2016年02月19日　　15:00

CiNet 1F Conference room A

Dan V. Nicolau

Professor
Head of the Department of Bioengineering
McGill University

担当 ： 田口 隆久 （CiNet 副センター長）

Abstract:

Many important problems, e.g., cryptography, network routing, require the exploration a large number of
candidate solutions. Because the time required for solving these problems grows exponentially with their
size, electronic computers, which operate sequentially, cannot solve them in reasonable timeframe.
Unfortunately, the parallel-computation approaches proposed so far, e.g., DNA-, and quantumcomputing,
suffer from fundamental and practical drawbacks, which prevented their implementation. On
the other hand, biological entities, from microorganisms to humans, process information in parallel,
routinely, for essential tasks, such as foraging, searching for available space, competition, and
cooperation. However, aside of their sheer complexity, parallel biological processes are difficult to
harness for parallel computation because of a fundamental difference: biological entities process analog
information, e.g., concentration gradients, whereas computing devices process numbers.
Two major classes of motile, self-propelled biological agents could be envisaged: protein linear
molecular motors, where cytoskeletal filaments, such as actin filaments or microtubules are propelled by
surface-immobilized molecular motors, such as myosin or kinesin, respectively; and microorganisms,
such as fungi, motile bacteria and algae. While the technology involving the use of molecular motorspropelled
agents advanced steadily in the last two decades, fungi and bacteria are also natural choices for
the exploration of microfluidics networks encoding mathematical problems. For instance, the growth
behaviour and optimality of space-searching algorithms of several fungal species has been tested in
microfluidic mazes and networks. First, it was found that the growth behaviour of all species was
strongly modulated by the geometry of micro-confinement. Second, the fungi used a complex growth
and space-searching strategy comprising two algorithmic subsets: (i) long-range directional memory of
individual hyphae and (ii) inducement of branching by physical obstruction. Third, stochastic simulations
using experimentally measured parameters showed that this strategy maximizes both survival and
biomass homogeneity in micro-confined networks, producing optimal results only when both algorithms
are synergistically used.
The presentation conclude with an overview the several research directions regarding the computation
and simulation using biological entities in microfluidics structures, weighing the opportunities and
challenges offered by various technological avenues.