Networks Of Chemical Oscillators Research Articles

Cancer classification with a network of chemical oscillators.

We discuss chemical information processing considering dataset classifiers formed with a network of interacting droplets. Our arguments are based on computer simulations of droplets in which a photosensitive variant of the Belousov-Zhabotinsky (BZ) reaction proceeds. By applying optical control we can adjust the time evolution of individual droplets and prepare the network to perform a specific computational task. We demonstrate that chemical classifiers made of droplets can be designed in computer simulations based on evolutionary algorithms. The mutual information between the dataset and the observed time evolution of droplets in the network is taken as the fitness function in the optimization process. We show that a classifier of the Wisconsin Breast Cancer Dataset made of a relatively small number of droplets can distinguish between malignant and benign forms of cancer with an accuracy exceeding 97%. The reliability of the optimized chemical classifiers of this dataset as a function of optimization time, number of droplets involved in data processing and the method of extracting the output information is discussed.

Excitability Modulation of Oscillating Media in 3D-Printed Structures.

Excitation and oscillation are central to living systems. For excitable systems, which can be brought into oscillation by an external stimulus, the excitation threshold is a crucial parameter. This is evident for neurons, which only generate an action potential when exposed to a sufficiently high concentration of excitatory neurotransmitters, which may only be achieved when multiple presynaptic axons deliver their action potential simultaneously to the synaptic cleft. Dynamic systems composed of relatively simple chemicals are of interest because they can serve as a model for physiological processes or can be exploited to implement chemical computing. With these applications in mind, we have studied the properties of the oscillatory Belousov-Zhabotinsky (BZ) reaction in 3D-printed reaction vessels with open channels of different dimensions. It is demonstrated that the channel geometry can be used to modulate the excitability of the BZ medium, switching a continuously oscillating medium to an excitable medium. Because large networks of channel-connected reaction wells of different depth can easily be fabricated by 3D printing, local excitability modulation could be built into the structure of the reaction vessel itself, opening the way to more extensive experimentation with networks of chemical oscillators.