COPASI---a COmplex PAthway SImulator
Bioinformatics
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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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.