2015, 12(5): 937-964. doi: 10.3934/mbe.2015.12.937

Uncertainty quantification in modeling HIV viral mechanics

1. 

Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212

2. 

Center for Research in Scienti c Computation, North Carolina State University, Raleigh, NC 27695-8212, United States, United States, United States, United States, United States, United States

Received  January 2014 Revised  April 2015 Published  June 2015

We consider an in-host model for HIV-1 infection dynamics developed and validated with patient data in earlier work [7]. We revisit the earlier model in light of progress over the last several years in understanding HIV-1 progression in humans. We then consider statistical models to describe the data and use these with residual plots in generalized least squares problems to develop accurate descriptions of the proper weights for the data. We use recent parameter subset selection techniques [5,6] to investigate the impact of estimated parameters on the corresponding selection scores. Bootstrapping and asymptotic theory are compared in the context of confidence intervals for the resulting parameter estimates.
Citation: H. T. Banks, Robert Baraldi, Karissa Cross, Kevin Flores, Christina McChesney, Laura Poag, Emma Thorpe. Uncertainty quantification in modeling HIV viral mechanics. Mathematical Biosciences & Engineering, 2015, 12 (5) : 937-964. doi: 10.3934/mbe.2015.12.937
References:
[1]

S. Abdella, N. Wabe and E. Yesuf, Management of common adverse effects in the era of highly active antiretroviral therapy in south east Ethiopia,, North American Journal of Medical Sciences, 3 (2011), 499.  doi: 10.4297/najms.2011.3499.  Google Scholar

[2]

B. M. Adams, H. T. Banks, M. Davidian and E. S. Rosenberg, Model fitting and prediction with HIV treatment interruption data,, Center for Research in Scientific Computation Technical Report CRSC-TR05-40, 69 (2007), 05.   Google Scholar

[3]

A. Alexaki, Y. Liu and B. Wigdahl, Cellular reservoirs of HIV-1 and their role in viral persistence,, Current HIV Research, 6 (2008), 388.  doi: 10.2174/157016208785861195.  Google Scholar

[4]

J. Ananworanich, C. Vandergeeten, N. Chomchey and N. Chomont, Early ART Intervention Restricts the Seeding of the HIV Reservoir in Long-Lived Central Memory CD4+ T Cells,, Program and Abstracts of the 20 Conference on Retroviruses and Opportunistic Infections, (2013).   Google Scholar

[5]

A. Cintron-Arias, H. T. Banks, A. Capaldi and A. L. Lloyd, A sensitivity matrix based methodology for inverse problem formulation, CRSC Tech. Rep. CRSC-TR09-09, April, 2009;, J. Inverse and Ill-posed Problems, 17 (2009), 545.  doi: 10.1515/JIIP.2009.034.  Google Scholar

[6]

H. T. Banks, A. Cintron-Arias and F. Kappel, Parameter selection methods in inverse problem formulation,, in Mathematical Modeling and Validation in Physiology, 2064 (2013), 43.  doi: 10.1007/978-3-642-32882-4_3.  Google Scholar

[7]

H. T. Banks, M. Davidian, S. Hu, G. M. Kepler and E. S. Rosenberg, Modeling HIV immune response and validation with clinical data,, Journal of Biological Dynamics, 2 (2008), 357.  doi: 10.1080/17513750701813184.  Google Scholar

[8]

H. T. Banks, K. Holm and D. Robbins, Standard error computations for uncertainty quantification in inverse problems: Asymptotic theory vs. bootstrapping,, Mathematical and Computer Modeling, 52 (2010), 1610.  doi: 10.1016/j.mcm.2010.06.026.  Google Scholar

[9]

H. T. Banks, S. Hu and W. Clayton Thompson, Modeling and Inverse Problems in the Presence of Uncertainty,, CRSC Press/ Taylor and Frances Publishing, (2014).   Google Scholar

[10]

H. T. Banks, D. F. Kapraun, W. Clayton Thompson, C. Peligero, J. Argilaguet and A. Meyerhans, A novel statistical analysis and interpretation of flow cytometry data,, Journal of Biological Dynamics, 7 (2013), 96.  doi: 10.1080/17513758.2013.812753.  Google Scholar

[11]

H. T. Banks and H. T. Tran, Mathematical and Experimental Modeling of Physical and Biological Processes,, Taylor Francis/CRC Press, (2009).   Google Scholar

[12]

