scholarly journals Data-Driven Modeling Identifies TIRAP-Independent MyD88 Activation Complex and Myddosome Assembly Strategy in LPS/TLR4 Signaling

2020 ◽  
Vol 21 (9) ◽  
pp. 3061 ◽  
Author(s):  
Xiang Li ◽  
Chuan-Qi Zhong ◽  
Zhiyong Yin ◽  
Hong Qi ◽  
Fei Xu ◽  
...  

TLR4 complexes are essential for the initiation of the LPS-induced innate immune response. The Myddosome, which mainly contains TLR4, TIRAP, MyD88, IRAK1/4 and TRAF6 proteins, is regarded as a major complex of TLR4. Although the Myddosome has been well studied, a quantitative description of the Myddosome assembly dynamics is still lacking. Furthermore, whether some unknown TLR4 complexes exist remains unclear. In this study, we constructed a SWATH-MS data-based mathematical model that describes the component assembly dynamics of TLR4 complexes. In addition to Myddosome, we suggest that a TIRAP-independent MyD88 activation complex is formed upon LPS stimulation, in which TRAF6 is not included. Furthermore, quantitative analysis reveals that the distribution of components in TIRAP-dependent and -independent MyD88 activation complexes are LPS stimulation-dependent. The two complexes compete for recruiting IRAK1/4 proteins. MyD88 forms higher-order assembly in the Myddosome and we show that the strategy to form higher-order assembly is also LPS stimulation-dependent. MyD88 forms a long chain upon weak stimulation, but forms a short chain upon strong stimulation. Higher-order assembly of MyD88 is directly determined by the level of TIRAP in the Myddosome, providing a formation mechanism for efficient signaling transduction. Taken together, our study provides an enhanced understanding of component assembly dynamics and strategies in TLR4 complexes.

Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.


Author(s):  
Shams Kalam ◽  
Rizwan Ahmed Khan ◽  
Shahnawaz Khan ◽  
Muhammad Faizan ◽  
Muhammad Amin ◽  
...  

Author(s):  
K. Midzodzi Pekpe ◽  
Djamel Zitouni ◽  
Gilles Gasso ◽  
Wajdi Dhifli ◽  
Benjamin C. Guinhouya

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