Observation conflict resolution in steady-state metabolic network dynamics analysis.
Observation conflict resolution in steady-state metabolic network dynamics analysis.
复制标题
稳态代谢网络动力学分析中的观察冲突解决。
DOI：
10.1142/s0219720012400045
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发表时间：
2012
影响因子：
1
通讯作者：
Ozsoyoglu,Gultekin
中科院分区：
文献类型：
--
作者：
Cicek,AErcument;Ozsoyoglu,Gultekin
SMDA是最近提出的计算工具，其（i）通过（哺乳动物）代谢网络数据库捕获代谢网络及其规则，（ii）给定一组代谢观察结果，模仿生物化学家的推理，并有效地定位所有可能的代谢活化/失活替代方案。然而，许多因素可能导致SMDA算法排除可行的场景。这些因素包括（i）观察（测量）中的固有误差容限，（ii）缺乏将测量分类为正常与异常的知识，以及（iii）选择高度受限的代谢子网络进行查询。在这项工作中，我们提出并正式这些障碍。然后，我们提出了技术来消除它们，并提出了我们提出的技术的实验评估。
SMDA is a recently proposed computational tool that (i) captures a metabolic network and its rules via a (mammalian) metabolic network database, (ii) given a set of metabolic observations, mimics the reasoning of a biochemist, and locates efficiently all possible metabolic activation/inactivation alternatives. However, many factors may cause the SMDA algorithm to eliminate feasible scenarios. These factors include (i) inherent error margins in observations (measurements), (ii) lack of knowledge to classify measurements as normal versus abnormal, and (iii) choosing a highly constrained metabolic sub-network to query against. In this work, we present and formalize these obstacles. Then, we propose techniques to eliminate them, and present an experimental evaluation of our proposed techniques.