Deriving a genetic regulatory network from an optimization principle
Thomas R. Sokolovski, Thomas Gregor, William Bialek, Gašper Tkačik. Proceedings of the National Academy of Science 122 (2), e2402925121 (2025).
Abstract
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 contexts. 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.
Significance Statement
Information crucial for life is represented by surprisingly low concentrations of key molecules, yet cells use these small signals to make reliable decisions. Could the mechanisms of life have been tuned to extract as much information as possible from a limited number of molecules? We apply this physical principle to a genetic network that controls pattern formation in early development of the fruit fly embryo, searching for the settings of 50+ internal parameters that give each cell the maximum possible information about its position in the embryo. The resulting optimal networks recapitulate many features of the real network, quantitatively. This approach makes tradeoffs explicit, rationalizes the network architecture, and provides perspectives on the interplay of chance and necessity.