Dynamic energy budget theory

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The Dynamic Energy Budget (DEB) theory is a formal metabolic theory which provides a single quantitative framework to dynamically describe the aspects of metabolism (energy and mass budgets) of all living organisms at the individual level, based on assumptions about energy uptake, storage, and utilization [1][2] [3] [4][5] [6] [7] [8] [9]. The DEB theory adheres to stringent thermodynamic principles, is motivated by universally observed patterns, is non-species specific, and links different levels of biological organization (cells, organisms, and populations) as prescribed by the implications of energetics [8][9] [10][11]. Models based on the DEB theory have been successfully applied to over a 1000 species with real-life applications ranging from conservation, aquaculture, general ecology, and ecotoxicology [12][13] (see also the Add-my-pet collection). The theory is contributing to the theoretical underpinning of the emerging field of metabolic ecology.

The explicitness of the assumptions and the resulting predictions enable testing against a wide variety of experimental results at the various levels of biological organization [1] [2][8][14] [15]. The theory explains many general observations, such as the body size scaling relationships of certain physiological traits, and provides a theoretical underpinning to the widely used method of indirect calorimetry [4] [7] [8] [16]. Several popular empirical models are special cases of the DEB model, or very close numerical approximations [1] [16][17] .

Theoretical background[edit]

The theory presents simple mechanistic rules that describe the uptake and allocation of energy (and nutrients) and the consequences for physiological organization throughout an organism's life cycle, including the relationships of energetics with aging and effects of toxicants [1] [2][4][6][8]. Assumptions of the DEB theory are delineated in an explicit way, the approach clearly distinguishes mechanisms associated with intra‐ and interspecific variation in metabolic rates, and equations for energy flows are mathematically derived following the principles of physics and simplicity [1][2][18][19].

Cornerstones of the theory are:

  • conservation of mass, energy and time,
  • relationships between surface area and volume
  • stoichiometric constraints on production
  • organizational uncoupling of metabolic modules (assimilation, dissipation, growth)
  • strong and weak homeostasis (composition of compartments is constant; composition of the organism is constant when the food is constant)
  • substrate(s) from the environment is/are first converted to reserve(s) before being used for further metabolism

The theory specifies that an organism is made up two main compartments: (energy) reserve and structure. Assimilation of energy is proportional to surface area of the structure, and maintenance is proportional to its volume. Reserve does not require maintenance. Energy mobilization will depend on the relative amount of the energy reserve, and on the interface between reserve and structure. Once mobilized, the energy is split into two branches:

  • a fixed proportion (termed kappa, κ) is allocated to growth (increase of structural mass) and maintenance of structure, while
  • the remaining proportion (1- κ) is allocated to processes of maturation (increase in complexity, installation of regulation systems, preparation for reproduction) and maintaining the level of attained maturity (including, e.g., maintenance of defense systems).

The κ-rule therefore states that the processes of growth and maturation do not directly compete. Maintenance needs to be paid before allocating energy to other processes. [4][8]

In the context of energy acquisition and allocation, the theory recognizes three main developmental stages: embryo, which does not feed or reproduce, juvenile, which feeds but does not reproduce, and adult, which both feeds and is allocating energy to reproduction. Transitions between these life stages occur at events specified as birth and puberty, which are reached when energy invested into maturation (tracked as 'level of maturity') reaches a certain threshold. Maturity does not increase in the adult stage, and maturity maintenance is proportional to maturity [1][2] [4] [8].

Biochemical composition of reserve and structure is considered to be that of generalised compounds, and is constant (the assumption of strong homeostasis) but not necessarily identical. Biochemical transformation from food to reserve (assimilation), and from reserve to structure (growth) include overhead costs. These overheads, together with processes of somatic and maturity maintenance and reproduction overheads (inefficiencies in transformation from reserve to reproductive material), all contribute to the consumption of oxygen and production of carbon dioxide, i.e. metabolism [1][4][6][8].

