Predicting functional associations from metabolism using bi-partite network algorithms
- Balaji Veeramani^{1, 2} and
- Joel S Bader^{1, 2}Email author
https://doi.org/10.1186/1752-0509-4-95
© Veeramani and Bader; licensee BioMed Central Ltd. 2010
Received: 16 July 2009
Accepted: 14 July 2010
Published: 14 July 2010
Abstract
Background
Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.
Results
We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.
Conclusions
We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.
Background
High quality metabolic reconstructions are available for many organisms and provide a rich scaffold for interpreting data from high-throughput biological experiments. The topology of a metabolic network, defined by connections between enzymes and metabolites, can be used to predict genetic interactions, transcriptional correlations and disease co-morbidity [1–3].
Previous studies have used the topology of the metabolic network to predict co-expression of transcripts for yeast metabolic enzymes [4]. This study first removed high-degree metabolites from the bipartite metabolic network, generated an enzyme-only network by connecting enzymes that shared at least 1 remaining metabolite, and calculated the shortest-path distance between all pairs of enzymes. Shorter distances were correlated with stronger co-expression. Similar procedures, also excluding high-degree metabolites from consideration, were used recently in a study linking diseases to metabolic enzymes [3].
Methods that involve calculation of optimal fluxes subject to constraints, such as flux coupling [5], have performed better than local topological metrics based on shared neighbours in predicting transcript co-expression. Flux coupling methods are much more computationally expensive than topological analysis, however. Furthermore, flux coupling methods suffer from the disadvantage that reactions with small flux values (and hence the enzymes involved in those reactions) are typically removed from the network. This is a problem if an enzyme of interest is removed from the network based on low reaction flux.
Our goal is to provide improved topological measures for enzyme functional associations from metabolic networks without the need for expensive calculations of optimal fluxes or sampling over feasible flux space. The motivation of our approach is that methods that count shared metabolites, or methods that generate a p-value for shared metabolites based on a hypergeometric distribution, essentially assume a flat degree distribution for metabolites. High-degree metabolites violate the assumption of a flat degree distribution, and the hypergeometric distribution is inappropriate for calculating p-values for metabolite sharing. Randomization methods based on rewiring, which maintain the observed degree distribution, are robust to high-degree metabolites but unfortunately are computationally expensive.
In this work, we provide a series of scores that are the Bayesian equivalent of the hypergeometric distribution, but adjusted for the known metabolite degree distributions. These scores are fast to calculate, essentially no more expensive than a hypergeometric p-value, and much faster than any methods that require rewiring permutations, flux sampling, or flux optimization. Results from applying these methods to metabolic networks in yeast demonstrate performance better than previous methods based on local connectedness. The results also reveal functional associations that are not captured by conventional metabolic pathway definitions, but which are inherent in the network structure.
Results
Overview
Metabolic networks can be represented as bipartite graphs with edges between enzymes and metabolites. An enzyme can use a metabolite in multiple unique reactions involving distinct subsets of other metabolites, and the number of unique reactions defines an integer-valued edge weight.
In this work, we introduce a more sophisticated local method that also corrects for metabolite degree, discounting the contribution of highly connected metabolites like water, protons, and ATP (Figure 1B). Our new local methods are motivated by Bayesian model selection using the log-likelihood ratio of a null model (random connectivity between enzymes and metabolites) to an alternative model. The number of shared metabolites is modelled as a Poisson distribution for both the null and alternative models. For the alternative model, the Poisson parameter is the maximum likelihood estimate for the observed network, which is the observed number of shared metabolites. For the null model, the Poisson parameter is estimated from a random network model (virtually identical to the leading contribution to the hypergeometric distribution). We present results for an improved Poisson model that uses knowledge of the observed metabolite degree distribution.
Methods termed "Global" are capable of generating rankings for enzymes that are not directly connected by metabolites by using the full network topology. Examples are shortest paths and the more robust graph diffusion kernel (GDK) (Figure 1C). GDKs are non-local in that they sample over all paths between two enzymes, rather than just the shortest paths defined by shared metabolites. GDKs have been successfully applied to functional inference in metabolic networks [7]. Recently parity-specific kernels been used to analyze genetic interaction networks [8]. We also used a method based on the Pearson correlation of the weighted metabolite-enzyme edge connectivity structure between two enzymes.
