Coming from the background of theoretical physics, Geoffrey West looked at the field of biology from the angle of complex adaptive systems. Given the seemingly infinite diversity of life, he set out to find the basic underlying principles that run through biological systems, hoping in the end to transfer those principles to social (human-made) systems like cities or companies. For this grand endeavour, biology served as the point of departure, the beginning of a journey.
Since the works of Carl von Linné and Charles Darwin (and their many contributors, collaborators and successors), our view of nature in general, and of biology in specific, is nicely structured and compartmentalized. Based on physical appearance (such as shape, size, colour) and other characteristics (like behaviour or habitat preference), every species of plant, fungus, or animal has its place in this orderly system. The tree of life that evolved from this straightforward logic serves to explain the origin and family relations between species; but it does not allow any comparison across its different branches.
Networks
West wanted to see through this mesmerizing diversity and searched for regularities and patterns that are common across the enormous range of shapes and forms of life on this planet. In his quest for a physics-based order, he found that networks could serve as the unifying element. If you look for them, you’ll find networks all over biology, within cells, in individual organisms, and even up to the ecosystem level.
A tree is something like a vertical network: look at the trunk, the branches, the twigs, and finally the leaves; go further into the leaves and the veins therein; or turn the other way and consider the roots and how they branch out into ever finer elements. Or consider your own body: your nervous system is an information network; the cardiovascular system distributes energy and resources; you might consider your kidney as your sewage system; while the neural network of your brain executes information processing. And it’s not hard to find similar networks within individual cells as well. Networks are literally everywhere —acting as transport systems within an organism.
The widespread success of networks is founded in their resilience: when a part of a network is not available —it might be clotted, separated, or fail otherwise— the organism itself can carry on. Maybe not at peak performance; but a local failure within a network does usually not result in global network collapse. That is the reason why networks are omnipresent in biology, throughout the evolution, independent of the concrete design of a species. And this omnipresence that made networks the ideal subject of West’s further work.
Scaling
Once he had established that clear target, West and his team took a deep dive into available data on existing species. They looked at a vast array of physiological variables ranging from blood flow to energy needs to length of the aorta. Now, intuitively we might expect that such parameters would scale linearly with size: at double network size, they should simply double as well, shouldn’t they? Well, they don’t.
What West and his team found through their extensive research is –in mathematical terms– a ¾ power law: the investigated parameters increased with the size of a species taken to the three-fourth power. What that means in practical terms? Take the intuitive idea again: at very first glance we’d expect that a species of double size should require double the material to build it and double the energy to run it. Doubling the size should require 100% more resources. That would be linear scaling (a power law with an exponent of 1). But West and his team found sublinear scaling (where the exponent of the power law is smaller than 1), which means in the specific case of a ¾ power law that at double size, a species requires only 85% more material and energy. In other words, that bigger species uses 15% fewer resources just because it is bigger: that’s genuine economies of scale. The bigger a species is, the more resource efficient it is.
To be fair, the concept of a ¾ power law had been expressed already in the 1930s by the biologist Max Kleiber, who had investigated the relations between metabolic rate (he used heat production as the indicator) and size (weight) for many animal species. What West and his team achieved is therefore a confirmation of Kleiber’s earlier work, built on thorough analyse of vast heaps of data, delivering quantifiable results across a range of physiological parameters far wider than Kleiber’s initial findings. Even more importantly, other scientists built on West’s results and identified a considerable number of additional parameters that followed that same relation. This is, according to West, a universal principle: physiological parameters of biological systems scale with organism size in a sublinear way, independent of the specific design.
Growth
There’s one additional concept that is of central importance to West’s further work: growth, the physiological maturation process of an organism during its lifetime. Intuitively, we are aware that every plant and animal goes through a considerable period of “rapidly increasing size” at the beginning of its life. Again, West went through the available data to articulate his concept of growth more systematically.
What he found is a universal pattern of sigmoidal growth. Independent of design, growth of an individual follows an S-shaped curve of rather rapid growth early in the organism’s life that slows down after a while and stops ultimately. Early on, the available energy is directly invested in the organism’s own growing; but once the organism gets sufficiently close to adult size, energy is increasingly shifted to reproduction; the fully grown-up organism invests comparatively little energy in maintenance, while most energy is directed towards off-spring. This growth pattern appears to be the most energy efficient from the perspective of both, the individual organism and the entire species.
While Geoffrey West builds on earlier works of biologists, he employs the perspective of a physicist to identify underlying concepts and to articulate them clearly. He put them into mathematical language and dug through the available data to run real numbers in order to underpin these rather abstract ideas. But this was only the prelude.
What about social systems?
With the concepts of scaling and growth derived from biology and firmly established, West had his toolbox in place to move on to investigating social systems like cities and companies, asking himself: Is London a great big whale? Is Walmart an elephant? What are the similarities –and differences– between biological systems and social systems? And that’s where I got riveted: How does all of that relate to innovation?
More to follow soon.
understanding innovation 