The answer is complexity. All of these systems can be described as a set of many interacting components forming a web. Such a web, also known as a complex network, has been mathematically shown to display common patterns of organization.
I was lucky enough to attend a lecture by Lee Hood recently, Founder of Seattle-based Institute for Systems Biology. A goal of systems biology is to formulate models for the biological networks that are predictive of both the kinetic and equilibrium behavior of biological systems, be it a cell or a regulatory network.
Systems Biology offers a compelling promise of predictive, preventative and personalized medicine. Clearly, if we really understood how a complex system works, we would have the ability to elicit certain long-term behavior from that system. Dr.Hood thinks that we are 10-20 years away from medicine based on the understanding of how biological systems work.
For systems biology research to be effective, biologists, engineers and mathematicians need to work together and agree on a model system and a set of experimental and computational approaches. In other words, the success of systems biology once again hinges on convergence, a concept discussed on this blog previously.
Fortunately, in recent years, mathematicians and computer scientists have discovered important properties of complex networks, suggesting that common rules shape the emergence of complexity.
These developments have inspired new applications of the theory in other fields, such as neuroscience. It’s been suggested, for example (see “Graph theoretical analysis of complex networks in the brain”), that the human brain can be modeled as a complex network. They may have a so-called “small-world structure” at both the anatomical and functional connectivity levels. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration.
There is also increasing evidence that various types of brain disease such as Alzheimer’s disease, schizophrenia, brain tumours and epilepsy may be associated with the functional network’s deviations from the optimal small-world pattern.
Those are the biological implications.
Are there any learnings or applications for organizations in complex systems?
This is a fascinating mathematical problem described by Ricard V. Solé in the essay “Origins of innovation in tinkered networks.” To me, the most practical aspect of his work is the insight into the stability of such complex networks – be it a biological system or an innovative organization, which the MaRS Venture Group works with all the time.
“The networks display strong similarities, including the presence of small world properties (namely short chains connecting any pair of elements) and heterogeneity: most elements have a few links whereas a handful of them (the hubs) have many connections. Together with these structural properties, tinkered networks (as we call them) exhibit a high fragility under the removal or damage of hubs and a high robustness under random mutations.”
How can the science of complex networks help emerging companies? One take away is that complex systems, such as knowledge networks, are quite strong even in the presence of chaos and adapt well, but watch your hub: if something happens to them, everything crumbles.