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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
生物 網絡s change dynamically as 蛋白質 components are synthesized and degraded. Understanding the time- dependence and, in a multicellular organism, tissue-dependence of a 網絡 leads to insight beyond a view that collapses time-varying interactions into a single static map. Conventional algorithms are limited to analyzing evolving 網絡s by reducing them to a series of unrelated snapshots. Here we introduce an approach that groups 蛋白質s according to shared interaction patterns through a dynamical hierarchical 隨機 block model. 蛋白質 membership in a block is permitted to evolve as interaction patterns shift over time and space, representing the spatial organization of cell types in a multicellular organism. The spatiotemporal 演化 of the 蛋白質 components are inferred from transcript profiles, using Arabidopsis
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