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Engineering Approaches to Biomolecular Motors: From in vitro to in vivo Poster Abstracts
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Fungal Space Searching Can Outperform Standard Algorithms
Hsin-Yu V. Lin
, Dan V. Nicolau,
McGill University, Department of Bioengineering, Canada.
The ecological success of basidiomycetous fungi, accounting for ~30% of known fungal species,
can be attributed to the efficient expansion of branched filaments (hyphae) when seeking out
nutritional resources in the surrounding environment. Despite the fact that these fungi naturally
colonize 3D micro-structured media, their growth behaviour has been primarily studied on flat
surfaces. Fortunately, microfluidics provides a versatile methodology for the probing the fungal
various space search strategies.
Solving mazes is a difficult algorithmic exercise, which is why mazes are used to estimate the
optimality of the behavioural response, or intelligence, of many higher organisms including ants,
bees, mice, rats, octopi, and humans, as well as artificial intelligence-enabled robots. When
presented to “intelligence-testing” geometries, e.g., mazes, fungi use a natural program for
searching the available space. While different species present different variants of this fungal
program, its framework is common and it consists of the interplay of two ‘sub-routines’:
collision-induced branching, and directional memory. These studies also demonstrated that the
natural program comprising the two ‘sub-routines’ is markedly superior to variants where one of
these is, or both are suppressed.
A comparison of the performance of the natural algorithm against those of several standard space
searching ones revealed that fungi consistently outperforms Depth-First-Search (DFS) algorithm.
Although the performance of the natural algorithms is inferior to that of’ informed algorithms’,
e.g., A*, this under-performance does not importantly increase with the increase of the size of
the maze. These findings encourage a systematic effort to harvest the natural space searching
algorithms used by microorganisms, which, if efficient, can be reverse-engineered for graph and
tree search strategies