DEB models[edit]

All dynamic energy budget models follow the energy budget of an individual organism throughout its life cycle; by contrast,"static" energy budget models describe a specific life stage or size of an organism [14][20]. The main advantage of the DEB-theory based model over most other models is its description of energy assimilation and utilization (reserve dynamics) simultaneously with decoupled processes of growth, development/ maturation, and maintenance [11][21] [22]. DEB theory specifies reserves as separate from structure: these are the two state variables that contribute to physical volume, and (in combination with reproduction buffer of adults) fully define the size of an individual. Maturity (also a state variable of the model) tracks how much energy has been invested into maturation, and therefore determines the life stage of the organism relative to maturity levels at which life stage transitions (birth and puberty) occur. Dynamics of the state variables are given by ordinary differential equations which include the major processes of energy uptake and use: assimilation, mobilization, maintenance, growth, maturation, and reproduction [1][2] [4][5] [7] [8].

  • Food is transformed into reserve, which fuels all other metabolic processes. The feeding rate is proportional to the surface area; food handling time and the transformation efficiency from food to reserve are independent of food density.
  • A fixed fraction (kappa) of mobilized reserve is allocated to somatic maintenance plus growth (soma), the rest on maturity maintenance plus maturation or reproduction. Maintenance has priority over other processes. Somatic maintenance is proportional to structural body volume, and maturity maintenance to maturity. Heating costs for endotherms and osmostic work (for fresh water organisms) are somatic maintenance costs that are proportional to surface area.
  • Stage transitions occur if the cumulated investment into maturation exceeds threshold values. Life stages typically are: embryo, juvenile, and adult. Reserve that is allocated to reproduction is first accumulated in a buffer. The rules for converting the buffer to gametes are species-specific (e.g. spawning can be once per season).

Parameters of the model are individual specific, but similarities between individuals of the same species yield species-specific parameter estimations [8][14][23]. DEB parameters are estimated from several types of data simultaneously [14][23][24][25]. Routines for data entry and parameter estimation are available as free software package DEBtool implemented in the MATLAB environment, with the process of model construction explained in a Wiki-style manual. Estimated parameters are collected in the online library called the Add-my-pet project.

The standard DEB model[edit]

The standard model quantifies the metabolism of an isomorph (organism that does not change in shape during ontogeny) that feeds on one type of food with a constant composition (therefore the weak homeostasis applies, i.e. the chemical composition of the body is constant). The state variables of the individual are 1 reserve, 1 structure, maturity, and (in the adult stage) the reproduction buffer. Parameter values are constant throughout life. The reserve density at birth equals that of the mother at egg formation. Foetuses develop similarly, but receive unrestricted amount of reserve from the mother during development.

Extensions of the standard model[edit]

DEB theory has been extended into many directions, such as

  • effects of changes in shape during growth (e.g. V1-morphs and V0-morphs)
  • non-standard embryo->juvenile->adult transitions, for example in holometabolic insects [26]
  • inclusion of more types of food (substrate), which requires synthesizing units to model
  • inclusion of more reserves (which is necessary for organisms that do not feed on other organisms) and more structures (which is necessary to deal with plants), or a simplified version of the model (DEBkiss) applicable in ecotoxicology [27][28]
  • the formation and excretion of metabolic products (which is a basis for syntrophic relationships, and useful in biotechnology)
  • the production of free radicals (linked to size and nutritional status) and their effect on survival (aging)
  • the growth of body parts (including tumours)
  • effects of chemical compounds (toxicants) on parameter values and the hazard rate (which is useful to establish no effect concentrations for environmental risk assessment): the DEBtox method
  • processes of adaptation (gene expression) to the availability of substrates (important in biodegradation)

A list and description of most common typified models can be found here.


The main criticism is directed to the formal presentation of the theory (heavy mathematical jargon), number of listed parameters, the symbol heavy notation, and the fact that modeled (state) variables and parameters are abstract quantities which cannot be directly measured, all making it less likely to reach its intended audience (ecologists) and be an "efficient" theory [2][18][19][29].

