While their laboratory studies the brain and consciousness, Marshall and colleagues write that the tool works equally well on biological systems, applying it to understand a fast-growing type of yeast used in laboratory experiments.
“We describe an algorithm that explains how to understand all the causal relationships in the system,” says Albantakis. “And sometimes the parts together have effects that can’t be found by looking at the individual level.”
Marshall says the paper offers an argument that to understand biological systems, sometimes a coarse grain is superior to the fine grain.
“There is a commonly held view that systems should be studied at the finest possible scale, and that higher levels of description are merely approximations that are necessary in practice,” Marshall says. “The result of our work is that – contrary to this view – causal power can increase at higher levels of description.”
Marshall says the hope is to create a practical tool that offers scientists another way to analyze and understand complicated biological systems by studying their larger-scale components. He says the mathematically rigorous algorithm is fully general, and thus applies across many disciplines.
“One benefit of our analysis is that it is observer independent – it provides an objective measure of causal power that can be used to ‘carve nature at its joints’ and identify the scales at which complex interactions in physical systems come into focus,” Marshall says.
The study was published in the journal PLOS-Computational Biology. The senior author is Giulio Tononi, MD, PhD, director of the Wisconsin Center for Sleep and Consciousness. The work was supported by the Templeton World Charity Foundation, Inc.
