12 results on '"Zou, Xiufen"'
Search Results
2. The Data-Based Mathematical Modeling and Parameter Identification in JAK-STAT Signaling Pathway by Using a Hybrid Evolutionary Algorithm
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Zhang, Wei, Zou, Xiufen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cai, Zhihua, editor, Hu, Chengyu, editor, Kang, Zhuo, editor, and Liu, Yong, editor
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- 2010
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3. Dynamic analysis of a drug resistance evolution model with nonlinear immune response.
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Wang, Tengfei and Zou, Xiufen
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DRUG resistance , *DRUG analysis , *IMMUNE response , *CANCER cells , *IMMUNE system - Abstract
Recent studies have utilized evolutionary mechanisms to impede the emergence of drug-resistant populations. In this paper, we develop a mathematical model that integrates hormonal treatment, immunotherapy, and the interactions among three cell types: drug-sensitive cancer cells, drug-resistant cancer cells and immune effector cells. Dynamical analysis is performed, examining the existence and stability of equilibria, thereby confirming the model's interpretability. Model parameters are calibrated using available prostate cancer data and literature. Through bifurcation analysis for drug sensitivity under different immune effector cells recruitment responses, we find that resistant cancer cells grow rapidly under weak recruitment response, maintain at a low level under strong recruitment response, and both may occur under moderate recruitment response. To quantify the competitiveness of sensitive and resistant cells, we introduce the comprehensive measures R 1 and R 2 , respectively, which determine the outcome of competition. Additionally, we introduce the quantitative indicators C I E 1 and C I E 2 as comprehensive measures of the immune effects on sensitive and resistant cancer cells, respectively. These two indicators determine whether the corresponding cancer cells can maintain at a low level. Our work shows that the immune system is an important factor affecting the evolution of drug resistance and provides insights into how to enhance immune response to control resistance. • The proposed model is validated by theoretical analysis and experimental data fitting. • MTD treatment promotes and no treatment inhibits resistant cells. • Weaker recruitment of immune effector cells makes drug resistance more likely to occur. • Different drug strategies are suit to patients with varied immune responses. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Data-Driven Mathematical Modeling and Dynamical Analysis for SARS-CoV-2 Coinfection with Bacteria.
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Zhou, Yu, Huang, Mingzhan, Jiang, Ying'an, and Zou, Xiufen
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COVID-19 ,SARS-CoV-2 ,BACTERIAL diseases ,PANDEMICS ,MATHEMATICAL models ,MIXED infections ,ORDINARY differential equations - Abstract
The ongoing coronavirus disease-2019 (COVID-19) pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has serious influences on human health and economy. The available clinical data suggest that patients infected by SARS-CoV-2 have the possibility of simultaneous infection of bacteria. In this study, we present a data-driven mathematical model for coinfection of SARS-CoV-2 and bacteria to investigate the dynamics of COVID-19 progress. Specifically, based on the statistical analysis of different clinical data from China and some other countries, a system model with ordinary differential equations (ODEs) in four variables, i.e. SARS-CoV-2, bacteria, neutrophils and lymphocytes, is established. We further validate our model through theoretical analysis and fitting of different clinical data. Moreover, through numerical simulations and bifurcation analysis, we find that bacterial infection and immune-related parameters in certain ranges lead to the system transitions among three steady states, i.e. mild, severe and death. We also analyzed the influence of the time it takes for patients to switch from a high-risk area to a low-risk area on the recovery time. These results reveal that the coinfection of viruses and bacteria can explain the changes in neutrophils and lymphocytes, and that initial bacterial infection and immune-related parameters have great influences on the severity degree and mortality in COVID-19 patients. Together, our model and quantitative analysis suggest that preventing bacterial infection and enhancing immune ability during the early phase of infections could be a potential treatment option for high-risk COVID-19 patients. [ABSTRACT FROM AUTHOR]
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- 2021
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5. SCCLRR: A Robust Computational Method for Accurate Clustering Single Cell RNA-Seq Data.