However, more recent publications aim to present the DEB theory in an "easier to digest" content to "bridge the ecology-mathematics gap" [2][18][19]. The general methodology of estimation of DEB parameters from data is described in van der Meer 2006; Kooijman et al 2008 shows which particular compound parameters can be estimated from a few simple observations at a single food density and how an increasing number of parameters can be estimated if more quantities are observed at several food densities. A natural sequence exists in which parameters can be known in principle. In addition, routines for data entry and scripts for parameter estimation are available as a free and documented software package DEBtool, aiming to provide a ready-to-use tool for users with less mathematical and programing background. Number of parameters, also pointed as relatively sparse for a bioenergetic model [10][20], vary depending on the main application and, because the whole life cycle of an organism is defined, the overall number of parameters per data-set ratio is relatively low [14][15] [30]. Linking the DEB (abstract) and measured properties is done by simple mathematical operations which include auxiliary parameters (also defined by the DEB theory and included in the DEBtool routines), and include also switching between energy-time and mass-time contexts [2][1][31][9]. Add my pet (AmP) project explores parameter pattern values across taxa. The DEB notation is a result of combining the symbols from the main fields of science (biology, chemistry, physics, mathematics) used in the theory, while trying to keep the symbols consistent [8]. As the symbols themselves contain a fair bit of information [1][2][8] (see DEB notation document), they are kept in most of the DEB literature.

Compatibility (and applicability) of DEB theory/models with other approaches[edit]

Dynamic energy budget theory presents a quantitative framework of metabolic organization common to all life forms, which could help to understand evolution of metabolic organization since the origin of life [5][8][10]. As such, it has a common aim with the other widely used metabolic theory: the West-Brown-Enquist (WBE) metabolic theory of ecology, which prompted side-by-side analysis of the two approaches [3][14][15] [32] . Though the two theories can be regarded as complimentary to an extent [11][33], they were built on different assumptions and have different scope of applicability [3][11][14][15]. In addition to a more general applicability, the DEB theory does not suffer from consistency issues pointed out for the WBE theory [3][11][15].


  • Add my pet (AmP) project is a collection of DEB models for over 1000 species, and explores patterns in parameter values across taxa. Routines for parameter exploration are available in AmPtool.
  • Models based on DEB theory can be linked to more traditional bioenergetic models without deviating from the underlying assumptions [11] [31]. This allows comparison and testing of model performance .
  • A DEB-module (physiological model based on DEB theory) was successfully applied to reconstruct and predict physiological responses of individuals under environmental constraints [34][35] [36]
  • A DEB-module is also featured in the eco-toxicological mechanistic models (DEBtox implementation) for modeling the sublethal effects of toxicants (e.g., change in reproduction or growth rate) [27][28][37][38][39]
  • Generality of the approach and applicability of the same mathematical framework to organisms of different species and life stages enables inter- and intra-species comparisons on the basis of parameter values [3][21], and theoretical/empirical exploration of patterns in parameter values in the evolutionary context [40], focusing for example on develompent [41][42] [22][43], energy utilization in a specific environment [44][45][46], reproduction [47], comparative energetics [48][49], and toxicological sensitivity linked to metabolic rates [50].
  • Studying patterns in body size scaling relationships: The assumptions of the model quantify all energy and mass fluxes in an organism (including heat, dioxygen, carbon dioxide, ammonia) while avoiding using the allometric relationships [8][21] [40]. In addition, same parameters describe same processes across species: for example, heating costs of endotherms (proportional to surface area) are regarded separate to volume-linked metabolic costs of both ectotherms and endotherms, and cost of growth, even though they all contribute to metabolism of the organism [8]. Rules for the co-variation of parameter values across species are implied by model assumptions, and the parameter values can be directly compared without dimensional inconsistencies which might be linked to allometric parameters [14][21]. Any eco-physiological quantity that can be written as function of DEB parameters which co-vary with size can, for this reason, also be written as function of the maximum body size [8].
  • DEB theory provides constraints on the metabolic organisation of sub-cellular processes [4] [10]. Together with rules for interaction between individuals (competition, syntrophy, prey-predator relationships), it also provides a basis to understand population and ecosystem dynamics [10] [51].

