Stochastic neural network models for gene regulatory networks
Stochastic neural network models for gene regulatory networks
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DOI：
10.1109/cec.2003.1299570
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Tianhai Tian;K. Burrage
中科院分区：
文献类型：
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作者：
Tianhai Tian;K. Burrage
基因表达谱分析技术的最新进展提供了大量的基因表达数据。这就提出了通过数学建模对基因组动态进行功能性理解的可能性。由于基因表达涉及内在噪声，随机模型对于更好地描述基因调控网络至关重要。然而，大规模基因表达数据集的随机建模仍处于非常早期的发展阶段。本文通过在神经网络模型中引入随机过程，提出了一些能够描述大规模基因网络中间调控的随机模型。泊松随机变量被用来表示合成和退化过程中的随机事件。对于具有归一化浓度的表达数据，使用指数或正态随机变量来实现波动。使用一个网络与三个基因，我们展示了如何使用随机模拟研究噪声的影响下的基因表达模式的鲁棒性和稳定性，以及如何使用随机模型来预测细胞群体中的表达水平的统计分布。讨论表明，随机神经网络模型可以更好地描述基因调控网络，并为数学模型的合理性提供了衡量标准。
Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in population of cells. The discussion suggest that stochastic neural network models can give better description of gene regulatory networks and provide criteria for measuring the reasonableness o mathematical models.