J. N. Blankson, D. Persaud and R. F. Siliciano, The challenge of viral reservoirs in HIV-1 infection,, Annual Review of Medicine, 53 (2002), 557.  doi: 10.1146/annurev.med.53.082901.104024.  Google Scholar

[13]

R. J. Carroll and D. Ruppert, Transformation and Weighting in Regression,, Chapman & Hall, (1988).  doi: 10.1007/978-1-4899-2873-3.  Google Scholar

[14]

R. J. Carroll, C. F. J. Wu and D. Ruppert, The effect of estimating weights in weighted least squares,, J. Amer. Statistical Assoc., 83 (1988), 1045.  doi: 10.1080/01621459.1988.10478699.  Google Scholar

[15]

M. Catalfamo, C. Wilhelm, L. T. Cheung, M. Proschan, T. Friesen, J. H. Park, J. Adelsberger, M. Baseler, F. Maldarelli, R. Davey, G. Roby, C. Rehm and C. Lane, CD4+ and CD8+ T cell immune activation during chronic HIV infection: roles of homeostasis, HIV, type I IFN, and IL-7,, Journal of Immunology, 186 (2011), 2106.  doi: 10.4049/jimmunol.1002000.  Google Scholar

[16]

N. W. Cummins and A. D. Badley, Anti-apoptotic mechanisms of HIV: lessons and novel approaches to curing HIV,, Cellular and Molecular Life Sciences: CMLS, 70 (2013), 3355.   Google Scholar

[17]

C. Dalmasso, W. Carpentier, L. Meyer, C. Rouzioux, C. Goujard, M.-L. Chaix, O. Lambotte, V. Avettand-Fenoel, S. Le Clerc, L. D. de Senneville, C. Deveau, F. Boufassa, P. Debré, J.-F. Delfraissy, P. Broet and I. Theodorou, Distinct genetic loci control plasma HIV-RNA and cellular HIV-DNA levels in HIV-1 infection: the ANRS Genome Wide Association 01 study,, PloS one, 3 (2008).   Google Scholar

[18]

M. Davidian, Nonlinear Models for Univariate and Multivariate Response,, ST 762 Lecture Notes, (2007).   Google Scholar

[19]

M. Davidian and D. M. Giltinan, Nonlinear Models for Repeated Measurement Data,, Chapman and Hall, (2000).   Google Scholar

[20]

B. Descours, V. Avettand-Fenoel, C. Blanc, A. Samri, A. Mélard, V. Supervie, I. Theodorou, G. Carcelain, C. Rouzioux and B. Autran, Immune responses driven by protective human leukocyte antigen alleles from long-term nonprogressors are associated with low HIV reservoir in central memory CD4+ T cells,, Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 54 (2012), 1495.   Google Scholar

[21]

T. J. DiCiccio and B. Efron, Bootstrap confidence intervals,, Statistical Science, 11 (1996), 189.  doi: 10.1214/ss/1032280214.  Google Scholar

[22]

B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans,, CBMS 38, (1982).   Google Scholar

[23]

M. Egger, M. May, G. Chêne, A. N. Phillips, B. Ledergerber, F. Dabis, D. Costagliola, A. D'Arminio Monforte, F. de Wolf, P. Reiss, J. Lundgren, A. Justice, S. Staszewski, C. Leport, R. Hogg, S. Robert, C. Sabin, M. Gill, B. Salzberger and J. Sterne, Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: A collaborative analysis of prospective studies,, Lancet, 360 (2002), 119.   Google Scholar

[24]

T. H. Finkel, G. Tudor-Williams, N. K. Banda, M. F. Cotton, T. Curiel, C. Monks, T. W. Baba, R. M. Ruprecht and A. Kupfer, Apoptosis occurs predominantly in bystander cells and not in productively infected cells of HIV- and SIV-infected lymph nodes,, Nature Medicine, 1 (1995), 129.   Google Scholar

[25]

O. Lambotte, F. Boufassa, Y. Madec, A. Nguyen, C. Goujard, L. Meyer, C. Rouzioux, A. Venet and J.-F. Delfraissy, HIV controllers: A homogeneous group of HIV-1-infected patients with spontaneous control of viral replication,, Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 41 (2005), 1053.   Google Scholar

[26]