Many more examples of applications have been published in scientific literature [12].

External links[edit]

  • DEBwiki -main page with links to events, software tools, collections, research groups etc linked to DEB theory
  • Add my pet (AmP) project portal - collection of species for which DEB model parameter values were estimated and implications, inter-species parameter patterns
  • Zotero DEB library - collection of scientific literature on the DEB theory
  • DEB Information page

Further reading[edit]

See also[edit]


  1. ^ a b c d e f g h i j Sousa, Tânia; Domingos, Tiago; Kooijman, S. A. L. M. (2008). "From empirical patterns to theory: a formal metabolic theory of life". Philosophical Transactions of the Royal Society of London B: Biological Sciences. 363 (1502): 2453–2464. doi:10.1098/rstb.2007.2230. ISSN 0962-8436. PMC 2606805Freely accessible. PMID 18331988. 
  2. ^ a b c d e f g h i j Jusup, Marko; Sousa, Tânia; Domingos, Tiago; Labinac, Velimir; Marn, Nina; Wang, Zhen; Klanjšček, Tin. "Physics of metabolic organization". Physics of Life Reviews. 20: 1–39. doi:10.1016/j.plrev.2016.09.001. 
  3. ^ a b c d e van der Meer, Jaap (2006). "Metabolic theories in ecology". Trends in Ecology & Evolution. 21 (3): 136–140. doi:10.1016/j.tree.2005.11.004. ISSN 0169-5347. 
  4. ^ a b c d e f g h Kooijman, S. A. L. M. (2001). "Quantitative aspects of metabolic organization: a discussion of concepts". Philosophical Transactions of the Royal Society of London B: Biological Sciences. 356 (1407): 331–349. doi:10.1098/rstb.2000.0771. ISSN 0962-8436. PMC 1088431Freely accessible. PMID 11316483. 
  5. ^ a b c Kooijman, S. A. L. M.; Troost, T. A. (2007-02-01). "Quantitative steps in the evolution of metabolic organisation as specified by the Dynamic Energy Budget theory". Biological Reviews. 82 (1): 113–142. doi:10.1111/j.1469-185x.2006.00006.x. ISSN 1469-185X. 
  6. ^ a b c M., Kooijman, S. A. L. (1993). Dynamic energy budgets in biological systems : theory and applications in ecotoxicology. Cambridge: Cambridge University Press. ISBN 0521452236. OCLC 29596070. 
  7. ^ a b c M., Kooijman, S. A. L. (2000). Dynamic energy and mass budgets in biological systems. Kooijman, S. A. L. M. (2nd ed ed.). Cambridge, UK: Cambridge University Press. ISBN 0521786088. OCLC 42912283. 
  8. ^ a b c d e f g h i j k l m n o p Kooijman, S. A. L. M. (2010). Dynamic Energy Budget Theory for Metabolic Organisation. Cambridge University Press. ISBN 9780521131919. 
  9. ^ a b c Sousa, Tânia; Domingos, Tiago; Poggiale, J.-C.; Kooijman, S. A. L. M. (2010). "Dynamic energy budget theory restores coherence in biology". Philosophical Transactions of the Royal Society of London B: Biological Sciences. 365 (1557): 3413–3428. doi:10.1098/rstb.2010.0166. ISSN 0962-8436. PMID 20921042. 
  10. ^ a b c d e Nisbet, R. M.; Muller, E. B.; Lika, K.; Kooijman, S. A. L. M. "From molecules to ecosystems through dynamic energy budget models". Journal of Animal Ecology. 69 (6): 913–926. doi:10.1111/j.1365-2656.2000.00448.x. 
  11. ^ a b c d e f Maino, James L.; Kearney, Michael R.; Nisbet, Roger M.; Kooijman, Sebastiaan A. L. M. (2014-01-01). "Reconciling theories for metabolic scaling". Journal of Animal Ecology. 83 (1): 20–29. doi:10.1111/1365-2656.12085. ISSN 1365-2656. 
