Discrete Dynamic Modeling: A Network Approach for Systems Pharmacology
Discrete Dynamic Modeling: A Network Approach for Systems Pharmacology
复制标题
离散动态建模：系统药理学的网络方法
DOI：
10.1007/978-3-319-44534-2_5
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
R. Albert
中科院分区：
文献类型：
--
作者：
S. Steinway;Rui;R. Albert
系统药理学是一个跨学科领域，旨在将系统生物学的理论和实验工具应用于药物开发。目标是超越药物与它结合的靶标之间的相互作用，探索药物对受疾病影响的细胞网络的作用。多年来，关于基因、蛋白质和小分子之间的调节关系的大量信息已经获得。同样，在疾病期间，这些系统的放松管制也已广为人知。然而，许多知识差距仍然存在。有大量的定性或相对的信息与信号通路的激活有关，但缺乏动力学和时间信息。离散动态建模提供了一种方法，通过整合零碎的和定性的相互作用信息来创建信号转导途径的预测模型。使用离散动态建模，生物调节关系的结构（静态）网络可以在不使用动力学参数的情况下转换为数学模型。该模型可以描述生物系统随时间的动态变化，无论是在正常情况下还是在扰动情况下。在本章中，我们讨论离散动态建模的基本原理，因为它与系统药理学有关。作为一个例子，我们将这种方法应用于先前构建的表皮衍生生长因子受体（EGFR）信号的药效学模型。我们(1)将该模型转化为两种类型的离散模型，布尔模型和三状态模型，(2)展示EGFR抑制剂（如吉非替尼）如何抑制肿瘤生长，(3)模拟基因组变异如何增强EGFR抑制肿瘤生长的效果。我们认为离散动态模型可用于促进系统药理学的许多目标。其中包括了解个体差异如何导致药物反应的变异性，以及根据个体遗传差异确定哪种药物是最好的。
Systems pharmacology is an interdisciplinary field that aims to apply the theoretical and experimental tools of systems biology to drug development. The goal is to go beyond the interaction between a drug and the target to which it binds to explore drug effects on the cellular networks affected by disease. Over the years, vast amounts of information about the regulatory relationships among genes, proteins, and small molecules have been acquired. Similarly, there is much known about the deregulation of these systems during disease. However, many knowledge gaps still exist. There is an abundance of qualitative or relative information related to the activation of signaling pathways, but a paucity of kinetic and temporal information. Discrete dynamic modeling provides a means to create predictive models of signal transduction pathways by integrating fragmentary and qualitative interaction information. Using discrete dynamic modeling, a structural (static) network of biological regulatory relationships can be translated into a mathematical model without the use of kinetic parameters. This model can describe the dynamics of a biological system over time, both in normal and in perturbation scenarios. In this chapter, we discuss the fundamentals of discrete dynamic modeling as it pertains to systems pharmacology. As an example, we apply this methodology to a previously constructed pharmacodynamic model of epidermal derived growth factor receptor (EGFR) signaling. We (1) translate this model into two types of discrete models, a Boolean model and a three-state model, (2) show how the effects of an EGFR inhibitor (such as gefitinib) can suppress tumor growth, and (3) model how genomic variants can augment the effect of EGFR inhibition in tumor growth. We argue that discrete dynamic models can be used to facilitate many of the goals of systems pharmacology. These include understanding how individual differences contribute to variability in drug response and determining which drugs would be best depending on individual genetic differences.
影响因子：
2
作者：
Albert, R;Othmer, HG
通讯作者：
Othmer, HG
影响因子：
2
作者：
Chaves, M;Albert, R;Sontag, ED
通讯作者：
Sontag, ED