Event Title

Parameterization of Variable Growth in an Individual Based Model of Freshwater Phytoplankton Mortality

Mentor 1

Dr. John Berges

Location

Union Wisconsin Room

Start Date

24-4-2015 2:30 PM

End Date

24-4-2015 3:45 PM

Description

Current models of phytoplankton dynamics include terms for growth, but they rarely address mortality. In previous work, an individual-based model of phytoplankton in a pond ecosystem was created that explicitly included death triggered by environmental factors (temperature, irradiance and nutrients). However, in this model, death was triggered by somewhat arbitrary thresholds, stochastically, and without involvement of any subcellular biochemical mechanisms. In the current project, the model has been modified (using the Netlogo modeling environment) by developing subcellular components that represent our current understanding of the biochemical mechanisms involved in cell death, specifically including production of reactive oxygen species (ROS) in response to specific metabolic events, and induction of cell death proteases (metacaspases) that require nitrogen and energy. In correctly parameterizing the new model, it is critical to understand how much variability to allow individual phytoplankton cells, e.g. given a mean population rate of growth or nutrient uptake, or sensitivity to a condition causing death, how much variation should there be among individuals? In order to begin to estimate this, we have conducted laboratory growth experiments on multiple sets of very small numbers of cultured phytoplankton cells, using a novel approach. Two representative species, a chlorophyte (Pseudokirchneriella subcapitata) and a diatom (Cyclotella), were grown in 96 well microplates, starting with dilutions that should be equivalent to 1 to 2 cells per well, and monitored for growth rate, maximum cell numbers at plateau and variable fluorescence emissions at plateau (an index of photosynthetic competence) using a fluorescence plate reader (Molecular Dynamics, model) with an excitation wavelength 460 nm and an emission wavelength of 680 nm (i.e. matching those of chlorophyll a).So far the model behavior is fairly similar to that of the previous versions, in that there is initial phase of little growth followed by an exponential growth phase which leads to the cells reaching carrying capacity of the system. At this point our culture crashes, which suggests that our current parameterization is incorrect and need to be further refined. As for the growth of the cells in the microplates we have seen that the cells grow better in the clear over the black polypropylene plates and that having the light source on the bottom of the plate works better than below the plates, as this helped to minimize the heating of the plates. Our ultimate goal in incorporating of cell death mechanisms with the associated metabolic costs into our model is to allow comparison of the competitive abilities of hypothetical models cells that either possess or do not possess these mechanism. This will help us to understand how cell death may have evolved in unicellular organisms to begin with. Since multicellular life evolve from unicellular life, understanding the origins of cell death may provide insight into human diseases that result from dysfunctional cell, including Parkinson’s and certain forms of dementia.

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Apr 24th, 2:30 PM Apr 24th, 3:45 PM

Parameterization of Variable Growth in an Individual Based Model of Freshwater Phytoplankton Mortality

Union Wisconsin Room

Current models of phytoplankton dynamics include terms for growth, but they rarely address mortality. In previous work, an individual-based model of phytoplankton in a pond ecosystem was created that explicitly included death triggered by environmental factors (temperature, irradiance and nutrients). However, in this model, death was triggered by somewhat arbitrary thresholds, stochastically, and without involvement of any subcellular biochemical mechanisms. In the current project, the model has been modified (using the Netlogo modeling environment) by developing subcellular components that represent our current understanding of the biochemical mechanisms involved in cell death, specifically including production of reactive oxygen species (ROS) in response to specific metabolic events, and induction of cell death proteases (metacaspases) that require nitrogen and energy. In correctly parameterizing the new model, it is critical to understand how much variability to allow individual phytoplankton cells, e.g. given a mean population rate of growth or nutrient uptake, or sensitivity to a condition causing death, how much variation should there be among individuals? In order to begin to estimate this, we have conducted laboratory growth experiments on multiple sets of very small numbers of cultured phytoplankton cells, using a novel approach. Two representative species, a chlorophyte (Pseudokirchneriella subcapitata) and a diatom (Cyclotella), were grown in 96 well microplates, starting with dilutions that should be equivalent to 1 to 2 cells per well, and monitored for growth rate, maximum cell numbers at plateau and variable fluorescence emissions at plateau (an index of photosynthetic competence) using a fluorescence plate reader (Molecular Dynamics, model) with an excitation wavelength 460 nm and an emission wavelength of 680 nm (i.e. matching those of chlorophyll a).So far the model behavior is fairly similar to that of the previous versions, in that there is initial phase of little growth followed by an exponential growth phase which leads to the cells reaching carrying capacity of the system. At this point our culture crashes, which suggests that our current parameterization is incorrect and need to be further refined. As for the growth of the cells in the microplates we have seen that the cells grow better in the clear over the black polypropylene plates and that having the light source on the bottom of the plate works better than below the plates, as this helped to minimize the heating of the plates. Our ultimate goal in incorporating of cell death mechanisms with the associated metabolic costs into our model is to allow comparison of the competitive abilities of hypothetical models cells that either possess or do not possess these mechanism. This will help us to understand how cell death may have evolved in unicellular organisms to begin with. Since multicellular life evolve from unicellular life, understanding the origins of cell death may provide insight into human diseases that result from dysfunctional cell, including Parkinson’s and certain forms of dementia.