  12. ^ a b "Zotero DEB library of scientific literature". 
  13. ^ van der Meer, Jaap; Klok, Chris; Kearney, Michael R.; Wijsman, Jeroen W.M.; Kooijman, Sebastiaan A.L.M. "35years of DEB research". Journal of Sea Research. 94: 1–4. doi:10.1016/j.seares.2014.09.004. 
  14. ^ a b c d e f g h van der Meer, Jaap (2006). "An introduction to Dynamic Energy Budget (DEB) models with special emphasis on parameter estimation". Journal of Sea Research. 56 (2): 85–102. doi:10.1016/j.seares.2006.03.001. ISSN 1385-1101. 
  15. ^ a b c d e Kearney, Michael R.; White, Craig R. (2012-11-01). "Testing Metabolic Theories". The American Naturalist. 180 (5): 546–565. doi:10.1086/667860. ISSN 0003-0147. 
  16. ^ a b Kooijman, S.A.L.M. (1988). "The von Bertalanffy growth rate as a function of physiological parameters: a comparative analysis". In Hallam, G Thomas; Gross, J. L.; Levin, A.S. Mathematical Ecology - Proceedings Of The Autumn Course Research Seminars International Ctr For Theoretical Physics. #N/A. pp. 3–45. ISBN 9789814696777. 
  17. ^ "Energy budgets can explain body size relations". Journal of Theoretical Biology. 121 (3): 269–282. 1986-08-07. doi:10.1016/S0022-5193(86)80107-2. ISSN 0022-5193. 
  18. ^ a b c Ledder, Glenn (2014). "The Basic Dynamic Energy Budget Model and Some Implications". Letters in Biomathematics. 1:2: Pages 221–233. 
  19. ^ a b c Sarà, Gianluca; Rinaldi, Alessandro; Montalto, Valeria (2014-12-01). "Thinking beyond organism energy use: a trait-based bioenergetic mechanistic approach for predictions of life history traits in marine organisms". Marine Ecology. 35 (4): 506–515. doi:10.1111/maec.12106. ISSN 1439-0485. 
  20. ^ a b Lika, Konstadia; Nisbet, Roger M. (2000-10-01). "A Dynamic Energy Budget model based on partitioning of net production". Journal of Mathematical Biology. 41 (4): 361–386. doi:10.1007/s002850000049. ISSN 0303-6812. 
  21. ^ a b c d Zonneveld, C; Kooijman, S (1993). "Comparative kinetics of embryo development". Bulletin of Mathematical Biology. 55 (3): 609–635. doi:10.1016/s0092-8240(05)80242-3. 
  22. ^ a b Mueller, Casey A.; Augustine, Starrlight; Kooijman, Sebastiaan A.L.M.; Kearney, Michael R.; Seymour, Roger S. "The trade-off between maturation and growth during accelerated development in frogs". Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology. 163 (1): 95–102. doi:10.1016/j.cbpa.2012.05.190. 
  23. ^ a b Marques, G. M., Lika, K., Augustine, S., Pecquerie, L., Domingos, T. and Kooijman, S. A. L. M. "The AmP project: Comparing Species on the Basis of Dynamic Energy Budget Parameters". www.bio.vu.nl. Retrieved 2018-04-05. 
  24. ^ Lika, Konstadia; Kearney, Michael R.; Freitas, Vânia; Veer, Henk W. van der; Meer, Jaap van der; Wijsman, Johannes W.M.; Pecquerie, Laure; Kooijman, Sebastiaan A.L.M. "The "covariation method" for estimating the parameters of the standard Dynamic Energy Budget model I: Philosophy and approach". Journal of Sea Research. 66 (4): 270–277. doi:10.1016/j.seares.2011.07.010. 
