Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA

How does the regulatory machinery of an animal cell ensure its survival during large-scale biochemical and phenotypic transitions? When a cell is strongly perturbed by an environmental stimulus, it can either die or persist with compensatory changes. But what do the dynamics of individual genes look like during this process of adaptation? In a previous technical paper, two approaches (drug treatments and polysome isolation) were used in tandem to demonstrate the effects of perturbation on cellular phenotype. In this paper, we can use these data in tandem with a discrete, first-order feedback model that incorporates leaky components to better characterize adaptive responses of mRNA regulation related to information processing in the cell. By evaluating the dynamic relationship between mRNA associated with transcription (translatome) and mRNA associated with the polysome (transcriptome) at multiple timepoints, hypothetical conditions for decay and aggregation are found and discussed. Our feedback model allows for the approximation of fluctuations and other aspects of cellular information processing, in addition to the derivation of three information processing principles. These results will lead us to a better understanding of how mRNA provides variable information over time to the complex intracellular environment, particularly in the context of large-scale phenotypic change.