1 of 2

1

Project Objectives

Vision

​

Problem Statement

​

Impact

Project Title: Network Analysis of Coupled Microbial Metabolic Networks: Scaling of Flux Identifiability with Community Complexity

13C metabolic flux analysis (13C-MFA) has been extended from single organisms to two-member co-cultures, but scaling to complex microbial communities remains unsolved. The fundamental bottleneck is unknown: is flux identifiability (the measurements needed to constrain all fluxes) limited by network topology, and how does it scale with community size? No systematic graph-theoretic analysis has addressed whether community metabolic networks decompose into independently solvable modules or form tightly coupled systems that require exponentially more data.

Use network analysis methods (eg. SVD, modularity detection, centrality) on coupled stoichiometric networks built from AGORA2 genome-scale reconstructions to characterize how flux identifiability scales with community size, and whether topological modularity enables tractable decomposition of community-scale 13C-MFA.

  1. Construct coupled stoichiometric networks from AGORA2 models for communities of 2–10 organisms
  2. Compute SVD rank and null space dimension as a function of community size
  3. Perform community detection (Newman’s modularity matrix eigenvector method) to test whether cross-feeding breaks organism-level modularity
  4. Compute centrality of inter-organism exchange reactions to identify information bottlenecks
  5. Assess whether topological decomposition enables modular 13C-MFA

Determines whether community-scale 13C-MFA is fundamentally limited by network complexity or can be made tractable through modular decomposition, guiding experimental design for emerging microbiome flux studies.

2 of 2

References (so far)

  1. Gebreselassie & Antoniewicz. “13C-MFA of co-cultures: A novel approach.” Metab Eng (2015).
  2. García Martín et al. “A peptide-based method for 13C MFA in microbial communities.” PLoS Comput Biol (2014).
  3. Heinken et al. “Genome-scale metabolic reconstruction of 7,302 human microorganisms...” Nat Biotechnol (2023).
  4. Liu, Slotine, Barabási. “Controllability of complex networks.” Nature 473 (2011).
  5. Newman. “Modularity and community structure in networks.” PNAS (2006).

​

2