Deriving a genetic regulatory network from an optimization principle

• 成果类型: Article
• 署名作者: Sokolowski, Thomas R.; Gregor, Thomas; Bialek, William; Tkacik, Gasper
• 署名单位: Institute of Science & Technology - Austria; Princeton University; Princeton University; Pasteur Network; Universite Paris Cite; Institut Pasteur Paris; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Biology (INSB); Rockefeller University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-15321
• DOI: 10.1073/pnas.2402925121
• 发表日期: 2025-01-07
• 关键词: positional information; expression patterns; EVOLUTION; embryo; morphogenesis; optimality; physics; rules

摘要

Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks.