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多对多蛋白质相互作用网络的计算能力。

The computational capabilities of many-to-many protein interaction networks.

机构信息

Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Biological Design Center, Boston University, Boston, MA 02215, USA.

Department of Medicine and Life Sciences, Pompeu Fabra University, 08003 Barcelona, Spain.

出版信息

Cell Syst. 2023 Jun 21;14(6):430-446. doi: 10.1016/j.cels.2023.05.001.

Abstract

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors.

摘要

许多生物电路由多组蛋白质变体组成,这些变体以多对多或混杂的方式相互作用。这些架构可以提供强大的计算能力,这在多细胞生物中尤为关键。理解这些电路中的生化计算原理可以允许更精确地控制细胞行为。然而,由于这些系统具有大量相互作用的分子成分、部分冗余和细胞上下文依赖性,因此它们的分析本质上很困难。在这里,我们讨论了最近的实验和理论进展,这些进展开始揭示混杂电路如何进行计算、这些计算在自然生物背景下扮演什么角色,以及混杂架构如何应用于设计合成多细胞行为。

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