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Understanding cellular engagement with its environment is essential to control and monitor metabolism. Molecular Communication theory (MC) offers a computational means to identify environmental perturbations that direct or signify cellular behaviors by quantifying the information about a molecular environment that is transmitted through a metabolic system. We developed an model that integrates conventional flux balance analysis metabolic modeling (FBA) and MC to mechanistically expand the scope of MC, and thereby uniquely blends mechanistic biology and information theory to understand how substrate consumption is captured reaction activity, metabolite excretion, and biomass growth. This is enabled by defining several channels through which environmental information transmits in a metabolic network. The information flow in bits that is calculated through this workflow further determines the maximal metabolic effect of environmental perturbations on cellular metabolism and behaviors, since FBA simulates maximal efficiency of the metabolic system. We exemplify this method on two intestinal symbionts – Bacteroides thetaiotaomicron and Methanobrevibacter smithii – and visually consolidated the results into constellation diagrams that facilitate interpretation of information flow from given environments and thereby cultivate the design of controllable biological systems. The unique confluence of metabolic modeling and information theory in this model advances basic understanding of cellular metabolism and has applied value for the Internet of Bio-Nano Things, synthetic biology, microbial ecology, and autonomous laboratories. 

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.