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丝裂原活化蛋白激酶(MAPK)级联反应的黄-费雷尔模型中的最小振荡子网络。

Minimal oscillating subnetwork in the Huang-Ferrell model of the MAPK cascade.

作者信息

Hadač Otto, Muzika František, Nevoral Vladislav, Přibyl Michal, Schreiber Igor

机构信息

Department of Chemical Engineering, University of Chemistry and Technology, Prague, Czech Republic.

出版信息

PLoS One. 2017 Jun 21;12(6):e0178457. doi: 10.1371/journal.pone.0178457. eCollection 2017.

Abstract

Prompted by the recent growing evidence of oscillatory behavior involving MAPK cascades we present a systematic approach of analyzing models and elucidating the nature of biochemical oscillations based on reaction network theory. In particular, we formulate a minimal biochemically consistent mass action subnetwork of the Huang-Ferrell model of the MAPK signalling that provides an oscillatory response when a parameter controlling the activation of the top-tier kinase is varied. Such dynamics are either intertwined with or separated from the earlier found bistable/hysteretic behavior in this model. Using the theory of stability of stoichiometric networks, we reduce the original MAPK model, convert kinetic to convex parameters and examine those properties of the minimal subnetwork that underlie the oscillatory dynamics. We also use the methods of classification of chemical oscillatory networks to explain the rhythmic behavior in physicochemical terms, i.e., we identify of the role of individual biochemical species in positive and negative feedback loops and describe their coordinated action leading to oscillations. Our approach provides an insight into dynamics without the necessity of knowing rate coefficients and thus is useful prior the statistical evaluation of parameters.

摘要

鉴于近期越来越多的证据表明丝裂原活化蛋白激酶(MAPK)级联存在振荡行为,我们提出了一种基于反应网络理论分析模型并阐明生化振荡本质的系统方法。特别是,我们构建了MAPK信号转导的黄-费雷尔模型的一个最小生化一致质量作用子网,当控制顶级激酶激活的参数变化时,该子网会产生振荡响应。这种动力学与该模型中早期发现的双稳态/滞后行为相互交织或分离。利用化学计量网络稳定性理论,我们简化了原始的MAPK模型,将动力学参数转换为凸参数,并研究了构成振荡动力学基础的最小子网的那些性质。我们还使用化学振荡网络分类方法从物理化学角度解释节律行为,即我们确定了各个生化物种在正反馈和负反馈回路中的作用,并描述了它们导致振荡的协同作用。我们的方法在无需知道速率系数的情况下就能深入了解动力学,因此在参数的统计评估之前很有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137b/5479530/0cb0ad16f5c1/pone.0178457.g001.jpg

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