Anderson Graham A, Liu Xuedong, Ferrell James E
Chemical and Systems Biology, Stanford University Medical Center, Stanford, CA, USA.
Methods Mol Biol. 2012;880:53-67. doi: 10.1007/978-1-61779-833-7_4.
When several genes or proteins modulate one another's activity as part of a network, they sometimes produce behaviors that no protein could accomplish on its own. Intuition for these emergent behaviors often cannot be obtained simply by tracing causality through the network in discreet steps. Specifically, when a network contains a feedback loop, biologists need specialized tools to understand the network's behaviors and their necessary conditions. This analysis is grounded in the mathematics of ordinary differential equations. We, however, will demonstrate the use of purely graphical methods to determine, for experimental data, the plausibility of two network behaviors, bistability and irreversibility. We use the Xenopus laevis oocyte maturation network as our example, and we make special use of iterative stability analysis, a graphical tool for determining stability in two dimensions.
当多个基因或蛋白质作为网络的一部分相互调节彼此的活性时，它们有时会产生单个蛋白质无法独自完成的行为。对于这些涌现行为的直觉通常无法简单地通过在网络中按离散步骤追踪因果关系来获得。具体而言，当网络包含反馈回路时，生物学家需要专门的工具来理解网络行为及其必要条件。这种分析基于常微分方程的数学。然而，我们将展示使用纯图形方法来确定实验数据中两种网络行为（双稳态和不可逆性）的合理性。我们以非洲爪蟾卵母细胞成熟网络为例，并特别利用迭代稳定性分析，这是一种用于确定二维稳定性的图形工具。