S. Lodi, L. Meyer, A. Kelleher, M. Rosinska, J. Ghosn, M. Sannes and K. Porter, Immunovirologic control 24 months after interruption of antiretroviral therapy initiated close to HIV seroconversion,, Archives of Internal Medicine, 172 (2012), 1251.   Google Scholar

[27]

B. Max and R. Sherer, Management of the adverse effects of antiretroviral therapy and medication adherence,, Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 30 (2000).   Google Scholar

[28]

T. H. Mogensen, J. Melchjorsen, C. S. Larsen and S. R. Paludan, Innate immune recognition and activation during HIV infection,, Retrovirology, 7 (2010).   Google Scholar

[29]

N. Ngo-Giang-Huong, C. Deveau, I. Da Silva, I. Pellegrin, A. Venet, M. Harzic, M. Sinet, J. F. Delfraissy, L. Meyer, C. Goujard and C. Rouzioux, Proviral HIV-1 DNA in subjects followed since primary HIV-1 infection who suppress plasma viral load after one year of highly active antiretroviral therapy,, AIDS, 15 (2001), 665.   Google Scholar

[30]

T. Schreiber and A. Schmitz, Surrogate time series,, Physica D: Nonlinear Phenomena, 142 (2000), 346.  doi: 10.1016/S0167-2789(00)00043-9.  Google Scholar

[31]

G. A. F. Seber and C. J. Wild, Nonlinear Regression,, J. Wiley & Sons, (1989).  doi: 10.1002/0471725315.  Google Scholar

[32]

L. Shan, K. Deng, N. S. Shroff, C. M. Durand, S. A. Rabi, H.-C. Yang, H. Zhang, J. B. Margolick, J. N. Blankson and R. F. Siliciano, Stimulation of HIV-1-specific cytolytic T lymphocytes facilitates elimination of latent viral reservoir after virus reactivation,, Immunity, 36 (2012), 491.   Google Scholar

[33]

B. W. Silverman, Density Estimation for Statistics and Data Analysis,, Chapman and Hall, (1986).  doi: 10.1007/978-1-4899-3324-9.  Google Scholar

[34]

R. M. van der Sluis, T. van Montfort, G. Pollakis, R. W. Sanders, D. Speijer, B. Berkhout and R. E. Jeeninga, Dendritic cell-induced activation of latent HIV-1 provirus in actively proliferating primary T lymphocytes,, PLoS Pathogens, 9 (2013).   Google Scholar

[35]

L. Sompayrac, How the Immune System Works,, Wiley, (2003).   Google Scholar

[36]

R. Steuer, J. Kurths, C. O. Daub, J. Weise and J. Selbig, The mutual information: Detecting and evaluating dependencies between variables,, Bioinformatics, 18 (2002).   Google Scholar

[37]

M. Strain, S. Little, E. Daar, D. Havlir, H. Gunthard, R. Lam, O. Daly, J. Nguyen, C. Ignacio, C. Spina, D. Richman and J. Wong, Effect of treatment, during primary infection, on establishment and clearance of cellular reservoirs of HIV-1,, The Journal of Infectious Diseases, 191 (2005), 1410.   Google Scholar

[38]

R. M. Welsh, K. Bahl, H. D. Marshall and S. L. Urban, Type 1 interferons and antiviral CD8+ T cell responses,, PLoS Pathogens, 8 (2012).   Google Scholar

[39]

N. Zhang and M. J. Bevan, CD8(+) T cells: Foot soldiers of the immune system,, Immunity, 35 (2011), 161.   Google Scholar

show all references

References:
[1]

S. Abdella, N. Wabe and E. Yesuf, Management of common adverse effects in the era of highly active antiretroviral therapy in south east Ethiopia,, North American Journal of Medical Sciences, 3 (2011), 499.  doi: 10.4297/najms.2011.3499.  Google Scholar

[2]

B. M. Adams, H. T. Banks, M. Davidian and E. S. Rosenberg, Model fitting and prediction with HIV treatment interruption data,, Center for Research in Scientific Computation Technical Report CRSC-TR05-40, 69 (2007), 05.   Google Scholar

[3]

A. Alexaki, Y. Liu and B. Wigdahl, Cellular reservoirs of HIV-1 and their role in viral persistence,, Current HIV Research, 6 (2008), 388.  doi: 10.2174/157016208785861195.  Google Scholar

[4]

