摘要
One of the most complex questions in quantitative biology is how to manage noise sources and the subsequent consequences for cell functions. Noise in genetic networks is inevitable, as chemical reactions are probabilistic and often, genes, mRNAs and proteins are present in variable numbers per cell. Previous research has focused on counting these numbers using experimental methods such as complex fluorescent techniques or theoretical methods by characterizing the probability distribution of mRNAs and proteins numbers in cells. In this work, we propose a modeling based approach;we build a mathematical model that is used to predict the number of mRNAs and proteins over time, and develop a computational method to extract the noise-related information in such a biological system. Our approach contributes to answering the question of how the number of mRNA and proteins change in living cells over time and how these changes induce noise. Moreover, we calculate the entropy of the system;this turns out to be important information for prediction which could allow us to understand how noise information is generated and expanded.
One of the most complex questions in quantitative biology is how to manage noise sources and the subsequent consequences for cell functions. Noise in genetic networks is inevitable, as chemical reactions are probabilistic and often, genes, mRNAs and proteins are present in variable numbers per cell. Previous research has focused on counting these numbers using experimental methods such as complex fluorescent techniques or theoretical methods by characterizing the probability distribution of mRNAs and proteins numbers in cells. In this work, we propose a modeling based approach;we build a mathematical model that is used to predict the number of mRNAs and proteins over time, and develop a computational method to extract the noise-related information in such a biological system. Our approach contributes to answering the question of how the number of mRNA and proteins change in living cells over time and how these changes induce noise. Moreover, we calculate the entropy of the system;this turns out to be important information for prediction which could allow us to understand how noise information is generated and expanded.
