Individual-based modeling of phytoplankton: Evaluating approaches for applying the cell quota model

Hellweger, Ferdi L.; Kianirad, Ehsan

doi:10.1016/j.jtbi.2007.08.020

Cited by 28 publications

(23 citation statements)

References 33 publications

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“…However, for the purpose of this investigation, the current routine's results are acceptable. The optimization algorithm found variability for the parameters that was within the range of values used in other models (10,23,31,39) and observed in laboratory and field populations (14,15). The CV values of the optimization run with the lowest error were adopted for the baseline simulation (Table 3).…”

Section: Resultsmentioning

confidence: 99%

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Use of Agent-Based Modeling To Explore the Mechanisms of Intracellular Phosphorus Heterogeneity in Cultured Phytoplankton

Fredrick

Berges

Twining

et al. 2013

Appl Environ Microbiol

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There can be significant intraspecific individual-level heterogeneity in the intracellular P of phytoplankton, which can affect the population-level growth rate. Several mechanisms can create this heterogeneity, including phenotypic variability in various physiological functions (e.g., nutrient uptake rate). Here, we use modeling to explore the contribution of various mechanisms to the heterogeneity in phytoplankton grown in a laboratory culture. An agent-based model simulates individual cells and their intracellular P. Heterogeneity is introduced by randomizing parameters (e.g., maximum uptake rate) of daughter cells at division. The model was calibrated to observations of the P quota of individual cells of the centric diatom Thalassiosira pseudonana, which were obtained using synchrotron X-ray fluorescence (SXRF). A number of simulations, with individual mechanisms of heterogeneity turned off, then were performed. Comparison of the coefficient of variation (CV) of these and the baseline simulation (i.e., all mechanisms turned on) provides an estimate of the relative contribution of these mechanisms. The results show that the mechanism with the largest contribution to variability is the parameter characterizing the maximum intracellular P, which, when removed, results in a CV of 0.21 compared to a CV of 0.37 with all mechanisms turned on. This suggests that nutrient/element storage capabilities/mechanisms are important determinants of intrapopulation heterogeneity. Phytoplankton play an important role in the ecology and biogeochemistry of freshwater and marine systems. Thus, understanding their composition and physiology (e.g., carbon fixation rate) is important. The elemental composition of phytoplankton was originally considered to be relatively uniform (1). However, subsequent research has shown that it can vary between species (2) or clones (3) and within a species under different conditions (4, 5). The composition can also vary within a species under the same conditions. Two measurements of intracellular P are commonly made: cell-based P (mol P/cell), which we refer to as P quota, and biomass-normalized P (mol P/mol C), which we call internal P content. While P quota is generally easier to measure on a cellspecific basis, it is the internal P content that is important for many physiological processes. Recent observations of intracellular P in individual cells have shown significant intraspecific heterogeneity in laboratory and field populations, with coefficients of variations (CV) ranging from 0.08 to 2.11 for P quota and 0.10 to 2.10 for internal P content (Table 1).Heterogeneity in intracellular P may have important consequences because it can result in heterogeneity in physiology (e.g., photosynthesis rate), which can have significant effects on community ecology (6). Specifically, for phytoplankton the specific growth rate (biomass accumulation) is generally considered to depend on the cell quota of the limiting nutrient according to Droop's function (4). Because the function is nonlinear, heterogenei...

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Section: Resultsmentioning

confidence: 99%

“…The original formulation proposed by Droop (4) uses the P quota (q and q 0 in units of P/cell), but it is also commonly applied with internal P content (q and q 0 in units of mol P/mol C) (9,23). If q drops below q 0 , then the cell does not perform photosynthesis.…”

Section: Methodsmentioning

confidence: 99%

Use of Agent-Based Modeling To Explore the Mechanisms of Intracellular Phosphorus Heterogeneity in Cultured Phytoplankton

Fredrick

Berges

Twining

et al. 2013

Appl Environ Microbiol

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“…Individuals reproduce when their carbon content increases from a minimum, m 0 , to m $ 2m 0 . At the point of division, the parent's biomass is divided between the two offspring, with a small partitioning inequality introduced in order to avoid artifactual synchronization effects (Hellweger and Kianirad 2007). …”

Section: Methodsmentioning

confidence: 99%

Environmental selection and resource allocation determine spatial patterns in picophytoplankton cell size

