Effect of weight-added regulatory networks on constraint-based metabolic models of Escherichia coli.
Though the traditional flux balance analysis (FBA) has successfully predicted intracellular fluxes using stoichiometry, linear programming, and metabolic pathways, it has not automatically reflected any potential genetic effects in response to the environmental changes in the metabolic pathways. Recently, attempts have been made to impose regulatory constraints described as a binary system, such as if-then rules using Boolean logic, on the traditional FBA. Yet this binary system has limited the representation of complex interactions between transcriptional factors and target genes. In addition, it is difficult to intuitively or visually recognize changes to the interactions among stimuli, sensors/regulatory proteins, and target genes due to the properties of the if-then rule systems. Thus, in the current work, in order to improve upon the previous approaches, we have (1) determined weight values after deducing from the inequality signs of the relative strengths of interactions between sensors/regulators and target genes based on the experimental data of gene expression, (2) divided expression level into eight levels, and (3) constructed and incorporated weight-added regulatory networks using the defined symbols within the FBA. Finally, a model system with the central metabolic pathway of Escherichia coli was examined under the aerobic batch culture with glucose and acetate reutilization and the aerobic and anaerobic batch culture with glucose only to demonstrate our suggested approach.
Lee
Sung Gun
SG
Department of Chemical Engineering, College of Engineering, Pusan National University, Pusan 609-735, Republic of Korea. lee_73@pusan.ac.kr
Park
Jun Hyung
JH
Hou
Bo Kyeng
BK
Kim
Young Han
YH
Kim
Cheol Min
CM
Hwang
Kyu Suk
KS
D019342:Acetic Acid / Q000378:metabolism; D000332:Aerobiosis; D000693:Anaerobiosis; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism*; D015870:Gene Expression; D005798:Genes, Bacterial; D005947:Glucose / Q000378:metabolism; D008954:Models, Biological; D049490:Systems Biology
D005947:Glucose; D019342:Acetic Acid