TY - JOUR
T1 - Description and analysis of metabolic connectivity and dynamics in the human red blood cell
AU - Kauffman, Kenneth J.
AU - Pajerowski, John David
AU - Jamshidi, Neema
AU - Palsson, Bernhard O.
AU - Edwards, Jeremy S.
N1 - Funding Information: Financial support was provided in part by a National Science Foundation Graduate Research Fellowship DGE-9616051 (to K.J.K.), a NASA Graduate Student Researchers Fellowship NGTS-50367 (to K.J.K.), the National Institutes of Health 1 P20 RR15588-01, the Department of Energy DE-FG02–01ER63200, and the University of Delaware Research Foundation LTR20010426.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/0035996969
U2 - 10.1016/S0006-3495(02)75198-9
DO - 10.1016/S0006-3495(02)75198-9
M3 - Article
C2 - 12124254
SN - 0006-3495
VL - 83
SP - 646
EP - 662
JO - Biophysical Journal
JF - Biophysical Journal
IS - 2
ER -