Common principles and best practices for engineering microbiomes

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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.

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Fig. 1: The design–build–test–learn cycle for microbiome engineering.
Fig. 2: Top-down and bottom-up approaches to design microbiomes.
Fig. 3: Building self-assembled and synthetic microbiomes.
Fig. 4: Testing microbiome function.
Fig. 5: Learning fundamental principles for microbiome engineering.


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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 ( 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 (, 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, 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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Correspondence to Christopher E. Lawson or Katherine D. McMahon.

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Microbiome science

Discovery and testing of fundamental principles governing microbiome assembly and function.

Microbiome engineering

Leveraging fundamental scientific principles and quantitative design to create microbiomes that perform desired functions.


An obligately mutualistic process that is mediated by metabolite cross-feeding between two or more organisms that cannot be catalysed by one organism alone.


Sets of emergent functions of a microbiome resulting from the interactions between individual microbial genomes (metagenome) and their interaction with the environment.

Ecological engineering

The process of designing and operating bioreactors and other engineered systems to foster the development of specific microbial communities that can perform desired functions.

Functional guilds

Groups of organisms that use similar resources (for example, electron donors, electron acceptors or carbon source) and occupy a similar ecological niche.

Keystone species

An organism that has a disproportionately large effect on maintaining the microbiome’s function and microbial interactions (both between microorganisms and with the environment).

Flux balance analysis

A constraint-based mathematical modelling technique for simulating metabolic fluxes through a metabolic network reconstructed from genomic information.

Ensemble modelling

Use of multiple models to address uncertainty by simulating a set of possibilities and selecting those consistent with measured data.

Machine learning

A technique used to build predictive models through patterns and inferences obtained from sample data rather than explicit or mechanistic relationships.

Technoeconomic assessment

A tool used to evaluate the technical and economic viability of an integrated process through a combination of process design, modelling and economic evaluation.

Life cycle analysis

A tool used to evaluate the environmental impacts associated with all stages of a product’s life, such as energy and water consumption, and air pollutant and greenhouse gas emissions.

Self-assembled microbiome

A microbiome built through environmental manipulation that selects for desired functions.

Synthetic microbiome

A microbiome built by combining predefined axenic or enrichment cultures to achieve a desired function.

Integrative and conjugative elements

Mobile genetic elements able to integrate into DNA sites via site-specific recombination that carry genes encoding the machinery necessary for conjugation.


An analytical technique to quantify extracellular small-molecule metabolites from environmental and/or biological samples typically through gas/liquid chromatography–mass spectrometry or nuclear magnetic resonance spectroscopy.

Off-gas analysis

The monitoring of gas flow rate and chemical composition (for example, carbon dioxide, hydrogen, methane) produced from a biological system.

Structure–function relationships

The influence of the microbiome’s three-dimensional spatial organization on its function.

Generalized Lotka–Volterra equations

A set of ordinary differential equations used to represent population dynamics based on experimentally inferred species interaction parameters.

Fundamental niche

The entire set of environmental conditions in which an organism can survive and reproduce (that is, an organism’s niche in the absence of interspecific competition).

Realized niche

The set of environmental conditions used by a species after consideration of interspecific competition (competition, predation and other factors).

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