Antoneli Fernando, Golubitsky Martin, Stewart Ian
Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP 05508-090, Brazil.
Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA.
J Theor Biol. 2018 May 14;445:103-109. doi: 10.1016/j.jtbi.2018.02.026.
The internal state of a cell is affected by inputs from the extra-cellular environment such as external temperature. If some output, such as the concentration of a target protein, remains approximately constant as inputs vary, the system exhibits homeostasis. Special sub-networks called motifs are unusually common in gene regulatory networks (GRNs), suggesting that they may have a significant biological function. Potentially, one such function is homeostasis. In support of this hypothesis, we show that the feed-forward loop GRN produces homeostasis. Here the inputs are subsumed into a single parameter that affects only the first node in the motif, and the output is the concentration of a target protein. The analysis uses the notion of infinitesimal homeostasis, which occurs when the input-output map has a critical point (zero derivative). In model equations such points can be located using implicit differentiation. If the second derivative of the input-output map also vanishes, the critical point is a chair: the output rises roughly linearly, then flattens out (the homeostasis region or plateau), and then starts to rise again. Chair points are a common cause of homeostasis. In more complicated equations or networks, numerical exploration would have to augment analysis. Thus, in terms of finding chairs, this paper presents a proof of concept. We apply this method to a standard family of differential equations modeling the feed-forward loop GRN, and deduce that chair points occur. This function determines the production of a particular mRNA and the resulting chair points are found analytically. The same method can potentially be used to find homeostasis regions in other GRNs. In the discussion and conclusion section, we also discuss why homeostasis in the motif may persist even when the rest of the network is taken into account.
细胞的内部状态会受到细胞外环境输入的影响,比如外部温度。如果某些输出,比如目标蛋白的浓度,在输入变化时大致保持恒定,那么该系统就表现出稳态。在基因调控网络(GRN)中,一种被称为模体的特殊子网络异常常见,这表明它们可能具有重要的生物学功能。其中一种潜在功能可能就是稳态。为支持这一假设,我们证明了前馈环基因调控网络能产生稳态。在这里,输入被归纳为一个仅影响模体中第一个节点的单一参数,而输出则是目标蛋白的浓度。该分析使用了无穷小稳态的概念,当输入 - 输出映射有一个临界点(导数为零)时就会出现这种情况。在模型方程中,可以使用隐函数求导来定位这些点。如果输入 - 输出映射的二阶导数也为零,那么这个临界点就是一个平台点:输出大致呈线性上升,然后趋于平稳(稳态区域或平台),接着又开始上升。平台点是稳态的常见成因。在更复杂的方程或网络中,数值探索必须辅助分析。因此,就寻找平台点而言,本文给出了一个概念验证。我们将此方法应用于一个模拟前馈环基因调控网络的标准微分方程族,并推断出平台点的存在。该函数决定了一种特定mRNA的产生,并且通过解析找到了相应的平台点。同样的方法有可能用于在其他基因调控网络中找到稳态区域。在讨论和结论部分,我们还讨论了即使考虑网络的其余部分,模体中的稳态为何可能依然存在。