Repository logo
Log in(current)
Repository logoMIT Open ScholarshipDSpace@MIT
  1. Home
  2. MIT Open Access Articles
  3. MIT Open Access Articles
  4. Exploring the gap between dynamic and constraint-based models of metabolism

Exploring the gap between dynamic and constraint-based models of metabolism

Thumbnail Image
Download
Name

Tidor_Exploring the gap.pdf

Size

3.95 MB

Format

Adobe PDF

Checksum (MD5)

76216ba27a441537c5f8471e674a5656

Author(s)
Machado, Daniel
•
Costa, Rafael S.
•
Rocha, Isabel
•
Tidor, Bruce
•
Ferreira, Eugenio C.
Date Issued
January 2012
Journal
Metabolic Engineering
Publisher
Elsevier
Citation
Machado, Daniel, Rafael S. Costa, Eugenio C. Ferreira, Isabel Rocha, and Bruce Tidor. “Exploring the Gap Between Dynamic and Constraint-Based Models of Metabolism.” Metabolic Engineering 14, no. 2 (March 2012): 112–119.
Version
Author's final manuscript
Abstract
Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of Escherichia coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
http://creativecommons.org/licenses/by-nc-nd/4.0/
Persistent DSpace Link
http://hdl.handle.net/1721.1/101078
DOI of Published Version
https://doi.org/10.1016/j.ymben.2012.01.003
Repository logo
PrivacyPermissionsAccessibilityContact us
Repository logo
Notify us about copyright concerns.