Special Issue "Metabolic Network Models"
A special issue of Metabolites (ISSN 2218-1989).
Deadline for manuscript submissions: closed (31 August 2012)
Dr. Kyongbum Lee (Website)
Department of Chemical and Biological Engineering, Tufts University, Room 142, 4 Colby Street, Medford, MA 02155, USA
Interests: adipose tissue metabolism; liver drug transformation; dynamic models of metabolic networks; targeted metabolomics
Network models have been instrumental in advancing quantitative knowledge of cellular metabolism by characterizing the systems-level features and properties that arise from the biochemical interactions between metabolites, enzymes and regulatory molecules. Network models are now widely used in both basic and applied studies, ranging from investigations on the evolutionary origins of hierarchical modularity in metabolism to design of synthetic pathways for the overproduction of commercially useful molecules. By exploiting parallel advances in genomics, proteomics and bioinformatics, significant progress has been achieved in modeling metabolic networks, especially in the reconstruction and characterization of whole cell metabolic networks.
Many challenges remain, however, in developing dynamic models capable of predicting the response of cellular metabolism to environmental perturbations or genetic modifications. Given the size and complexity of metabolic networks, new modeling approaches are needed to incorporate existing and new knowledge on regulation, account for uncertainty, and systematically construct an identifiable model whose parameters can be robustly estimated from data. Therefore, this special issue of Metabolites will be dedicated for publishing current advances on dynamic metabolic network models, multi-scale and multi-resolution models, transcriptional and allosteric regulation, integration with signaling and other biochemical networks, parameter estimation from metabolomics data, and modeling of noise and uncertainty.
Prof. Kyongbum Lee
- metabolic network
- dynamic model
- model reduction
- transcriptional regulation
- allosteric regulation
- metabolomics data
- parameter estimation
- network flexibility
- robust design