Clark

Lenton

Williams

et al. 2013

Limnology & Oceanography

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Here we describe a new trait-based model for cellular resource allocation that we use to investigate the relative importance of different drivers for small cell size in phytoplankton. Using the model, we show that increased investment in nonscalable structural components with decreasing cell size leads to a trade-off between cell size, nutrient and light affinity, and growth rate. Within the most extreme nutrient-limited, stratified environments, resource competition theory then predicts a trend toward larger minimum cell size with increasing depth. We demonstrate that this explains observed trends using a marine ecosystem model that represents selection and adaptation of a diverse community defined by traits for cell size and subcellular resource allocation. This framework for linking cellular physiology to environmental selection can be used to investigate the adaptive response of the marine microbial community to environmental conditions and the adaptive value of variations in cellular physiology.A key challenge facing marine biogeochemical modelers is how best to represent the important role that diverse, rapidly evolving microbial populations play in marine biogeochemical cycles. Simple models, which may include just a single state variable for all phytoplankton (e.g., Fasham et al. 1990), often ignore the distinct functional role that different taxa perform (e.g., silicifying, or calcifying organisms), while more complicated models, which attempt to explicitly resolve multiple functional types (Le Quéré et al. 2005), can face severe practical problems in terms of the number of organism-level measurements and parameters required to describe the model (Anderson 2005;Flynn 2006). A still more fundamental problem lies in how best to represent adaptive or evolutionary processes, which are typically ignored in current state-of-the-art marine ecosystem models.Recent approaches have attempted to address some of these issues (Follows and Dutkiewicz 2011). For example, Follows et al. (2007) used a Monte-Carlo sampling method with a marine ecosystem model that included a diverse phytoplankton community, described by a set of empirically motivated traits with trade-offs. Emergent patterns in phytoplankton biogeography were in broad agreement with observations, illustrating the importance of environmental selection in determining spatial and temporal patterns in phytoplankton biogeography. The same model has also been used to examine drivers for latitudinal patterns in biodiversity (Barton et al. 2010) and the effect of chromatic adaptation on community structure in oligotrophic environments (Hickman et al. 2010). Meanwhile, Bruggeman and Kooijman (2007) described a biodiversity-based marine ecosystem model in which species were defined by a generic model with continuous trait values for investment in nutrient-and light-harvesting machinery. In their model, trade-offs between traits emerged naturally as a result of different allocation strategies. When configured for a representative oligotrophic open-ocean sit...

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“…Methods: Development of agent-based EBPR model. The agent based model developed was implemented using the iAlgae framework previously described by Hellweger and Kianirad (2007). The model simulates the behavior of PAOs, GAOs and general heterotroph population in an EBPR system.…”

Section: Model/data Comparison Of Heterogeneitymentioning

confidence: 99%

“…In the biological sciences, ABMs have traditionally been applied to higher-level organisms (e.g. fish, wolves), but there is a recent trend to simulate microbes (see review in Hellweger and Bucci 2009), such as the cell quota distribution in phytoplankton (Hellweger and Kianirad 2007). More recently, agent-based models (ABMs) have been applied to biological wastewater treatment systems.…”

Section: Introductionmentioning

confidence: 99%

Is the whole the sumof its parts? Agent-basedmodelling of wastewater treatment systems

Schuler

Majed

Bucci

et al. 2011

Water Science and Technology

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Agent-based models (ABMS) simulate individual units within a system, such as the bacteria in a biological wastewater treatment system. This paper outlines past, current and potential future applications of ABMs to wastewater treatment. ABMs track heterogeneities within microbial populations, and this has been demonstrated to yield different predictions of bulk behaviors than the conventional, "lumped" approaches for enhanced biological phosphorus removal (EBPR) completely mixed reactors systems. Current work included the application of the ABM approach to bacterial adaptation/evolution, using the model system of individual EBPR bacteria that are allowed to evolve a kinetic parameter (maximum glycogen storage) in a competitive environment. The ABM approach was successfully implemented to a simple anaerobic-aerobic system and it was found the differing initial states converged to the same optimal solution under uncertain hydraulic residence times associated with completely mixed hydraulics. In another study, an ABM was developed and applied to simulate the heterogeneity in intracellular polymer storage compounds, including polyphosphate (PP), in functional microbial populations in enhanced biological phosphorus removal (EBPR) process. The simulation results were compared to the experimental measurements of single-cell abundance of PP in polyphosphate accumulating organisms (PAOs), performed using Raman microscopy. The model-predicted heterogeneity was generally consistent with observations, and it was used to investigate the relative contribution of external (different life histories) and internal (biological) mechanisms leading to heterogeneity. In the future, ABMs could be combined with computational fluid dynamics (CFD) models to understand incomplete mixing, more intracellular states and mechanisms can be incorporated, and additional experimental verification is needed.

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Individual-based modeling of phytoplankton: Evaluating approaches for applying the cell quota model

Cited by 28 publications

References 33 publications

Use of Agent-Based Modeling To Explore the Mechanisms of Intracellular Phosphorus Heterogeneity in Cultured Phytoplankton

Use of Agent-Based Modeling To Explore the Mechanisms of Intracellular Phosphorus Heterogeneity in Cultured Phytoplankton

Environmental selection and resource allocation determine spatial patterns in picophytoplankton cell size

Is the whole the sumof its parts? Agent-basedmodelling of wastewater treatment systems

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