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 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. A computational model of odour plume propagation and experimental data are used to devise a general search algorithm for movement strategies in chemotaxis, based on sporadic cues and partial information. The strategy is termed 'infotaxis' as it locally maximizes the expected rate of information gain.