Design and fabrication of networks for bacterial computing

Falco C M J M van Delft,Viola Tokárová,Dan V Nicolau,Anja van Langen-Suurling,Charles de Boer,Frank W A Dirne,Ondřej Kašpar,Ayyappasamy Sudalaiyadum Perumal

doi:10.1088/1367-2630/ac1d38

Abstract

Non-deterministic polynomial (NP-) complete problems, whose number of possible solutions grows exponentially with the number of variables, require by necessity massively parallel computation. Because sequential computers, such as solid state-based ones, can solve only small instances of these problems within a reasonable time frame, parallel computation using motile biological agents in nano- and micro-scale networks has been proposed as an alternative computational paradigm. Previous work demonstrated that protein molecular motors-driven cytoskeletal filaments are able to solve a small instance of an NP complete problem, i.e. the subset sum problem, embedded in a network. Autonomously moving bacteria are interesting alternatives to these motor driven filaments for solving such problems, because they are easier to operate with, and have the possible advantage of biological cell division. Before scaling up to large computational networks, bacterial motility behaviour in various geometrical structures has to be characterised, the stochastic traffic splitting in the junctions of computation devices has to be optimized, and the computational error rates have to be minimized. In this work, test structures and junctions have been designed, fabricated, tested, and optimized, leading to specific design rules and fabrication flowcharts, resulting in correctly functioning bio-computation networks.

Highlights

Combinatorial mathematical problems, including nondeterministic polynomial-time (NP-) complete problems, have a number of possible solutions that increases exponentially with the problem size, which, in turn, makes them intractable for conventional sequentially operating electronic computers [1,2,3,4,5]
The vessel was opened to let silanization take place under the vapours for ∼2 h; (ii) Sylgard 184 kit (polydimethylsiloxane (PDMS)) was mixed in 10:1 ratio and stirred ∼3 min; (iii) the PDMS mixture was poured over the master structure and put in a vacuum chamber to remove air bubbles (∼2 h); (iv) the petri dish was either placed in the oven overnight at 60 ◦C or kept under room temperature during 2–3 d for curing: (v) the chip was cut around the structure and slowly peeled off from the master (the master was usually covered with a new PDMS mixture to keep it free from contamination (following steps (ii)–(v))
In previous work on reactive ion etch (RIE) of transition metals, using a Luxtron fluoroptic thermometer, it was shown that plasma etching-generated heat is able to raise the temperature on the wafer surface far above the pre-set cooling temperature of the electrode [27]

Summary

Introduction

Combinatorial mathematical problems, including nondeterministic polynomial-time (NP-) complete problems, have a number of possible solutions that increases exponentially with the problem size, which, in turn, makes them intractable for conventional sequentially operating electronic computers [1,2,3,4,5]. To DNA computing, which requires impractically large amounts of DNA when scaling up [6,7,8,9], and quantum computing, which appears to be limited in scale by de-coherence and by the small number of qubits that can be integrated [10], massively parallel computation employing motile biological agents in networks has been proposed to solve such problems [11]. The solutions of the problem can be derived from the end points and trajectories of the agents in the network

Objectives

Methods

Results

Conclusion