Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

Abstract

We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

“…Traditionally, the field has been developed to understand social systems [21,22]. Nevertheless, the tools are simply foundational from a mathematical perspective, and can be flexibly shifted to other fields to understand complex functions of biological [23-26], technological [27,28], and physical [29-31] systems.…”

A Novel Index for Predicting Health Status Using Species-level Gut Microbiome Profiling

“…Despite the strong reproducibility in classification performance demonstrated on the validation datasets, more is left to be desired in regards to achieving higher accuracies. We conjecture that misclassifications were partly because of the complex 13,14,48 , stochastic 49,50 , and highly personalized 22,51 nature of gut microbiome ecologies; all of which complicate the identification and validation of a reliable and robust signature of health. In addition, we cannot rule out the possibility of inaccurate diagnoses for the original phenotype labels.…”

The best models of metabolism

“…While most reverse‐engineering methods and applications have targeted gene networks, some have been applied to metabolic networks as well. Depending on the data, these inferences have addressed static networks or dynamic systems …”

“…Depending on the data, these inferences have addressed static networks 49-52 or dynamic systems. [53][54][55][56][57][58][59][60][61][62] The machine learning inferences may target not only the connectivity of a network but also the distribution of metabolic flux magnitudes at a steady state. For the latter, two methodological frameworks have been developed.…”