  Many biological systems approach physical limits to their performance,
motivating the idea that their behavior and underlying mechanisms could be
determined by such optimality. Nevertheless, optimization as a predictive
principle has only been applied in very simplified setups. Here, in contrast,
we explore a mechanistically-detailed class of models for the gap gene network
of the Drosophila embryo, and determine its 50+ parameters by optimizing the
information that gene expression levels convey about nuclear positions, subject
to physical constraints on the number of available molecules. Optimal networks
recapitulate the architecture and spatial gene expression profiles of the real
organism. Our framework makes precise the many tradeoffs involved in maximizing
functional performance, and allows us to explore alternative networks to
address the questions of necessity vs contingency. Multiple solutions to the
optimization problem may be realized in closely related organisms.