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Zhang, Wei, Li, Yuanyuan, and Zou, Xiufen
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EUCLIDEAN distance ,RNA sequencing ,MATHEMATICAL models ,COSINE function ,DATABASES ,CELLS - Abstract
Single-cell RNA transcriptome data present a tremendous opportunity for studying the cellular heterogeneity. Identifying subpopulations based on scRNA-seq data is a hot topic in recent years, although many researchers have been focused on designing elegant computational methods for identifying new cell types; however, the performance of these methods is still unsatisfactory due to the high dimensionality, sparsity and noise of scRNA-seq data. In this study, we propose a new cell type detection method by learning a robust and accurate similarity matrix, named SCCLRR. The method simultaneously captures both global and local intrinsic properties of data based on a low rank representation (LRR) framework mathematical model. The integrated normalized Euclidean distance and cosine similarity are used to balance the intrinsic linear and nonlinear manifold of data in the local regularization term. To solve the non-convex optimization model, we present an iterative optimization procedure using the alternating direction method of multipliers (ADMM) algorithm. We evaluate the performance of the SCCLRR method on nine real scRNA-seq datasets and compare it with seven state-of-the-art methods. The simulation results show that the SCCLRR outperforms other methods and is robust and effective for clustering scRNA-seq data. (The code of SCCLRR is free available for academic https://github.com/wzhangwhu/SCCLRR). [ABSTRACT FROM AUTHOR]
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- 2021
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6. Mathematical modeling of HIV-like particle assembly in vitro.
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Liu, Yuewu and Zou, Xiufen
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GAG proteins , *UNIQUENESS (Mathematics) , *NONLINEAR equations , *PARAMETER estimation , *MATHEMATICAL bounds - Abstract
In vitro , the recombinant HIV-1 Gag protein can generate spherical particles with a diameter of 25–30 nm in a fully defined system. It has approximately 80 building blocks, and its intermediates for assembly are abundant in geometry. Accordingly, there are a large number of nonlinear equations in the classical model. Therefore, it is difficult to compute values of geometry parameters for intermediates and make the mathematical analysis using the model. In this work, we develop a new model of HIV-like particle assembly in vitro by using six-fold symmetry of HIV-like particle assembly to decrease the number of geometry parameters. This method will greatly reduce computational costs and facilitate the application of the model. Then, we prove the existence and uniqueness of the positive equilibrium solution for this model with 79 nonlinear equations. Based on this model, we derive the interesting result that concentrations of all intermediates at equilibrium are independent of three important parameters, including two microscopic on-rate constants and the size of nucleating structure. Before equilibrium, these three parameters influence the concentration variation rates of all intermediates. We also analyze the relationship between the initial concentration of building blocks and concentrations of all intermediates. Furthermore, the bounds of concentrations of free building blocks and HIV-like particles are estimated. These results will be helpful to guide HIV-like particle assembly experiments and improve our understanding of the assembly dynamics of HIV-like particles in vitro . [ABSTRACT FROM AUTHOR]
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- 2017
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7. Understanding inhibition of viral proteins on type I IFN signaling pathways with modeling and optimization
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Zou, Xiufen, Xiang, Xueshuang, Chen, Yan, Peng, Tao, Luo, Xuelian, and Pan, Zishu
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VIRAL proteins , *INTERFERONS , *CHEMICAL inhibitors , *CELLULAR signal transduction , *MATHEMATICAL models , *GENETIC algorithms , *DOUBLE-stranded RNA , *QUANTITATIVE chemical analysis - Abstract
Abstract: The interferon system provides a powerful and universal intracellular defense mechanism against viruses. As one part of their survival strategies, many viruses have evolved mechanisms to counteract the host type I interferon (IFN-α/β) responses. In this study, we attempt to investigate virus- and double-strand RNA (dsRNA)-triggered type I IFN signaling pathways and understand the inhibition of IFN-α/β induction by viral proteins using mathematical modeling and quantitative analysis. Based on available literature and our experimental data, we develop a mathematical model of virus- and dsRNA-triggered signaling pathways leading to type I IFN gene expression during the primary response, and use the genetic algorithm to optimize all rate constants in the model. The consistency between numerical simulation results and biological experimental data demonstrates that our model is reasonable. Further, we use the model to predict the following phenomena: (1) the dose-dependent inhibition by classical swine fever virus (CSFV) Npro or Erns protein is observed at a low dose and can reach a saturation above a certain dose, not an increase; (2) Erns and Npro have no synergic inhibitory effects on IFN-β induction; (3) the different characters in an important transcription factor, phosphorylated IRF3 (IRF3p), are exhibited because Npro or Erns counteracted dsRNA- and virus-triggered IFN-β induction by targeting the different molecules in the signaling pathways and (4) Npro inhibits the IFN-β expression not only by interacting with IFR3 but also by affecting its complex with MITA. Our approaches help to gain insight into system properties and rational therapy design, as well as to generate hypotheses for further research. [Copyright &y& Elsevier]
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- 2010
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8. Modeling specificity in the yeast MAPK signaling networks
