Computational Identification of Bacterial Communities

Stable bacterial polymorphism on a single limiting resource may appear if between the evolved strains metabolic interactions take place that allow the exchange of essential nutrients [8]. Towards an attempt to predict the possible outcome of longrunning evolution experiments, a network based on the metabolic capabilities of homogeneous populations of every single gene knockout strain (nodes) of the bacterium E. coli is reconstructed. Potential metabolic interactions (edges) are allowed only between strains of different metabolic capabilities. Bacterial communities are determined by finding cliques in this network. Growth of the emerged hypothetical bacterial communities is simulated by extending the metabolic flux balance analysis model of Varma et al [2] to embody heterogeneous cell population growth in a mutual environment. Results from aerobic growth on 10 different carbon sources are presented. The upper bounds of the diversity that can emerge from single-cloned populations of E. coli such as the number of strains that appears to metabolically differ from most strains (highly connected nodes), the maximum clique size as well as the number of all the possible communities are determined. Certain single gene deletions are identified to consistently participate in our hypothetical bacterial communities under most environmental conditions implying a pattern of growth-condition- invariant strains with similar metabolic effects. Moreover, evaluation of all the hypothetical bacterial communities under growth on pyruvate reveals heterogeneous populations that can exhibit superior growth performance when compared to the performance of the homogeneous wild-type population.

• Eleftheria Tzamali
• Panayiota Poirazi
• Ioannis G. Tollis
• Martin Reczko

• Bacterial polymorphism
• clique identification
• dynamic FBA
• evolution
• metabolic interactions.

[1] D. Treves, S. Manning, and J. Adams, "Repeated evolution of an
acetate-crossfeeding polymorphism in long-term populations of
Escherichia coli," Mol. Biol. Evol., vol. 15, no. 7, pp. 789-797, 1998.
[2] A. Varma, and B.O. Palsson, "Stoichiometric flux balance models
quantitatively predict growth and metabolic by-product secretion in
wild-type Escherichia coli W3110," Appl. Environ. Microbiol., vol. 60,
no. 10, pp. 3724-31, 1994.
[3] E. Tzamali, and M. Reczko, "The benefit of cooperation: Identifying
growth efficient interacting strains of Escherichia coli using metabolic
flux balance models," 8th IEEE International conference on
bioinformatics and bioengineering, Greece, 2008.
[4] J.L. Reed, et al., "An expanded genome-scale model of Escherichia coli
K-12 (iJR904 GSM/GPR)," Genome Biol, vol. 4, no. 9, pp. R54, 2003.
[5] Scott A Becker, Adam M Feist, Monica L Mo, Gregory Hannum,
Bernhard ├ÿ Palsson & Markus J Herrgard, "Quantitative prediction of
cellular metabolism with constraint-based models: the COBRA
Toolbox," Nature Protocols, vol. 2, pp. 727-738, 2007.
[6] Janet M. Six, Ioannis G. Tollis, "Effective Graph Visualization Via
Node Grouping," infovis, pp.51-58, 2001 IEEE Symposium on
Information Visualization (InfoVis 2001), 2001
[7] Paul Rainey, Angus Buckling, Rees Kassen and Michael Travisano,
"The emergence and maintenance of diversity: insights from
experimental bacterial populations," Tree, vol. 15, pp. 243-247, 2000.
[8] R. F. Rosenzweig, R. R. Sharp, D. S. Treves, and J. Adams, "Microbial
Evolution in a Simple Unstructured Environment: Genetic
Differentiation in Escherichia Coli," Genetics, vol. 137(4), pp. 903-
917, 1994
[9] Patric R. J. Ostergard, "A New Algorithm for the Maximum-Weight
Clique Problem," Electronic Notes in Discrete Mathematics, 6th Twente
Workshop on Graphs and Combinatorial Optimization, vol. 3, pp. 153-
156, 1999.