scholarly journals MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

2015 ◽  
Vol 11 (1) ◽  
pp. 137-145 ◽  
Author(s):  
A. Marcel Willemsen ◽  
Diana M. Hendrickx ◽  
Huub C. J. Hoefsloot ◽  
Margriet M. W. B. Hendriks ◽  
S. Aljoscha Wahl ◽  
...  

This paper presents MetDFBA, a new approach incorporating experimental metabolomics time-series into constraint-based modeling. The method can be used for hypothesis testing and predicting dynamic flux profiles.

2020 ◽  
Vol 117 (10) ◽  
pp. 3006-3017 ◽  
Author(s):  
Carolina Shene ◽  
Paris Paredes ◽  
Liset Flores ◽  
Allison Leyton ◽  
Juan A. Asenjo ◽  
...  

2012 ◽  
Vol 110 (3) ◽  
pp. 792-802 ◽  
Author(s):  
K. Höffner ◽  
S. M. Harwood ◽  
P. I. Barton

2013 ◽  
Vol 163 (2) ◽  
pp. 637-647 ◽  
Author(s):  
E. Grafahrend-Belau ◽  
A. Junker ◽  
A. Eschenroder ◽  
J. Muller ◽  
F. Schreiber ◽  
...  

2019 ◽  
Author(s):  
Lin Liu ◽  
Alexander Bockmayr

AbstractIntegrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic flux balance analysis (dFBA), resource balance analysis (RBA) or dynamic enzyme-cost flux balance analysis (deFBA) extending classical flux balance analysis (FBA) in various directions, there have been no constraint-based methods so far that allow predicting the dynamics of metabolism taking into account both macromolecule production costs and regulatory events.In this paper, we introduce a new constraint-based modeling framework named regulatory dynamic enzyme-cost flux balance analysis (r-deFBA), which unifies dynamic modeling of metabolism, cellular resource allocation and transcriptional regulation in a hybrid discrete-continuous setting.With r-deFBA, we can predict discrete regulatory states together with the continuous dynamics of reaction fluxes, external substrates, enzymes, and regulatory proteins needed to achieve a cellular objective such as maximizing biomass over a time interval. The dynamic optimization problem underlying r-deFBA can be reformulated as a mixed-integer linear optimization problem, for which there exist efficient solvers.


2010 ◽  
Vol 12 (2) ◽  
pp. 150-160 ◽  
Author(s):  
Adam L. Meadows ◽  
Rahi Karnik ◽  
Harry Lam ◽  
Sean Forestell ◽  
Brad Snedecor

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