Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

<div><p>Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in <em>Saccharomyces cerevisiae</em>.</p> </div

Yeast metabolic chassis designs for diverse biotechnological products

Supplementary information accompanies this paper at http://www.nature.com/srepThe diversity of industrially important molecules for which microbial production routes have been experimentally demonstrated is rapidly increasing. The development of economically viable producer cells is, however, lagging behind, as it requires substantial engineering of the host metabolism. A chassis strain suitable for production of a range of molecules is therefore highly sought after but remains elusive. Here, we propose a genome-scale metabolic modeling approach to design chassis strains of Saccharomyces cerevisiae - a widely used microbial cell factory. For a group of 29 products covering a broad range of biochemistry and applications, we identified modular metabolic engineering strategies for re-routing carbon flux towards the desired product. We find distinct product families with shared targets forming the basis for the corresponding chassis cells. The design strategies include overexpression targets that group products by similarity in precursor and cofactor requirements, as well as gene deletion strategies for growth-product coupling that lead to non-intuitive product groups. Our results reveal the extent and the nature of flux re-routing necessary for producing a diverse range of products in a widely used cell factory and provide blueprints for constructing pre-optimized chassis strains.This study was supported by BMBF, Germany (project: DeYeastLibrary, Grant number: 031A343A, ERA-IB-2/003/2013) and FCT, Portugal (Project: DeYeastLibrary, ERA-IB-2/003/2013). We thank A. Zelezniak and E. Valentini for help in drawing the yeast pathway figure, and S. Sheridan for proofreading and comments on the manuscript

MiMBl shows robust simulation results while using alternative stoichiometry representations – illustration using a toy-model.

<p><b>a</b>) Toy-model: <i>R1</i> to <i>R7</i> and <i>A</i> to <i>D</i> represent reactions and metabolites, respectively. In the wild-type, or reference, flux goes from <i>A</i> to <i>D</i> via <i>R5</i>. <i>R6</i> and <i>R2–R3–R4</i> are two alternative pathways for flux re-distribution after deletion of <i>R5</i>. <b>b</b>) Flux through reactions <i>R2</i> (full symbols) and <i>R6</i> (open symbols) obtained after simulation of minimization of metabolic adjustment with lMoMA (black), quadratic MoMA (qMoMA, gray) and MiMBl (red) using numerically different but biochemically equivalent representations of reaction <i>R6</i> (given by different scaling factor θ<i>_R6</i>, <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002758#s3" target="_blank">Methods</a></b>). <b>c</b>) Formulation of objective functions of minimization of metabolic adjustment for lMoMA, qMoMA and MiMBl (<b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002758#s3" target="_blank">Methods</a></b>). <b>d</b>) Optimal objective function value (distance) obtained for minimization of metabolic adjustment using lMoMA (black), qMoMA (gray) and MiMBl (red) as function of θ<i>_R6</i>.</p

Sensitivity of MiMBl towards the use of alternative reference flux distributions.

<p><b>a</b>) The histogram shows the distribution of variability in the predicted growth of single gene knockout mutants while using 500 different FBA alternative optima as reference flux distributions. <b>b</b>) Case study of <i>YLR377C</i> knockout simulations using different reference flux distributions as input. The predicted growth varies between 50–100% of that of the wild-type.</p

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.

Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair's activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds

Understanding genetic interactions by using MiMBl.

<p><b>a, b</b>) The accuracy of genetic interaction predictions by FBA, lMoMA and MiMBl was assessed by calculating the sensitivity and precision for positive (a) and negative (b) interactions. Sensitivity was calculated as the fraction of experimentally observed interactions captured by the algorithm, while precision was estimated as the fraction of experimentally observed interactions among the predicted interactions. <b>c</b>) Venn diagram showing the overlap of the correctly predicted interactions by FBA, MiMBl and lMoMA. <b>d, e</b>) Distribution of the graph theoretical distances, within the yeast metabolic network, between the interacting genes captured by FBA (d) and MiMBl (e). As MiMBl also captured the majority of FBA predicted interactions, only exclusive MiMBl interactions are shown in (e). <b>f</b>) The <i>S. cerevisiae</i> genetic interactions network correctly predicted by MiMBl and/or FBA (FBA – dashed line, MiMBl – dotted line, both – full line). Positive and negative interactions are distinguished by color (orange and blue, respectively) and the opacity of the edges is inversely proportional to the network distance between the corresponding genes. Gray-filled nodes represent genes that display both positive and negative interactions. Gray areas enclose isoenzymes where at least one of them was found to interact with other genes in the metabolic network.</p

Minimization of overall intracellular flux leads to divergent predictions for flux distribution when using biochemically equivalent stoichiometry representations.

<p>Shown are predicted fluxes through key pathways within the <i>S. cerevisiae</i> central carbon metabolism, using numerically different but biochemically equivalent stoichiometric representation of reaction <i>RPI1</i> (θ<i>_RPI1</i>, <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002758#s3" target="_blank">Methods</a></b>). θ<i>_RPI1</i> is represented on the x-axis, while fold-change of fluxes relatively to θ = 1 is represented on the y-axis. A representative reaction from each of the pathways was selected to illustrate the flux re-arrangement; <i>FBA1</i> for glycolysis, <i>ZWF1</i> for pentose phosphate pathway, <i>CIT1</i> for tricarboxilic acid cycle and <i>NID1</i> for oxidative phosphorylation. Note that θ = 1 is an arbitrary reference, as the stoichiometric representation of any reaction is subjective, often scaled to have coefficient of 1 for one of the reactants/products.</p

Polarization of microbial communities between competitive and cooperative metabolism

Resource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here, we use genome-scale metabolic modelling to assess the potential for resource competition and metabolic cooperation in large co-occurring groups (up to 40 members) across thousands of habitats. Our analysis reveals two distinct community types, which are clustered at opposite ends of a spectrum in a trade-off between competition and cooperation. At one end are highly cooperative communities, characterized by smaller genomes and multiple auxotrophies. At the other end are highly competitive communities, which feature larger genomes and overlapping nutritional requirements, and harbour more genes related to antimicrobial activity. The latter are mainly present in soils, whereas the former are found in both free-living and host-associated habitats. Community-scale flux simulations show that, whereas competitive communities can better resist species invasion but not nutrient shift, cooperative communities are susceptible to species invasion but resilient to nutrient change. We also show, by analysing an additional data set, that colonization by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our results highlight the bifurcation between competitive and cooperative metabolism in the assembly of natural communities and its implications for community modulation

Formulation of different biological objective functions using MiMBl.

*<p><b>Note:</b> Biomass production within metabolic models is typically represented as a single reaction accounting for all the biomass constitutes. Therefore, FBA and MiMBl are equivalent for maximizing biomass.</p