Department of Biological Sciences, Florida State University, Tallahassee, Florida, USA.
PLoS Comput Biol. 2011 Apr;7(4):e1001124. doi: 10.1371/journal.pcbi.1001124. Epub 2011 Apr 21.
Biological systems are characterized by a high number of interacting components. Determining the role of each component is difficult, addressed here in the context of biological oscillations. Rhythmic behavior can result from the interplay of positive feedback that promotes bistability between high and low activity, and slow negative feedback that switches the system between the high and low activity states. Many biological oscillators include two types of negative feedback processes: divisive (decreases the gain of the positive feedback loop) and subtractive (increases the input threshold) that both contribute to slowly move the system between the high- and low-activity states. Can we determine the relative contribution of each type of negative feedback process to the rhythmic activity? Does one dominate? Do they control the active and silent phase equally? To answer these questions we use a neural network model with excitatory coupling, regulated by synaptic depression (divisive) and cellular adaptation (subtractive feedback). We first attempt to apply standard experimental methodologies: either passive observation to correlate the variations of a variable of interest to system behavior, or deletion of a component to establish whether a component is critical for the system. We find that these two strategies can lead to contradictory conclusions, and at best their interpretive power is limited. We instead develop a computational measure of the contribution of a process, by evaluating the sensitivity of the active (high activity) and silent (low activity) phase durations to the time constant of the process. The measure shows that both processes control the active phase, in proportion to their speed and relative weight. However, only the subtractive process plays a major role in setting the duration of the silent phase. This computational method can be used to analyze the role of negative feedback processes in a wide range of biological rhythms.
生物系统的特点是存在大量相互作用的组件。确定每个组件的作用具有一定难度,本研究在生物振荡的背景下对此进行了探讨。节律性行为可能是正反馈相互作用的结果,正反馈促进了高活性和低活性之间的双稳性,而缓慢的负反馈则在高活性和低活性状态之间切换系统。许多生物振荡器包括两种类型的负反馈过程:除法(降低正反馈回路的增益)和减法(增加输入阈值),它们都有助于系统在高活性和低活性状态之间缓慢移动。我们能否确定每种类型的负反馈过程对节律性活动的相对贡献?是否有一种占主导地位?它们是否对等控制活跃相和静止相?为了回答这些问题,我们使用了具有兴奋性耦合的神经网络模型,由突触抑制(除法)和细胞适应(减法反馈)调节。我们首先尝试应用标准实验方法:要么被动观察,将感兴趣的变量的变化与系统行为相关联,要么删除一个组件以确定该组件是否对系统至关重要。我们发现,这两种策略可能会得出相互矛盾的结论,而且其解释力充其量是有限的。相反,我们通过评估过程的时间常数对活跃相(高活性)和静止相(低活性)持续时间的敏感性,来开发一种计算过程贡献的方法。该方法表明,两种过程都以与其速度和相对权重成比例的方式控制活跃相。然而,只有减法过程在设置静止相持续时间方面起着主要作用。这种计算方法可用于分析负反馈过程在广泛的生物节律中的作用。