Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

2010 ◽  
Vol 7 (3) ◽  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

2012 ◽  
Hemanth Tummala ◽  
Hilal S. Khalil ◽  
Katarzyna Goszcz ◽  
Maria Grazia Tupone ◽  
Vili Stoyanova ◽  

2013 ◽  
Vol 40 (6Part33) ◽  
pp. 552-552
F Guan ◽  
R Mohan ◽  
J Dinh ◽  
M Kerr ◽  
L Perles ◽  

2019 ◽  
Vol 218 ◽  
pp. 481-504 ◽  
Caroline Gauchotte-Lindsay ◽  
Thomas J. Aspray ◽  
Mara Knapp ◽  
Umer Z. Ijaz

We present here a data-driven systems biology framework for the rational design of biotechnological solutions for contaminated environments with the aim of understanding the interactions and mechanisms underpinning the role of microbial communities in the biodegradation of contaminated soils.

2019 ◽  
Vol 13 (3) ◽  
pp. 272
Michael F. Keating ◽  
Tom Q. Vallim ◽  
Ben L. Parker ◽  
Marcus M. Seldin ◽  
Elizabeth J. Tarling ◽  

2011 ◽  
Vol 11 (1) ◽  
Sara Gracie ◽  
Craig Pennell ◽  
Gunvor Ekman-Ordeberg ◽  
Stephen Lye ◽  

2003 ◽  
Vol 79 (12) ◽  
pp. 717-725 ◽  
D. Faller ◽  
U. Klingmüller ◽  
J. Timmer

2021 ◽  
John Lagergren ◽  
Mikaela Cashman ◽  
Verónica G. Melesse Vergara ◽  
Paul R. Eller ◽  
Joao Gabriel Felipe Machado Gazolla ◽  

AbstractPredicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution. The results are captured as networks, in which edges between geolocations are defined if their historical climates exceed a similarity threshold. We then apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37 × 1018 operations/sec) and one of the largest (168.7 × 1021 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Correlation and network analysis methods of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.

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