Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

Abstract. The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO 2 ) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO 2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO 2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO 2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

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Background: This study aimed to analyze the role of Dickopff1 [DKK1] as a potential differentiating agent for the neuroblastoma cell line SHSY5Y and neurospheres derived from it. Materials and Methods: SHSY5Y neurospheres were formed from undifferentiated adherent cultures. The cellular properties and gene expression were used to study the effect of DKK1 on SHSY5Y-formed neurospheres. Its effect on SHSY5Y neuronal differentiation was also studied. Results: SHSY5Y adherent undifferentiated cells were grown as neurospheres. Treatment of neurospheres resulted in their fragmentation. It also resulted in reduced mRNA expression of markers of cancer stem cells, pluripotency, and proliferation [p≤0.05]. DKK1 treatment also resulted in reduced mRNA expression of β-catenin and TCF genes. There was significantly higher expression of neuronal differentiation genes in SHSY5Y adherent cells grown in complete DMEM media containing DKK1 as compared to cells grown without DKK1. We also found DKK1 synergized with retinoic acid-induced differentiation of neuroblastoma cells. Conclusion: DKK1 was able to convincingly abrogate neurosphere formation and promote neuronal differentiation of SHSY5Y cells, including synergy with retinoic acid. This was accompanied by corresponding changes in mRNA markers of cancer stem cells, pluripotency and proliferation. These results may have implications for neuroblastoma stemness and differentiation.