Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments
Fig 3
RIPTiDe identifies established biological differences in E. coli str. K-12 substr. MG1655 across in vitro growth conditions.
Without increased user supervision, RIPTiDe correctly predicts behavior and context-specific pathways for E. coli str. K-12 substr. MG1655 (iJO1366), while simultaneously producing functional models that can be characterized phenotypically. (A) NMDS ordination of Bray-Curtis dissimilarities between exchange reaction flux samples for each version of iJO1366. Table legend indicates flux constraints placed on iJO1366 prior to flux sampling. Flux samples from RIPTiDe transcriptome contextualization without exchange constraints are not significantly different from those where media condition was set a priori (p-value > 0.05). The gray check denotes that the max growth constraint is inherently integrated into the RIPTiDe workflow. Significant differences evaluated by PERMANOVA. (B) Comparison of metabolic reactions included among RIPTiDe-contextualized transcriptomes with iJO1366. The majority of reactions (71.5%) are conserved across models within a central set of pathways. All exchange reaction bounds set ±1000 prior to contextualization. (C) Comparison of importance (essentiality) for conserved genes across pruned RIPTiDe models. 105 core essential genes were identified across all groups (S3 Table). Hierarchical clustering reveals context-specific pathway essentially, labeled across the bottom axis, based on the environment in which the bacterium is growing. All exchanges set to ±1000 prior to contextualization.