A paradigm for viewing biologic systems as scale-free networks based on energy efficiency: implications for present therapies and the future of evolution.
A network constitutes an abstract description of the relationships among entities, respectively termed links and nodes. If a power law describes the probability distribution of the number of links per node, the network is said to be scale-free. Scale-free networks feature link clustering around certain hubs based on preferential attachments that emerge due either to merit or legacy. Biologic systems ranging from sub-atomic to ecosystems represent scale-free networks in which energy efficiency forms the basis of preferential attachments. This paradigm engenders a novel scale-free network theory of evolution based on energy efficiency. As environmental flux induces fitness dislocations and compels a new meritocracy, new merit-based hubs emerge, previously merit-based hubs become legacy hubs, and network recalibration occurs to achieve system optimization. To date, Darwinian evolution, characterized by innovation sampling, variation, and selection through filtered termination, has enabled biologic progress through optimization of energy efficiency. However, as humans remodel their environment, increasing the level of unanticipated fitness dislocations and inducing evolutionary stress, the tendency of networks to exhibit inertia and retain legacy hubs engender maladaptations. Many modern diseases may fundamentally derive from these evolutionary displacements. Death itself may constitute a programmed adaptation, terminating individuals who represent legacy hubs and recalibrating the network. As memes replace genes as the basis of innovation, death itself has become a legacy hub. Post-Darwinian evolution may favor indefinite persistence to optimize energy efficiency. We describe strategies to reprogram or decommission legacy hubs that participate in human disease and death.
Stanford University, Department of Radiology, 470 University Avenue, Palo Alto, CA 94301, USA. ayun@stanford.edu
Anthony J
AJ
Yun
Patrick Y
PY
Lee
John D
JD
Doux
D000818:Animals; D005075:Biological Evolution; D004526:Efficiency; D004734:Energy Metabolism; D005544:Forecasting; D006801:Humans; D008962:Models, Theoretical