  25. ^ Kooijman, S. A. L. M.; Sousa, T.; Pecquerie, L.; Meer, J. Van Der; Jager, T. (2008-11-01). "From food-dependent statistics to metabolic parameters, a practical guide to the use of dynamic energy budget theory". Biological Reviews. 83 (4): 533–552. doi:10.1111/j.1469-185x.2008.00053.x. ISSN 1469-185X. 
  26. ^ Llandres, Ana L.; Marques, Gonçalo M.; Maino, James L.; Kooijman, S. A. L. M.; Kearney, Michael R.; Casas, Jérôme (2015-08-01). "A dynamic energy budget for the whole life‐cycle of holometabolous insects". Ecological Monographs. 85 (3). doi:10.1890/14-0976.1/. ISSN 1557-7015. 
  27. ^ a b Jager, Tjalling; Zimmer, Elke I. "Simplified Dynamic Energy Budget model for analysing ecotoxicity data". Ecological Modelling. 225: 74–81. doi:10.1016/j.ecolmodel.2011.11.012. 
  28. ^ a b Jager, Tjalling; Martin, Benjamin T.; Zimmer, Elke I. "DEBkiss or the quest for the simplest generic model of animal life history". Journal of Theoretical Biology. 328: 9–18. doi:10.1016/j.jtbi.2013.03.011. 
  29. ^ Marquet, Pablo A.; Allen, Andrew P.; Brown, James H.; Dunne, Jennifer A.; Enquist, Brian J.; Gillooly, James F.; Gowaty, Patricia A.; Green, Jessica L.; Harte, John (2014-08-01). "On Theory in Ecology". BioScience. 64 (8): 701–710. doi:10.1093/biosci/biu098. ISSN 0006-3568. 
  30. ^ Kearney, Michael R.; Domingos, Tiago; Nisbet, Roger (2015-04-01). "Dynamic Energy Budget Theory: An Efficient and General Theory for Ecology". BioScience. 65 (4): 341–341. doi:10.1093/biosci/biv013. ISSN 0006-3568. 
  31. ^ a b Nisbet, Roger M.; Jusup, Marko; Klanjscek, Tin; Pecquerie, Laure (2012-03-15). "Integrating dynamic energy budget (DEB) theory with traditional bioenergetic models". Journal of Experimental Biology. 215 (6): 892–902. doi:10.1242/jeb.059675. ISSN 0022-0949. PMID 22357583. 
  32. ^ White, Craig R.; Kearney, Michael R.; Matthews, Philip G. D.; Kooijman, Sebastiaan A. L. M.; Marshall, Dustin J. (2011-12-01). "A Manipulative Test of Competing Theories for Metabolic Scaling". The American Naturalist. 178 (6): 746–754. doi:10.1086/662666. ISSN 0003-0147. 
  33. ^ Brown, James H.; Gillooly, James F.; Allen, Andrew P.; Savage, Van M.; West, Geoffrey B. (2004-07-01). "TOWARD A METABOLIC THEORY OF ECOLOGY". Ecology. 85 (7): 1771–1789. doi:10.1890/03-9000. ISSN 1939-9170. 
  34. ^ Kearney, Michael; Porter, Warren (2009-04-01). "Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges". Ecology Letters. 12 (4). doi:10.1111/j.1461-0248.2008.01277.x/full. ISSN 1461-0248. 
  35. ^ Huey, Raymond B.; Kearney, Michael R.; Krockenberger, Andrew; Holtum, Joseph A. M.; Jess, Mellissa; Williams, Stephen E. (2012-06-19). "Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation". Phil. Trans. R. Soc. B. 367 (1596): 1665–1679. doi:10.1098/rstb.2012.0005. ISSN 0962-8436. PMID 22566674. 
  36. ^ Kearney, Michael; Simpson, Stephen J.; Raubenheimer, David; Helmuth, Brian (2010-11-12). "Modelling the ecological niche from functional traits". Philosophical Transactions of the Royal Society of London B: Biological Sciences. 365 (1557): 3469–3483. doi:10.1098/rstb.2010.0034. ISSN 0962-8436. PMID 20921046. 
  37. ^ Jager, Tjalling (2015-08-11). "Making sense of chemical stress". leanpub.com (in undefined). Retrieved 2018-04-04. 
