Dynamics in Decision Making: How Cellular Networks Encode And Decode Temporal Information

5.项目1.决策中的关键技术：细胞网络如何编码和 解码时间信息 总结 越来越多的证据表明，信号的动力学-特定途径的活动如何随着时间的推移而变化， 时间的函数-可能在细胞信息传输的特异性中起核心作用。一个一般 假设是，不同的外部输入（不同的生长因子，压力等）可以将信息编码在 中枢信号传导节点如何被激活的动力学（即持续与瞬时激活;不同的 频率激活）。反过来，这些不同的动态特性可以被下游网络解码， 以产生不同的细胞反应程序。尽管如此，这种动态编码假说一直是 很难测试，因为我们缺乏系统地干扰信号动态的工具。我们有 最近开发了一套细胞光遗传学开关，使我们能够激活关键的细胞内调节 节点与轻（如Ras，MAPK，cAMP，转录）。因为我们可以用光来激活这些节点， 任意的时间模式，它们是系统地询问细胞如何编码和解码的强大工具 动态信息我们建议将联合收割机系统性光遗传学刺激与定量反应相结合 分析以研究许多典型的细胞决策系统（哺乳动物细胞增殖、酵母 应激反应和干细胞分化）。这些研究将使我们有更深层次的定量认识 细胞信息如何在信号动力学中被编码。此外，它们还应提供一个基础， 更深入地了解动态变化如何在癌症等疾病中发挥作用，以及动态变化如何在癌症中发挥作用。 刺激也可能提供新的方式来调节和控制细胞行为，特别是在 工程化治疗细胞（例如项目3包括干细胞的工程化动态控制 差异化）。我们还希望学习如何设计信号网络， filters. 首席研究员：EL-SAMAD 研究者：EL-SAMAD、LIM、THOMSON、KROGAN、LI

5. PROJECT 1. DYNAMICS IN DECISION MAKING: HOW CELLULAR NETWORKS ENCODE AND DECODE TEMPORAL INFORMATION SUMMARY There is growing evidence that the dynamics of signaling – how the activity of specific pathways changes as a function of time – may play a central role in the specificity of cellular information transmission. One general hypothesis is that distinct external inputs (different growth factors, stresses, etc.) can encode information in the dynamics of how central signaling nodes are activated (i.e. sustained vs transient activation; different frequency activation). In turn, these distinct dynamic properties could be decoded by downstream networks in order to yield distinct cellular response programs. Nonetheless, this dynamic encoding hypothesis has been difficult to test, because we have lacked the tools to systematically perturb signaling dynamics. We have recently developed a suite of cellular optogenetic switches that allow us to activate key intracellular regulatory nodes with light (e.g. Ras, MAPK, cAMP, transcription). Because we can use light to activate these nodes with arbitrary temporal patterns, they are powerful tools to systematically interrogate how cells encode and decode dynamical information. We propose to combine systematic optogenetic stimulation with quantitative response profiling to study a number of canonical cellular decision making systems (mammalian cell proliferation, yeast stress responses, and stem cell differentiation). These studies will give us a deeper quantitative understanding of how cellular information can be encoded in signaling dynamics. In addition, they should provide a basis for a deeper understanding of how changes in dynamics play a role in diseases such as cancer and how dynamic stimulation might also provide new modalities to modulate and control cellular behavior, especially in engineered therapeutic cells (e.g. PROJECT 3 includes engineering dynamic control of stem cell differentiation). We also hope to learn how to engineer signaling networks that can act as specific dynamic filters. LEAD Investigator: EL-SAMAD Investigators: EL-SAMAD, LIM, THOMSON, KROGAN, LI