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.
"Other interpretations of the model are discussed in Sec. 4. Information theory  allows general high-level descriptions of systems, permitting to hide away irrelevant details for the purposes of a model  . In particular, information theory provides a natural framework to analyse cells' decision-making processes in uncertainty where the mechanisms need not to be modelled    . "
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
Information Theory and Applications Workshop; 02/2014
[Show abstract][Hide abstract] ABSTRACT: The 50th anniversary of the classic Monod-Wyman-Changeux (MWC) model provides an opportunity to survey the broader conceptual and quantitative implications of this quintessential biophysical model. Using statistical mechanics, the mathematical implementation of the MWC concept links problems that seem otherwise to have no ostensible biological connection including ligand-receptor binding, ligand-gated ion channels, chemotaxis, chromatin structure and gene regulation. Hence, a thorough mathematical analysis of the MWC model can illuminate the performance limits of a number of unrelated biological systems in one stroke. The goal of our review is two-fold. First, we describe in detail the general physical principles that are used to derive the activity of MWC molecules as a function of their regulatory ligands. Second, we illustrate the power of ideas from information theory and dynamical systems for quantifying how well the output of MWC molecules tracks their sensory input, giving a sense of the "design" constraints faced by these receptors.
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