Belousov-Zhabotinsky liquid marbles in robot control

© 2019 Elsevier B.V. We show how to control the movement of a wheeled robot using on-board liquid marbles made of Belousov-Zhabotinsky solution droplets coated with polyethylene powder. Two stainless steel, iridium coated electrodes were inserted in a marble and the electrical potential recorded was used to control the robot's motor. We stimulated the marble with a laser beam. It responded to the stimulation by pronounced changes in the electrical potential output. The electrical output was detected by the robot. The robot changed its trajectory in response to the stimulation. The results open new horizons for applications using oscillatory chemical reactions in robotics

Outline of an evolutionary morphology generator towards the modular design of a biohybrid catheter

Biohybrid machines (BHMs) are an amalgam of actuators composed of living cells with synthetic materials. They are engineered in order to improve autonomy, adaptability and energy efficiency beyond what conventional robots can offer. However, designing these machines is no trivial task for humans, provided the field’s short history and, thus, the limited experience and expertise on designing and controlling similar entities, such as soft robots. To unveil the advantages of BHMs, we propose to overcome the hindrances of their design process by developing a modular modeling and simulation framework for the digital design of BHMs that incorporates Artificial Intelligence powered algorithms. Here, we present the initial workings of the first module in an exemplar framework, namely, an evolutionary morphology generator. As proof-of-principle for this project, we use the scenario of developing a biohybrid catheter as a medical device capable of arriving to hard-to-reach regions of the human body to release drugs. We study the automatically generated morphology of actuators that will enable the functionality of that catheter. The primary results presented here enforced the update of the methodology used, in order to better depict the problem under study, while also provided insights for the future versions of the software module

Towards a slime Mould-FPGA interface

© 2015, Korean Society of Medical and Biological Engineering and Springer. Purpose: The plasmodium of slime mouldPhysarum polycephalum: is a multinucleate single celled organism which behaves as a living amorphous unconventional computing substrate. As an excitable, memristive cell that typically assumes a branching or stellate morphology, slime mould is a unique model organism that shares many key properties of mammalian neurons. There are numerous studies that reveal the computing abilities of the plasmodium realized by the formation of tubular networks connecting points of interest. Recent research demonstrating typical responses in electrical behaviour of the plasmodium to certain chemical and physical stimuli has generated interest in creating an interface between.P. polycephalum: and digital logic, with the aim to perform computational tasks with the resulting device.Methods: Through a range of laboratory experiments, wemeasure plasmodial membrane potential via a non-invasive method and use this signal to interface the organism with a digital system.Results: This digital system was demonstrated to perform predefined basic arithmetic operations and is implemented in a field-programmable gate array (FPGA). These basic arithmetic operations, i.e. counting, addition, multiplying, use data that were derived by digital recognition of membrane potential oscillation and are used here to make basic hybrid biologicalartificial sensing devices.Conclusions: We present here a low-cost, energy efficient and highly adaptable platform for developing next-generation machine-organism interfaces. These results are therefore applicable to a wide range of biological/medical and computing/electronics fields

Street map analysis with excitable chemical medium

© 2018 American Physical Society. Belousov-Zhabotinsky (BZ) thin layer solution is a fruitful substrate for designing unconventional computing devices. A range of logical circuits, wet electronic devices, and neuromorphic prototypes have been constructed. Information processing in BZ computing devices is based on interaction of oxidation (excitation) wave fronts. Dynamics of the wave fronts propagation is programed by geometrical constraints and interaction of colliding wave fronts is tuned by illumination. We apply the principles of BZ computing to explore a geometry of street networks. We use two-variable Oregonator equations, the most widely accepted and verified in laboratory experiments BZ models, to study propagation of excitation wave fronts for a range of excitability parameters, with gradual transition from excitable to subexcitable to nonexcitable. We demonstrate a pruning strategy adopted by the medium with decreasing excitability when wider and ballistically appropriate streets are selected. We explain mechanics of streets selection and pruning. The results of the paper will be used in future studies of studying dynamics of cities and characterizing geometry of street networks

Evolutionary algorithms designing nanoparticle cancer treatments with multiple particle types

There is a rich history of evolutionary algorithms tackling optimization problems where the most appropriate size of solutions, namely the genome length, is unclear a priori. Here, we investigated the applicability of this methodology on the problem of designing a nanoparticle (NP) based drug delivery system targeting cancer tumors. Utilizing a treatment comprised of multiple types of NPs is expected to be more effective due to the higher complexity of the treatment. This paper begins by using the well-known NK model to explore the effects of fitness landscape ruggedness on the evolution of genome length and, hence, solution complexity. The size of novel sequences and variations of the methodology with and without sequence deletion are also considered. Results show that whilst landscape ruggedness can alter the dynamics of the process, it does not hinder the evolution of genome length. On the contrary, the expansion of genome lengths can be encouraged by the topology of such landscapes. These findings are then explored within the aforementioned real-world problem. Variable sized treatments with multiple NP types are studied via an agent-based open source physics-based cell simulator. We demonstrate that the simultaneous evolution of multiple types of NPs leads to more than 50% reduction in tumor size. In contrast, evolution of a single NP type leads to only 7% reduction in tumor size. We also demonstrate that the initial stages of evolution are characterized by a fast increase in solution complexity (addition of new NP types), while later phases are characterized by a slower optimization of the best NP composition. Finally, the smaller the number of NP types added per mutation step, the shorter the length of the typical solution found

Metameric representations on optimization of nano particle cancer treatment

In silico evolutionary optimization of cancer treatment based on multiple nano-particle (NP) assisted drug delivery systems was investigated in this study. The use of multiple types of NPs is expected to increase the robustness of the treatment, due to imposing higher complexity on the solution tackling a problem of high complexity, namely the physiology of a tumor. Thus, the utilization of metameric representations in the evolutionary optimization method was examined, along with suitable crossover and mutation operators. An open-source physics-based simulator was utilized, namely PhysiCell, after appropriate modifications, to test the fitness of possible treatments with multiple types of NPs. The possible treatments could be comprised of up to ten types of NPs, simultaneously injected in an area close to the cancerous tumour. Initial results seem to suffer from bloat, namely the best solutions discovered are converging towards the maximum amount of different types of NPs, however, without providing a significant return in fitness when compared with solutions of fewer types of NPs. As the large diversity of NPs will most probably prove to be quite toxic in lab experiments, we opted for methods to reduce the bloat, thus, resolve to therapies with fewer types of NPs. Namely, the bloat control methods studied here were removing types of NPs from the optimization genome as part of the mutation operator and applying parsimony pressure in the replacement operator. By utilizing these techniques, the treatments discovered are composed of fewer types of NPs, while their fitness is not significantly smaller