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ABSTRACT Trophic interactions between microbes are postulated to determine whether a host microbiome is healthy or causes predisposition to disease. Two abundant taxa, the Gram-negative heterotrophic bacterium Bacteroides thetaiotaomicron and the methanogenic archaeon Methanobrevibacter smithii , are proposed to have a synergistic metabolic relationship. Both organisms play vital roles in human gut health; B. thetaiotaomicron assists the host by fermenting dietary polysaccharides, whereas M. smithii consumes end-stage fermentation products and is hypothesized to relieve feedback inhibition of upstream microbes such as B. thetaiotaomicron . To study their metabolic interactions, we defined and optimized a coculture system and used software testing techniques to analyze growth under a range of conditions representing the nutrient environment of the host. We verify that B. thetaiotaomicron fermentation products are sufficient for M. smithii growth and that accumulation of fermentation products alters secretion of metabolites by B. thetaiotaomicron to benefit M. smithii . Studies suggest that B. thetaiotaomicron metabolic efficiency is greater in the absence of fermentation products or in the presence of M. smithii . Under certain conditions, B. thetaiotaomicron and M. smithii form interspecies granules consistent with behavior observed for syntrophic partnerships between microbes in soil or sediment enrichments and anaerobic digesters. Furthermore, when vitamin B 12 , hematin, and hydrogen gas are abundant, coculture growth is greater than the sum of growth observed for monocultures, suggesting that both organisms benefit from a synergistic mutual metabolic relationship. IMPORTANCE The human gut functions through a complex system of interactions between the host human tissue and the microbes which inhabit it. These diverse interactions are difficult to model or examine under controlled laboratory conditions. We studied the interactions between two dominant human gut microbes, B. thetaiotaomicron and M. smithii , using a seven-component culturing approach that allows the systematic examination of the metabolic complexity of this binary microbial system. By combining high-throughput methods with machine learning techniques, we were able to investigate the interactions between two dominant genera of the gut microbiome in a wide variety of environmental conditions. Our approach can be broadly applied to studying microbial interactions and may be extended to evaluate and curate computational metabolic models. The software tools developed for this study are available as user-friendly tutorials in the Department of Energy KBase. 

The natural communication ability of cells is explored in this paper by providing preliminary results in the estimation of the Mutual Information (MI) of signaling pathway communication channels. These results, based on an application of Molecular Communication (MC) and information theory concepts to multi-scale integrated Flux-Balance Analysis (iFBA) models are a first step to evaluate the potential of cells and their biochemical processes as a substrate for enabling engineered MC channels for the future internet of Bio-Nano Things. 

Signal transduction pathways are chemical communication channels embedded in biological cells, and they propagate information from the environment to regulate cell growth and proliferation, among other cell's behaviors. Disruptions in the normal functionalities of these channels, mostly resulting from mutations in the underlying genetic code, can be leading causes of diseases, such as cancer. Motivated by the increasing availability of public data on genetic code expression in cell tissue samples, i.e., transcriptomics, and the emerging field of molecular communication, a novel data-driven approach based on experimental data mining and communication theory is proposed in this paper. This approach is an alternative to existing computational models of these pathways in the context of cancer, which often appear to oversimplify the complexity of the underlying mechanisms. In contrast, a computational methodology is here derived to estimate the difference in information propagation performance of signal transduction pathways in healthy and diseased cells, solely based on transcriptomic data. This methodology is built upon a molecular communication abstraction of information flow through the pathway and its correlation with the expression of the underlying DNA genes. Numerical results are presented for a case study based on the JAK-STAT pathway in kidney cancer, and correlated with the occurrence of pathway gene mutations in the available data.