Yet more elaborate flux balance analysis methods sample flux states that are feasible under steady-state constraints. Flux correlations can then be used to rank enzyme pairs for functional associations (Figure 1D). Other flux balance methods have generated functional associations by predicting synergistic or buffering epistatic interactions for deleting pairs of enzymes from the network.
Performance of local methods
Performance is assessed primarily by the ability to predict synthetic lethal genetic interactions between metabolic enzymes, and secondarily by the ability to identify classes of enzymes with similar Gene Ontology (GO) annotations. The synthetic lethal interactions provide a direct link to testable experiments. The database annotations are not necessarily testable, but instead show whether inference from computational models is consistent with known biology.
We generated rank ordered lists of enzyme pairs based on the local methods. Performance was assessed from the receiver operating characteristic (ROC) curve using the area under the curve (AUC), and from the precision-recall (PR) curve using the maximum F-score, the harmonic mean of precision and recall. Known positives were taken from experimentally reported synthetic lethal/growth defect interactions recorded in the BioGRID database. There were 170 growth defect/lethal interactions in which both genes involved were part of the metabolic network model. Known negatives were defined as gene pairs where each gene has at least one synthetic lethal interaction, and one of the two has at least 5 synthetic lethal interactions as a query in a high-throughput screen, to exclude pairs that might not have been tested experimentally.
Performance of global topology methods
Overall, incorporating global information through the graph diffusion kernel improves the performance in identifying synthetic lethal pairs. Adding edge weights to the graph diffusion kernel does not appear to improve performance. Further comparisons with the graph diffusion kernel use the unweighted model only, as it is simpler.
Global topology with metabolic constraints
We next considered possible improvements that use the knowledge that the network edges represent a flux balance model for metabolism. While others have investigated models that investigate the robustness of metabolism to pairwise gene deletions [9], correlations of fluxes through enzymes provide improved predictions of genetic interactions [2]. We therefore used the flux sampling approach to calculate enzyme correlations, whose absolute values were used to rank enzyme pairs. The flux sampling excludes reactions with negligible flux, which reduces the model to 477 metabolites, 582 reactions and 469 enzymes and reduces the known positive pairs to 69 genetic interactions.
The epistatic estimates, obtained from previous work [9], do not perform as well in predicting synthetic lethality.
Compartmentalized metabolism
Compartment distribution of reactions.
Compartment | Number of reactions |
---|---|
Cytosol | 599 |
Mitochondria | 154 |
Extracellular | 122 |
Peroxisome | 58 |
Nucleus | 13 |
Golgi | 6 |
Endoplasmic reticulum | 4 |
Vacuole | 2 |
Associated with 2 or more compartments | 308 |
Synthetic lethality and shared compartments.
SL | Non-SL | |
---|---|---|
Share | 149 | 199,644 |
Do not share | 21 | 81,061 |
To test these competing possibilities, we applied the local and global methods to a network in which compartment information was removed. The yeast metabolic network we used in this study specifies compartments for enzymes and metabolites [10]. We reasoned that identical reactions occurring in different compartments can functionally compensate for each other due to diffusion or transport of metabolites across compartment boundaries. We therefore generated a simplified network that ignores the cellular compartments of the metabolites. Removing the compartments reduced the number of metabolites from 1061 to 646.
Assessment based on database annotations
We used published methods to assess the ability of different ranking methods to identify enzyme pairs with similar database annotations [11]. This assessment tests the significance of the hypothesis that the average coupling score between all pairs of genes associated with a GO term is higher than between pairs of genes associated with different GO terms. All 835 GO terms mapping to at least 5 and less than 100 enzymes in the network were considered, comprising 538 biological process (BP), 209 molecular function (MF), and 88 cellular compartment (CC). If the null is rejected by the Benjamini-Hochberg procedure at a starting p-value of 0.05/(number of GO terms tested) [12], then the GO term is termed consistent. The fraction of consistent GO terms was computed by this procedure for each method. The assessment was performed for the complete compartment-based network, the reduced compartment-based network with enzymes with negligible fluxes removed, and the network with compartments removed. The category size of 5-100 matches the original publication. Assessment with category sizes of 2-100 and 2-200 yield similar results, but with more categories overall [Additional file 2].
