Two wrongs do not make a right: the assumption that an inhibitor acts as an inverse activator.

IF 2.2 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2024-07-05 DOI:10.1007/s00285-024-02118-4
Chathranee Jayathilaka, Robyn Araujo, Lan Nguyen, Mark Flegg
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Abstract

Models of biochemical networks are often large intractable sets of differential equations. To make sense of the complexity, relationships between genes/proteins are presented as connected graphs, the edges of which are drawn to indicate activation or inhibition relationships. These diagrams are useful for drawing qualitative conclusions in many cases by the identifying recurring of topological motifs, for example positive and negative feedback loops. These topological features are usually classified under the presumption that activation and inhibition are inverse relationships. For example, inhibition of an inhibitor is often classified the same as activation of an activator within a motif classification, effectively treating them as equivalent. Whilst in many contexts this may not lead to catastrophic errors, drawing conclusions about the behavior of motifs, pathways or networks from these broad classes of topological feature without adequate mathematical descriptions can lead to obverse outcomes. We investigate the extent to which a biochemical pathway/network will behave quantitatively dissimilar to pathway/ networks with similar typologies formed by swapping inhibitors as the inverse of activators. The purpose of the study is to determine under what circumstances rudimentary qualitative assessment of network structure can provide reliable conclusions as to the quantitative behaviour of the network. Whilst there are others, We focus on two main mathematical qualities which may cause a divergence in the behaviour of two pathways/networks which would otherwise be classified as similar; (i) a modelling feature we label 'bias' and (ii) the precise positioning of activators and inhibitors within simple pathways/motifs.

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两错不成全:假定抑制剂起着反向激活剂的作用。
生化网络模型通常是一组难以理解的大型微分方程。为了理解其复杂性,基因/蛋白质之间的关系以连接图的形式呈现,其边缘表示激活或抑制关系。在许多情况下,通过识别拓扑结构的重复出现,例如正反馈回路,这些图表有助于得出定性结论。这些拓扑特征通常根据激活和抑制是反向关系这一假设进行分类。例如,在图案分类中,抑制剂的抑制通常与激活剂的激活相同,实际上将它们视为等价物。虽然在很多情况下,这可能不会导致灾难性的错误,但在没有充分数学描述的情况下,从这些拓扑特征的大类中得出关于图案、路径或网络行为的结论,可能会导致相反的结果。我们研究了生化通路/网络与具有相似类型的通路/网络在多大程度上会表现出量变上的差异,这些相似类型的通路/网络是通过将抑制剂作为激活剂的逆向交换而形成的。这项研究的目的是确定在什么情况下,对网络结构的初步定性评估可以为网络的定量行为提供可靠的结论。虽然还有其他方法,但我们将重点放在两个主要的数学特性上,它们可能会导致两个原本被归类为相似的通路/网络的行为出现分歧;(i) 我们称之为 "偏差 "的建模特征;(ii) 激活剂和抑制剂在简单通路/主题中的精确定位。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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