COMPUTATIONAL APPROACHES TO STUDY MICROBES AND MICROBIOMES

Casey S. Greene1, James A. Foster2, Bruce A. Stanton3, Deborah A. Hogan3, Yana Bromberg4,5


1Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania
2Institute of Bioinformatics and Evolutionary Studies, University of Idaho
3Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth
4Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University
5Institute for Advanced Study, Technische Universität München
Email: csgreene@upenn.edu, foster@uidaho.edu, Bruce.A.Stanton@dartmouth.edu, Deborah.A.Hogan@dartmouth.edu, yana@bromberglab.org

Pacific Symposium on Biocomputing 21:557-565(2016)

© 2016 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.


Abstract

Technological advances are making large-scale measurements of microbial communities commonplace. These newly acquired datasets are allowing researchers to ask and answer questions about the composition of microbial communities, the roles of members in these communities, and how genes and molecular pathways are regulated in individual community members and communities as a whole to effectively respond to diverse and changing environments. In addition to providing a more comprehensive survey of the microbial world, this new information allows for the development of computational approaches to model the processes underlying microbial systems. We anticipate that the field of computational microbiology will continue to grow rapidly in the coming years. In this manuscript we highlight both areas of particular interest in microbiology as well as computational approaches that begin to address these challenges.


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