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首页> 外文会议>Pacific Symposium on Biocomputing 2004; Jan 6-10, 2004; Hawaii, USA >A MIXED INTEGER LINEAR PROGRAMMING (MILP) FRAMEWORK FOR INFERRING TIME DELAY IN GENE REGULATORY NETWORKS
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A MIXED INTEGER LINEAR PROGRAMMING (MILP) FRAMEWORK FOR INFERRING TIME DELAY IN GENE REGULATORY NETWORKS

机译：用于推断基因调控网络中时间延迟的混合整数线性编程（MILP）框架

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摘要

In this paper, an optimization based modeling and solution framework for inferring gene regulatory networks while accounting for time delay is described. The proposed framework uses the basic linear model of gene regulation. Boolean variables are used to capture the existence of discrete time delays between the various regulatory relationships. Subsequently, the time delay that best fits the expression profiles is inferred by minimizing the error between the predicted and experimental expression values. Computational experiments are conducted for both in numero and real expression data sets. The former reveal that if time delay is neglected in a system a priori known to be characterized with time delay then a significantly larger number of parameters are needed to describe the system dynamics. The real microarray data example reveals a considerable number of time delayed interactions suggesting that time delay is ubiquitous in gene regulation. Incorporation of time delay leads to inferred networks that are sparser. Analysis of the amount of variance in the data explained by the model and comparison with randomized data reveals that accounting for time delay explains more variance in real rather than randomized data.
机译：在本文中，描述了一种基于优化的建模和解决方案框架，用于在考虑时间延迟的情况下推断基因调控网络。提出的框架使用基因调控的基本线性模型。布尔变量用于捕获各种监管关系之间离散时间延迟的存在。随后，通过最小化预测和实验表达值之间的误差，推断出最适合表达谱的时间延迟。针对数字和真实表达数据集进行了计算实验。前者表明，如果忽略了系统中的时间延迟（已知的先验时间延迟特征），则需要大量参数来描述系统动力学。真实的微阵列数据示例显示了大量的时间延迟相互作用，这表明时间延迟在基因调控中无处不在。合并时间延迟会导致推断的网络稀疏。对模型解释的数据中的方差量进行分析，并与随机数据进行比较，结果表明，考虑到时间延迟，可以解释实际数据而不是随机数据中的更多方差。

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