A metabolic model was developed to describe the degradation of thiodiglycol (TDG) by Alcaligenes xylosoxydans ssp. xylosoxydans (SH91). Experimental evidence has suggested there is a metabolic capacity constraint that limits the speci®c thiodiglycol utilization rate, leads to metabolic byproducts, and ultimately inhibits the overall degradation process. A global inhibition function, g, was introduced to mathematically characterize this constraint based on thiodiglycol consumption. The resulting model described well the dynamic response of cells consuming thiodiglycol in several reactor con®gurations (batch, repeated batch, repeated fed batch) and was used to optimize overall thiodiglycol degradation as well as de®ne conditions for optimizing conversion to byproducts [(2-hydroxyethyl)thio]acetic acid (HETA) and thiodiglycolic acid (TDGA), which are of commercial interest. IntroductionA gram-negative bacterium, Alcaligenes xylosoxydans ssp. xylosoxydans (SH91), was used to degrade thiodiglycol (TDG) in a sulfur mustard mineralization process. In our previous work [1], an Andrews inhibition model was used to describe the SH91 growth and TDG uptake in media containing less than 100 mM TDG. Subsequently, in experiments with high initial concentration (up to 600 mM), we identi®ed and quanti®ed several metabolites including [(2-hydroxyethyl)thio] acetic acid (HETA) and thiodiglycolic acid (TDGA), that were produced by SH91 [2, 3]. TDG is converted to HETA by an NAD + -dependent butanol dehydrogenase [4], which is also used to convert HETA to TDGA, resulting in two NADH produced per processed TDG. The pathway is depicted in Fig. 1. Accordingly, a model is presented here to describe SH91 growth in TDG medium that yields metabolic byproducts, HETA and TDGA. The model comprises three principal¯uxes, TDG®HETA, HETA®TDGA, and TDGA®Cell Mass. A global inhibition factor, g, is employed to describe the inhibition phenomena we have observed at high TDG concentration. This model describes a capacity constraint for the assimilation of TDG by the cell mass that, in turn, results in HETA and TDGA accumulation as an over¯ow phenomenon. The resulting model successfully described experimental results for batch (initial TDG concentration varied from 8 to 170 mM), repeated-batch (TDG feed concentration 270 mM), as well as repeated fed-batch experiments with linear feed (TDG feed concentration 290 mM). Materials and methods2.1 Microorganism, media, and culture conditions SH91, a gram-negative bacterium, consumes TDG as the sole carbon source for growth. Stock cultures were maintained on TDG medium of the following composition (per liter): TDG, 30 mM; ammonium sulfate, 2 g; potassium phosphate dibasic, 2 g; and modi®ed Wolin salts solution (WS42, [1]), 10 mL. Incubated cells ($0.3 OD) were added to the fermenters (1.5 L working volume, New Brunswick Scienti®c) as inocula (1.5% v/v). The best growth conditions were 30°C and pH 8 [3, 5]. Analytical methodsCell concentration was determined by optical density (OD 600 , Milton Roy, Spec21). Dry ...
Background There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best. Results We derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models. Conclusions Alongside the derivation and implementation of perplexity for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.
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