Document Detail


Observation conflict resolution in steady-state metabolic network dynamics analysis.
MedLine Citation:
PMID:  22809305     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Steady state metabolic network dynamics analysis (SMDA) is a recently proposed computational metabolomics tool that (i) captures a metabolic network and its rules via a metabolic network database, (ii) mimics the reasoning of a biochemist, given a set of metabolic observations, and (iii) locates efficiently all possible metabolic activation/inactivation (flux) alternatives. However, a number of factors may cause the SMDA algorithm to eliminate feasible flux 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 subnetwork to query against. In this work, we first present and formalize these obstacles. Then, we propose techniques to eliminate them and present an experimental evaluation of our proposed techniques.
Authors:
A Ercument Cicek; Gultekin Ozsoyoglu
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of bioinformatics and computational biology     Volume:  10     ISSN:  0219-7200     ISO Abbreviation:  J Bioinform Comput Biol     Publication Date:  2012 Feb 
Date Detail:
Created Date:  2012-07-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101187344     Medline TA:  J Bioinform Comput Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  1240004     Citation Subset:  IM    
Affiliation:
Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH 44106, USA.
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