IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v312y2015icp281-291.html
   My bibliography  Save this article

Algebraic equilibrium solution of tissue nitrogen quota in algae and the discrepancy between calibrated parameters and physiological properties

Author

Listed:
  • Port, Alexander
  • Bryan, Karin R.
  • Pilditch, Conrad A.
  • Hamilton, David P.
  • Bischof, Kai

Abstract

Tissue nutrient concentrations are a key factor in determining primary production in a variety of algae, for example the marine macroalga Ulva. We present a novel algebraic solution to calculate the equilibrium tissue nitrogen concentration or “quota” Q. The solution is derived from a classical mechanistic description of “luxury uptake” in marine macroalgae using a computer algebra system. Forced by ammonium (NH4+) and nitrate plus nitrite (NOx−) concentrations, water temperature and irradiance, equilibrium Q can be calculated directly without the need for numerical integration, and the model performs well in reproducing observations of Q in frondose Ulva spp. A Sobol’ global sensitivity analysis reveals that the degree of uncertainty in physiological parameters has a similar magnitude of influence on model output as the typical environmental range of nutrient forcing data. The environmental forcing variables NH4+ and NOx− together account for 60% of variance in model output, while the two most influential physiological parameters together account for another 32% of variance. Repeated parameter calibrations with random first guesses and evolutionary adaptations lead to broad and even multimodal distributions for some parameters, as well as values at the extremes of their literature ranges. This shows that although model performance as quantified by statistical measures is high, individual calibrations are not sufficient to give reliable parameter estimates that can be interpreted as physiological system properties.

Suggested Citation

  • Port, Alexander & Bryan, Karin R. & Pilditch, Conrad A. & Hamilton, David P. & Bischof, Kai, 2015. "Algebraic equilibrium solution of tissue nitrogen quota in algae and the discrepancy between calibrated parameters and physiological properties," Ecological Modelling, Elsevier, vol. 312(C), pages 281-291.
  • Handle: RePEc:eee:ecomod:v:312:y:2015:i:c:p:281-291
    DOI: 10.1016/j.ecolmodel.2015.05.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380015002409
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2015.05.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Brush, Mark J. & Nixon, Scott W., 2010. "Modeling the role of macroalgae in a shallow sub-estuary of Narragansett Bay, RI (USA)," Ecological Modelling, Elsevier, vol. 221(7), pages 1065-1079.
    2. P. Kaelo & M. M. Ali, 2006. "Some Variants of the Controlled Random Search Algorithm for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 130(2), pages 253-264, August.
    3. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    4. Zaldívar, J.M. & Bacelar, F.S. & Dueri, S. & Marinov, D. & Viaroli, P. & Hernández-García, E., 2009. "Modeling approach to regime shifts of primary production in shallow coastal ecosystems," Ecological Modelling, Elsevier, vol. 220(21), pages 3100-3110.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hongwei Shao & Tao Peng & Zhiwei Ji & Jing Su & Xiaobo Zhou, 2013. "Systematically Studying Kinase Inhibitor Induced Signaling Network Signatures by Integrating Both Therapeutic and Side Effects," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-16, December.
    2. Canal-Vergés, Paula & Potthoff, Michael & Hansen, Flemming Thorbjørn & Holmboe, Nikolaj & Rasmussen, Erik Kock & Flindt, Mogens R., 2014. "Eelgrass re-establishment in shallow estuaries is affected by drifting macroalgae – Evaluated by agent-based modeling," Ecological Modelling, Elsevier, vol. 272(C), pages 116-128.
    3. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
    4. Andrew White & Malachi Tolman & Howard D Thames & Hubert Rodney Withers & Kathy A Mason & Mark K Transtrum, 2016. "The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-26, December.
    5. Elba Raimúndez & Simone Keller & Gwen Zwingenberger & Karolin Ebert & Sabine Hug & Fabian J Theis & Dieter Maier & Birgit Luber & Jan Hasenauer, 2020. "Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-21, March.
    6. Joseph D Taylor & Samuel Winnall & Alain Nogaret, 2020. "Estimation of neuron parameters from imperfect observations," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-22, July.
    7. Xinxian Shao & Andrew Mugler & Justin Kim & Ha Jun Jeong & Bruce R Levin & Ilya Nemenman, 2017. "Growth of bacteria in 3-d colonies," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-19, July.
    8. Agus Hartoyo & Peter J Cadusch & David T J Liley & Damien G Hicks, 2019. "Parameter estimation and identifiability in a neural population model for electro-cortical activity," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-27, May.
    9. Christian A Tiemann & Joep Vanlier & Maaike H Oosterveer & Albert K Groen & Peter A J Hilbers & Natal A W van Riel, 2013. "Parameter Trajectory Analysis to Identify Treatment Effects of Pharmacological Interventions," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-15, August.
    10. Mikhail Chernov & Brett R. Dunn & Francis A. Longstaff, 2018. "Macroeconomic-Driven Prepayment Risk and the Valuation of Mortgage-Backed Securities," The Review of Financial Studies, Society for Financial Studies, vol. 31(3), pages 1132-1183.
    11. Zachary R Fox & Brian Munsky, 2019. "The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-23, January.
    12. Claudia Schillings & Mikael Sunnåker & Jörg Stelling & Christoph Schwab, 2015. "Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-16, August.
    13. Giorgos Minas & David A Rand, 2017. "Long-time analytic approximation of large stochastic oscillators: Simulation, analysis and inference," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-23, July.
    14. Marc Hafner & Heinz Koeppl & Martin Hasler & Andreas Wagner, 2009. "‘Glocal’ Robustness Analysis and Model Discrimination for Circadian Oscillators," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-10, October.
    15. Belssing Taruvinga, 2019. "Solving Selected Problems on American Option Pricing with the Method of Lines," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2019, March.
    16. Joshua Russell-Buckland & Christopher P Barnes & Ilias Tachtsidis, 2019. "A Bayesian framework for the analysis of systems biology models of the brain," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-29, April.
    17. Borut Kirn, 2019. "Visualization of Myocardial Strain Pattern Uniqueness with Respect to Activation Time and Contractility: A Computational Study," Data, MDPI, vol. 4(2), pages 1-7, May.
    18. Andrew J K Conlan & Ellen Brooks Pollock & Trevelyan J McKinley & Andrew P Mitchell & Gareth J Jones & Martin Vordermeier & James L N Wood, 2015. "Potential Benefits of Cattle Vaccination as a Supplementary Control for Bovine Tuberculosis," PLOS Computational Biology, Public Library of Science, vol. 11(2), pages 1-27, February.
    19. Rodrigo P. Rocha & Loren Koçillari & Samir Suweis & Michele Filippo De Grazia & Michel Thiebaut Schotten & Marco Zorzi & Maurizio Corbetta, 2022. "Recovery of neural dynamics criticality in personalized whole-brain models of stroke," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    20. Farzaneh Khajouei & Saurabh Sinha, 2018. "An information theoretic treatment of sequence-to-expression modeling," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-24, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:312:y:2015:i:c:p:281-291. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.