Predictive modeling of signaling crosstalk during C. elegans vulval development.
Predictive modeling of signaling crosstalk during C. elegans vulval development.
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秀丽隐杆线虫期间信号传导串扰的预测建模。
DOI：
10.1371/journal.pcbi.0030092
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发表时间：
2007-05
影响因子：
4.3
通讯作者：
Henzinger, Thomas A.
中科院分区：
文献类型：
--
作者：
Fisher, Jasmin;Piterman, Nir;Hajnal, Alex;Henzinger, Thomas A.
秀丽隐杆线虫外阴发育为研究动物发育过程中细胞命运决定和模式形成过程提供了重要的范例。虽然许多控制外阴细胞命运的基因已被确定，但它们如何协调自己产生一个强大的和不变的细胞命运模式尚未完全理解。在这里，我们已经开发了一个动态的计算模型，结合目前的机制理解基因相互作用在这个图案化的过程。我们的模型的一个关键特征是包含表皮生长因子受体（EGFR）和LIN-12/Notch信号通路之间的多种串扰模式，它们共同决定了六个外阴前体细胞（VPC）的命运。计算分析，使用模型检查技术，提供了新的生物学见解的监管网络VPC的命运规范和预测新的负反馈回路。此外，我们的分析表明，大多数影响外阴发育的突变导致稳定的命运模式，尽管VPC之间的同步性的变化。对这种稳健性基础的计算搜索表明，EGFR介导的诱导信号传导和LIN-12 /Notch介导的侧向信号传导途径的顺序激活是实现稳定细胞命运模式的关键。我们通过实验证明了野生型动物中诱导和侧向信号传导途径的激活与显示不稳定命运模式的突变体中顺序信号传导的丢失之间的时间延迟;因此，验证了我们的建模工作提供的两个关键预测。通过我们的建模研究获得的见解进一步证实了执行和分析机械模型来研究复杂生物行为的有用性。系统生物学旨在获得对生命系统的系统级理解。为了实现这样的理解，我们需要建立方法和技术来理解生物系统的全部复杂性。其中一种尝试是使用设计用于构建和分析复杂计算机化系统的方法来模拟生物系统。用一种动态的、可执行的语言描述生物学中的机制模型，为表示时间和并行性提供了很大的优势，而时间和并行性是生物行为的重要特征。此外，自动分析方法可用于确保计算模型与其所基于的生物数据的一致性。我们已经开发了一个动态的计算模型，描述了目前的机械理解细胞命运的决定，在C。elegans vulval development，为研究动物发育提供了一个重要的范式。我们的模型是现实的，再现了最新的实验观察，允许在硅片实验，并分析自动工具。我们的模型的分析提供了新的见解的时间方面的细胞命运图案化的过程，并预测新的模式之间的相互作用的信号通路。这些生物学的见解，这也是实验验证，进一步证实了动态计算模型的有用性，以调查复杂的生物行为。
Caenorhabditis elegans vulval development provides an important paradigm for studying the process of cell fate determination and pattern formation during animal development. Although many genes controlling vulval cell fate specification have been identified, how they orchestrate themselves to generate a robust and invariant pattern of cell fates is not yet completely understood. Here, we have developed a dynamic computational model incorporating the current mechanistic understanding of gene interactions during this patterning process. A key feature of our model is the inclusion of multiple modes of crosstalk between the epidermal growth factor receptor (EGFR) and LIN-12/Notch signaling pathways, which together determine the fates of the six vulval precursor cells (VPCs). Computational analysis, using the model-checking technique, provides new biological insights into the regulatory network governing VPC fate specification and predicts novel negative feedback loops. In addition, our analysis shows that most mutations affecting vulval development lead to stable fate patterns in spite of variations in synchronicity between VPCs. Computational searches for the basis of this robustness show that a sequential activation of the EGFR-mediated inductive signaling and LIN-12 / Notch-mediated lateral signaling pathways is key to achieve a stable cell fate pattern. We demonstrate experimentally a time-delay between the activation of the inductive and lateral signaling pathways in wild-type animals and the loss of sequential signaling in mutants showing unstable fate patterns; thus, validating two key predictions provided by our modeling work. The insights gained by our modeling study further substantiate the usefulness of executing and analyzing mechanistic models to investigate complex biological behaviors. Systems biology aims to gain a system-level understanding of living systems. To achieve such an understanding, we need to establish the methodologies and techniques to understand biological systems in their full complexity. One such attempt is to use methods designed for the construction and analysis of complex computerized systems to model biological systems. Describing mechanistic models in biology in a dynamic and executable language offers great advantages for representing time and parallelism, which are important features of biological behavior. In addition, automatic analysis methods can be used to ensure the consistency of computational models with biological data on which they are based. We have developed a dynamic computational model describing the current mechanistic understanding of cell fate determination during C. elegans vulval development, which provides an important paradigm for studying animal development. Our model is realistic, reproduces up-to-date experimental observations, allows in silico experimentation, and is analyzable by automatic tools. Analysis of our model provides new insights into the temporal aspects of the cell fate patterning process and predicts new modes of interaction between the signaling pathways involved. These biological insights, which were also validated experimentally, further substantiate the usefulness of dynamic computational models to investigate complex biological behaviors.
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影响因子：
64.8
作者：
Alon, U;Surette, MG;Leibler, S
通讯作者：
Leibler, S
影响因子：
4.3
作者：
Efroni S;Harel D;Cohen IR
通讯作者：
Cohen IR
影响因子：
64.8
作者：
Barkai, N;Leibler, S
通讯作者：
Leibler, S
影响因子：
7
作者：
Efroni, S;Harel, D;Cohen, LR
通讯作者：
Cohen, LR
DOI：
10.1073/pnas.0409433102
发表时间：
2005-02-08
影响因子：
11.1
作者：
Fisher, J;Piterman, N;Harel, D
通讯作者：
Harel, D