That is what an international team of researchers led by Prof. Nicolau, the Chair of the Department of Bioengineering at McGill, believe.
They’ve published an article on the subject this week in the Proceedings of the National Academy of Sciences (PNAS), in which they describe a model of a biological computer that they have created that is able to process information very quickly and accurately using parallel networks in the same way that massive electronic super computers do.
Except that the model bio supercomputer they have created is a whole lot smaller than current supercomputers, uses much less energy, and uses proteins present in all living cells to function.
PNAS Proceedings of the National Academy of Science -Parallel computation with molecular-motor-propelled agents in nanofabricated networks
“We’ve managed to create a very complex network in a very small area,” says Dan Nicolau, Sr. with a laugh. He began working on the idea with his son, Dan Jr., more than a decade ago and was then joined by colleagues from Germany, Sweden and The Netherlands, some 7 years ago. “This started as a back of an envelope idea, after too much rum I think, with drawings of what looked like small worms exploring mazes.”
The model bio-supercomputer that the Nicolaus (father and son) and their colleagues have created came about thanks to a combination of geometrical modelling and engineering knowhow (on the nano scale). It is a first step, in showing that this kind of biological supercomputer can actually work.
The circuit the researchers have created looks a bit like a road map of a busy and very organized city as seen from a plane. Just as in a city, cars and trucks of different sizes, powered by motors of different kinds, navigate through channels that have been created for them, consuming the fuel they need to keep moving.
More sustainable computing
But in the case of the biocomputer, the city is a chip measuring about 1.5 cm square in which channels have been etched. Instead of the electrons that are propelled by an electrical charge and move around within a traditional microchip, short strings of proteins (which the researchers call biological agents) travel around the circuit in a controlled way, their movements powered by ATP, the chemical that is, in some ways, the juice of life for everything from plants to politicians.
Because it is run by biological agents, and as a result hardly heats up at all, the model bio-supercomputer that the researchers have developed uses far less energy than standard electronic supercomputers do, making it more sustainable. Traditional supercomputers use so much electricity, that they heat up a lot and then need to be cooled down, often requiring their own power plant to function.
Moving from model to reality
Although the model bio supercomputer was able to very efficiently tackle a complex classical mathematical problem by using parallel computing of the kind used by supercomputers, the researchers recognize that there is still a lot of work ahead to move from the model they have created to a full-scale functional computer.
”This would not have been possible without the enthusiasm and hard work of Prof. Linke, who is also co-corresponding author, and his group, Prof. Prof. Månsson and his group - both from Sweden, Prof. Diez and his group from Germany, and Dr. Van Delft from Philips, The Netherlands. Now that this model exists as a way of successfully dealing with a single problem, there are going to be many others who will follow up and try to push it further, using different biological agents, for example,” says Nicolau. “It’s hard to say how soon it will be before we see a full scale bio super-computer. One option for dealing with larger and more complex problems may be to combine our device with a conventional computer to form a hybrid device. Right now we’re working on a variety of ways to push the research further.”
Electronic computers are extremely powerful at performing a high number of operations at very high speeds, sequentially. However, they struggle with combinatorial tasks that can be solved faster if many operations are performed in parallel. Here, we present proof-of-concept of a parallel computer by solving the specific instance of a classical nondeterministic-polynomial-time complete (“NP-complete”) problem, the subset sum problem. The computer consists of a specifically designed, nanostructured network explored by a large number of molecular-motor-driven, protein filaments. This system is highly energy efficient, thus avoiding the heating issues limiting electronic computers. We discuss the technical advances necessary to solve larger combinatorial problems than existing computation devices, potentially leading to a new way to tackle difficult mathematical problems.
The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.
SOURCES - McGill University, PNAS