Integrative computational biology

Jan Baumbach

Jan Baumbach studied Applied Computer Science in the Natural Sciences at Bielefeld University in Germany. His research career started at Rothamsted Research in Harpenden (UK) where he worked on computational methods for the integration of molecular biology data. He returned to the Center for Biotechnology in Bielefeld for his PhD studies on the reconstruction of bacterial transcriptional regulatory networks. He developed CoryneRegNet, the reference database and analysis platform for corynebacterial gene regulations. Afterwards, at the University of California at Berkeley, he worked in the Algorithms group of Richard Karp on protein homology detection. In Berkeley, he also developed Transitivity Clustering, a novel clustering framework for large-scale biomedical data sets. Since 2010, Jan has been head of the Computational Systems Biology group at the Max Planck Institute for Informatics and the Cluster of Excellence for Multimodal Computing and Interaction at Saarland University in Saarbrücken, Germany. His current research concentrates on the combined analysis of biological networks together with OMICS data, the modeling of genetic expression pathways as well as the efficient clustering of large-scale data sets.

This issue of Integrative Biology is dedicated to several research areas of computational biology, commonly called bioinformatics. Bioinformatics is a cross-sectional computer science research area driven by the necessity of analyzing huge, often noisy, biomedical data sets. Nowadays, computer-aided studies for unravelling molecular mechanisms with physiological relevance are no longer exceptional; they have become commonplace. Bioinformatics approaches are ideally utilized iteratively: Drawing conclusions from wet-lab data by utilizing integrated software approaches alternates with the prediction of new wet-lab targets, and the design of new experiments. In order to understand how cells survive, reproduce, grow, form organs and alter their behavior in accordance to changing environmental conditions, biological data must be combined appropriately to form a comprehensive picture. This is integrative computational biology.

The following small, but diverse, selection of articles from different bioinformatics areas describe integrated computational studies that I believe to be interesting, informative and educating to the reader.

The first step of data analysis is data collection, pre-processing and integration. James et al. (DOI:10.1039/c2ib00123c) studied the impact of noise and data quality of manually curated databases to the accuracy of integrated analyses. Thereby they highlight a need for biological data management systems that aid this task. Vu et al. (DOI:10.1039/c2ib00146b) introduce such a wet-lab information management software, specialized for efficient analysis of DNA barcodes. Köster et al. (DOI:10.1039/c2ib00126h) describe a text mining approach that helps with the task of integrating protein–protein interaction networks from scientific papers. Another way of finding such interactions is presented by Memišević et al. (DOI:10.1039/c2ib00140c): The C-GRAAL algorithm compares any kind of biological networks for identifying patterns of pathogen interactions with host proteins, solely from network topology, i.e. without node alignment. Daminelli et al. (DOI: 10.1039/c2ib00154c) find connections between drugs and their molecular biological targets by using a bi-clique completion approach. Pauling et al. (DOI:10.1039/c2ib00132b) concentrate on different molecular networks. The authors use computational methods for identifying evolutionarily conserved genetic switches for genes responsible for pathogenicity and virulence. They provide a reference database and analysis platform for transcriptional gene regulatory networks of EHEC and EAEC bacteria, close relatives of the harmless wet-lab strain E. coli K-12. Given such networks, whether reconstructed from wet-lab data or through computational predictions, we now may aim for case-specific, combined analyses of these networks together with OMICS data, i.e. transcriptomics, metabolomics, proteomics or epigenomics assays. With KeyPathwayMiner, Alcaraz et al. (DOI: 10.1039/c2ib00133k) attack this computationally intense problem by simulating artificial ants that contribute with their swarm intelligence to extracting dysregulated sub-networks specific for Huntington’s disease, as well as colon cancer. A similar enrichment study was performed by Wittkop et al. (DOI: 10.1039/c2ib00136e). Pinto et al. (DOI:10.1039/c2ib00092j) analyze the core stimulon of Corynebacterium pseudotuberculosis in accordance with known transcriptional regulatory interactions. TrAnsFuSe is the biologically specialized strategy of Harel et al. (DOI:10.1039/c2ib00131d) for improved identification of oxidoreductases.

Whether specialized or general, rather applied or methological, we ensure the typical 3i-criteria for publication in Integrative Biology (Insight, Innovation, Integration) through a rigorous selection and reviewing process. Thus, I sincerely believe that you will enjoy reading this themed issue.

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