Performance summary statistic (AUC and F-score) and percentage consistent GO terms.
Scores | Performance on 170 SL pairs (full network) | Performance on 69 SL pairs(reduced network) | Genes in full network, 283 GO terms (152-BP; 85-MF; 46-CC) | Genes in reduced network, 203 GO terms (110-BP; 60-MF; 33-CC) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
AUC (ROC) | F-Score (PR) | AUC (ROC) | F-Score (PR) | Total | BP | MF | CC | Total | BP | MF | CC | |
Compartments | ||||||||||||
Shared Mets | 0.69 | 0.16 | 0.75 | 0.26 | 61 | 52 | 69 | 78 | 50 | 41 | 62 | 61 |
Shared Mets (low degree) | 0.67 | 0.15 | 0.76 | 0.27 | 65 | 64 | 61 | 74 | 55 | 58 | 45 | 61 |
Hypergeometric p-value | 0.69 | 0.21 | 0.76 | 0.43 | 72 | 64 | 79 | 85 | 63 | 55 | 75 | 67 |
Bayes Score | 0.70 | 0.21 | 0.76 | 0.43 | 72 | 64 | 79 | 83 | 63 | 55 | 75 | 67 |
Poisson Score | 0.70 | 0.20 | 0.76 | 0.40 | 72 | 64 | 79 | 85 | 60 | 52 | 73 | 64 |
Poisson Score (met. degree) | 0.70 | 0.21 | 0.79 | 0.47 | 81 | 73 | 94 | 83 | 74 | 65 | 90 | 73 |
Graph diffusion kernel score | 0.74 | 0.23 | 0.79 | 0.39 | 94 | 90 | 99 | 98 | 92 | 87 | 97 | 100 |
Graph diffusion kernel score (weighted edges) | 0.72 | 0.22 | 0.79 | 0.39 | 93 | 88 | 99 | 98 | 92 | 87 | 97 | 100 |
Flux Correlation | - | - | 0.87 | 0.49 | - | - | - | - | 64 | 66 | 53 | 73 |
Poisson Score (met. associations, deg.) | 0.70 | 0.21 | 0.79 | 0.47 | 81 | 73 | 93 | 83 | 74 | 65 | 92 | 70 |
Scaled epistasis | - | - | 0.54 | 0.08 | - | - | - | - | - | - | - | - |
No Compartments | ||||||||||||
Shared mets. | 0.68 | 0.09 | 0.73 | 0.22 | 55 | 47 | 69 | 54 | 49 | 38 | 67 | 52 |
Shared mets. (low deg. mets.) | 0.66 | 0.18 | 0.73 | 0.26 | 71 | 72 | 76 | 54 | 64 | 67 | 62 | 58 |
Hypergeometric P-value | 0.69 | 0.13 | 0.76 | 0.39 | 66 | 59 | 78 | 65 | 57 | 45 | 73 | 67 |
Bayes Score | 0.69 | 0.13 | 0.76 | 0.39 | 66 | 59 | 78 | 65 | 57 | 45 | 73 | 67 |
Poisson Score | 0.69 | 0.12 | 0.75 | 0.33 | 64 | 57 | 76 | 65 | 56 | 44 | 73 | 64 |
Poisson Score (met. degree) | 0.72 | 0.31 | 0.80 | 0.49 | 78 | 72 | 93 | 72 | 75 | 65 | 92 | 76 |
Graph diffusion kernel score | 0.72 | 0.26 | 0.79 | 0.38 | 94 | 91 | 99 | 93 | 91 | 85 | 98 | 94 |
Graph diffusion kernel score (weighted edges) | 0.70 | 0.25 | 0.79 | 0.4 | 93 | 90 | 99 | 93 | 89 | 86 | 93 | 88 |
Poisson score(met. associations, deg.) | 0.72 | 0.30 | 0.79 | 0.48 | 80 | 74 | 93 | 72 | 75 | 65 | 90 | 79 |
We investigated whether the models with or without compartments perform better using a binomial test under the null hypothesis that each of the two models has equal probability of performing better for a particular method. Tests were performed separately for AUC/ROC, PR/F-score, and the three GO categories, and separately for the full and reduced network. In all cases except GO/Cellular Compartment, two-tailed tests showed no significant deviations from the null hypothesis at p = 0.05. The test for Cellular Compartment is significant (p = 0.0396), with improved consistency for models that include compartment information.
