The Molecular Nose

分子的NOSE哺乳动物细胞使用更多的基因来调节生物过程，而不是执行它们。这些过程包括所有基本过程，如细胞生长、分化、存活、新陈代谢，以及感知和产生与环境沟通的正确生物分子补充的能力。通过将生物过程组织成网络，复杂性进一步提高。了解这种复杂网络的行为和反应对于解决生物学中的重大问题至关重要。为了满足这一需求，我们将建立一个多路传感器平台，该平台可以评估和量化哺乳动物细胞中生化网络功能状态的动态变化，并使用这些数据在系统范围的水平上重建细胞网络相互作用及其动态行为。支撑这一平台的概念从根本上不同于生物科学中用来评估细胞功能的现有方法，类似于电子鼻，在电子鼻中，传感器阵列首先用单个刺激来训练，以建立反应模式库，随后使用该库来解开复杂的输入。分子鼻将使用人工转录报告程序同时监测细胞群体或单个细胞中数百个网络组件的输出，并设计软件框架和算法进行功能分析。分子鼻将有三个版本。其中一个将使用分子生物学工具构建，并将允许使用大型阵列(多达1000个)的传感器。然而，它需要裂解细胞进行测量，因为探测器在细胞外部。该版本对于培训系统和建立大型响应模式库将特别有用。第二个版本将通过将单个传感器及其相应的探测器集成到条码纳米颗粒上来构建，这些纳米颗粒将被引入细胞中，并使用表面增强共振拉曼散射光谱进行读取。这种设置将使用较少的传感器(最多30个)，但可以用于实时监测活细胞的反应。同时，我们将开发可控制地将这些粒子库引入细胞的方法。第三个版本是将基于质粒的感应器文库稳定整合到胚胎小鼠干细胞中，目的是产生转基因感应器鼠。干细胞还可以用于器官培养和体外分化系统。分子鼻将使系统测试和合理解释细胞网络的行为成为可能。这项技术是通用的，在单细胞和细胞群体中都有广泛的应用，包括突出的生物学问题，如药物效应的分析和副作用的预测；干细胞分化以期最终控制分化；为支持组织工程而指定细胞命运；通过合成工程途径生产所需蛋白质和代谢物的遗传和生化网络；以及适应性网络反应和进化的研究。目前，我们缺乏有效的实验工具来分析这些复杂的相互作用。

THE MOLECULAR NOSEMammalian cells use more genes to regulate biological processes than to carry them out. These include all fundamental processes such as cell growth, differentiation, survival, metabolism and the ability to sense and produce the correct complement of biomolecules to communicate with the environment. Complexity is further enhanced by the organization of biological processes as networks. Understanding the behaviour and responses of such complex networks will be crucial to solve eminent questions in biology. To address this need we will build a multiplexed sensor platform that can assess and quantify dynamic changes in the functional state of biochemical networks in mammalian cells, and use these data to reconstruct cell network interactions and their dynamic behaviour on a systems wide level. The concept underpinning this platform is fundamentally different from existing methods used in the biological sciences to assess cell function, and similar to the Electronic Nose , where an array of sensors is first trained with individual stimuli to establish a library of response patterns which subsequently are used to deconvolute complex inputs. The Molecular Nose will monitor the outputs of several hundred network components simultaneously in cell populations or single cells using artificial transcriptional reporters, and design a software framework and algorithms for their functional analysis. The Molecular Nose will be built in three versions. One will be constructed using molecular biology tools, and will permit to use a large array (up to 1000) of sensors. However, it requires the cell being lysed for the measurement as the detector is outside of the cell. This version will be particularly useful for training the system and establishing a large library of response patterns. The second version will be built by integrating both individual sensors and their corresponding detectors onto bar-coded nanoparticles which will be introduced into cells and read using surface enhanced resonance Raman scattering spectroscopy. This setup will use a smaller number of sensors (up to 30), but can be used to monitor responses in living cells in real time. In parallel we will develop methods for the controlled introduction of these particle libraries into cells. The third version is the stable integration of a plasmid based sensor library into the embryonic mouse stem cells with the aim to generate a transgenic sensor mouse. The stem cells also can be used for organotypic cultures and in vitro differentiation systems.The Molecular Nose will enable the systematic testing and rational interpretation of the behaviour of cellular networks. The technique is generic with a wide range of applications in both single cells and cell populations, including eminent biological problems such as the analysis of drug effects and prediction of side effects; stem cell differentiation with a view to eventually control differentiation; cell fate specification in order to support tissue engineering; genetic and biochemical networks for the production of desired proteins and metabolites by synthetically engineered pathways; and the investigation of adaptive network responses and evolution. Currently, we are lacking efficient experimental tools to analyse these complex interactions.