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Zou, Xiufen, Peng, Tao, and Pan, Zishu
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LEAVENING agents , *YEAST , *CELLS , *PROTEIN kinases - Abstract
Abstract: Cells sense several kinds of stimuli and trigger corresponding responses through signaling pathways. As a result, cells must process and integrate multiple signals in parallel to maintain specificity and avoid erroneous cross-talk. In this study, we focus our theoretical effort on understanding specificity of a model network system in yeast, Saccharomyces cerevisiae, which contains three mitogen-activated protein kinase (MAPK) signal transduction cascades that share multiple signaling components. The cellular response to the pheromone, the filamentous growth and osmotic pressure stimuli in yeast is described and an integrative mathematical model for the three MAPK cascades is developed using available literature and experimental data. The theoretical framework for analyzing the specificity of signaling networks [Bardwell, L., Zou, X.F., Nie, Q., Komarova, N.L., 2007. Mathematical models of specificity in cell signaling. Biophys. J. 92, 3425–3441] is extended to include multiple interacting pathways with shared components. Simulations are also performed with any one stimulus, with any two simultaneous stimuli, and with the simultaneous application of the three stimuli. The interactions between the three pathways are systematically investigated. Moreover, the specificity and fidelity of this model system are calculated using our newly developed concept under different stimuli or with specific mutants. Our simulated and calculated results demonstrate that the yeast MAPK signaling network can achieve specificity and fidelity by filtering out spurious cross-talk between the relevant pathways through different mechanisms, such as scaffolding, cross-inhibiting, and feedback control. Proof that Pbs2 and Hog1 are essential for the maintenance of signaling specificity is presented. Our studies provide novel insights into integration of relevant signaling pathways in a biological system and the mechanisms conferring specificity in cellular signaling networks. [Copyright &y& Elsevier]
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- 2008
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9. A theoretical framework for specificity in cell signaling.
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Komarova, Natalia L, Zou, Xiufen, Nie, Qing, and Bardwell, Lee
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CYTOLOGY , *CELLULAR control mechanisms , *CELL physiology , *PROTEIN kinases , *GENETIC transduction , *CELL communication - Abstract
Different cellular signal transduction pathways are often interconnected, so that the potential for undesirable crosstalk between pathways exists. Nevertheless, signaling networks have evolved that maintain specificity from signal to cellular response. Here, we develop a framework for the analysis of networks containing two or more interconnected signaling pathways. We define two properties, specificity and fidelity, that all pathways in a network must possess in order to avoid paradoxical situations where one pathway activates another pathway's output, or responds to another pathway's input, more than its own. In unembellished networks that share components, it is impossible for all pathways to have both mutual specificity and mutual fidelity. However, inclusion of either of two related insulating mechanisms—compartmentalization or the action of a scaffold protein—allows both properties to be achieved, provided deactivation rates are fast compared to exchange rates. [ABSTRACT FROM AUTHOR]
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- 2005
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10. Identifying optimal adaptive therapeutic schedules for prostate cancer through combining mathematical modeling and dynamic optimization.
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Liu, Ruiyang, Wang, Shun, Tan, Xuewen, and Zou, Xiufen
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PROSTATE cancer , *PROSTATE cancer patients , *DYNAMIC models , *MATHEMATICAL models , *STATISTICAL decision making , *ABIRATERONE acetate - Abstract
• A new framework based on a dynamic optimization problem identifies the adaptive therapeutic schedule for prostate cancer. • A competitive model between drug-sensitive and drug-resistant cells is established and validated using available data. • The stability of equilibrium points and existence of a periodic solution indicate the feasibility of adaptive therapy. • Our therapeutic framework is superior to the current approaches in the total drug dose and the survival time. Adaptive therapy, a new therapeutic strategy, is increasingly applied in the treatment of cancer. According to the evolutionary game theory of cancer cells, adaptive therapy is understood as the dynamic regulation of cancer cells. However, it is a great challenge to identify the therapeutic period and dose in adaptive therapy. In this study, we establish a competitive model between drug-sensitive and drug-resistant cancer cells. Theoretical analysis, including the stability of equilibrium points and the existence of periodic solutions, validates the interpretability of the model. The available data for prostate cancers are used to identify the model parameters. Furthermore, we propose a new dynamic optimization problem with constraints to establish the adaptive therapeutic schedule for prostate cancer, and an optimal set of decisions in this optimization problem represents the therapeutic period and dose in adaptive therapy. Through numerical simulations and quantitative analysis, we compare our therapeutic schedule and current adaptive therapeutic schedules in patients with prostate cancer in three aspects: the total drug dose, the peak number of drug-resistant cancer cells and the survival time. These results demonstrate that our therapeutic framework is superior to the current adaptive therapeutic schedules. The proposed therapeutic framework could provide an identifiable adaptive therapy to achieve personal and precise treatment for prostate cancer. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Mathematical modeling and dynamic analysis for cancer resistance incorporating persister cells.