Flux correlation performs best in the Biological Process category. A surprising result here is that overall flux correlation performs worse than GDK and Poisson (metabolite degree, with or without metabolite associations) with only 70% consistent GO terms. It is possible the flux correlation method could be improved with longer runs that reduce the statistical noise in the correlations, although the flux calculations already require over 100× more computer time than any of the other methods (see Computational cost below).
Discussion
Performance of local, global, and flux-based methods
We have investigated the performance of three classes of methods for predicting functional associations in metabolic networks: (i) local methods, based primarily on the metabolites shared by two metabolic enzymes; (ii) global methods, based on the probability that a random walk started at one enzyme will visit a second enzyme; (iii) flux-based methods that use flux balance to identify enzymes with correlated fluxes. The local and global methods are fast and generally applicable to other types of networks, whereas the flux-based methods are computationally expensive and dedicated to metabolism (or other networks that have similar conservation-of-mass constraints). In terms of performance in predicting functional associations, however, the dedicated flux-based methods have typically been superior. Developing fast methods with performance similar to expensive flux-based methods has been a challenge.
Previous local and global methods have had difficulties with high-degree metabolites. For local methods, metabolites such as water and ATP are often shared by enzymes with very different functions. For global methods, these metabolites introduce many short paths through the network. Often, high-degree metabolites are removed from a network prior to analysis. This approach is undesirable because it introduces an ad hoc tuning parameter, which can lead to over-fitting, and it excludes potentially interesting metabolites from the analysis.
The hypothesis motivating this work is that the difficulties from high-degree metabolites arise from an implicit assumption of a narrow metabolite degree distribution, as opposed to the known long-tailed degree distribution. The hypergeometric distribution for shared metabolites corrects for enzyme degree, but not for metabolite degree. Consequently a high-degree metabolite such as water is given the same weight as a low-degree metabolite when counting shared metabolites. Intuitively, high-degree metabolites should be down-weighted. Our improved local method uses the known enzyme and metabolite degrees to generate a degree-corrected score with excellent performance.
The global method we examine, a graph diffusion kernel on the bipartite enzyme-metabolite network, also includes a degree normalization that down-weights the contribution of high-degree metabolites (and high-degree enzymes). This method is somewhat more expensive than the local methods, requiring a full matrix inverse rather than sparse matrix multiplication.
The graph diffusion kernel explores the topology of the metabolic network using random walks that visit metabolites and enzymes. Enzyme-metabolite edges are treated as undirected, permitting random walkers to traverse both directions even for a unidirectional reaction. There are no constraints on the flux of random walkers through any enzyme, and the stoichiometry of a metabolite as a reactant or product is ignored.
Flux-balance methods go beyond graph diffusion by adding constraints specific to metabolic networks. Enzyme fluxes are coupled by mass balance and reaction stoichiometry, and correlations between enzyme fluxes can propagate through the network. These additional constraints capture more of the biological reality of metabolism than either shared metabolites or graph diffusion. Predictive performance is also better, presumably because of the biological constraints. A curious point is that flux sampling, with a uniform sample over the feasible space, performs better than calculations of epistatic effects based on reductions to an optimized fitness objective function. This may indicate errors in the assumed objective function for cellular fitness. The main drawback of flux-balance methods is the high computational cost.
In summary, graph diffusion methods have become a method of choice for analyzing many types of networks. While the degree-corrected local methods provide a substantial improvement over previous local methods, the graph diffusion kernel using the entire network topology performs somewhat better. Dedicated flux-sampling methods are slightly better for predicting genetic interactions, but take over 100× longer to calculate and are much more difficult to implement.
