The application of information theory to biochemical signaling systems

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Physical Biology (Impact Factor: 2.54). 08/2012; 9(4):045011. DOI: 10.1088/1478-3975/9/4/045011
Source: PubMed


Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell's signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon's information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.

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    • "To the best of our knowledge, there are only a small number of reviews on the applications of information theory to the biochemical signaling systems [2] [3] [4] [5] [6] and reverse engineering of cellular networks [7] [8], and there is not a more comprehensive review to demonstrate the wide applicability of information theory in systems biology by the emphasis on all types of biological networks. Therefore, the purpose of this review, as indicated in Fig. 1, is to give a survey on most of the existing studies on biological networks, which are founded on the concepts of information theory. "
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    ABSTRACT: "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.
    Full-text · Article · Dec 2015 · Seminars in Cell and Developmental Biology
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    • "Other interpretations of the model are discussed in Sec. 4. Information theory [57] allows general high-level descriptions of systems, permitting to hide away irrelevant details for the purposes of a model [48] [40]. In particular, information theory provides a natural framework to analyse cells' decision-making processes in uncertainty where the mechanisms need not to be modelled [36] [66] [6] [53]. "
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    ABSTRACT: We consider a simple information-theoretic model of communication, in which two species of bacteria have the option of exchanging information about their environment, thereby improving their chances of survival. For this purpose, we model a system consisting of two species whose dynamics in the world are modelled by a bet-hedging strategy. It is well known that such models lend themselves to elegant information-theoretical interpretations by relating their respective long-term growth rate to the information the individual species has about its environment. We are specifically interested in modelling how this dynamics are affected when the species interact cooperatively or in an antagonistic way in a scenario with limited resources. For this purpose, we consider the exchange of environmental information between the two species in the framework of a game. Our results show that a transition from a cooperative to an antagonistic behaviour in a species results as a response to a change in the availability of resources. Species cooperate in abundance of resources, while they behave antagonistically in scarcity.
    Full-text · Article · May 2015
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    • "In this regard, several information theoretical based approaches were proposed to model, analyse an design the inter-cellular communications (see e.g. [5], [6], [7] for recent reviews). "
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    ABSTRACT: Inspired by the parallels between information cod-ing in morphogenesis and information coding in computer communication, we introduce a new model for coupled discrete memoryless channels in which the error probability of one channel depends on the output of the other channel. The model is motivated by a type of cell-cell communication. It is shown that coupling will lead to higher sum capacities with both optimal input distribution and with uniform input distribution under joint coding. Thereby, nature can achieve more than the sum of the individual capacities (synergistic effect). We compare this result with the maximum achievable sum capacity by arbitrary ideal coupling using Majorization theory. Finally, we illustrate the model with applications from wireless communications.
    Full-text · Conference Paper · Feb 2014
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