AI Article Synopsis
- The human red blood cell (hRBC) metabolic network, while simpler than other cellular networks, requires computational modeling for effective study.
- Systems science approaches, including phase planes and statistical analysis, help identify key dynamic features and "pools" of metabolites within hRBC metabolism.
- The research highlights the value of systems science in providing insights into the functioning of individual enzymes and metabolites, enhancing our understanding of complex biological systems.
Article Abstract
The human red blood cell (hRBC) metabolic network is relatively simple compared with other whole cell metabolic networks, yet too complicated to study without the aid of a computer model. Systems science techniques can be used to uncover the key dynamic features of hRBC metabolism. Herein, we have studied a full dynamic hRBC metabolic model and developed several approaches to identify metabolic pools of metabolites. In particular, we have used phase planes, temporal decomposition, and statistical analysis to show hRBC metabolism is characterized by the formation of pseudoequilibrium concentration states. Such equilibria identify metabolic "pools" or aggregates of concentration variables. We proceed to define physiologically meaningful pools, characterize them within the hRBC, and compare them with those derived from systems engineering techniques. In conclusion, systems science methods can decipher detailed information about individual enzymes and metabolites within metabolic networks and provide further understanding of complex biological networks.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1302176 | PMC |
| http://dx.doi.org/10.1016/S0006-3495(02)75198-9 | DOI Listing |
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