Higher teleost fishes, including zebrafish and fugu, have duplicated their Hox genes relative to the gene inventory of other gnathostome lineages. The most widely accepted theory contends that the duplicate Hox clusters orginated synchronously during a single genome duplication event in the early history of ray-finned fishes. In this contribution we collect and re-evaluate all publicly available sequence information. In particular, we show that the short Hox gene fragments from published PCR surveys of the killifish Fundulus heteroclitus, the medaka Oryzias latipes and the goldfish Carassius auratus can be used to determine with little ambiguity not only their paralog group but also their membership in a particular cluster. Together with a survey of the genomic sequence data from the pufferfish Tetraodon nigroviridis we show that at least percomorpha, and possibly all eutelosts, share a system of 7 or 8 orthologous Hox gene clusters. There is little doubt about the orthology of the two teleost duplicates of the HoxA and HoxB clusters. A careful analysis of both the coding sequence of Hox genes and of conserved non-coding sequences provides additional support for the “duplication early” hypothesis that the Hox clusters in teleosts are derived from eight ancestral clusters by means of subsequent gene loss; the data remain ambiguous, however, in particular for the HoxC clusters. Assuming the “duplication early” hypothesis we use the new evidence on the Hox gene complements to determine the phylogenetic positions of gene-loss events in the wake of the cluster duplication. Surprisingly, we find that the resolution of redundancy seems to be a slow process that is still ongoing. A few suggestions on which additional sequence data would be most informative for resolving the history of the teleostean Hox genes are discussed.

Background: Reliable prediction of RNA–RNA binding energies is crucial, e.g. for the understanding on RNAi, microRNA–mRNA binding and antisense interactions. The thermodynamics of such RNA–RNA interactions can be understood as the sum of two energy contributions: (1) the energy necessary to ‘open’ the binding site and (2) the energy gained from hybridization. Methods: We present an extension of the standard partition function approach to RNA secondary structures that computes the probabilities Pu[i, j] that a sequence interval [i, j] is unpaired. Results: Comparison with experimental data shows that Pu[i, j] can be applied as a significant determinant of local target site accessibility for RNA interference (RNAi). Furthermore, these quantities can be used to rigorously determine binding free energies of short oligomers to large mRNA targets. The resource consumption is comparable with a single partition function computation for the large target molecule. We can show that RNAi efficiency correlates well with the binding energies of siRNAs to their respective mRNA target.

Despite spectacular progress in biophysics, molecular biology and biochemistry our ability to predict the dynamic behavior of multicellular systems under different conditions is very limited. An important reason for this is that still not enough is known about how cells change their physical and biological properties by genetic or metabolic regulation, and which of these changes affect the cell behavior. For this reason, it is difficult to predict the system behavior of multicellular systems in case the cell behavior changes, for example, as a consequence of regulation or differentiation. The rules that underlie the regulation processes have been determined on the time scale of evolution, by selection on the phenotypic level of cells or cell populations. We illustrate by detailed computer simulations in a multi-scale approach how cell behavior controlled by regulatory networks may emerge as a consequence of an evolutionary process, if either the cells, or populations of cells are subject to selection on particular features. We consider two examples, migration strategies of single cells searching a signal source, or aggregation of two or more cells within minimal multiscale models of biological evolution. Both can be found for example in the life cycle of the slime mold Dictyostelium discoideum. However, phenotypic changes that can lead to completely different modes of migration have also been observed in cells of multi-cellular organisms, for example, as a consequence of a specialization in stem cells or the de-differentiation in tumor cells. The regulatory networks are represented by Boolean networks and encoded by binary strings. The latter may be considered as encoding the genetic information (the genotype) and are subject to mutations and crossovers. The cell behavior reflects the phenotype. We find that cells adopt naturally observed migration strategies, controlled by networks that show robustness and redundancy. The model simplicity allow us to unambiguously analyze the regulatory networks and the resulting phenotypes by different measures and by knockouts of regulatory elements. We illustrate that in order to maintain a cells' phenotype in case of a knockout, the cell may have to be able to deal with contradictory information. In summary, both the cell phenotype as well as the emerged regulatory network behave as their biological counterparts observed in nature.

In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and consequently, a vast amount of microarray data sets are produced. Having access to this variety of repositories, researchers would like to incorporate this data in their analyses processes to increase the statistical significance of their results. Such analyses processes are typical examples of data-intensive processes. In general, data-intensive processes are characterized by (i) a sequence of functional operations processing large amount of data and (ii) the transportation and transformation of huge data sets between the functional operations. To support data-intensive processes, an efficient and scalable environment is required, since the performance is a key factor today. The service-oriented architecture (SOA) is beneficial in this area according to process orchestration and execution. However, the current realization of SOA with Web services and BPEL includes some drawbacks with regard to the performance of the data propagation between Web services. Therefore, we present in this paper our data-aware service-oriented approach to efficiently support such data-intensive processes.