Disagreement between network-based predictions and database annotations
We therefore selected examples with high GDK scores and low semantic similarity. A GDK threshold of score ≥50 focused attention on the top-ranked GDK pairs, where on average the semantic similarity corresponds to only 23 genes annotated to the parent category of a pair. We then set a threshold of semantic similarity ≤2. This value is substantially below the semantic similarity of gene pairs selected at random, and was chosen to yield a number of example that was feasible for case-by-case analysis, a data set of 101 pairs denoted putative false positives [Additional file 3].
Categories of gene pairs with high network association and low semantic similarity.
Category | Number of Gene Pairs (of 101) |
---|---|
Transport-Synthesis | 66 |
Unannotated | 12 |
Pathway boundary, Quinone metabolism | 7 |
Pathway boundary, Glycolysis | 4 |
Secondary activity | 3 |
Pathway boundary, N-acetylation | 2 |
Pathway boundary, Purine metabolism | 2 |
Pathway boundary, TCA | 2 |
Pathway boundary, Fumarate metabolism | 1 |
Pathway boundary, Glycoprotein synthesis | 1 |
Pathway boundary, Redox | 1 |
The transport-synthesis category is the largest, with about 65% of the cases. In these examples, the GDK predicts an association between enzymes responsible for synthesis and transport (usually extracellular to intracellular) of the same metabolite. Thus, both enzymes are fulfilling the same role of increasing the intracellular concentration of a metabolite. The structure of the GO annotations does not reflect this close functional association, however. Examples include transport of choline/ethanolime (HNM1-CKI1), allantoate (DAL5-DAL1), sterols (AUS1-ERG27), and uridine (FUI1-URK1).
The final category, pathway-boundary, arises when the boundary between two well-accepted pathways cuts through a metabolite. Enzymes that connect to this metabolite are then annotated to very different pathways, despite a close network-level association. These cases are responsible for 20% of the total. Examples include associations between enzymes in the TCA cycle and those using TCA metabolites for amino acid synthesis, enzymes with different roles in glycoprotein synthesis, and enzymes responsible for quinone metabolism [Additional file 3].
Conclusions
In analyzing large networks, it has become common to delete high-degree vertices. This practice is questionable. It depends on an arbitrary high-degree cutoff, usually without any clear break in a vertex degree distribution. It can remove vertices that are of interest, and it can introduce unknown biases into the analysis.
Here we have introduced methods that are readily applied to networks with high-degree hubs. Local methods use known degree distributions to correct for high-degree enzymes and metabolites, and global methods use graph diffusion kernels to rank the association between pairs of enzymes. We show that these methods outperform previous methods that eliminate high-degree vertices from the networks. The context is cellular metabolism, where high-degree metabolites like water and ATP are shared by many enzymes. Our methods are able to infer functional associations between enzymes, without being misled by sharing of these high-degree metabolites.
In several cases, enzymes predicted by network analysis to have high functional association have very little similarity in database annotations. Some of these cases are due to a discrepancy between the metabolic reconstruction, which records a reaction for an enzyme, and the annotation database, which lacks information or omits a secondary activity. Two additional patterns were observed, however, which relate to the structure of Gene Ontology hierarchies. First, enzymes that are responsible for synthesis and transport of the same metabolite often have little annotation similarity. Second, conventional pathway definitions may place two enzymes with strong network-level associations on opposite sides of a pathway boundary.
The methods developed here should be applicable in general to other bipartite networks, particularly those with high-degree hubs.
Methods
Yeast Metabolic network reconstruction
The Yeast metabolic network used in our study was obtained from the database maintained by systems biology group, University of California, San Diego [10, 13]. The file "Sc_{-}iND750_{-}GlcMM.xml" corresponding to the minimal media condition was obtained from http://gcrg.ucsd.edu/Downloads/Cobra_Toolbox. This network has 1061 metabolites, 1266 reactions and 750 genes. The stoichiometry matrix S(m, r) provides the number of metabolites m consumed or produced in reaction r. The reaction-gene association matrix E(r, e) in the metabolic network indicates whether reaction r can be catalyzed by enzyme e.
