Fungi Use Efficient Algorithms for the Exploration of Microfluidic Networks

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.

Parallel computation with molecular-motor-propelled agents in nanofabricated networks

“…Specifically, cytoskeletal filaments can self-replicate as they traverse the network by enzymatic splitting and simultaneous elongation (31,32). Alternatively, self-propelled, dividing microorganisms can be used as agents (33)(34)(35). Thus, the larger the network, the more the agents will multiply, in an exponential fashion.…”

Probing the growth dynamics of Neurospora crassa with microfluidic structures

“…Due to its biocompatibility, PDMS has been used extensively in biological research projects. These include sensor (Park & Shuler 2003) and tissue engineering (Folch & Toner 1998;Desai 2000;Jiang et al 2005), manipulation of cell shape (Singhvi et al 1994a(Singhvi et al , 1994bChen et al 1998;Kane et al 1999;Leclerc et al 2003), function and migration (Jiang et al 2005;Hanson et al 2006), examination of individual cell and populationlevel bacterial behaviour (Binz et al 2010;Park et al 2003aPark et al , 2003b, diagnostic cell arrays (Ziauddin & Sabatini 2001;Griscom et al 2002), and biocomputation .…”

“…Despite increasing efforts to identify the specific biological mechanisms and organelles responsible for the polarisation of hyphal growth and the regular branching pattern, these mechanisms still require considerable examination. The efforts to investigate the biological fundamentals of hyphal growth include light microscopy (Brunswick 1924;Girbardt 1969;Freitag et al 2004;Fricker et al 2008;Uchida et al 2008), electron microscopy (Girbardt 1969;Grove & Bracker 1970;Riquelme et al 2002), chemical (Riquelme et al 1998), genetic (Emerson 1963;Riquelme et al 2002;Seiler & Plamann 2003;Verd ın et al 2009), andtheoretical (Bartnicki-Garcia et al 1989;Riquelme et al 1998;Klumpp & Lipowsky 2003;Darrah et al 2006;Hanson et al 2006;Nicolau et al 2006;Sugden et al 2007) approaches.…”

A biomimetic algorithm for the improved detection of microarray features

“…It has been demonstrated recently that biological systems have evolved algorithms that are mathematically efficient, inter alia, for the exploration of available space [8] and finding nutrients [9]. This contribution attempts to test an algorithm that mimics a bacterial-"patented" algorithm for the search of available space and nutrients to find, "zeroin" and eventually delimitate the features existent on the features on the microarray surface.…”