Optimal decoding of NFkB signaling dynamic

The encoder/decoder paradigm suggests that signaling networks transform information about the extracellular environment into specific signaling patterns that are then read by downstream effectors to control cellular behavior. Previous work used information theoretical tools to analyze the fidelity of encoding using dynamic signaling patterns. However, as the overall fidelity depends on both encoding and decoding, it is important to consider information loss during signal decoding. Here we used NFkB signaling as a model to understand the accuracy of signal decoding. Using a detailed mathematical model we simulated realistic NFkB signaling patterns with different degrees of variability. The NFkB patterns were used as an input to a simple gene expression model. Analysis of information transmission between ligand and NFkB and ligand and gene expression allow us to determine information loss in both encoding and decoding steps. Information loss could occur due to biochemical noise or due to lack of specificity in decoding response. We found that noise free decoding has very little information loss suggesting that decoding through gene expression can preserve specificity in NFkB patterns. As expected, information transmission through a noisy decoder suffers from information loss. Interestingly, this effect can be mitigated by a specific choice of decoding parameters that can substantially reduce information loss due to biochemical noise during signal decoding. Overall our results show that optimal decoding of dynamic patterns can preserve ligand specificity to maximize the accuracy of cellular response to environmental cues. Synopsis The fidelity of signal transduction depends on the accurate encoding of ligand information in specific signaling patterns and the reliable decoding of these patterns by downstream gene expression machinery. We present an analysis of the accuracy of decoding processes in the case of the transcription factor NFkB. We show that noiseless decoding can preserve ligand identity with minimal information loss. Noisy decoding does result in information loss, an effect that can be largely mitigated by choice of optimal decoding parameter values. Decoding of dynamic signaling patterns by a simple gene model can preserve most of the information about ligand identity. Noisy decoding will result in information loss, but this effect can be mitigated by the optimal choice of decoding parameters. Improvement in decoding is a result of decreased variability in gene expression patterns.