传递函数方法理解信号转导网络的周期性强迫。

IF 2 4区生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Physical biology Pub Date : 2023-03-29 DOI:10.1088/1478-3975/acc300

Nguyen H N Tran, Andrew H A Clayton

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引用次数: 0

摘要

信号转导网络负责将生化信号从细胞外传递到细胞内环境。了解这些网络的动态有助于理解它们的生物过程。信号通常以脉冲和振荡的方式传递。因此，了解这些网络在脉动和周期性刺激下的动力学是有用的。一个工具就是传递函数。本教程概述了传递函数方法背后的基本理论，并介绍了一些简单信号转导网络的例子。

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Transfer function approach to understanding periodic forcing of signal transduction networks.

Signal transduction networks are responsible for transferring biochemical signals from the extracellular to the intracellular environment. Understanding the dynamics of these networks helps understand their biological processes. Signals are often delivered in pulses and oscillations. Therefore, understanding the dynamics of these networks under pulsatile and periodic stimuli is useful. One tool to do this is the transfer function. This tutorial outlines the basic theory behind the transfer function approach and walks through some examples of simple signal transduction networks.

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来源期刊

Physical biology 生物-生物物理

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.