J. Ananworanich, C. Vandergeeten, N. Chomchey and N. Chomont, Early ART Intervention Restricts the Seeding of the HIV Reservoir in Long-Lived Central Memory CD4+ T Cells,, Program and Abstracts of the 20 Conference on Retroviruses and Opportunistic Infections, (2013).   Google Scholar

[5]

A. Cintron-Arias, H. T. Banks, A. Capaldi and A. L. Lloyd, A sensitivity matrix based methodology for inverse problem formulation, CRSC Tech. Rep. CRSC-TR09-09, April, 2009;, J. Inverse and Ill-posed Problems, 17 (2009), 545.  doi: 10.1515/JIIP.2009.034.  Google Scholar

[6]

H. T. Banks, A. Cintron-Arias and F. Kappel, Parameter selection methods in inverse problem formulation,, in Mathematical Modeling and Validation in Physiology, 2064 (2013), 43.  doi: 10.1007/978-3-642-32882-4_3.  Google Scholar

[7]

H. T. Banks, M. Davidian, S. Hu, G. M. Kepler and E. S. Rosenberg, Modeling HIV immune response and validation with clinical data,, Journal of Biological Dynamics, 2 (2008), 357.  doi: 10.1080/17513750701813184.  Google Scholar

[8]

H. T. Banks, K. Holm and D. Robbins, Standard error computations for uncertainty quantification in inverse problems: Asymptotic theory vs. bootstrapping,, Mathematical and Computer Modeling, 52 (2010), 1610.  doi: 10.1016/j.mcm.2010.06.026.  Google Scholar

[9]

H. T. Banks, S. Hu and W. Clayton Thompson, Modeling and Inverse Problems in the Presence of Uncertainty,, CRSC Press/ Taylor and Frances Publishing, (2014).   Google Scholar

[10]

H. T. Banks, D. F. Kapraun, W. Clayton Thompson, C. Peligero, J. Argilaguet and A. Meyerhans, A novel statistical analysis and interpretation of flow cytometry data,, Journal of Biological Dynamics, 7 (2013), 96.  doi: 10.1080/17513758.2013.812753.  Google Scholar

[11]

H. T. Banks and H. T. Tran, Mathematical and Experimental Modeling of Physical and Biological Processes,, Taylor Francis/CRC Press, (2009).   Google Scholar

[12]

J. N. Blankson, D. Persaud and R. F. Siliciano, The challenge of viral reservoirs in HIV-1 infection,, Annual Review of Medicine, 53 (2002), 557.  doi: 10.1146/annurev.med.53.082901.104024.  Google Scholar

[13]

R. J. Carroll and D. Ruppert, Transformation and Weighting in Regression,, Chapman & Hall, (1988).  doi: 10.1007/978-1-4899-2873-3.  Google Scholar

[14]

R. J. Carroll, C. F. J. Wu and D. Ruppert, The effect of estimating weights in weighted least squares,, J. Amer. Statistical Assoc., 83 (1988), 1045.  doi: 10.1080/01621459.1988.10478699.  Google Scholar

[15]

M. Catalfamo, C. Wilhelm, L. T. Cheung, M. Proschan, T. Friesen, J. H. Park, J. Adelsberger, M. Baseler, F. Maldarelli, R. Davey, G. Roby, C. Rehm and C. Lane, CD4+ and CD8+ T cell immune activation during chronic HIV infection: roles of homeostasis, HIV, type I IFN, and IL-7,, Journal of Immunology, 186 (2011), 2106.  doi: 10.4049/jimmunol.1002000.  Google Scholar

[16]

N. W. Cummins and A. D. Badley, Anti-apoptotic mechanisms of HIV: lessons and novel approaches to curing HIV,, Cellular and Molecular Life Sciences: CMLS, 70 (2013), 3355.   Google Scholar

[17]

C. Dalmasso, W. Carpentier, L. Meyer, C. Rouzioux, C. Goujard, M.-L. Chaix, O. Lambotte, V. Avettand-Fenoel, S. Le Clerc, L. D. de Senneville, C. Deveau, F. Boufassa, P. Debré, J.-F. Delfraissy, P. Broet and I. Theodorou, Distinct genetic loci control plasma HIV-RNA and cellular HIV-DNA levels in HIV-1 infection: the ANRS Genome Wide Association 01 study,, PloS one, 3 (2008).   Google Scholar

[18]

M. Davidian, Nonlinear Models for Univariate and Multivariate Response,, ST 762 Lecture Notes, (2007).   Google Scholar

[19]

