TranSYS ESR 05 Andrew Walakira, at the University of Ljubljana, announces latest publication on “Guided extraction of genome-scale metabolic models for the integration and analysis of omics data” published in Computational and Structural Biotechnology Journal
The advent of high-throughput technologies has allowed us to generate large volumes of different types of omics data. This has made possible to study organisms at cellular level. However, we know that life is sustained by a network of reactions that define a biological system. This implies that studying biological processes at systems level yields a better understanding of the underlying mechanisms. Studying biological systems in vivo is often very complicated for lower organisms, and even impossible for higher organisms like humans. This is due to the high costs involved and also for ethical reasons. In silico computational models, in the form of genome scale metabolic models (GEMs), have been developed to fill this void. Here, omics data is integrated in a reference GEM to yield context specific models which can then be used to study a specific condition at systems level. However, the nature and capability of a context specific model is greatly determined by the choice of the data integration algorithm, also known as model extraction method (MEM). In this work, we introduce a protocol for the efficient extraction of context specific GEMs in a reproducible way. Furthermore, we describe approaches that can be used to analyse the results obtained with the selected MEM, and to put these results in a biological context.
This work was the effort of scientists from the Center for Genomics and Bio Chips, Faculty of Medicine, University of Ljubljana and the Faculty of Computer and Information Science, University of Ljubljana. This work was funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 860895.
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Author: Andrew Walakia
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