Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'.
Other applications of information theory to cell regulation have been developed, which consider bounds on information transmission in biological systems, such as finding the minimum rate at which information must be transmitted in the system to ensure the readout of the signal remains within a fixed value of the signal -- these bounds have much to do with the approach that views cells and organisms as trying to decoding'' noisy environmental signals and making optimal decisions based on these data @cite_47 . Information theory has been used to discuss chemotaxis @cite_74 @cite_19 , i.e. navigation on the basis of noisy inputs.
@cite_19 @cite_47 @cite_74
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Cells send and receive signals through pathways that have been defined in great detail biochemically, and it is often presumed that the signals convey only level information. Cell signaling in the presence of noise is extensively studied but only rarely is the speed required to make a decision considered. However, in the immune system, rapidly developing embryos, and cellular response to stress, fast and accurate actions are required. Statistical theory under the rubric of “exploit–explore” quantifies trade-offs between decision speed and accuracy and supplies rigorous performance bounds and algorithms that realize them. We show that common protein phosphorylation networks can implement optimal decision theory algorithms and speculate that the ubiquitous chemical modifications to receptors during signaling actually perform analog computations. We quantify performance trade-offs when the cellular system has incomplete knowledge of the data model. For the problem of sensing the time when the composition of a ligand mixture changes, we find a nonanalytic dependence on relative concentrations and specify the number of parameters needed for near-optimal performance and how to adjust them. The algorithms specify the minimal computation that has to take place on a single receptor before the information is pooled across the cell. Cells must respond to environmental changes to remain viable, yet the information they receive is often noisy. Through a biochemical implementation of Bayes's rule, we show that genetic networks can act as inference modules, inferring from intracellular conditions the likely state of the extracellular environment and regulating gene expression appropriately. By considering a two-state environment, either poor or rich in nutrients, we show that promoter occupancy is proportional to the (posterior) probability of the high nutrient state given current intracellular information. We demonstrate that single-gene networks inferring and responding to a high environmental state infer best when negatively controlled, and those inferring and responding to a low environmental state infer best when positively controlled. Our interpretation is supported by experimental data from the lac operon and should provide a basis for both understanding more complex cellular decision-making and designing synthetic inference circuits. Information processing and information flow occur at many levels in the course of an organism's development and throughout its lifespan. Biological networks inside cells transmit information from their inputs (e.g. the concentrations of proteins or other signaling molecules) to their outputs (e.g. the expression levels of various genes). Moreover, cells do not exist in isolation, but they constantly interact with one another. We study the information flow in a model of interacting genetic networks, which are represented as Boolean graphs. It is observed that the information transfer among the networks is not linearly dependent on the amount of nodes that are able to influence the state of genes in surrounding cells. Abstract Simulation-based and information theoretic models for a diffusion-based short-range molecular communication channel between a nano-transmitter and a nano-receiver are constructed to analyze information rates between channel inputs and outputs when the inputs are independent and identically distributed (i.i.d.). The total number of molecules available for information transfer is assumed to be limited. It is also assumed that there is a maximum tolerable delay bound for the overall information transfer. Information rates are computed via simulation-based methods for different time slot lengths and transmitter–receiver distances. The rates obtained from simulations are then compared to those computed using information theoretic channel models which provide upper bounds for information rates. The results indicate that a 4-input–2-output discrete channel model provides a very good approximation to the nano-communication channel, particularly when the time slot lengths are large and the distance between the transmitter and the receiver is small. It is shown through an extensive set of simulations that the information theoretic channel capacity with i.i.d. inputs can be achieved when an encoder adjusts the relative frequency of binary zeros to be higher (between 50 and 70 for the scenarios considered) than binary ones, where a ‘zero’ corresponds to not releasing and a ‘one’ corresponds to releasing a molecule from the transmitter.