M. Davidian and D. M. Giltinan, Nonlinear Models for Repeated Measurement Data,, Chapman and Hall, (2000).   Google Scholar

[20]

B. Descours, V. Avettand-Fenoel, C. Blanc, A. Samri, A. Mélard, V. Supervie, I. Theodorou, G. Carcelain, C. Rouzioux and B. Autran, Immune responses driven by protective human leukocyte antigen alleles from long-term nonprogressors are associated with low HIV reservoir in central memory CD4+ T cells,, Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 54 (2012), 1495.   Google Scholar

[21]

T. J. DiCiccio and B. Efron, Bootstrap confidence intervals,, Statistical Science, 11 (1996), 189.  doi: 10.1214/ss/1032280214.  Google Scholar

[22]

B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans,, CBMS 38, (1982).   Google Scholar

[23]

M. Egger, M. May, G. Chêne, A. N. Phillips, B. Ledergerber, F. Dabis, D. Costagliola, A. D'Arminio Monforte, F. de Wolf, P. Reiss, J. Lundgren, A. Justice, S. Staszewski, C. Leport, R. Hogg, S. Robert, C. Sabin, M. Gill, B. Salzberger and J. Sterne, Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: A collaborative analysis of prospective studies,, Lancet, 360 (2002), 119.   Google Scholar

[24]

T. H. Finkel, G. Tudor-Williams, N. K. Banda, M. F. Cotton, T. Curiel, C. Monks, T. W. Baba, R. M. Ruprecht and A. Kupfer, Apoptosis occurs predominantly in bystander cells and not in productively infected cells of HIV- and SIV-infected lymph nodes,, Nature Medicine, 1 (1995), 129.   Google Scholar

[25]

O. Lambotte, F. Boufassa, Y. Madec, A. Nguyen, C. Goujard, L. Meyer, C. Rouzioux, A. Venet and J.-F. Delfraissy, HIV controllers: A homogeneous group of HIV-1-infected patients with spontaneous control of viral replication,, Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 41 (2005), 1053.   Google Scholar

[26]

S. Lodi, L. Meyer, A. Kelleher, M. Rosinska, J. Ghosn, M. Sannes and K. Porter, Immunovirologic control 24 months after interruption of antiretroviral therapy initiated close to HIV seroconversion,, Archives of Internal Medicine, 172 (2012), 1251.   Google Scholar

[27]

B. Max and R. Sherer, Management of the adverse effects of antiretroviral therapy and medication adherence,, Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 30 (2000).   Google Scholar

[28]

T. H. Mogensen, J. Melchjorsen, C. S. Larsen and S. R. Paludan, Innate immune recognition and activation during HIV infection,, Retrovirology, 7 (2010).   Google Scholar

[29]

N. Ngo-Giang-Huong, C. Deveau, I. Da Silva, I. Pellegrin, A. Venet, M. Harzic, M. Sinet, J. F. Delfraissy, L. Meyer, C. Goujard and C. Rouzioux, Proviral HIV-1 DNA in subjects followed since primary HIV-1 infection who suppress plasma viral load after one year of highly active antiretroviral therapy,, AIDS, 15 (2001), 665.   Google Scholar

[30]

T. Schreiber and A. Schmitz, Surrogate time series,, Physica D: Nonlinear Phenomena, 142 (2000), 346.  doi: 10.1016/S0167-2789(00)00043-9.  Google Scholar

[31]

G. A. F. Seber and C. J. Wild, Nonlinear Regression,, J. Wiley & Sons, (1989).  doi: 10.1002/0471725315.  Google Scholar

[32]

L. Shan, K. Deng, N. S. Shroff, C. M. Durand, S. A. Rabi, H.-C. Yang, H. Zhang, J. B. Margolick, J. N. Blankson and R. F. Siliciano, Stimulation of HIV-1-specific cytolytic T lymphocytes facilitates elimination of latent viral reservoir after virus reactivation,, Immunity, 36 (2012), 491.   Google Scholar

[33]

B. W. Silverman, Density Estimation for Statistics and Data Analysis,, Chapman and Hall, (1986).  doi: 10.1007/978-1-4899-3324-9.  Google Scholar

[34]

R. M. van der Sluis, T. van Montfort, G. Pollakis, R. W. Sanders, D. Speijer, B. Berkhout and R. E. Jeeninga, Dendritic cell-induced activation of latent HIV-1 provirus in actively proliferating primary T lymphocytes,, PLoS Pathogens, 9 (2013).   Google Scholar

