Information flow in a mammalian signal transduction pathway

哺乳动物的信号转导网络将有关细胞外配体存在和浓度的详细信息传递到细胞核，并通过改变基因表达来改变细胞行为。由于信号转导通路与典型的通信系统有着惊人的相似之处，信息论的框架可以直接应用于更好地理解细胞信号。在当前优先计划InKoMBio的资助期内，我们采用单细胞实验和信息论计算相结合的方法，确定了原型MAPK途径的信息传递能力。令人惊讶的是，我们的结果表明，信令网络传输的信息不到一位。它不是忠实地报告配体EGF的胞外浓度，而是以二元方式做出反应。此外，分子噪声干扰了对输入信号存在的健壮编码，进一步限制了信息内容。我们观察到另外两个信号通路，TGFbeta/SMAD和P53网络的通道容量同样有限。由于在不同生物模型系统中的许多研究表明，细胞可以通过信号通路获得更多关于环境的信息，我们的目标是调查是什么限制了单个细胞水平的信息传输能力，以及细胞如何最大限度地从外部和内部来源获得信息量，以确保适当的生理反应。我们假设这些通路整合了来自细胞环境的信息，这可以解释通道容量明显较低的原因。因此，我们建议使用信息论、单细胞实验和数学模型来研究背景信息的影响，解决以下具体问题：(I)细胞状态如何影响对外部信号的反应，(Ii)先前刺激的背景如何影响反应，以及(Iii)不同路径上的上下文相关信号传递的共同原则是什么？我们将使用活细胞成像和免疫荧光分析来测量信号和上下文，并使用条件互信息、上下文树和简约贝叶斯网络计算上下文信息的贡献。为了对潜在的分子机制有一个预测性的理解，我们将扩展现有的通路的数学模型，以包括提供背景和分析其信息理论属性的相互作用的调节过程。使用网络扰动，我们将通过实验验证模型预测。

The mammalian signal transduction network relays detailed information about the presence and concentration of ligands on the outside of the cell to the nucleus, and alters cellular behavior by changing gene expression. Since signal transduction pathways exhibit striking similarities to typical communication systems, the framework of information theory can be directly applied to better understand cellular signaling. During the current funding period of the priority program InKoMBio, we determined the information transmission capacities of the prototypic MAPK pathway using a combination of single cell experimentation and information theoretical calculations. Surprisingly, our results indicate that the signaling network transmits less than one bit of information. Rather than faithfully reporting extracellular concentrations of the ligand EGF, it responds in a binary manner. In addition, molecular noise interferes with a robust encoding of the presence of the input signal, limiting the information content even further. We observed similarly limited channel capacities for two other signaling pathways, the TGFbeta/SMAD and p53 networks.As many studies in different biological model systems suggest that cells can gain more information than one bit about their environment using signaling pathways, we aim to investigate what is limiting the information transmission capabilities at the single cell level and how cells maximize the amount of information gained from external and internal sources to ensure a proper physiological response. We hypothesize that the pathways integrate information from the cellular context, which could explain the apparently low channel capacity. We therefore propose to use information theory, single cell experimentation and mathematical modeling to study the influence of contextual information, by addressing the following specific questions: (i) how does the state of a cell influence the response to an external signal, (ii) how does the context of previous stimuli influence the response and (iii) what are common principles of context-dependent signaling across different pathways? We will use live-cell imaging and immunofluorescence assays to measure signaling and context, and calculate the contribution of contextual information using conditional mutual information, context trees and parsimonious Bayesian networks. To gain a predictive understanding of the underlying molecular mechanisms, we will expand existing mathematical models of the pathways to include the interacting regulatory processes that provide context and analyze their information theoretical properties. Using network perturbations, we will experimentally validate model predictions.