Information content and optimization of self-organized developmental systems

• 成果类型: Article
• 署名作者: Brueckner, David B.; Tkacik, Gasper
• 署名单位: Institute of Science & Technology - Austria
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9782
• DOI: 10.1073/pnas.2322326121
• 发表日期: 2024-06-04
• 关键词: positional information; symmetry-breaking; spatial-pattern; MODEL; notch; activation; FRAMEWORK; feedback

摘要

A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction-diffusion models of selforganization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes. Significance 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 underlying regulatory motifs, 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.