论文标题
从基因表达的随机轨迹中提取信息
Extracting Information from Stochastic Trajectories of Gene Expression
论文作者
论文摘要
基因表达是一个随机过程，其中细胞产生对生命功能必不可少的生物分子。现代实验方法可以随着时间的推移在单细胞和单分子分辨率下测量生物分子。数学模型用于理解这些实验。实验和模型的代码允许一个人使用模型设计最佳实验，并找到提供尽可能多的有关相关模型参数信息的实验。在这里，我们为从连续时间马尔可夫过程中采样的轨迹提供了一种渔民信息，通常用于建模生物系统，并将结果应用于随机基因表达的潜在相关测量。我们在两个常用的基因表达模型上验证了结果，并表明它可用于优化模拟单细胞荧光显微镜实验的测量周期。最后，我们使用Fisher信息和相互信息之间的联系来得出非线性调节基因表达的通道能力。
Gene expression is a stochastic process in which cells produce biomolecules essential to the function of life. Modern experimental methods allow for the measurement of biomolecules at single-cell and single-molecule resolution over time. Mathematical models are used to make sense of these experiments. The codesign of experiments and models allows one to use models to design optimal experiments, and to find experiments which provide as much information as possible about relevant model parameters. Here, we provide a formulation of Fisher information for trajectories sampled from the continuous time Markov processes often used to model biological systems, and apply the result to potentially correlated measurements of stochastic gene expression. We validate the result on two commonly used models of gene expression and show it can be used to optimize measurement periods for simulated single-cell fluorescence microscopy experiments. Finally, we use a connection between Fisher information and mutual information to derive channel capacities of nonlinearly regulated gene expression.
