Information- and Communication-Centric Approach in Cell Metabolism for Analyzing Behavior of Microbial Communities

Abstract

Microorganisms naturally form community ecosystems to improve fitness in diverse environments and conduct otherwise intractable processes. Microbial communities are therefore central to biogeochemical cycling, human health, agricultural productivity, and technologies as nuanced as nanotechnology-enabled devices; however, the combinatorial scaling of exchanges with the environment that predicate community functions are experimentally untenable. Several computational tools have been presented to capture these exchanges, yet, no attempt has been made to understand the total information flow to a community from its environment. We therefore adapted a recently developed model for singular organisms, which blends molecular communication and the Shannon Information theory to quantify information flow, to communities and exemplify this expanded model on idealized communities: one of Escherichia coli (E. coli) and Pseudomonas fluorescens to emulate an ecological community and the other of Bacteroides thetaiotaomicron (B. theta) and Kleb Ciella to emulate a human microbiome interaction. Each of these sample communities exhibit critical syntrophy in certain environmental conditions, which should be evident through our community mutual information model. We further explored alternative frameworks for constructing community genome-scale metabolic models (GEMs) - mixed-bag and compartmentalized. Our study revealed that information flow is greater through communities than isolated models, and that the mixed-bag framework conducts greater information flow than the compartmentalized framework for community GEMs, presumably because the latter is encumbered with transport reactions that are absent in the former. This community Mutual Information model is furthermore wrapped as a KBase Application (Run Flux Mutual Information Analysis, RFMIA) for optimum accessibility to biological investigators. We anticipate that this unique quantitative approach to consider information flow through metabolic systems will accelerate both basic and applied discovery in diverse biological fields. Author SummaryMicroorganisms frequently communicate information via information-bearing molecules, which must be fundamentally understood to engineer biological cells that properly engage with their environments, such as the envisioned Internet of Bio-NanoThings. The study of these molecular communications has employed information and communication theory to analyze the exchanged information via chemical reactions and molecular transport. We introduce an information- and communication-centric computational approach to estimate the information flow in biological cells and its impacts on the behavior of single organisms and communities. This study complements our previous work of cell metabolism by developing an end-to-end perspective of molecular communication based on enzyme-regulated reactions. We explore the mutual information using Shannon information theory, measured in bits, between influential nutrients and cellular growth rate. The developed RFMIA computational tool is deployed in the U.S. Department of Energys Systems Biology Knowledgebase, where it quantitatively estimates information flow in both organism and community metabolic networks and extends recent developments in computer communications to explore and explain a new biology for the open-source community.