Application of Diagnostic Test Methods To The Classification Of Time Series With Discrete Values

Discrete-value time series are sequences of measurements where each measurement is a discrete (categorical or integer) value. These time series are widely used in various fields, and their classification and clustering are essential for data analysis. This article presents the possibility of applying diagnostic test methods to such time series and estimates the probability of finding ``matching tests''

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Spontaneous Reaction Silencing in Metabolic Optimization

Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical non-optimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.Comment: 34 pages, 6 figure

Theoretical maximum fluxes of secondary metabolite production.

<p>The heat map shows relative maximal fluxes of the final biosynthetic step in the metabolic pathways leading 15 different secondary metabolites, which were incorporated into the genome-scale metabolic models of 41 actinobacteria. Flux balance analysis was performed on the minimal medium described by Alam et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051511#pone.0051511-Alam1" target="_blank">[17]</a>. White indicates a high relative flux level, red indicates a low relative flux level (as % of the maximally obtained value across all species, displayed at the top of the figure). In the heatmap on the left, the number of model reactions and metabolites, the genome sizes and the number of secondary metabolite biosynthesis gene clusters (predicted using antiSMASH <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051511#pone.0051511-Medema4" target="_blank">[54]</a>) are plotted.</p

Pareto front calculation between biomass production and secondary metabolite biosynthesis.

<p>Pareto fronts are given for four species and three different natural products. To estimate secondary metabolite production, the flux rate through the final step in the biosynthetic pathway of the corresponding compound was used as a proxy.</p

Comparison of parsing capabilities of MultiMetEval with other FBA frameworks.

<p>Table showing SBML parsing abilities of the most popular FBA tools. Only the MultiMetEval parser is able to successfully process SBML models from SEED <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051511#pone.0051511-Henry1" target="_blank">[11]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051511#pone.0051511-DeJongh1" target="_blank">[28]</a>, KGML <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051511#pone.0051511-Kanehisa1" target="_blank">[29]</a> and COBRA <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051511#pone.0051511-Becker1" target="_blank">[30]</a>.</p

Table and plot output from the Pareto front calculation routine.

<p>The first steps are identical to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051511#pone-0051511-g001" target="_blank">Figure 1</a>, except that only one organism is selected and two reactions are selected to calculate their trade-off.</p

Workflow of comparative metabolic analysis in MultiMetEval.

<p>Workflow of comparative metabolic analysis in MultiMetEval.</p