Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations.

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.

Case study: data management strategies in an integrated pathway tool.

This paper describes the development strategies for an integrated tool to support scientists in the creative exploration of data relating to biochemical pathways. The multiple user groups, diverse functionalities, and many types and sources of data demanded a flexible yet coherent approach. This paper summarises the software requirements and the implied modules and functions, and focuses on the design decisions relevant to the representation, management and flow of data. Finally, several case studies in the use of the software are described and evaluated, and recommendations are made for future work.

Elucidation of an archaeal replication protein network to generate enhanced PCR enzymes.

Thermostable DNA polymerases are an important tool in molecular biology. To exploit the archaeal repertoire of proteins involved in DNA replication for use in PCR, we elucidated the network of proteins implicated in this process in Archaeoglobus fulgidus. To this end, we performed extensive yeast two-hybrid screens using putative archaeal replication factors as starting points. This approach yielded a protein network involving 30 proteins potentially implicated in archaeal DNA replication including several novel factors. Based on these results, we were able to improve PCR reactions catalyzed by archaeal DNA polymerases by supplementing the reaction with predicted polymerase co-factors. In this approach we concentrated on the archaeal proliferating cell nuclear antigen (PCNA) homologue. This protein is known to encircle DNA as a ring in eukaryotes, tethering other proteins to DNA. Indeed, addition of A. fulgidus PCNA resulted in marked stimulation of PCR product generation. The PCNA-binding domain was determined, and a hybrid DNA polymerase was constructed by grafting this domain onto the classical PCR enzyme from Thermus aquaticus, Taq DNA polymerase. Addition of PCNA to PCR reactions catalyzed by the fusion protein greatly stimulated product generation, most likely by tethering the enzyme to DNA. This sliding clamp-induced increase of PCR performance implies a promising novel micromechanical principle for the development of PCR enzymes with enhanced processivity.