A morphometric method for correcting phytoplankton cell volume estimates

Cell volume calculations are often used to estimate biomass of natural phytoplankton assemblages. Such estimates may be questioned due to morphological differences in the organisms present. Morphometric analysis of 8 species representative of phytoplankton types found in the Great Lakes shows significant differences in cell constituent volumes. Volume of physiologically inert wall material ranges from nil, in some flagellates, to over 20% of the total cell volume in certain diatoms. Likewise, “empty” vacuole may comprise more than 40% of the total cell volume of some diatoms, but less than 3% of the volume of some flagellates. In the organisms investigated, the total carbon containing cytoplasm ranged from 52% to 98% of the total cell volume and the metabolizing biovolume ranged from 30% to 82%. Although these differences complicate direct biomass estimation, morphometric analysis at the ultrastructural level may provide ecologically valuable insights.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41732/1/709_2005_Article_BF01275650.pd

Modeling MYC-dependent regulation of gene expression and cell metabolism in B-cell lymphomas

The oncogene MYC plays an important role in B cell lymphoma pathogenesis. Despite more than 30 years of MYC research there are still open questions concerning its function and how to target MYC in lymphomagenesis. Thus, this work aims to examine the causal relationships between MYC and the transcriptome and metabolome in a B cell lymphoma cell line by computational methods. The data set covers RNA-seq and mass spectrometry measurements of the same cell line. The underlying data is purely observational, no intervention is needed since causal inference techniques enable virtual experiments in theory. The first part of this thesis addresses three issues: First, the analysis of the RNA-seq data from cells with overexpressed MYC is challenging since MYC is a transcriptional amplifier. There is no de novo activation of genes by the elevated MYC, but an amplification of all presently expressed genes. This behavior is accompanied with an increase in cell size and an increase of RNA amount. Thus, the comparison of lymphoma cells with a high MYC expression with normal B cells by RNA-seq standard pipelines is difficult, since current normalization methods require a constant RNA amount across samples. I present a method that uses Drosophila melanogaster cells as a spike-in to calibrate the data to the number of cells in the sample (Taruttis et al., 2017). I demonstrate that, in case of transcriptional amplification in the B cell lymphoma cell line the use of an external spike-in is mandatory to observe the global gene expression changes. Furthermore, the Drosophila melanogaster spike-in normalization outperforms other calibration methods, including the use of the commercially available ERCC spike-ins. Second, Maathuis et al. (2010) presented the first high throughput analysis of virtual intervention experiments. Their ground-breaking IDA method (Maathuis et al., 2009) will have a lasting effect on the field of systems biology. Further developments of the IDA method recommended a subsampling strategy for the estimation of causal effects from observational data (Stekhoven et al., 2012). I extend IDA and its extension CStaR by analyzing the distribution of causal effects and call the method Accumulation IDA (aIDA) (Taruttis et al., 2015). aIDA improves the prediction of causal effects in comparison to Maathuis et al. (2009) and (Stekhoven et al., 2012). Third, causal structure learning by the PC algorithm (Spirtes and Glymour, 1991; Kalisch and Bühlmann, 2007), the first step of both IDA and aIDA, assumes that the underlying structure is sparse. However, the application of the spike-in methods to B cell lymphoma data sets with MYC overexpression results in highly correlated data. Thus, the underlying causal structure is highly likely not sparse. I assume that this is a consequence of the global role of Myc in gene expression (Lin et al., 2012; Nie et al., 2012). Thus, we observe no technical artifact but a real biological process. I show that using the MMHC algorithm instead of the PC algorithm together with my accumulation method outperforms aIDA for highly correlated datasets. However, the MMHC-aIDA method breaks down, too, when the density of the underlying causal structure becomes too high. The second part of the thesis presents a causal inference analysis of a B cell lymphoma cell line. We decided for the P493-6 cell line due to its doxycycline-dependent promoter to switch MYC on or off, which allows for an examination of the causal relationships of MYC under the same epigenetic conditions. RNA-seq and mass spectrometry data are measurements of the transcriptome and the metabolome of the cell line and are the input of the causal inference analysis. I show that the selection of the method to estimate the causal effects highly depends on the data structure. While the highly correlated RNA seq dataset shows the best results with the MMHC-aIDA method, the mass spectrometry data performs well with aIDA. The analysis of RNA-seq data shows that MYC upregulates most of genes in the dataset. MYC further shows a positive causal effect on most of the metabolites. These findings are in line with the hypothesis that MYC is a transcriptional amplifier. Some of the causal effects of MYC on the transcriptome and metabolome are already known, others can be high priority candidates for future wet lab experiments