基于误差最小化的生物调节规则
Rules for biological regulation based on error minimization
摘要：
基因表达的控制涉及复杂的机制，在设计上表现出很大的变化。例如，基因可以通过激活物的结合（阳性对照）或阻遏物的解结合（阴性对照）来开启。是什么决定了每个基因控制模式的选择？这项研究提出了规则的基因调控的基础上的假设，自由监管网站暴露于非特异性结合错误，而网站结合到他们的同源监管机构的保护错误。因此，所选机制在大部分时间内将网站绑定到其指定的监管机构，从而最大限度地减少健身减少错误。这为经验证明的Savageau需求规则提供了解释：在自然环境中经常需要的基因往往受到激活因子的调节，而很少需要的基因往往受到阻遏因子的调节;在这两种情况下，位点在大多数时间内都是结合的，错误最小化。误差最小化的适应性优势似乎是容易选择的。本方法还可以生成多调节器系统的规则。误差最小化框架提出了几个实验验证的假设。它也可以应用于其他生物调节系统，如涉及蛋白质相互作用的那些。
英文摘要：
The control of gene expression involves complex mechanisms that show large variation in design. For example, genes can be turned on either by the binding of an activator (positive control) or the unbinding of a repressor (negative control). What determines the choice of mode of control for each gene? This study proposes rules for gene regulation based on the assumption that free regulatory sites are exposed to nonspecific binding errors, whereas sites bound to their cognate regulators are protected from errors. Hence, the selected mechanisms keep the sites bound to their designated regulators for most of the time, thus minimizing fitness-reducing errors. This offers an explanation of the empirically demonstrated Savageau demand rule: Genes that are needed often in the natural environment tend to be regulated by activators, and rarely needed genes tend to be regulated by repressors; in both cases, sites are bound for most of the time, and errors are minimized. The fitness advantage of error minimization appears to be readily selectable. The present approach can also generate rules for multi-regulator systems. The error-minimization framework raises several experimentally testable hypotheses. It may also apply to other biological regulation systems, such as those involving protein-protein interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
