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Zhang L, Liu G, Kong M, Li T, Wu D, Zhou X, Yang C, Xia L, Yang Z, Chen L. Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model. Bioinformatics 2021; 37:1554-1561. [PMID: 31350562 DOI: 10.1093/bioinformatics/btz542] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 06/17/2019] [Accepted: 07/19/2019] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway. RESULTS We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway. AVAILABILITY AND IMPLEMENTATION We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Le Zhang
- College of Computer Science.,Medical Big Data Center, Sichuan University, Chengdu 610065, China.,Chongqqing Zhongdi Medical Information Technology Co., Ltd, Chongqing 401320, China
| | - Guangdi Liu
- College of Computer and Information Science, Southwest University, Chongqing 400715, China.,Library of Chengdu University, Chengdu University, Chengdu 610106, China
| | - Meijing Kong
- College of Computer and Information Science, Southwest University, Chongqing 400715, China
| | - Tingting Li
- College of Mathematics and Statistics, Southwest University, Chongqing 400715, China
| | - Dan Wu
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Xiaobo Zhou
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Chuanwei Yang
- Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lei Xia
- Cancer Center, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing 400042, China
| | - Zhenzhou Yang
- Cancer Center, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing 400042, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.,Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China
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Energy Saving Maximization of Balanced and Unbalanced Distribution Power Systems via Network Reconfiguration and Optimum Capacitor Allocation Using a Hybrid Metaheuristic Algorithm. ENERGIES 2021. [DOI: 10.3390/en14113205] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The main aim of this work was the maximization of the energy saving of balanced and unbalanced distribution power systems via system reconfiguration and the optimum capacitor’s bank choice, which were estimated by using a new algorithm: modified Tabu search and Harper sphere search (MTS-HSSA). The results demonstrated that the proposed method is appropriate for energy saving and improving performance compared with other methods reported in the literature for IEEE 33-bus adopted systems, including large scale systems such as IEEE 119 and the IEEE 123 unbalanced distribution system. Moreover, it can be used for unbalanced distribution systems distributed generators (DGs). The results demonstrated that the proposed method (the optimal choice of shunt capacitor (SC) banks and the optimal reconfiguration via the proposed algorithm) is appropriate for energy saving compared with different strategies for energy saving, which included distributed generation (DG) at different cost levels.
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