Information content and optimization of self-organized developmental systems

Development relies on the ability of cells to self-organize into patterns of different cell types that underlie the formation of tissues and organs. Such patterning occurs in a reproducible manner despite the inevitable presence of noise. However, how to generically quantify the patterning performance of different biological self-organizing systems has remained unclear. Here we develop an information-theoretic framework and use it to analyze a wide range of models of self-organization. Our approach can be used to define and measure the information content of observed patterns, to functionally assess the importance of various patterning mechanisms, and to predict optimal operating regimes and parameters for self-organizing systems. This framework represents a unifying mathematical language to describe biological self-organization across diverse systems.