Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning.
Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning.
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DOI：
10.1016/j.mbs.2008.08.013
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发表时间：
2009-01
影响因子：
4.3
通讯作者：
Faeder JR
中科院分区：
文献类型：
--
作者：
An GC;Faeder JR
细胞内信号/合成通路的特征越来越广泛。然而，虽然这些途径可以在静态图表中显示，但实际上它们存在着一定程度的动态复杂性，这是导致细胞行为异质性的原因。多条平行路径同时存在并相互作用，限制了将各种已确定的机制整合为一个紧密结合的整体的能力。计算方法被认为是连接这些知识的一种手段，以帮助理解整个系统动力学。由于生物医学研究的最终目标是识别和发展治疗方式，计算表示法必须有足够的细节来促进这一“工程”过程。增加挑战的是，这种类型的表示必须在不完全知识的永久状态下发生。我们提出了一种建模方法来解决这一挑战，既详细又定性。这种方法被称为“动态知识表示法”，旨在成为科学发现迭代周期的一个组成部分。利用细胞内信号通路模拟软件BioNetGen(BNG)对Toll样受体4(TLR-4)信号转导通路进行了模拟。该模型的信息基础是一系列关于(TLR-4)信号调制的参考文献，以及一些具体的初步研究论文，以帮助表征该途径中特定的机制步骤。该模型详细说明了所表示的途径的组成，但定性说明了用于执行反应的具体反应系数。通过产生肿瘤坏死因子(TNF)来衡量对模拟脂多糖(LPS)注射的反应性。模拟运行包括评估在10、100、1000和10000注射脂多糖的初始剂量依赖反应，以及随后在模拟时间的∼27h增加脂多糖在10、100、1000和10000以及在10000的第二剂量注射的预适应行为。对该模型的“敲除”版本的模拟允许进一步检查信号级联中的相互作用。该模型呈现出剂量依赖性的肿瘤坏死因子对内毒素刺激增加的反应曲线。预适应模拟显示，预适应剂量具有类似的剂量依赖关系，导致对随后的内毒素攻击的反应减弱--这是一种“耐受”动态。这些反应与文献中报道的动力学相吻合。此外，模拟的“基因敲除”结果表明，以锌环指蛋白A20和抑制因子kappa B蛋白(IκB)为代表的双重负反馈控制机制的存在和必要性，以便有效地衰减初始刺激信号和随后的预适应“耐受”行为。我们提供了一个使用TLR-4信号通路的详细的、定性的动态知识表示的例子，它的控制机制和关于预适应的整体行为。这种方法的目的是演示一种方法，该方法将在基础科学级别生成的大量机械知识转换为可执行的框架，该框架可以提供一种“概念模型验证”的手段。这既允许“检查”机械假说的动态后果，也允许创建针对生物医学研究的工程目标的整体模型的模块组件。希望这份文件能增加以这种方式表达和交流知识的使用，并促进全社会知识的串联和整合。
Intracellular signaling/synthetic pathways are being increasingly extensively characterized. However, while these pathways can be displayed in static diagrams, in reality they exist with a degree of dynamic complexity that is responsible for heterogeneous cellular behavior. Multiple parallel pathways exist and interact concurrently, limiting the ability to integrate the various identified mechanisms into a cohesive whole. Computational methods have been suggested as a means of concatenating this knowledge to aid in the understanding of overall system dynamics. Since the eventual goal of biomedical research is the identification and development of therapeutic modalities, computational representation must have sufficient detail to facilitate this “engineering” process. Adding to the challenge, this type of representation must occur in a perpetual state of incomplete knowledge. We present a modeling approach to address this challenge that is both detailed and qualitative. This approach is termed “dynamic knowledge representation,” and is intended to be an integrated component of the iterative cycle of scientific discovery. BioNetGen (BNG), a software platform for modeling intracellular signaling pathways, was used to model the toll-like receptor 4 (TLR-4) signal transduction cascade. The informational basis of the model was a series of reference papers on modulation of (TLR-4) signaling, and some specific primary research papers to aid in the characterization of specific mechanistic steps in the pathway. This model was detailed with respect to the components of the pathway represented, but qualitative with respect to the specific reaction coefficients utilized to execute the reactions. Responsiveness to simulated lipopolysaccharide (LPS) administration was measured by tumor necrosis factor (TNF) production. Simulation runs included evaluation of initial dose-dependent response to LPS administration at 10, 100, 1000, and 10000, and a subsequent examination of preconditioning behavior with increasing LPS at 10, 100, 1000 and 10000 and a secondary dose of LPS at 10000 administered at ∼27 h of simulated time. Simulations of “knockout” versions of the model allowed further examination of the interactions within the signaling cascade. The model demonstrated a dose-dependent TNF response curve to increasing stimulus by LPS. Preconditioning simulations demonstrated a similar dose-dependency of preconditioning doses leading to attenuation of response to subsequent LPS challenge—a “tolerance” dynamic. These responses match dynamics reported in the literature. Furthermore, the simulated “knockout” results suggested the existence and need for dual negative feedback control mechanisms, represented by the zinc ring-finger protein A20 and inhibitor kappa B proteins (IκB), in order for both effective attenuation of the initial stimulus signal and subsequent preconditioned “tolerant” behavior. We present an example of detailed, qualitative dynamic knowledge representation using the TLR-4 signaling pathway, its control mechanisms and overall behavior with respect to preconditioning. The intent of this approach is to demonstrate a method of translating the extensive mechanistic knowledge being generated at the basic science level into an executable framework that can provide a means of “conceptual model verification.” This allows for both the “checking” of the dynamic consequences of a mechanistic hypothesis and the creation of a modular component of an overall model directed at the engineering goal of biomedical research. It is hoped that this paper will increase the use of knowledge representation and communication in this fashion, and facilitate the concatenation and integration of community-wide knowledge.
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影响因子：
9.9
作者：
Oda, Kanae;Kitano, Hiroaki
通讯作者：
Kitano, Hiroaki
影响因子：
5.5
作者：
Marshall, John C.
通讯作者：
Marshall, John C.
DOI：
10.1126/stke.3442006re6
发表时间：
2006-07-18
期刊：
Science's STKE : signal transduction knowledge environment
影响因子：
--
作者：
Hlavacek, William S;Faeder, James R;Fontana, Walter
通讯作者：
Fontana, Walter
影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
Wang, J
影响因子：
1.4
作者：
An, Gary;Faeder, James;Vodovotz, Yoram
通讯作者：
Vodovotz, Yoram