A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation

The
growing availability of multiomic data provides a highly comprehensive
view of cellular processes at the levels of mRNA, proteins, metabolites,
and reaction fluxes. However, due to probabilistic interactions between
components depending on the environment and on the time course, casual,
sometimes rare interactions may cause important effects in the cellular
physiology. To date, interactions at the pathway level cannot be measured
directly, and methodologies to predict pathway cross-correlations
from reaction fluxes are still missing. Here, we develop a multiomic
approach of flux-balance analysis combined with Bayesian factor modeling
with the aim of detecting pathway cross-correlations and predicting
metabolic pathway activation profiles. Starting from gene expression
profiles measured in various environmental conditions, we associate
a flux rate profile with each condition. We then infer pathway cross-correlations
and identify the degrees of pathway activation with respect to the
conditions and time course using Bayesian factor modeling. We test
our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments,
thus predicting the functionality of particular groups of reactions
and how it varies over time. In a dynamic environment, our method
can be readily used to characterize the temporal progression of pathway
activation in response to given stimuli.