论文标题
随机生化网络中的动态信息传递
Dynamic Information Transfer in Stochastic Biochemical Networks
论文作者
论文摘要
我们开发了数值和分析方法，以计算两个分子成分的完整路径之间的相互信息，这些路径嵌入了较大的反应网络中。特别是，我们专注于连续的马尔可夫链形式主义，通常用于描述涉及较低丰富分子物种的细胞内过程。以前，当两个分子成分直接相互作用而没有中间分子成分时，我们已经展示了如何计算此系统的路径互信息。在这项工作中，我们将这种方法推广到涉及任意数量分子成分的生化网络。我们提出了一种有效的蒙特卡洛方法以及一个分析近似，以计算路径互信息，并显示如何将其分解为一对捕获两个网络组件之间信息的因果流动的转移熵。我们将我们的方法应用于简单的三节点前馈网络中研究信息传输，以及更复杂的正反馈系统，该系统在两个亚稳态模式之间随机切换。
We develop numerical and analytical approaches to calculate mutual information between complete paths of two molecular components embedded into a larger reaction network. In particular, we focus on a continuous-time Markov chain formalism, frequently used to describe intracellular processes involving lowly abundant molecular species. Previously, we have shown how the path mutual information can be calculated for such systems when two molecular components interact directly with one another with no intermediate molecular components being present. In this work, we generalize this approach to biochemical networks involving an arbitrary number of molecular components. We present an efficient Monte Carlo method as well as an analytical approximation to calculate the path mutual information and show how it can be decomposed into a pair of transfer entropies that capture the causal flow of information between two network components. We apply our methodology to study information transfer in a simple three-node feedforward network, as well as a more complex positive feedback system that switches stochastically between two metastable modes.
