Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method.
Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method.
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DOI：
10.1371/journal.pcbi.0020082
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发表时间：
2006-07-21
影响因子：
4.3
通讯作者：
Germain RN
中科院分区：
文献类型：
--
作者：
Meier-Schellersheim M;Xu X;Angermann B;Kunkel EJ;Jin T;Germain RN
真核化学传感的信号网络是受体介导的跨膜信号、脂质修饰、蛋白质易位以及膜结合和胞质成分的差异激活/失活的复杂组合。因此，它为组合计算和实验分析提供了特别有趣的挑战。我们开发了一种新颖的详细分子信号传导模型，当用于模拟对吸引剂环磷酸腺苷 (cAMP) 的反应时，对盘基网柄菌化学传感做出了重要的预测。这些预测，包括出人意料地存在空间不对称、多相、环磷酸腺苷诱导的 PTEN 易位和磷脂酰肌醇-(3,4,5)P3 生成，通过定量单细胞显微镜进行实验验证，使我们对当前生物体中趋化剂诱导的生化极化的标准模型提出重大修改。这一成功建模工作的关键是使用“Simmune”，这是一种新软件包，支持轻松开发和测试细胞行为的详细计算表示。直观的界面允许用户根据特定分子结合位点相互作用的定义和分子的亚细胞定位来定义复杂的信号网络。它自动将此类输入转换为空间解析的模拟和所得信号网络的动态图形表示，可以以与湿实验室实验程序非常相似的方式进行探索。 Simmune 的这些功能对于此处介绍的模型开发和分析至关重要，并且可能在细胞生物学许多方面的计算研究中有用。细胞可以根据细胞外化学物质（化学引诱剂）浓度的微小局部差异来调整其迁移方向。了解这一过程（化学传感）需要分析响应细胞内信号分子的时间和位置依赖性行为，这对于实验和计算研究来说都是一个特别有趣的挑战。在这里，作者报告了一种新的详细分子模型的开发和测试，该模型是阿米巴盘基网柄菌化学传感装置与化学引诱剂环磷酸腺苷的反应。使用该模型进行的计算机模拟预测了两种重要细胞内信号分子的浓度和位置的意外且先前未报告的变化模式。 These predictions were experimentally verified using microscopy, suggesting the need for modifications to the current “standard” model of eukaryotic chemosensing.他们的模型中的高度细节是通过名为“Simmune”的新软件套件实现的，该软件套件允许生物学家使用图形界面输入有关分子相互作用的信息。无需用户编写任何方程，软件即可自动构建整体反应网络，模拟模型，并提供多种方式查看模拟细胞的生化情况。这种新工具应该可以帮助生物学家将细胞生物过程的定性表征转化为定量的预测模型。
The signaling network underlying eukaryotic chemosensing is a complex combination of receptor-mediated transmembrane signals, lipid modifications, protein translocations, and differential activation/deactivation of membrane-bound and cytosolic components. As such, it provides particularly interesting challenges for a combined computational and experimental analysis. We developed a novel detailed molecular signaling model that, when used to simulate the response to the attractant cyclic adenosine monophosphate (cAMP), made nontrivial predictions about Dictyostelium chemosensing. These predictions, including the unexpected existence of spatially asymmetrical, multiphasic, cyclic adenosine monophosphate–induced PTEN translocation and phosphatidylinositol-(3,4,5)P3 generation, were experimentally verified by quantitative single-cell microscopy leading us to propose significant modifications to the current standard model for chemoattractant-induced biochemical polarization in this organism. Key to this successful modeling effort was the use of “Simmune,” a new software package that supports the facile development and testing of detailed computational representations of cellular behavior. An intuitive interface allows user definition of complex signaling networks based on the definition of specific molecular binding site interactions and the subcellular localization of molecules. It automatically translates such inputs into spatially resolved simulations and dynamic graphical representations of the resulting signaling network that can be explored in a manner that closely parallels wet lab experimental procedures. These features of Simmune were critical to the model development and analysis presented here and are likely to be useful in the computational investigation of many aspects of cell biology. Cells can orient their migration in response to small local differences in the concentration of extracellular chemicals (chemoattractants). Understanding this process (chemosensing) requires analyzing the time and position-dependent behavior of the signaling molecules within the responding cell, making it an especially interesting challenge for both experimental and computational investigation. Here, the authors report the development and testing of a new detailed molecular model of the chemosensing apparatus of the amoeba Dictyostelium discoidium reacting to the chemoattractant cyclic adenosine monophosphate. Computer simulations performed using this model predicted unexpected and previously unreported patterns of changes in the concentration and location of two important intracellular signaling molecules. These predictions were experimentally verified using microscopy, suggesting the need for modifications to the current “standard” model of eukaryotic chemosensing. The high degree of detail in their model was made possible by a new software suite called “Simmune,” which allows biologists to enter information about molecular interactions using a graphical interface. Without requiring the user to write any equations, the software automatically constructs the overall reaction network, simulates the model, and provides several ways to view the biochemistry of simulated cells. This new tool should help biologists to translate qualitative representations of cell biological processes into quantitative, predictive models.
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影响因子：
4.8
作者：
Chan, PC;Chen, YL;Chen, HC
通讯作者：
Chen, HC
影响因子：
1.2
作者：
Huang, JHY;Rajkovic, A;Goode, S
通讯作者：
Goode, S
DOI：
10.1073/pnas.0932835100
发表时间：
2003-06-24
影响因子：
11.1
作者：
Das, S;Dixon, JE;Cho, WW
通讯作者：
Cho, WW
影响因子：
4.1
作者：
McConnachie, G;Pass, I;Downes, CP
通讯作者：
Downes, CP
影响因子：
56.9
作者：
Lodowski, DT;Pitcher, JA;Tesmer, JJG
通讯作者：
Tesmer, JJG