Alpha Galilieo reports – University of Southampton researchers, Dr de Planque, a biochemist, and Dr Zauner, a computer scientist, will adapt brain processes to a ‘wet’ information processing scenario by setting up chemicals in a tube which behave like the transistors in a computer chip
The project will run for three years and involves three complementary objectives.
1. Engineer lipid-coated water droplets, inspired by biological cells, containing an excitable chemical medium and then to connect the droplets into networks in which they can communicate through chemical signals.
2. Design information-processing architectures based on the droplets and to demonstrate purposeful information processing in droplet architectures.
3. Establish and explore the potential and limitations of droplet architectures.
Our system will copy some key features of neuronal pathways in the brain and will be capable of excitation, self-repair and self-assembly
Burch, H. J., Antoranz Contera, S., de Planque, M. R. R., Grobert, N. and Ryan, J. F. (2008) Doping of carbon nanotubes with nitrogen improves protein coverage whilst retaining correct conformation. Nanotechnology, 19 (38). p. 384001. ISSN 0957-4484
Toledo, N. C., de Planque, M. R. R., Antoranz Contera, S., Grobert, N. and Ryan, J. F. (2007) Lipid-modulated assembly of magnetized iron-filled carbon nanotubes in millimeter-scale structures. Japanese Journal of Applied Physics, 46 (4B). pp. 2799-2805.
Ramanujan, C. S., Sumitomo, K., de Planque, M. R. R., Hibino, H., Torimitsu, K. and Ryan, J. F. (2007) Self-assembly of vesicle nanoarrays on Si: A potential route to high-density functional protein arrays. Applied Physics Letters, 90 (3). 033901.
de Planque, M. R. R., Raussens, V., Antoranz Contera, S., Rijkers, D. T. S., Liskamp, R. M. J., Ruysschaert, J. M., Ryan, J. F., Separovic, F. and Watts, A. (2007) β-Sheet Structured β-Amyloid(1-40) Perturbs Phosphatidylcholine Model Membranes. Journal of Molecular Biology, 368 (4). pp. 982-997.
Lovell, C. J. and Zauner, K. P. (2009) Towards Algorithms for Autonomous Experimentation. In: Eighth International Conference on Information Processing in Cells and Tissues (IPCAT 2009), 5-9 April 2009, Ascona, Switzerland. pp. 150-152.
Lovell, C. J., Jones, G. and Zauner, K. P. (2009) Autonomous Experimentation: Coupling Machine Learning with Computer Controlled Microfluidics. In: ELRIG Drug Discovery, 7-8th September 2009, Liverpool.
Modelling biological systems is impaired by the cost of experimentally obtaining the data required to build the models. The resources available to perform experiments are typically very limited compared to the size of parameter spaces and the complexity of the systems under investigation. However, the confluence of lab automation and the low cost of computing resources make it practicable to apply a closed-loop strategy, where each experimental observation allows the computer to reason the experiment to perform next. By doing so, autonomous experimentation tries to capture the efficiency of experimentalists in navigating a seemingly boundless space of potential experiments. While computers can at most represent a very limited knowledge context in which they interpret their observations, they do have the benefit of being able to contemplate many thousands of hypotheses in parallel.
We will report on the development of an autonomous experimentation setup that devises hypotheses and decides on experiments which are then physically performed on a microfluidic device, all without human interaction. The purpose of our implementation is the investigation of biomolecular substrates for novel computing devices, however our approach is not specific to this application.