Reconstruction of an in silico metabolic model of _Arabidopsis thaliana_ through database integration

The number of genome-scale metabolic models has been rising quickly in recent years, and the scope of their utilization encompasses a broad range of applications from metabolic engineering to biological discovery. However the reconstruction of such models remains an arduous process requiring a high level of human intervention. Their utilization is further hampered by the absence of standardized data and annotation formats and the lack of recognized quality and validation standards.

Plants provide a particularly rich range of perspectives for applications of metabolic modeling. We here report the first effort to the reconstruction of a genome-scale model of the metabolic network of the plant _Arabidopsis thaliana_, including over 2300 reactions and compounds. Our reconstruction was performed using a semi-automatic methodology based on the integration of two public genome-wide databases, significantly accelerating the process. Database entries were compared and integrated with each other, allowing us to resolve discrepancies and enhance the quality of the reconstruction. This process lead to the construction of three models based on different quality and validation standards, providing users with the possibility to choose the standard that is most appropriate for a given application. First, a _core metabolic model_ containing only consistent data provides a high quality model that was shown to be stoichiometrically consistent. Second, an _intermediate metabolic model_ attempts to fill gaps and provides better continuity. Third, a _complete metabolic model_ contains the full set of known metabolic reactions and compounds in _Arabidopsis thaliana_.

We provide an annotated SBML file of our core model to enable the maximum level of compatibility with existing tools and databases. We eventually discuss a series of principles to raise awareness of the need to develop coordinated efforts and common standards for the reconstruction of genome-scale metabolic models, with the aim of enabling their widespread diffusion, frequent update, maximum compatibility and convenience of use by the wider research community and industry

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

Workflow of comparative metabolic analysis in MultiMetEval.

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