Structured Projection-Based Model Reduction With Application to Stochastic Biochemical Networks
Structured Projection-Based Model Reduction With Application to Stochastic Biochemical Networks
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DOI：
10.1109/tac.2017.2691315
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发表时间：
2015-10
影响因子：
6.8
通讯作者：
Aivar Sootla;James Anderson
中科院分区：
文献类型：
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作者：
Aivar Sootla;James Anderson
化学主方程（CME）是已知的提供生化反应网络的最高分辨率模型。不幸的是，即使模拟CME也可能是一项具有挑战性的任务。由于这个原因，已经提出了更简单的近似CME。在本文中，我们专注于这样一个模型，线性噪声近似（LNA）。具体来说，我们考虑最近提出的LNA时标分离方法的影响。我们表明，降阶LNA收敛到全阶模型的均方意义。以此为动机，我们推导出一个基于结构化投影的网络结构保持约简算法。我们讨论当这些结构化的投影存在，我们提出了凸优化算法，描述如何可以计算这样的投影。然后将该算法应用于酵母糖酵解途径的线性化随机LNA模型。
The chemical master equation (CME) is well known to provide the highest resolution models of a biochemical reaction network. Unfortunately, even simulating the CME can be a challenging task. For this reason, simpler approximations to the CME have been proposed. In this paper, we focus on one such model, the linear noise approximation (LNA). Specifically, we consider implications of a recently proposed LNA time-scale separation method. We show that the reduced-order LNA converges to the full-order model in the mean square sense. Using this as motivation, we derive a network structure-preserving reduction algorithm based on structured projections. We discuss when these structured projections exist and we present convex optimization algorithms that describe how such projections can be computed. The algorithms are then applied to a linearized stochastic LNA model of the yeast glycolysis pathway.