Generation and robustness of Boolean networks to model Clostridium difficile infection
One of the more common healthcare associated infection is Chronic diarrhea. This disease is caused by the bacterium Clostridium difficile which alters the normal composition of the human gut flora. The most successful therapy against this infection is the fecal microbial transplant (FMT). They displace C. difficile and contribute to gut microbiome resilience, stability and prevent further episodes of diarrhea. The microorganisms in the FMT their interactions and inner dynamics reshape the gut microbiome to a healthy state. Even though microbial interactions play a key role in the development of the disease, currently, little is known about their dynamics and properties. In this context, a Boolean network model for C. difficile infection (CDI) describing one set of possible interactions was recently presented. To further explore the space of possible microbial interactions, we propose the construction of a neutral space conformed by a set of models that differ in their interactions, but share the final community states of the gut microbiome under antibiotic perturbation and CDI. To begin with the analysis, we use the previously described Boolean network model and we demonstrate that this model is in fact a threshold Boolean network (TBN). Once the TBN model is set, we generate and use an evolutionary algorithm to explore to identify alternative TBNs. We organize the resulting TBNs into clusters that share similar dynamic behaviors. For each cluster, the associated neutral graph is constructed and the most relevant interactions are identified. Finally, we discuss how these interactions can either affect or prevent CDI.
KeywordsThreshold network Neutral space Evolutionary computation Microbiome Clostridium difficile infection
This work was supported by Basal grant of the Center for Mathematical Modeling AFB170001 (UMI2807 UCHILE-CNRS), Center for Genome Regulation FONDAP 15090007 (D.T., M.C., M.L., A.M.), CONICYT PFCHA/Beca Doctorado Nacional 2015/FOLIO 21150895 (D.T.), FONDECYT 11150679 (M.L.), ECOS C16E01 (E.G.) and Internal Grant of the Universidad Adolfo Ibañez (E.G.). We also thank to the National Laboratory for High Performance Computing NLHPC (ECM-02).
- Buffie CG, Jarchum I, Equinda M, Lipuma L, Gobourne A, Viale A, Ubeda C, Xavier J, Pamer EG (2012) Profound alterations of intestinal microbiota following a single dose of clindamycin results in sustained susceptibility to clostridium difficile-induced colitis. Infect Immun 80(1):62–73CrossRefGoogle Scholar
- Pérez-Cobas AE, Artacho A, Ott SJ, Moya A, Gosalbes MJ, Latorre A (2014) Structural and functional changes in the gut microbiota associated to clostridium difficile infection. Front Microbiol 5:335Google Scholar
- Robert F (2012) Discrete iterations: a metric study, vol 6. Springer, BerlinGoogle Scholar
- Ruz GA, Goles E (2012) Reconstruction and update robustness of the mammalian cell cycle network. In: IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB), 2012, pp 397–403. IEEEGoogle Scholar
- Schubert E, Koos A, Emrich T, Züfle A, Schmid KA, Zimek A (2015) A framework for clustering uncertain data. PVLDB 8(12):1976–1979. URL http://www.vldb.org/pvldb/vol8/p1976-schubert.pdf. Accessed 19 June 2017
- Wuensche A (1999) Classifying cellular automata automatically: finding gliders, filtering, and relating space-time patterns, attractor basins, and the z parameter. Complexity 4(3):47–66. https://doi.org/10.1002/(SICI)1099-0526(199901/02)4:3<47::AID-CPLX9>3.0.CO;2-V MathSciNetCrossRefGoogle Scholar