A wider probability distribution corresponds to an increase in the uncertainty of the cellular response and consequently, entropy. (B) Entropy can be understood as a measure of dispersion. The amount of mutual information between the stimulus and cellular response also suffers such that the greater the overlap between distributions, the less mutual information is communicated. Consequently, from the cell’s perspective, noise leads to a loss of information about the input. For sufficiently large noise, a cell which can encounter strong or weak stimuli cannot use its response to discern which stimulus was encountered with absolute precision. The magnitude of noise is evidenced in the breadth of the probability distribution of the response to a given stimulus. (A) Noise can limit the amount of information a cell can obtain about a stimulus In order to quantify the degree to which noise affects the fidelity of the message, or specifically to determine what a biological signaling system can or cannot communicate accurately, it is useful to turn to information theory. Likewise, both deterministic and stochastic mathematical models, although able to capture dynamic trends, require a priori knowledge or assumptions of the underlying molecular mechanisms and ultimately fail to describe how signaling fidelity is affected by variability. Traditional metrics for noise related to the standard deviation or variance primarily quantify the magnitude of noise and do not directly indicate the degree to which noise hampers the discrimination of different inputs. This inability to resolve distinct stimuli represents a loss of information about the input. If, for example, the distribution of responses elicited by a weak stimulus overlaps with the distribution elicited by a strong stimulus, a cell whose response value falls within the overlap will not be able to discern with absolute certainty which stimulus was present ( figure 1A). As a result, the message can get distorted and cells may not be able to acquire a precise perception of their surroundings.īiological noise can perhaps more adequately described as stochastic cell-cell variability and can be experimentally observed by sampling the distribution of responses by a group of genetically identical cells exposed to the same stimulus. Since the mechanisms behind this complex function are biochemical in nature, molecular noise can greatly hamper the propagation of signals. These control functions are commonly executed by dedicated sets of kinases and transcription factors to ensure that the appropriate cellular response is activated. Within the recipient cell, the information contained within the chemical messages must be captured and processed by the cell’s biochemical circuitry, which typically involves feedback loops, crosstalk, and delays. Each of these molecular signals can be thought of as being sent with the intent of communicating a specific message or action for the receiving cell to perform. In their in vivo environment, cells are constantly awash in a sea of hormones, cytokines, morphogens, and other receptor ligands released by other cells. 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. 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. 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. 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. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |