Knowledge-matching based computational framework for genome-scale metabolic model refinement
Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.
- Research Organization:
- Auburn University, AL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- SC0019181
- OSTI ID:
- 2478686
- Journal Information:
- Computer Aided Chemical Engineering, Journal Name: Computer Aided Chemical Engineering Vol. 49; ISSN 1570-7946
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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