Evaluation of rate law approximations in bottom-up kinetic models of metabolism.
The mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.



In this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.



Overall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches.
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Bin
B
Du
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Daniel C
DC
Zielinski
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Erol S
ES
Kavvas
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.|Center for Bioinformatics Tuebingen (ZBIT), Sand 1, University of Tuebingen, Tübingen, 72076, Germany.
Andreas
A
Dräger
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Justin
J
Tan
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Zhen
Z
Zhang
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Kayla E
KE
Ruggiero
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Garri A
GA
Arzumanyan
Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA. palsson@ucsd.edu.|Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA. palsson@ucsd.edu.|Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark. palsson@ucsd.edu.
Bernhard O
BO
Palsson
D004798:Enzymes / Q000378:metabolism*; D007700:Kinetics; D008954:Models, Biological
D004798:Enzymes
Approximate rate laws; Kinetic modeling; Mass action kinetics; Metabolic modeling; Michaelis-Menten kinetics