Abstract
Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions.
Highlights
How topology shapes dynamics is a long-standing question in the field of network theory [1,2]
In order to subdivide the metabolic reactions into essentiality classes, namely non-essential, conditional lethal, and essential, we quantify the relative essentiality of a reaction by computing optimal, i.e., maximizing biomass production, steady-state flux distributions for over more than 7 × 104 combinatorial minimal media conditions
No clear separation of conditional lethal from non-essential and essential reactions is achieved by this combinatorial approach (Figure 5b). These results indicate that UPUC, Synthetic accessibility (SA) and metabolic core (MC), albeit good essentiality predictors, do not provide the means for a topological characterization of medium-dependent essentiality
Summary
How topology shapes dynamics is a long-standing question in the field of network theory [1,2]. Many attempts have been formulated to understand the functional structure of metabolic networks from first principles using evolutionary, biochemical, or graph theoretical arguments [3,4,5,6,7,8]. Several works have argued that the network topology of metabolic systems is markedly optimized for robustness. Marr et al [9] used binary dynamic probes to demonstrate that on average fluctuations are dampened out in real metabolic networks. There seems to be a selection for minimal metabolic pathways, given the experimental conditions [10]. The accessible nutrients for a species may be inferred by analyzing the network topologies
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