Fungi Use Efficient Algorithms for the Exploration of Microfluidic Networks

Authors

  • Kristi L. Hanson,

    1. BioNanoEngineering Labs, Faculty of Engineering and Industrial Science, Swinburne University of Technology, John Street, Hawthorn, Victoria 3122, Australia
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  • Dan V. Nicolau Jr.,

    1. Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX1 3LB, UK
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  • Luisa Filipponi,

    1. BioNanoEngineering Labs, Faculty of Engineering and Industrial Science, Swinburne University of Technology, John Street, Hawthorn, Victoria 3122, Australia
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  • Lisen Wang,

    1. Department of Biomedical Engineering, University of California, Irvine, CA 92697-2715, USA
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  • Abraham P. Lee Prof.,

    1. Department of Biomedical Engineering, University of California, Irvine, CA 92697-2715, USA
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  • Dan V. Nicolau Prof.

    1. BioNanoEngineering Labs, Faculty of Engineering and Industrial Science, Swinburne University of Technology, John Street, Hawthorn, Victoria 3122, Australia
    2. Present address: Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, Liverpool, L69 3GJ, UK. Fax: (+44) 151-794-4540
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  • Authors’ contributions: D.V.N. conceived the experiments and designed the test microstructures; K.L.H. and L.F. jointly carried out experiments; D.V.N. Jr. performed the modeling and simulation; L.W. and A.P.L. fabricated the masters for poly(dimethylsiloxane) microstructures; L.F. compiled growth statistics; K.L.H., D.V.N., L.F. and D.V.N., Jr. analyzed and interpreted the data and wrote the paper.

Abstract

Fungi, in particular, basidiomycetous fungi, are very successful in colonizing microconfined mazelike networks (for example, soil, wood, leaf litter, plant and animal tissues), a fact suggesting that they may be efficient solving agents of geometrical problems. We therefore evaluated the growth behavior and optimality of fungal space-searching algorithms in microfluidic mazes and networks. First, we found that fungal growth behavior was indeed strongly modulated by the geometry of microconfinement. Second, the fungus used a complex growth and space-searching strategy comprising two algorithmic subsets: 1) long-range directional memory of individual hyphae and 2) inducement of branching by physical obstruction. Third, stochastic simulations using experimentally measured parameters showed that this strategy maximizes both survival and biomass homogeneity in microconfined networks and produces optimal results only when both algorithms are synergistically used. This study suggests that even simple microorganisms have developed adequate strategies to solve nontrivial geometrical problems.

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