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Dobrovolny HM. Mathematical Modeling of Virus-Mediated Syncytia Formation: Past Successes and Future Directions. Results Probl Cell Differ 2024; 71:345-370. [PMID: 37996686 DOI: 10.1007/978-3-031-37936-9_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
Many viruses have the ability to cause cells to fuse into large multi-nucleated cells, known as syncytia. While the existence of syncytia has long been known and its importance in helping spread viral infection within a host has been understood, few mathematical models have incorporated syncytia formation or examined its role in viral dynamics. This review examines mathematical models that have incorporated virus-mediated cell fusion and the insights they have provided on how syncytia can change the time course of an infection. While the modeling efforts are limited, they show promise in helping us understand the consequences of syncytia formation if future modeling efforts can be coupled with appropriate experimental efforts to help validate the models.
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Affiliation(s)
- Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
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Computational simulation of liver fibrosis dynamics. Sci Rep 2022; 12:14112. [PMID: 35982187 PMCID: PMC9388486 DOI: 10.1038/s41598-022-18123-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 08/05/2022] [Indexed: 11/08/2022] Open
Abstract
Liver fibrosis is a result of homeostasis breakdown caused by repetitive injury. The accumulation of collagens disrupts liver structure and function, which causes serious consequences such as cirrhosis. Various mathematical simulation models have been developed to understand these complex processes. We employed the agent-based modelling (ABM) approach and implemented inflammatory processes in central venous regions. Collagens were individually modelled and visualised depending on their origin: myofibroblast and portal fibroblast. Our simulation showed that the administration of toxic compounds induced accumulation of myofibroblast-derived collagens in central venous regions and portal fibroblast-derived collagens in portal areas. Subsequently, these collagens were bridged between central-central areas and spread all over areas. We confirmed the consistent dynamic behaviour of collagen formulation in our simulation and from histological sections obtained via in vivo experiments. Sensitivity analyses identified dead hepatocytes caused by inflammation and the ratio of residential liver cells functioned as a cornerstone for the initiation and progression of liver fibrosis. The validated mathematical model demonstrated here shows virtual experiments that are complementary to biological experiments, which contribute to understanding a new mechanism of liver fibrosis.
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You Y, Ru X, Lei W, Li T, Xiao M, Zheng H, Chen Y, Zhang L. Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme. BMC Bioinformatics 2020; 21:383. [PMID: 32938364 PMCID: PMC7646399 DOI: 10.1186/s12859-020-03674-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis. RESULTS Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma. CONCLUSIONS We summarize the whole process of the experiment and discuss how to expand our experiment in the future.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xufang Ru
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, P.R. China
| | - Wanjing Lei
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Tingting Li
- College of Mathematics and Statistics, Southwest University, Chongqing, 400715, P.R. China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Huiru Zheng
- School of Computing, Ulster University, Coleraine, Londonderry, Northern Ireland, UK
| | - Yujie Chen
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, P.R. China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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Garg A, Yuen S, Seekhao N, Yu G, Karwowski JAC, Powell M, Sakata JT, Mongeau L, JaJa J, Li-Jessen NYK. Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification. APPLIED SCIENCES (BASEL, SWITZERLAND) 2019; 9:2974. [PMID: 31372307 PMCID: PMC6675024 DOI: 10.3390/app9152974] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.
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Affiliation(s)
- Aman Garg
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Samson Yuen
- School of Communication Sciences and Disorders, McGill University, Montreal, QC H3A 1G1, Canada
| | - Nuttiiya Seekhao
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
| | - Grace Yu
- School of Communication Sciences and Disorders, McGill University, Montreal, QC H3A 1G1, Canada
| | | | - Michael Powell
- Virginia Tech Carilion Research Institute, Roanoke, VA 24016, USA
| | - Jon T. Sakata
- Department of Biology, McGill University, Montreal, QC H3A 1G1, Canada
| | - Luc Mongeau
- Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Joseph JaJa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
| | - Nicole Y. K. Li-Jessen
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
- School of Communication Sciences and Disorders, McGill University, Montreal, QC H3A 1G1, Canada
- Department of Otolaryngology–Head and Neck Surgery, McGill University, Montreal, QC H3A 1G1, Canada
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Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection. Int J Mol Sci 2017; 18:ijms18122592. [PMID: 29194393 PMCID: PMC5751195 DOI: 10.3390/ijms18122592] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 11/23/2017] [Accepted: 11/26/2017] [Indexed: 11/16/2022] Open
Abstract
Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.
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Mathematical and Computational Modeling in Complex Biological Systems. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5958321. [PMID: 28386558 PMCID: PMC5366773 DOI: 10.1155/2017/5958321] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 12/20/2016] [Accepted: 01/16/2017] [Indexed: 12/22/2022]
Abstract
The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
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Zhang L, Qiao M, Gao H, Hu B, Tan H, Zhou X, Li CM. Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation. NANOSCALE 2016; 8:14877-87. [PMID: 27460959 PMCID: PMC10150920 DOI: 10.1039/c6nr01637e] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Herein, we have developed a novel approach to investigate the mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model, experimental optimization of key parameters and experimental data validation of the predictive power of the model. The advantages of this study are that the impact of mechanical stimulation on bone regeneration in a porous biodegradable CaP scaffold is considered, experimental design is used to investigate the optimal combination of growth factors loaded on the porous biodegradable CaP scaffold to promote bone regeneration and the training, testing and analysis of the model are carried out by using experimental data, a data-mining algorithm and related sensitivity analysis. The results reveal that mechanical stimulation has a great impact on bone regeneration in a porous biodegradable CaP scaffold and the optimal combination of growth factors that are encapsulated in nanospheres and loaded into porous biodegradable CaP scaffolds layer-by-layer can effectively promote bone regeneration. Furthermore, the model is robust and able to predict the development of bone regeneration under specified conditions.
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Affiliation(s)
- Le Zhang
- College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China.
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Tong X, Chen J, Miao H, Li T, Zhang L. Correction: Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data. PLoS One 2016; 11:e0156823. [PMID: 27243622 PMCID: PMC4887019 DOI: 10.1371/journal.pone.0156823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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