the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical Note: A modified formulation of dynamic energy budget theory for faster computation of biological growth
Abstract. The mass conservation equation in the presence of boundary fluxes and chemical reactions from non-equilibrium thermodynamics is used to derive a modified dynamic energy budget (mDEB) model. Compared to the standard dynamic energy budget (sDEB) model (Kooijman, 2009), this modified formulation does not place the dilution effect in the mobilization kinetics of reserve biomass, and it maintains the partition principle for reserve mobilization dynamics for both linear and non-linear kinetics. Overall, the mDEB model shares most features with the sDEB model. However, for biological growth that requires multiple nutrients, the mDEB model is computationally much more efficient by not requiring numerical iterations for obtaining the specific growth rate. In an example of modelling the growth of Thalassiosira weissfloggi in a nitrogen-limiting chemostat, the mDEB model was found to have almost the same accuracy as the sDEB model, while requiring almost half of the computing time of the sDEB model. Since the sDEB model has been successfully applied in numerous studies, we believe that the mDEB model can help improve the modelling of biological growth and the associated ecosystem processes in various contexts.
- Preprint
(798 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 21 Dec 2024)
-
RC1: 'Comment on egusphere-2024-2282', Anonymous Referee #1, 17 Sep 2024
reply
This MS represents a useful contribution to bioenergetic modeling, beyond its stated emphasis as a technical note focused on accelerating computations of biological growth. This is because an overwhelmingly large proportion of the literature on DEB models is constructed round the assumptions of Kooijman’s “standard” DEB model – called sDEB in the paper. One of the author’s previous publications (Tang and Riley 2023) is an exception, an interesting reformulation (rDEB) that offers (and tests) a representation of reserve homeostasis with a more transparent link to known subcellular processes. The new model (mDEB) in this paper is a further variant that reduces in a limiting case to the sDEB model. The main emphasis, computational speed if of course important for two reasons: (i) population simulations and (ii) parameter estimation using computationally intensive methods. The latter is important as DEB models typically use state variables that are not directly observable.
The reasoning behind the mDEB model is presented clearly. However, if I understand it correctly, the “high enzyme condition” (line 134) is essential for the numerical improvements proposed to be valid for systems with multiple reserves. Equation (22) for synthesizing unit kinetics approximates the form in Kooijmans work in the limiting situation where there is no upper limit to the reaction rate of the final step (release of product). If this is included calculation of the growth rate (mu) may well require handling a set of implicit equations and there can be situations in more complex models where these do not have a unique solution (see Pfab et al 2022 - 10.1093/conphys/coac026 for an example). THis point does note require changes in the main text, but, if correct, might merit mention in the discussion.
A few very minor points:
- Derivation of the mDEB equations via equation (2) and Gauss’s theorem will intimidate some readers. Those who do understand eq (2) may worry about the first term of the RHS (divergence of J). This is zero everywhere in the interior of the cell (by assumption), so at minimum the text needs to state that the integral is over the volume (including the surface).
- In line 121, there is mention of “observed weak homeostasis”. I doubt this; weak homeostasis is a central assumption of sDEB, but I know of no examples where an organism’s composition remains constant through its lifetime.
- The sentence beginning line129 “the Von Bertalanffy…..” is not strictly accurate.
Citation: https://doi.org/10.5194/egusphere-2024-2282-RC1
Model code and software
model scripts Jinyun Tang https://github.com/jinyun1tang/bg_mdeb
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
141 | 40 | 65 | 246 | 4 | 5 |
- HTML: 141
- PDF: 40
- XML: 65
- Total: 246
- BibTeX: 4
- EndNote: 5
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1