New research sheds light on how molecular “machines” work together to speed up transport

New research by Simon Fraser University physicists Matthew Leighton and David Sivak is shedding new light on how to keep the systems of molecular “machines” within our cells working together.

Their research is a first step towards understanding how large numbers of molecular-scale machines can work together to achieve a common goal, illustrating design principles that may be essential for future engineering applications, such as creating medical nanomachines.

The researchers, whose work is newly published in the New Journal of Physics, sought to learn more about how large collections of these machines behave, and how their performance can be optimized for specific goals.

The malfunctioning of our molecular “motors”—which are in constant motion, converting different types of energy, transporting materials and assembling complex structures— is related to various human diseases, such as cardiomyopathies and Alzheimers.

The research examined how the number of motors used to transport a molecular “cargo” determines the behaviour of the system. “Specifically, we are interested in important metrics of performance like speed, precision, power consumption, and efficiency,” says Sivak, who holds SFU’s Canada Research Chair in Nonequilibrium Statistical Biophysics.

"These motors, too small to see even with the most powerful microscopes, are difficult to observe experimentally, so we applied mathematical models to understand their behaviour. Using a model we developed, we were able to derive simple scaling laws which explain how these different performance metrics depend on the number of motors." 

The pair believe that nanotechnology will play an increasingly large role in clinical medicine in the coming decades. Swarms of nanoscale machines may be well-suited to complex yet critical tasks such as drug delivery and cellular repair.

Their work highlights an important design principle that will guide future design of synthetic molecular machines—that in addition to tuning the properties of individual machines, the number of machines working collectively is an important parameter that can be adjusted to optimize performance.