Limits...
Metabolic modeling of endosymbiont genome reduction on a temporal scale.

Yizhak K, Tuller T, Papp B, Ruppin E - Mol. Syst. Biol. (2011)

Bottom Line: A fundamental challenge in Systems Biology is whether a cell-scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints.The model's network-based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints.Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. kerenyiz@post.tau.ac.il

ABSTRACT
A fundamental challenge in Systems Biology is whether a cell-scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Escherichia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network-based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.

Show MeSH

Related in: MedlinePlus

Mean in silico and phylogenetically loss time as a function of the k-robustness index. (A) Mean in silico gene loss time as a function of the number of backup reactions a gene has in the metabolic network (its k-robustness; Deutscher et al, 2006). (B) Phylogenetically reconstructed gene loss time, also as a function of gene backup number. Error bars in both cases represent the standard deviation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3094061&req=5

f4: Mean in silico and phylogenetically loss time as a function of the k-robustness index. (A) Mean in silico gene loss time as a function of the number of backup reactions a gene has in the metabolic network (its k-robustness; Deutscher et al, 2006). (B) Phylogenetically reconstructed gene loss time, also as a function of gene backup number. Error bars in both cases represent the standard deviation.

Mentions: What does the metabolic model reveal about the constraints that affect the timing of gene loss? To answer this, we examined the dependency of the predicted loss time of each gene on its intrinsic network-level properties. We find that the predicted gene loss time strongly depends on the number of functional backups that the corresponding reactions of a gene have in the network under a given medium. The latter is measured by the k-robustness index introduced in Deutscher et al (2006), where k=1 denotes essential genes, k=2 denotes genes involved in synthetic lethal pairs, k=3 involves genes with at least two other functional backups and so on. Accordingly, we find a very strong inverse Spearman's correlation of −0.84 (empirical P-value <9.9e−4) between the order of gene loss predicted in silico and the k-robustness levels of the genes (Figure 4A). Notably, when excluding essential genes (k=1) from the analysis, we still obtain a high inverse Spearman's correlation of −0.65 (empirical P-value <9.9e−4). This arises as poorly backed up genes (k=2) are more likely to be retained in the final networks evolved than genes with k>2 (P-value=2.35e−8), as when their sole backup gene is lost they then become essential. An analogous association is found between the gene loss time inferred from the phylogenetic reconstruction and the k-robustness levels, with a mean Spearman's correlation of −0.51 (empirical P-value <9.9e−4, Figure 4B) over the four Buchnera strains.


Metabolic modeling of endosymbiont genome reduction on a temporal scale.

Yizhak K, Tuller T, Papp B, Ruppin E - Mol. Syst. Biol. (2011)

Mean in silico and phylogenetically loss time as a function of the k-robustness index. (A) Mean in silico gene loss time as a function of the number of backup reactions a gene has in the metabolic network (its k-robustness; Deutscher et al, 2006). (B) Phylogenetically reconstructed gene loss time, also as a function of gene backup number. Error bars in both cases represent the standard deviation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094061&req=5

f4: Mean in silico and phylogenetically loss time as a function of the k-robustness index. (A) Mean in silico gene loss time as a function of the number of backup reactions a gene has in the metabolic network (its k-robustness; Deutscher et al, 2006). (B) Phylogenetically reconstructed gene loss time, also as a function of gene backup number. Error bars in both cases represent the standard deviation.
Mentions: What does the metabolic model reveal about the constraints that affect the timing of gene loss? To answer this, we examined the dependency of the predicted loss time of each gene on its intrinsic network-level properties. We find that the predicted gene loss time strongly depends on the number of functional backups that the corresponding reactions of a gene have in the network under a given medium. The latter is measured by the k-robustness index introduced in Deutscher et al (2006), where k=1 denotes essential genes, k=2 denotes genes involved in synthetic lethal pairs, k=3 involves genes with at least two other functional backups and so on. Accordingly, we find a very strong inverse Spearman's correlation of −0.84 (empirical P-value <9.9e−4) between the order of gene loss predicted in silico and the k-robustness levels of the genes (Figure 4A). Notably, when excluding essential genes (k=1) from the analysis, we still obtain a high inverse Spearman's correlation of −0.65 (empirical P-value <9.9e−4). This arises as poorly backed up genes (k=2) are more likely to be retained in the final networks evolved than genes with k>2 (P-value=2.35e−8), as when their sole backup gene is lost they then become essential. An analogous association is found between the gene loss time inferred from the phylogenetic reconstruction and the k-robustness levels, with a mean Spearman's correlation of −0.51 (empirical P-value <9.9e−4, Figure 4B) over the four Buchnera strains.

Bottom Line: A fundamental challenge in Systems Biology is whether a cell-scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints.The model's network-based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints.Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. kerenyiz@post.tau.ac.il

ABSTRACT
A fundamental challenge in Systems Biology is whether a cell-scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Escherichia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network-based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.

Show MeSH
Related in: MedlinePlus