Nuclear DNA and protein content evaluation in Taxus plant cell cultures using multiparameter flow cytometry

Plant cell cultures of Taxus provide the most reliable production methods for the anti-cancer drug paclitaxel. In order to comprehend the inherent culture heterogeneity and production variability in cell cultures, it is essential that the cellular metabolism is studied at the genomic level. Genomic stability in plant cell cultures is crucial as it affects cell growth and division, metabolite accumulation and protein synthesis. A rapid and efficient method to prepare nuclei suspensions from aggregated cell cultures of Taxus was employed. Methods were subsequently developed to simultaneously stain them for DNA and protein content using Propidium Iodide and Fluorescein Isothiocyanate respectively. Flow cytometry was used to analyze and quantify the DNA content and genome size of Taxus using known reference species as standards. Furthermore, their genomic stability was evaluated by correlating DNA content and genome size with cell size and complexity, protein content, and elicitation effects using multiparameter flow cytometry. These techniques to evaluate and correlate various culture characteristics can be very useful in designing superior bio processes for enhanced production.


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Approximately Counting Triangles in Sublinear Time

We consider the problem of estimating the number of triangles in a graph. This problem has been extensively studied in both theory and practice, but all existing algorithms read the entire graph. In this work we design a {\em sublinear-time\/} algorithm for approximating the number of triangles in a graph, where the algorithm is given query access to the graph. The allowed queries are degree queries, vertex-pair queries and neighbor queries. We show that for any given approximation parameter $0<\epsilon<1$, the algorithm provides an estimate $\widehat{t}$ such that with high constant probability, $(1-\epsilon)\cdot t< \widehat{t}<(1+\epsilon)\cdot t$, where $t$ is the number of triangles in the graph $G$. The expected query complexity of the algorithm is $\!\left(\frac{n}{t^{1/3}} + \min\left\{m, \frac{m^{3/2}}{t}\right\}\right)\cdot {\rm poly}(\log n, 1/\epsilon)$, where $n$ is the number of vertices in the graph and $m$ is the number of edges, and the expected running time is $\!\left(\frac{n}{t^{1/3}} + \frac{m^{3/2}}{t}\right)\cdot {\rm poly}(\log n, 1/\epsilon)$. We also prove that $\Omega\!\left(\frac{n}{t^{1/3}} + \min\left\{m, \frac{m^{3/2}}{t}\right\}\right)$ queries are necessary, thus establishing that the query complexity of this algorithm is optimal up to polylogarithmic factors in $n$ (and the dependence on $1/\epsilon$).Comment: To appear in the 56th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2015