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Qi, Ke, Wang, Shun, Xiao, Yuyang, and Zou, Xiufen
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MATHEMATICAL models , *DYNAMIC models , *COMPUTED tomography , *DRUG resistance , *CELL transformation , *TRP channels , *DATA analysis - Abstract
Drug resistance is a key impediment to cancer treatment, however, the resistance mechanism remains controversial. Experiment evidence indicated that persister cells, a subpopulation in a transient pseudo-dormant state, are posited to play a potential role in the emergence of resistance. In this study, we propose a novel mathematical model for describing the interactions among sensitive, persister, and resistant cells to qualitatively and quantitatively analyze how persister cells affect cancer evolution and unravel the underlying resistance mechanisms. The proposed model is validated by theoretical analysis and fitting of actual human CT scan and mice data. Theoretical analysis demonstrates the existence and stability of the multiple steady states, as well as the global asymptotical stability of a unique steady state. Furthermore, bifurcation analysis reveals that the key factors, including the apoptotic rate of sensitive cells and the transformation rate between sensitive and persister cells, induce bistable phenomenon and are characterized by double saddle–node and transcritical bifurcations. The bistable region indicates that persister cells under high stable steady state as a reservoir could promote the emergence of resistant cells conferring a proliferative advantage and conversely when they are in lower stable steady state, the source of resistant cells and resistance process will be controlled. Therefore, the first transcritical bifurcation point can be viewed as an early indicator to detect critical transitions from low resistance to high resistance. Together, the proposed model and quantitative results would provide new insights for searching for strategies in modulating and decreasing the risk of drug resistance. • A novel mathematical model is developed and validated by theoretical analysis and experimental data. • Stability and bifurcation analysis shows the complex dynamics, including bistability, saddle–node bifurcation, and transcritical bifurcation. • Persister cells under high and low stable steady states exhibit different levels of resistance. • The first transcritical bifurcation point can be viewed as an early indicator to detect critical transitions from low to high resistance. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways.
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Zhang, Wei, Tian, Tianhai, and Zou, Xiufen
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TYPE I interferons , *VIRAL disease prevention , *CELLULAR signal transduction , *STOCHASTIC analysis , *ORDINARY differential equations - Abstract
Type I interferon (IFN) signaling pathways play an essential role in the defense against early viral infections; however, the diverse and intricate molecular mechanisms of virus-triggered type I IFN responses are still poorly understood. In this study, we analyzed and compared two classes of models i.e., deterministic ordinary differential equations (ODEs) and stochastic models to elucidate the dynamics and stochasticity of type I IFN signaling pathways. Bifurcation analysis based on an ODE model reveals that the system exhibits a bistable switch and a one-way switch at high or low levels when the strengths of the negative and positive feedbacks are tuned. Furthermore, we compared the stochastic simulation results under the Master and Langevin equations. Both of the stochastic equations generate the bistable switch phenomenon, and the distance between two stable states are smaller than normal under the simulation of the Langevin equation. The quantitative computations also show that a moderate ratio between positive and negative feedback strengths is required to ensure a reliable switch between the different IFN concentrations that regulate the immune response. Moreover, we propose a multi-state stochastic model based on the above deterministic model to describe the multi-cellular system coupled with the diffusion of IFNs. The perturbation and inhibition analysis showed that the positive feedback, as well as noises, has little effect on the stochastic expression of IFNs, but the negative feedback of ISG56 on the activation of IRF7 has a great influence on IFN stochastic expression. Together, these results reveal that positive feedback stabilizes IFN gene expression, and negative feedback may be the main contribution to the stochastic expression of the IFN gene in the virus-triggered type I IFN response. These findings will provide new insight into the molecular mechanisms of virus-triggered type I IFN signaling pathways. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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