Tracking the shift to 'Postgenomics'

Current knowledge about the variety and complexity of the processes that allow regulated gene expression in living organisms calls for a new understanding of genes. A 'postgenomic' understanding of genes as entities constituted during genome expression is outlined and illustrated with specific examples that formed part of a survey research instrument developed by two of the authors for an ongoing empirical study of conceptual change in contemporary biology. Copyright (C) 2006 S. Karger AG, Basel.

Behavioural Genetics in the Postgenomics Era

There is growing evidence that the complexity of higher organisms does not correlate with the ‘complexity’ of the genome (the human genome contains fewer protein coding genes than corn, and many genes are preserved across species). Rather, complexity is associated with the complexity of the pathways and processes whereby the cell utilises the deoxyribonucleic acid molecule, and much else, in the process of phenotype formation. These pro- cesses include the activity of the epigenome, noncoding ribonucleic acids, alternative splicing and post-transla- tional modifications. Not accidentally, all of these pro- cesses appear to be of particular importance for the human brain, the most complex organ in nature. Because these processes can be highly environmentally reactive, they are a key to understanding behavioural plasticity and highlight the importance of the developmental process in explaining behavioural outcomes.

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.