Henri C. Jimbo and Matthew J. Craven
Bio-engineering, Modelling, Simulation
One of the most complex questions in noise biology is how to manage noise sources and their subsequent consequences for cell function. Noise in genetic networks is inevitable, as chemical reactions are of a probabilistic nature and many genes, mRNA and protein molecules are present in variable numbers per cell. Previous research focused on counting numbers of the above present by using experimental fluorescent techniques, or by using theoretical characterization of the probability distribution of mRNA and protein numbers in cells. In this work, we propose a new mathematical model to capture the dynamic of the number of mRNA and protein molecules over time, and develop a computational method to extract noise-related information in biological systems. Our approach helps answer the question of how the number of mRNA and protein molecules change in each cell of a population over time. Further, we calculate the noise uncertainty (entropy) of the biological system: this turns out to be important information for prediction. We also observe that the proposed approach for capturing noise in the system is efficient, allowing us to test ideas of how noise information is generated and expanded.
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