Coupling measures based on metabolic bipartite network
A bipartite network has two disjoint sets of vertices with edges only between vertices of different sets. In the case of the metabolic network, we consider enzymes e and the metabolites m as disjoint vertices in a bipartite graph. We use various metabolic coupling measures between two enzymes in this graph to predict synthetic lethal genetic interactions. Towards this goal, we use both methods from literature (based on shared metabolites [6], shared metabolites after removing high degree metabolites [3, 4]) and other methods proposed here.
Shared metabolite count (with and without hubs)
The coupling between metabolites can be calculated using the stoichiometry matrix as $\hat{S}{\hat{S}}^{T}$[6]. The elements of $\hat{S}$, denote the participation of a metabolite i in a reaction j with a value 1 and 0 otherwise ($\hat{S}$ is binary version of the stoichometry matrix, S). This idea extended to the coupling of genes based on the bipartite metabolic network could be represented as $\stackrel{\wedge}{M}{\stackrel{\wedge}{M}}^{T}$, where M^{ T } = $\hat{S}$E, and $\widehat{M}$ is the binary version of the matrix M. The element C_{ ij }of the matrix C now represents number of metabolites shared between enzyme i and enzyme j.
The metabolite degree is defined as the number of reactions in which a metabolite participates. In previous work, high-degree metabolites have been excluded from metabolite sharing (equivalent to ignoring the rows corresponding to metabolite hubs in the stoichiometry matrix) [3]. In our calculations of shared metabolites, the top 5% of metabolites were excluded (53 metabolites participating in 13 or more reactions).
Bayesian score
The combinatorial factor C(n, k) is n!/k!(n - k)!. This score increases when n_{12} is either larger or smaller than the value n_{1}n_{2}/n expected under the null hypothesis (analogous to a two-sided test). For n_{12} smaller than the null expectation, we used score(n_{12}) - |score(n_{12}) - score(n_{1}n_{2} / n)| to restrict attention to enrichment.
Hypergeometric p-value score
Poisson score
The absolute value for the second term in Eq. 3 ensures that large scores come from enrichment rather than depletion of shared metabolites.
Poisson score with metabolite degree
Poisson score with metabolite associations and degree
Results from this more complicated model are included in Table 1, but not discussed in the text as the method is more complicated yet performs no better than simpler Poisson score methods.
Graph diffusion kernel
The parameter γ controls the extent of diffusion, or equivalently the length of the random walks. These lengths are distributed exponentially, with the probability of a d-step walk proportional to e-^{γ d}. The results shown in this work are for a value of γ = 1. Results were not sensitive to the value of γ, with similar results over a range from 0.5 to 120. The entries in the kernel corresponding to the enzyme-enzyme relationships were then extracted to predict genetic interactions. For readability, GDK scores displayed in the figures are multiplied by 10^{4}.
Graph diffusion kernel with weighted edges
We also considered a version of the graph diffusion kernel with weighted edges. Here we used the full version of the matrix M(M^{ T }= $\hat{S}$E) in the adjacency matrix A rather than a binary version as used in the non-weighted case (Eq. 11). The element (M^{ T } )_{ ij } of the matrix M^{ T }= $\hat{S}$E represents the number of times a metabolite i is associated with the enzyme j through various reactions. Then kernel score with weighted edges is obtained using the same procedure as described above. Results are shown for γ = 1 and remained the same for higher γ values.
Scores without compartments
The model obtained from the BIGG database is a fully compartmentalized model with same metabolites localized to different compartments represented separately. Some metabolites may move between compartments freely, others through ion-channels by chemical gradients or through transporters. This may bring the reactions that use the metabolites that diffuse freely in different compartments closer in that they share either the substrates or products. To investigate the effect of this in our analysis, we combined same metabolites localized to different compartments (by adding the rows of the stoichometric matrix). Then we calculated all the metabolic coupling measures (except enzyme flux correlation measure) with the compartment-free metabolic network. There were 646 metabolites in the compartment-free network.
