Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

中文翻译：

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量化细胞内信号传导信息：机器学习的进展

细胞将有关细胞外环境的信息传递给其核心功能机器。研究细胞内信号传导途径传输信息的能力解决了有关生命系统的基本问题。在这里，我们回顾了如何使用信息论方法来通过功能多效性且受分子随机性影响的信号通路来量化信息传输。我们描述了如何利用机器学习的最新进展来解决复杂时间轨迹数据集的挑战，以及这些如何有助于我们理解细胞如何利用时间编码来适当适应环境扰动。