An adaptable in silico ensemble model of the arachidonic acid cascade†

IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Molecular omics Pub Date : 2024-06-03 DOI:10.1039/D3MO00187C
Megan Uttley, Grace Horne, Areti Tsigkinopoulou, Francesco Del Carratore, Aliah Hawari, Magdalena Kiezel-Tsugunova, Alexandra C. Kendall, Janette Jones, David Messenger, Ranjit Kaur Bhogal, Rainer Breitling and Anna Nicolaou
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Abstract

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

Abstract Image

花生四烯酸级联的可调整硅学集合模型
二十酸是一系列生物活性脂质,包括无处不在的脂肪酸花生四烯酸(AA)的衍生物。二十酸类在炎症中的密切参与促使人们开发出预测性的硅学模型,用于系统级的疾病机制探索、药物开发和动物模型替代。利用集合建模策略,我们开发了 AA 级联的计算模型。这种方法克服了固定参数建模的局限性,使可信的、热力学上可行的预测可视化。我们开发了一种质量评分方法,用于量化相对于实验数据的集合预测的准确性,测量过程的整体不确定性。蒙特卡洛集合建模用于量化预测置信度。使用质谱介质脂质组学测量 HaCaT 表皮角质细胞和 46BR.1N 真皮成纤维细胞在刺激(钙离子诱导剂(A23187)、紫外线辐射、三磷酸腺苷)和环氧化酶抑制剂(吲哚美辛)作用下产生的二十烷酸,证明了模型的适用性。实验结果和预测结果在质量上非常吻合,这表明该模型能够适应在 AA 释放和酶浓度分布方面存在差异的细胞类型。通过扩大网络拓扑结构以包括更多的反应,实验结果和预测结果之间的定量一致性可以得到改善。总之,我们的方法生成了一个适应性强、可调整的 AA 级联集合模型,该模型可量身定制以代表不同的细胞类型,并证明了硅学和体外方法的整合有助于加深对 AA 级联等复杂生物网络的理解。
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来源期刊
Molecular omics
Molecular omics Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
5.40
自引率
3.40%
发文量
91
期刊介绍: Molecular Omics publishes high-quality research from across the -omics sciences. Topics include, but are not limited to: -omics studies to gain mechanistic insight into biological processes – for example, determining the mode of action of a drug or the basis of a particular phenotype, such as drought tolerance -omics studies for clinical applications with validation, such as finding biomarkers for diagnostics or potential new drug targets -omics studies looking at the sub-cellular make-up of cells – for example, the subcellular localisation of certain proteins or post-translational modifications or new imaging techniques -studies presenting new methods and tools to support omics studies, including new spectroscopic/chromatographic techniques, chip-based/array technologies and new classification/data analysis techniques. New methods should be proven and demonstrate an advance in the field. Molecular Omics only accepts articles of high importance and interest that provide significant new insight into important chemical or biological problems. This could be fundamental research that significantly increases understanding or research that demonstrates clear functional benefits. Papers reporting new results that could be routinely predicted, do not show a significant improvement over known research, or are of interest only to the specialist in the area are not suitable for publication in Molecular Omics.
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