Modularity Analysis of Metabolic Networks Based on Shortest Retroactive Distances (ShReD).
Sridharan, Gautham.
2013
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Abstract: Cellular
metabolism is very complex. Large scale networks that are used for modeling single-cell
organism or tissue-specific systems typically comprise of several thousand reactions,
each representing a unique biochemical conversion of substrate to product. These in
silico models have the potential for predicting how a cell may respond to a perturbation
in the form of either a genetic ... read moreintervention or external stimulus. However, the sheer
complexity of these networks remains an impediment for the construction of predictive
kinetic ODE models, because the number of system parameters that need to be estimated
typically far exceeds the available experimental data and most estimated parameters are
not statistically identifiable. Alternatively, graph-based modeling of metabolic
networks, where reactions can be denoted by nodes and their interactions described by
directed edges, allow one to survey solely the topology of the network and identify
structural features that may offer predictable dynamics. Moreover, graph theoretical
tools allow for the discovery of modules, or a subset of reactions containing few inputs
and outputs, that together function in concert to isolate perturbations from propagating
to the rest of the network, a characteristic of metabolic robustness. In this regard,
the systematic modularity analysis serves to reduce the complexity of metabolic models
and identify modules that both confer robustness and reveal strong coupling among
reactions that may not necessarily be intuitive by viewing a two-dimensional cartography
of metabolism. In this thesis, the governing hypothesis is that retroactive, or
cyclical, interactions in the form of feedback loops and metabolic cycles engender
robustness, and serve as a defining structural feature for the systematic identification
of functional modules. As such, a graph-theoretical metric called the Shortest
Retroactive Distance (ShReD) is introduced to be used in conjunction with a known
network partition algorithm to produce a hierarchical tree of modules, each enriched in
cyclical pathways and allosteric feedback loops. Applied to a hepatocyte (liver cell)
metabolic network, the ShReD-based partition identifies a `redox' module that couples
reactions from apparently distant pathways such as glucose, pyruvate, lipid, and drug
metabolism through the shared production and consumption of NADPH, suggesting that
cofactors greatly influence the modularity of the network. Recognizing that metabolic
networks are not static, a metabolic flux-based edge weighting scheme is proposed to
capture the relative engagement between reaction nodes in the graph network. Applying
the ShReD-based partition algorithm to weighted adipocyte (fat cell) networks reveals
that major physiological changes such as cellular differentiation lead to substantial
reorganization in the modularity of the network. In addition, ShReD-based modularity
serves as a platform for a targeted motif search within functional modules to discover
novel metabolic substrate cycles (a.k.a. futile cycles), which have been recently
proposed to be targets for obesity and even cancer. Identifying these substrate cycles
requires elementary flux modes (EFM) computation, which would otherwise be infeasible on
a large scale network. Prospectively, modularity analysis of metabolic networks provides
theoretical guidance for which reaction rates and metabolite levels may be altered in
the face of a perturbation. To experimentally confirm predictions, targeted metabolomics
using tandem mass spectrometry (LC/MS-MS) is used to obtain absolute quantification of
metabolite concentrations. As an example, an in silico model predicts a set of
tryptophan-derived metabolites that can only be exclusively produced by the gut
microbiome and may have anti-inflammatory properties. In vivo levels of these
indole-backbone metabolite levels are quantified in cecum samples from mice at two
different age groups. Statistically significant differences between the two groups
suggest that age influences the microbiome composition as well as the metabolites they
produce.
Thesis (Ph.D.)--Tufts University, 2013.
Submitted to the Dept. of Chemical and Biological Engineering.
Advisors: Kyongbum Lee, and Soha Hassoun.
Committee: Steve Matson, and Arul Jayaraman.
Keyword: Chemical engineering.read less - ID:
- vt150w37b
- Component ID:
- tufts:22012
- To Cite:
- TARC Citation Guide EndNote
