School of Complex Adaptive Systems, Arizona State University, Tempe, Arizona, United States of America.
Banner Health Corporation, Phoenix, Arizona, United States of America.
PLoS Comput Biol. 2023 Sep 21;19(9):e1010704. doi: 10.1371/journal.pcbi.1010704. eCollection 2023 Sep.
In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.
在许多生物体中，基因之间的相互作用导致了多种功能状态，而相互作用的变化则可能导致向新状态的转变。这些转变可能与动力系统理论中的分叉（或临界点）有关。描述这些集体转变是系统生物学的主要挑战。在这里，我们开发了一种统计方法，可从高维基因表达数据中直接识别连续转变附近的双稳性。我们将该方法应用于来自蜜蜂的数据，其中在执行巢中任务的蜜蜂和离开巢去觅食的蜜蜂之间发生了已知的发育转变。我们的方法利用了转变附近基因表达水平分布的预期形状，成功地识别出双稳性的出现，并将其与已知参与行为转变的基因联系起来。这一概念验证表明，超越相关分析来推断基因表达分布的形状可能更普遍地用于从基因表达数据中识别集体转变。
