A number of papers have highlighted remarkably high levels of robustness in the biochemical processes that control cellular function. This robustness is achieved in spite of the 'inexact' and highly stochastic nature of molecular interactions. Averaging, thresholding, resynchronization, and feedback are used extensively in biological systems to achieve robustness. How did incremental evolutionary changes lead to such sophisticated control algorithms? Can these principles be abstracted and used to artificially evolve robust nonlinear control in engineered systems? We present an analysis of the mechanisms underlying biological evolution and offer a model of molecular evolution as incremental model building and optimization.
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