Jan
Baumbach
*a and
Richard
Röttger
b
aComputational Biology group, Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark. E-mail: jan.baumbach@imada.sdu.dk
bPractical Computer Science in BioMedicine, Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
The following small but diverse selection of articles from different bioinformatics areas describe integrated computational studies that we believe to be interesting, informative and educational for the reader.
An ongoing trend in computational biology is the enrichment of biological networks and/or models with additional biological datasets and a priori knowledge. This enhances the quality of the underlying models and allows for investigating available biological datasets together in a broader and more significant context.
Bender et al. (DOI: 10.1039/C4IB00175C) combine sequence and structure data in order to determine the relationship and the bioactivity of protease inhibitors against the Serine Protease family, using proteochemometric modelling. Sinha et al. (DOI: 10.1039/C4IB00124A) present a method for modeling the important Wnt signaling pathway by employing static Bayesian networks, which allow for integrating of prior biological knowledge, like epigenetic information and inter/intra-cellular factors. In the work of Sarajlić et al. (DOI: 10.1039/C4IB00125G), the connection of the disease–disease interaction of diabetes and aneurysm is investigated by combining different disease-related pathways and genetic information, in order to identify the most relevant proteins in these pathways. Another proficient way of combining different biological datasets is presented in the work of Pauling et al. (DOI: 10.1039/C4IB00137K). The authors integrate multiple OMICs datasets with hybrid interactome networks and identified dysregulated functional network modules with high breast cancer specificity. An even broader approach is taken by Pržulj et al. (DOI: 10.1039/C4IB00122B); the authors combine a multitude of different datasets into an “integrated disease network”. This approach allows for gaining knowledge on molecular mechanisms driving diseases and shared between diseases by a combined analysis of transcriptomic, proteomic, metabolomic and genomic data.
All presented analyses ultimately depend on the quality of the underlying networks and of the enrichment datasets. Azevedo et al. (DOI: 10.1039/C4IB00136B) analyze the ability of different BLAST+ metrics in order to predict protein–protein interactions. The work of Poongavanam et al. (DOI: 10.1039/C4IB00111G) deals with the problem of how the selection of the initial ligand structures influences the ranking of the binding affinity of the different compounds using a set of HIV-1 RNase H inhibitors.
Another way of benefiting from computational power and biological data is presented in a second contribution of Azevedo et al. (DOI: 10.1039/C4IB00140K). The authors employed computer simulations in order to predict the efficiency of potential new drugs, targeted against the two-signal transduction system in the multi-resistent bacteria C. pseudotuberculosis.
Through a rigorous paper selection and review process, we also ensured the usual 3-I criteria for publication in Royal Society of Chemistry's Integrative Biology: Insight, Innovation, and Integration. Sincerely, we believe that you will enjoy reading this themed issue on Computational Integrative Biology.
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