Enzyme flux correlation
The performance of various scores considered in this work were compared with enzyme flux correlation score used in our previous study [2]. Briefly, the method is based on feasible reaction fluxes obtained under stoichometric and reaction flux constraints at steady state [14]. The set of feasible reaction fluxes were sampled using a Markov random sampling algorithm under in silico medium similar to YPD [15]. Then reaction fluxes were transformed to enzyme fluxes. The enzyme flux correlation between two enzymes is then obtained by calculating the Pearson correlation coefficient over the various enzyme flux samples. The details of the calculations are available elsewhere [2]. This entire procedure from random sampling to calculating correlations was repeated 3 times with different random seeds, with no evidence of non-ergodic sampling among the three runs. Final predictions used absolute value of correlation averaged over three runs. A preliminary step before random sampling removes all blocked reactions which carry no flux. The reduced model used for flux sampling had 477 metabolites, 582 reactions and 469 enzymes. The flux sampling procedure was carried out with the COBRA MATLAB toolbox [16]. The absolute value of the flux correlation is used for ranking.
Scaled epistasis
The scaled epistasis values corresponding to the minimal media were obtained from a previous study [9]. The file "fitness_data_nominal.txt" containing the fitness of insilico single and double gene yeast knockouts were obtained from http://kishony.med.harvard.edu/prism/index.html. For calculating the AUC and F-score from scaled epistasis score, only enzyme pairs with epistasis score were considered. We did not calculate GO consistency scores for this method because it performed poorly for predicting genetic interactions.
Synthetic lethality data sources
Synthetic lethality data for this study was obtained from the BioGRID database (version 2.0.46) [17]. There were 97 synthetic lethal and 73 synthetic growth defect interactions in the BioGRID database that had both the genes in the yeast metabolic network. There were 39 synthetic lethal and 30 growth deflect interactions in the BioGRID database that had both the genes in the reduced model used in the flux sampling procedure.
Performance metrics
The ROC curves, AUC, F-score and PR curves were generated in R using the ROCR package [18]. The ROC and PR curves are shown with a downsampling option in the plot set to 5000.
Gene Ontology assessment
We used a procedure proposed in a previous study for validating the functional gene similarity measures [11]. We used gene ontology (GO) annotation terms from all the three categories biological process, cellular compartment and molecular function. A GO term is termed consistent if the average metabolite coupling score between all pairs of metabolic genes associated with the GO term is greater than genes that do not share a same annotation.
The percent of consistent GO terms were calculated for each bipartite coupling measure. We considered only GO terms associated with 5 though 100 genes. For each GO term, the pairwise coupling score between metabolic genes associated with it are averaged. The statistical significance of this averaged score is assessed by random shuffling of gene GO annotation associations, maintaining both the annotation and gene distribution. We calculated an empirical p-value based on 10,000 iterations for each GO term. These empirical p-values were corrected for multiple testing of many GO terms to control for false discovery rates [12]. The consistency score was obtained by the proportion of GO terms that were significant with a false discovery rate of 0.05. The GO gene associations of yeast corresponding in the file gene_{-}association.sgd was obtained from the Saccharomyces genome database, http://downloads.yeastgenome.org/literature_curation/.
Semantic Similarity
Semantic similarity was calculated as l_{ n }(N_{ T }/N_{ P }), where the total number of genes N_{ T }= 6310 for yeast, and the number of genes annotated to the closest parent category of two genes is N_{ P }[19, 20]. Semantic similarity values were calculated separately for the three main Gene Ontology hierarchies: Biological Process, Cellular Component, and Metabolic Function. These three values were then summarized by the maximum of the three to identify functional associations inferred from network structure that do not match any known annotation similarity.
Computational cost
The methods proposed in this work are computationally less expensive as compared to the flux correlation based approaches. The computation of flux correlation takes about 15 hours (each sampling run taking about 5 hours). Computing the other scores was performed as a single calculation that required only 9 minutes. The graph diffusion kernel, part of this single calculation, was computed directly as the inverse of the graph Laplacian rather than as a repeated matrix multiplication.
Declarations
Acknowledgements
BV and JSB acknowledge funding from NIH R24 DK082840. JSB acknowledges support from NSF CAREER MCB 0546446. We acknowledge help from Yasir Suhail in performing semantic similarity calculations.
