Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling.
Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling.
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DOI：
10.1186/1752-0509-3-98
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发表时间：
2009-09-28
影响因子：
--
通讯作者：
Theis FJ
中科院分区：
文献类型：
--
作者：
Wittmann DM;Krumsiek J;Saez-Rodriguez J;Lauffenburger DA;Klamt S;Theis FJ
对调控和信号网络的理解长期以来一直是系统生物学的核心目标。关于这些网络的知识主要是定性的，这允许布尔模型的构建，其中组件的状态是“关”或“开”。虽然这些模型通常能够捕捉网络的基本行为，但它们永远无法重现浓度水平的详细时间过程。然而，如今，实验产生越来越多的定量数据。因此，一个明显的问题是，如何使用定性模型来解释和预测这些实验的结果。在这篇文章中，我们提出了一个规范的方式转换布尔连续模型，其中使用多元多项式插值允许转换成一个系统的常微分方程（ODE）的逻辑运算。该方法是标准化的，可以很容易地应用于大型网络。其他的，更有限的方法来完成这项任务进行了简要的审查和比较。此外，我们还讨论和推广了布尔模型和连续模型之间关系的已有理论结果。作为一个测试用例的逻辑模型转换成一个广泛的连续的ODE模型描述的T细胞的激活。我们讨论了如何确定该模型的参数，以便定量的实验结果进行解释和预测，包括多个配体浓度和不同配体的结合亲和力的时间过程。这表明，从连续模型中，我们可以获得从离散模型中不明显的生物学见解。所提出的方法将促进建模和实验之间的互动。此外，它提供了一个简单的方法来应用定量分析方法定性描述的系统。
The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments. In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one. The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems.
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影响因子：
4.3
作者：
Saez-Rodriguez J;Simeoni L;Lindquist JA;Hemenway R;Bommhardt U;Arndt B;Haus UU;Weismantel R;Gilles ED;Klamt S;Schraven B
通讯作者：
Schraven B
影响因子：
--
作者：
ZADEH, LA
通讯作者：
ZADEH, LA
用交叉反应性非富含配体激活后自动反应性T细胞的自反应性降低。
影响因子：
15.3
作者：
Munder, Markus;Bettelli, Estelle;Monney, Laurent;Slavik, Jacqueline M;Nicholson, Lindsay B;Kuchroo, Vijay K
通讯作者：
Kuchroo, Vijay K
影响因子：
64.8
作者：
Daniels, Mark A.;Teixeiro, Emma;Palmer, Ed
通讯作者：
Palmer, Ed
影响因子：
4.4
作者：
Kemp, Melissa L.;Wille, Lucia;Lauffenburger, Douglas A.
通讯作者：
Lauffenburger, Douglas A.