BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities

Abstract

Motivation: Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet.

Results: Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism.

Availability and implementation: MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.

Figure 1.

Pearson correlations between simulated relative abundance and in vitro relative abundance with BN-BacArena and BacArena. For each species and time point, the mean simulated bacterial relative abundance (across 20 random runs) for the 11 validation foods was correlated with in vitro experimental levels after 20 h of fermentation. Abbreviations: A. putredinis, Alistipes putredinis; B. caccae, Bacteroides caccae; B. eggerthii, Bacteroides eggerthii; B. stercoris, Bacteroides stercoris; B. thetaiotaomicron, Bacteroides thetaiotaomicron; B. uniformis, Bacteroides uniformis; B. vulgatus, Bacteroides vulgatus; B. intestinihominis, Barnesiella intestinihominis; B. longum, Bifidobacterium longum; C. aerofaciens, Collinsella aerofaciens; D. invisus, Dialister invisus; D. formicigenerans, Dorea formicigenerans; F. prausnitzii, Faecalibacterium prausnitzii; O. splanchnicus, Odoribacter splanchnicus; P. distasonis, Parabacteroides distasonis; P. merdae, Parabacteroides merdae; R. bicirculans, Ruminococcus bicirculans; R. bromii, Ruminococcus bromii; S. variabile, Subdoligranulum variabile. See Supplementary Table S3 for more details

Figure 2.

Linear regression between simulated relative abundance and in vitro relative abundance with BN-BacArena and BacArena. The linear regression was performed considering simulated bacterial relative abundances across the different foods at 5–6 h and the in vitro results. (A) Simulation results using BN-BacArena and (B) BacArena. Abbreviations: A. putredinis, Alistipes putredinis; B. caccae, Bacteroides caccae; B. eggerthii, Bacteroides eggerthii; B. stercoris, Bacteroides stercoris; B. thetaiotaomicron, Bacteroides thetaiotaomicron; B. uniformis, Bacteroides uniformis; B. vulgatus, Bacteroides vulgatus; B. intestinihominis, Barnesiella intestinihominis; B. longum, Bifidobacterium longum; C. aerofaciens, Collinsella aerofaciens; D. invisus, Dialister invisus; D. formicigenerans, Dorea formicigenerans; F. prausnitzii, Faecalibacterium prausnitzii; O. splanchnicus, Odoribacter splanchnicus; P. distasonis, Parabacteroides distasonis; P. merdae, Parabacteroides merdae; R. bicirculans, Ruminococcus bicirculans; R. bromii, Ruminococcus bromii; S. variabile, Subdoligranulum variabile

Figure 3.

Cross-feeding interactions in BN-BacArena and BacArena for bacteria–bacteria relationships in the Bayesian network model. The cross-feeding interactions were estimated at the end of the simulations across the different foods and replicates. Number of cross-feeding interactions for the 19 predicted positive (A) and 20 negative (B) microbe–microbe interactions in the Bayesian network model. Note: ‘S1-S8’, for example, means that S1 regulates S8. Thus, a cross-feeding interaction implies that S1 produces an output metabolite that is received by S8. Abbreviations: S1, Alistipes putredinis; S2, Bacteroides caccae; S3, Bacteroides eggerthii; S4, Bacteroides stercoris; S5, Bacteroides thetaiotaomicron; S6, Bacteroides_uniformis; S7, Bacteroides_vulgatus; S8, Barnesiella_intestinihominis; S9, Bifidobacterium longum; S10, Collinsella aerofaciens; S11, Dialister invisus; S12, Dorea formicigenerans; S13, Faecalibacterium prausnitzii; S14, Odoribacter splanchnicus; S15, Parabacteroides distasonis; S16, Parabacteroides merdae; S17, Ruminococcus bicirculans; S18, Ruminococcus bromii; S19, Subdoligranulum variabile