Odor space and olfactory processing: Collective algorithms and neural implementation

Several basic olfactory tasks must be solved by highly olfactory animals, including background suppression, multiple object separation, mixture separation, and source identification. The large number N of classes of olfactory receptor cells—hundreds or thousands—permits the use of computational strategies and algorithms that would not be effective in a stimulus space of low dimension. A model of the patterns of olfactory receptor responses, based on the broad distribution of olfactory thresholds, is constructed. Representing one odor from the viewpoint of another then allows a common description of the most important basic problems and shows how to solve them when N is large. One possible biological implementation of these algorithms uses action potential timing and adaptation as the “hardware” features that are responsible for effective neural computation.

Collecting and harvesting biological data: the GPCRDB and NucleaRDB information systems

The amount of genomic and proteomic data that is entered each day into databases and the experimental literature is outstripping the ability of experimental scientists to keep pace. While generic databases derived from automated curation efforts are useful, most biological scientists tend to focus on a class or family of molecules and their biological impact. Consequently, there is a need for molecular class-specific or other specialized databases. Such databases collect and organize data around a single topic or class of molecules. If curated well, such systems are extremely useful as they allow experimental scientists to obtain a large portion of the available data most relevant to their needs from a single source. We are involved in the development of two such databases with substantial pharmacological relevance. These are the GPCRDB and NucleaRDB information systems, which collect and disseminate data related to G protein-coupled receptors and intra-nuclear hormone receptors, respectively. The GPCRDB was a pilot project aimed at building a generic molecular class-specific database capable of dealing with highly heterogeneous data. A first version of the GPCRDB project has been completed and it is routinely used by thousands of scientists. The NucleaRDB was started recently as an application of the concept for the generalization of this technology. The GPCRDB is available via the WWW at http://www.gpcr.org/7tm/ and the NucleaRDB at http://www.receptors.org/NR/.

Complete congruence between gene diversity estimates derived from genotypic data at enzyme and random amplified polymorphic DNA loci in black spruce.

Controversy still exists over the adaptive nature of variation of enzyme loci. In conifers, random amplified polymorphic DNAs (RAPDs) represent a class of marker loci that is unlikely to fall within or be strongly linked to coding DNA. We have compared the genetic diversity in natural populations of black spruce [Picea mariana (Mill.) B.S.P.] using genotypic data at allozyme loci and RAPD loci as well as phenotypic data from inferred RAPD fingerprints. The genotypic data for both allozymes and RAPDs were obtained from at least six haploid megagametophytes for each of 75 sexually mature individuals distributed in five populations. Heterozygosities and population fixation indices were in complete agreement between allozyme loci and RAPD loci. In black spruce, it is more likely that the similar levels of variation detected at both enzyme and RAPD loci are due to such evolutionary forces as migration and the mating system, rather than to balancing selection and overdominance. Furthermore, we show that biased estimates of expected heterozygosity and among-population differentiation are obtained when using allele frequencies derived from dominant RAPD phenotypes.