Kinetic Parameters Estimation of The Escherichia coli (E. coli) Model by Garra Rufa-inspired Optimization Algorithm (GRO)

• Published in: IEEE Access
• Main Author: Zain J.M.; Azrag M.A.K.; Yatin S.F.M.; Aldehim G.; Zain Z.M.; Shaiba H.; Alturki N.; Sakri S.; Mohamed A.; Jaber A.S.
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197533224&doi=10.1109%2fACCESS.2024.3422450&partnerID=40&md5=ed62e6c8dbb04b09f7c16d5bfaf2bd4f

Due to complex nature of metabolic pathways, E. coli metabolic model kinetic parameters are difficult to detect experimentally. Thus, obtaining accurate kinetic data for all reactions in an E. coli metabolic model is a technically-challenging process. So, Garra Rufa-inspired Optimization (GRO) Algorithm is applied to the primary metabolic network of E. coli as a model to estimate small-scale kinetic parameters and increase the kinetic accuracy. Also, the Differential Algebraic Equations (DAE) is used to represent the glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate routes, and acetate production pathways of Escherichia coli in the metabolic network. Based on the behavior of the Garra Rufa fish, a route is modelled in which particles are sorted into groups and each group is guided by the best value. In addition, the fitness of the group leaders determines whether or not these particles are able to switch groups. In this study, experimental data was used to estimate seven kinetic parameters. However, the numerical results of The Relative Error (RE) and the Mean Error (ME) reveal that the observed and anticipated data are in line with the results. As a result of this new method, it was discovered that small-scale and even whole-cell dynamic models can be estimated accurately. Authors