  38. ^ Jager, Tjalling; Heugens, Evelyn H. W.; Kooijman, Sebastiaan A. L. M. (2006-04-01). "Making Sense of Ecotoxicological Test Results: Towards Application of Process-based Models". Ecotoxicology. 15 (3): 305–314. doi:10.1007/s10646-006-0060-x. ISSN 0963-9292. 
  39. ^ Jager, Tjalling; Vandenbrouck, Tine; Baas, Jan; Coen, Wim M. De; Kooijman, Sebastiaan A. L. M. (2010-02-01). "A biology-based approach for mixture toxicity of multiple endpoints over the life cycle". Ecotoxicology. 19 (2): 351–361. doi:10.1007/s10646-009-0417-z. ISSN 0963-9292. 
  40. ^ a b Lika, Konstadia; Kearney, Michael R.; Kooijman, Sebastiaan A.L.M. "The "covariation method" for estimating the parameters of the standard Dynamic Energy Budget model II: Properties and preliminary patterns". Journal of Sea Research. 66 (4): 278–288. doi:10.1016/j.seares.2011.09.004. 
  41. ^ Kooijman, S.A.L.M.; Pecquerie, L.; Augustine, S.; Jusup, M. "Scenarios for acceleration in fish development and the role of metamorphosis". Journal of Sea Research. 66 (4): 419–423. doi:10.1016/j.seares.2011.04.016. 
  42. ^ Kooijman, S.A.L.M. "Metabolic acceleration in animal ontogeny: An evolutionary perspective". Journal of Sea Research. 94: 128–137. doi:10.1016/j.seares.2014.06.005. 
  43. ^ Kooijman, S. a. L. M. (1986-02-01). "What the hen can tell about her eggs: egg development on the basis of energy budgets". Journal of Mathematical Biology. 23 (2): 163–185. doi:10.1007/BF00276955. ISSN 0303-6812. 
  44. ^ Kooijman, S. A. L. M. (2013-03-01). "Waste to hurry: dynamic energy budgets explain the need of wasting to fully exploit blooming resources". Oikos. 122 (3): 348–357. doi:10.1111/j.1600-0706.2012.00098.x. ISSN 1600-0706. 
  45. ^ Lika, Konstadia; Augustine, Starrlight; Pecquerie, Laure; Kooijman, Sebastiaan A.L.M. "The bijection from data to parameter space with the standard DEB model quantifies the supply–demand spectrum". Journal of Theoretical Biology. 354: 35–47. doi:10.1016/j.jtbi.2014.03.025. 
  46. ^ Marn, Nina; Jusup, Marko; Legović, Tarzan; Kooijman, S.A.L.M.; Klanjšček, Tin. "Environmental effects on growth, reproduction, and life-history traits of loggerhead turtles". Ecological Modelling. 360: 163–178. doi:10.1016/j.ecolmodel.2017.07.001. 
  47. ^ Kooijman, Sebastiaan A. L. M.; Lika, Konstadia (2014-11-01). "Resource allocation to reproduction in animals". Biological Reviews. 89 (4): 849–859. doi:10.1111/brv.12082. ISSN 1469-185X. 
  48. ^ Lika, Konstadia; Kooijman, Sebastiaan A.L.M. "The comparative topology of energy allocation in budget models". Journal of Sea Research. 66 (4): 381–391. doi:10.1016/j.seares.2011.10.005. 
  49. ^ Kooijman, Sebastiaan A.L.M.; Lika, Konstadia. "Comparative energetics of the 5 fish classes on the basis of dynamic energy budgets". Journal of Sea Research. 94: 19–28. doi:10.1016/j.seares.2014.01.015. 
  50. ^ Baas, Jan; Kooijman, Sebastiaan A. L. M. (2015-04-01). "Sensitivity of animals to chemical compounds links to metabolic rate". Ecotoxicology. 24 (3): 657–663. doi:10.1007/s10646-014-1413-5. ISSN 0963-9292. 
  51. ^ Kooijman, S.A.L.M. (2014-03-11). "Population dynamics on basis of budgets". In Metz, Johan A.; Diekmann, Odo. The Dynamics of Physiologically Structured Populations. Springer. pp. 266–297. ISBN 9783662131596.