Novel Quantitative Approach for Predicting mRNA/Protein Counts in Living Cells
Novel Quantitative Approach for Predicting mRNA/Protein Counts in Living Cells作者机构：Department of Applied Mathematics and Statistics Waseda University Tokyo Japan Department of Applied Mathematics University of Paris 1 Pantheon Sorbonne Paris France Department of Urology Central Medical Hospital CEMAC Region Yaounde Cameroon Department of Mathematics Higher National School (ENS) Yaounde Cameroon
出 版 物：《Applied Mathematics》 (应用数学（英文）)
年 卷 期：2017年第8卷第8期
页 面：1128-1139页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：Applied Mathematics Embedded Control System Genetic Algorithm Optimization Biodynamics Stochastic Modelling Simulations Stochasticity Bioengineering and Medicine
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.
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