Despite broad scientific interest in harnessing the power of Earth’s microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design–build–test–learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.
Bibliographical noteFunding Information:
The authors acknowledge the College of Engineering at the University of Wisconsin-Madison, which provided financial support for a workshop during the Madison Microbiome Meeting on 27 April 2018, which all authors attended and at which all authors participated in discussions that led to the creation of this article. C.E.L. is supported by a Postgraduate Scholarships–Doctoral award from the National Sciences and Engineering Research Council of Canada and a Wisconsin Distinguished Graduate Fellowship. K.D.M. and D.R.N. acknowledge support from the National Science Foundation (CBET-1803055 and MCB-1518130) and the University of Wisconsin-Madison Wisconsin Alumni Research Foundation via the Microbiome Initiative. D.R.N. and B.F.P. acknowledge support from US Department of Energy (DOE) Great Lakes Bioenergy Research Center grants (DOE Office of Science BER DE-SC0018409). B.F.P. acknowledges support from the National Science Foundation (CBET-1703504 and MCB-1716594). M.A.O. and H.G.-M. are funded by the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the US DOE, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley Laboratory and the US DOE. H.G.-M. is also funded by the DOE Agile BioFoundry (http://agilebio-foundry.org), supported by the US DOE, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, through contract DE-AC02-05CH11231. H.G.-M. is also supported by the Basque Government through the Basque Center for Applied Mathematics 2018–2021 programme and by the Spanish Ministry of Economy and Competitiveness (MINECO) through BCAM Severo Ochoa excellence accreditation SEV-2017-071. F.E.L. acknowledges support by the US Department of Defense’s Strategic Environmental Research and Development Program and the Governor’s Chair programme through the University of Tennessee and Oak Ridge National Laboratory. D.G.W. acknowledges the support offered by a mobility fellowship of the Swiss National Science Foundation (Chemical Engineering Division, grant 151977), start-up fund of the Department of Biotechnology of the TU Delft, research grant of the Netherlands Organisation for Scientific Research (NWO, Applied and Engineering Sciences Division, project 15812), talent grants of the Soehngen Institute of Anaerobic Microbiology (SIAM, www.anaerobic-microbiology.eu) research program, and European Commission Horizon 2020 (Research and Innovation Action Saraswati 2.0, and Twinning Project REPARES). F.E.L. acknowledges support by the US Department of Defense’s Strategic Environmental Research and Development Program, the National Science Foundation (Dimensions DEB1831599), and the Governor’s Chair programme through the University of Tennessee and Oak Ridge National Laboratory. R.H. acknowledges support from the Gordon and Betty Moore Foundation (award GBMF5999) and the National Science Foundation (RII Track-2 FEC award 1736255).
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