Authors’ Affiliations
References
- Ihmels J, Levy R, Barkai N: Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae. Nat Biotechnol. 2004, 22: 86-92. 10.1038/nbt918View ArticlePubMedGoogle Scholar
- Veeramani B, Bader JS: Metabolic flux correlations, genetic interactions, and disease. J Comput Biol. 2009, 16 (2): 291-302. 10.1089/cmb.2008.14TTPubMed CentralView ArticlePubMedGoogle Scholar
- Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabási AL: The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci USA. 2008, 105 (29): 9880-9885. 10.1073/pnas.0802208105PubMed CentralView ArticlePubMedGoogle Scholar
- Kharchenko P, Church GM, Vitkup D: Expression dynamics of a cellular metabolic network. Mol Syst Biol. 2005, 1: 2005.0016- 10.1038/msb4100023PubMed CentralView ArticlePubMedGoogle Scholar
- Notebaart RA, Teusink B, Siezen RJ, Papp B: Co-regulation of metabolic genes is better explained by flux coupling than by network distance. PLoS Comput Biol. 2008, 4: e26- 10.1371/journal.pcbi.0040026PubMed CentralView ArticlePubMedGoogle Scholar
- Becker SA, Price ND, Palsson BO: Metabolite coupling in genome-scale metabolic networks. BMC Bioinformatics. 2006, 7: 111- 10.1186/1471-2105-7-111PubMed CentralView ArticlePubMedGoogle Scholar
- Tsuda K, Noble WS: Learning kernels from biological networks by maximizing entropy. Bioinformatics. 2004, 20 (Suppl 1): i326-i333. 10.1093/bioinformatics/bth906View ArticlePubMedGoogle Scholar
- Qi Y, Suhail Y, yi Lin Y, Boeke JD, Bader JS: Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions. Genome Res. 2008, 18 (12): 1991-2004. 10.1101/gr.077693.108PubMed CentralView ArticlePubMedGoogle Scholar
- Segrè D, Deluna A, Church GM, Kishony R: Modular epistasis in yeast metabolism. Nat Genet. 2005, 37: 77-83.PubMedGoogle Scholar
- Duarte NC, Herrgård MJ, Palsson BO: Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 2004, 14 (7): 1298-1309. 10.1101/gr.2250904PubMed CentralView ArticlePubMedGoogle Scholar
- Rokhlenko O, Shlomi T, Sharan R, Ruppin E, Pinter RY: Constraint-based functional similarity of metabolic genes: going beyond network topology. Bioinformatics. 2007, 23 (16): 2139-46.- 10.1093/bioinformatics/btm319View ArticlePubMedGoogle Scholar
- Benjamini YHY: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. 1995, 57: 289-Google Scholar
- Schellenberger J, Park J, Conrad T, Palsson B: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics. 2010, 11: 213- 10.1186/1471-2105-11-213PubMed CentralView ArticlePubMedGoogle Scholar
- Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol. 2004, 2 (11): 886-897. 10.1038/nrmicro1023View ArticlePubMedGoogle Scholar
- Harrison R, Papp B, Pál C, Oliver SG, Delneri D: Plasticity of genetic interactions in metabolic networks of yeast. Proc Natl Acad Sci USA. 2007, 104 (7): 2307-2312. 10.1073/pnas.0607153104PubMed CentralView ArticlePubMedGoogle Scholar
- Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc. 2007, 2 (3): 727-738. 10.1038/nprot.2007.99View ArticlePubMedGoogle Scholar
- Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, D535-D539. 34 Database,PubMed CentralView ArticlePubMedGoogle Scholar
- Sing T, Sander O, Beerenwinkel N, Lengauer T: ROCR visualizing classifier performance in R. Bioinformatics. 2005, 21 (20): 3940-3941. 10.1093/bioinformatics/bti623View ArticlePubMedGoogle Scholar
- Lord PW, Stevens RD, Brass A, Goble CA: Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics. 2003, 19 (10): 1275-1283. 10.1093/bioinformatics/btg153View ArticlePubMedGoogle Scholar
- Resnik P: Semantic Similarity in a Taxonomy An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research. 1999, 11: 95-130.Google Scholar
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