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Information theory has been successfully applied to biology with interesting results and applications, ranging from scientific discovery, to system modeling, and engineering. Novel concepts such as semantic and useful information have been proposed to address the peculiarity of biological systems in contrast to Shannon’s classical theory. In this paper, the concept of subjective information, previously observed as an emergent property in a simulated biological system with determinate char- acteristics, is further explored through the proposal of a novel metric for its quantification. This measure is based on a biological system’s ability to dynamically sense and react to environmental signals to achieve a goal. The novel metric is validated through the simulation of a computational model that enables its correlation with different strategies for information acquisition from the environment and processing. The obtained results indicate that the proposed measure of subjective information is reliable in quantifying the effectiveness of a biological system’s strategy in using information from the environment for its growth and survival. 

The realization of interfaces between the biological world and elec- tronics has the potential to propel the field of Molecular Commu- nication (MC) to novel frontiers. Plugging MC-enabled devices to our electrical cyber-world will enable revolutionary applications, especially in the biomedical field. By stemming from a seminal proof-of-concept prototype that enables communication between a biological system and an electrical circuit, based on redox biochem- ical reactions, this paper introduces the first frequency analysis of such system and its characterization in terms of communication performance (capacity). To achieve these results, made possible by a linearity property in the analytical model of the system, an em- pirical methodology is followed to obtain the frequency response and the noise power spectral density of the system from the results of a simulation framework. The latter was developed in prior work and made accessible publicly through a web app. A water filling capacity estimation algorithm is applied to the obtained results to give a preliminary idea on the communication performance of such system, which results in a transmission rate equivalent to 0.0587 bits/hour. While orders of magnitude slower than common electrical or optical communications, these results are in line with the inherent timescales of the biological systems envisioned to be interfaced with this technology. 

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. 

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a “subjective information”, i.e., trade-off between a living system’s capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival. 

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 optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical for-mulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems. 

Information processing has increasingly gained traction as a unifying and holistic concept to characterize biological systems. Current research has obtained important but limited results in applying information to understanding life, mainly because of inherent syntactic constraints embedded in a universally accepted theory, formulated for communication system engineering, rather than a universal characterization of nature. In this paper, we further the notion of "subjective information", which takes into account the relative importance of different information sources for distinct life functions. To this end, we develop a computational model of a microorganism that requires two metabolic substrates to survive and grow. The substrates have different spatial distributions, and the organism acquires information on their environmental concentrations and gradients through a noisy receptor-binding process, ultimately guiding its chemotaxis in the environment to increase the chances of growth and survival. Our simulation results reveal a trade-off between a living system's capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time, suggesting that a form of "subjective information" promotes growth and survival in life processes, rather than the classical, purely syntactic Shannon information. 

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.