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原文連結
論文資訊
- 類型：已發表論文
- 日期：2013-12-02
摘要
Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables-usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a 統計ly rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through 隨機 differential equations whose trajectories contain 資訊 about missing model parts. We first apply this semiautomatic procedure to a pharmacokineti
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