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Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions

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Abstract

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.

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Abbreviations

EDTA:

Ethylenediaminetetraacetic acid

GA:

Genetic algorithm

LB:

Luria-Bertani

OD:

Optical density

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Acknowledgements

We are grateful to Cornelia Sawatzky for critical revision of the manuscript. This research was supported by a fellowship from the Inland Northwest Research Alliance (INRA) Subsurface Science Research Institute, which is funded by the Department of Energy under contract DE-FG07-02ID14277.

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Authors and Affiliations

  1. Department of Biological and Agricultural Engineering, University of Idaho, P.O. Box 440904, Moscow, ID, 83844-0904, USA

    Frederik P. J. Vandecasteele & Thomas F. Hess

  2. Environmental Biotechnology Institute, University of Idaho, Moscow, ID, 83844-1052, USA

    Frederik P. J. Vandecasteele & Ronald L. Crawford

Authors
  1. Frederik P. J. Vandecasteele
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  2. Thomas F. Hess
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  3. Ronald L. Crawford
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Correspondence to Thomas F. Hess.

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Vandecasteele, F.P.J., Hess, T.F. & Crawford, R.L. Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions. Antonie van Leeuwenhoek 92, 83–93 (2007). https://doi.org/10.1007/s10482-006-9138-y

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  • DOI: https://doi.org/10.1007/s10482-006-9138-y

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