells expressing the AtGRIP fused to a DsRed2 fluorescent probe, showed extensive co-location with the Golgi marker alpha-mannosidase I in transformed tobacco protoplasts. GRIP-like sequences were also found in genomic databases of rice, maize, wheat and alfalfa, suggesting that this domain may be a useful Golgi marker for immunolocalisation studies. Despite low sequence identity amongst GRIP domains, the plant GRIP sequence was able to target to the Golgi of mammalian cells. Taken together, these data indicate that GRIP domain proteins might be implicated in a targeting mechanism that is conserved amongst eukaryotes.

The development of research supervision: 'Turning the light on private space'

Educational development for research supervisors is still a recent phenomenon. Early optional sessions on research supervision have now been replaced, particularly in the UK, continental Europe, and Australasia, by comprehensive and, in some cases, mandatory programs. Yet some of these programs focus solely on the administrative roles and responsibilities of supervisors, attempting to provide technical “fixes” that deny the genuine difficulties and complexities involved in supervision relationships. Some research supervisors resent the intrusion of educational developers into what many of them have regarded as a private pedagogical space. They interpret such programs as further instances of the quality assurance agendas of governments and university administrators, and are justifiably suspicious of what some describe as the colonial underpinnings of educational development. These reactions create tensions for educational developers. This article explores why educational development can be problematic for research supervisors. It then charts some current supervision educational development programs that seek to go beyond administrative interpretations of supervision. Finally, it examines whether the “Compassionate Rigour” supervision program, developed to address these difficulties, manages to respond respectfully and sensitively to supervisors’ educational development needs.

Leximancer concept mapping of patient case studies

Quantitative databases are limited to information identified as important by their creators, while databases containing natural language are limited by our ability to analyze large unstructured bodies of text. Leximancer is a tool that uses semantic mapping to develop concept maps from natural language. We have applied Leximancer to educational based pathology case notes to demonstrate how real patient records or databases of case studies could be analyzed to identify unique relationships. We then discuss how such analysis could be used to conduct quantitative analysis from databases such as the Coronary Heart Disease Database.