[35]

L. Sompayrac, How the Immune System Works,, Wiley, (2003).   Google Scholar

[36]

R. Steuer, J. Kurths, C. O. Daub, J. Weise and J. Selbig, The mutual information: Detecting and evaluating dependencies between variables,, Bioinformatics, 18 (2002).   Google Scholar

[37]

M. Strain, S. Little, E. Daar, D. Havlir, H. Gunthard, R. Lam, O. Daly, J. Nguyen, C. Ignacio, C. Spina, D. Richman and J. Wong, Effect of treatment, during primary infection, on establishment and clearance of cellular reservoirs of HIV-1,, The Journal of Infectious Diseases, 191 (2005), 1410.   Google Scholar

[38]

R. M. Welsh, K. Bahl, H. D. Marshall and S. L. Urban, Type 1 interferons and antiviral CD8+ T cell responses,, PLoS Pathogens, 8 (2012).   Google Scholar

[39]

N. Zhang and M. J. Bevan, CD8(+) T cells: Foot soldiers of the immune system,, Immunity, 35 (2011), 161.   Google Scholar

[1]

Wei Feng, Michael Freeze, Xin Lu. On competition models under allee effect: Asymptotic behavior and traveling waves. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5609-5626. doi: 10.3934/cpaa.2020256

[2]

Jiaquan Liu, Xiangqing Liu, Zhi-Qiang Wang. Sign-changing solutions for a parameter-dependent quasilinear equation. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020454

[3]

Anna Abbatiello, Eduard Feireisl, Antoní Novotný. Generalized solutions to models of compressible viscous fluids. Discrete & Continuous Dynamical Systems - A, 2021, 41 (1) : 1-28. doi: 10.3934/dcds.2020345

[4]

Justin Holmer, Chang Liu. Blow-up for the 1D nonlinear Schrödinger equation with point nonlinearity II: Supercritical blow-up profiles. Communications on Pure & Applied Analysis, , () : -. doi: 10.3934/cpaa.2020264

[5]

Zonghong Cao, Jie Min. Selection and impact of decision mode of encroachment and retail service in a dual-channel supply chain. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020167

[6]

Yolanda Guerrero–Sánchez, Muhammad Umar, Zulqurnain Sabir, Juan L. G. Guirao, Muhammad Asif Zahoor Raja. Solving a class of biological HIV infection model of latently infected cells using heuristic approach. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020431

[7]

Haixiang Yao, Ping Chen, Miao Zhang, Xun Li. Dynamic discrete-time portfolio selection for defined contribution pension funds with inflation risk. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020166

[8]

A. M. Elaiw, N. H. AlShamrani, A. Abdel-Aty, H. Dutta. Stability analysis of a general HIV dynamics model with multi-stages of infected cells and two routes of infection. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020441

[9]

Vivina Barutello, Gian Marco Canneori, Susanna Terracini. Minimal collision arcs asymptotic to central configurations. Discrete & Continuous Dynamical Systems - A, 2021, 41 (1) : 61-86. doi: 10.3934/dcds.2020218

[10]

Zhenzhen Wang, Tianshou Zhou. Asymptotic behaviors and stochastic traveling waves in stochastic Fisher-KPP equations. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020323

[11]

Scipio Cuccagna, Masaya Maeda. A survey on asymptotic stability of ground states of nonlinear Schrödinger equations II. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020450

[12]

Yongge Tian, Pengyang Xie. Simultaneous optimal predictions under two seemingly unrelated linear random-effects models. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020168

[13]

Annegret Glitzky, Matthias Liero, Grigor Nika. Dimension reduction of thermistor models for large-area organic light-emitting diodes. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020460

[14]

Yichen Zhang, Meiqiang Feng. A coupled $ p $-Laplacian elliptic system: Existence, uniqueness and asymptotic behavior. Electronic Research Archive, 2020, 28 (4) : 1419-1438. doi: 10.3934/era.2020075

[15]

Hoang The Tuan. On the asymptotic behavior of solutions to time-fractional elliptic equations driven by a multiplicative white noise. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020318

[16]

Yongxiu Shi, Haitao Wan. Refined asymptotic behavior and uniqueness of large solutions to a quasilinear elliptic equation in a borderline case. Electronic Research Archive, , () : -. doi: 10.3934/era.2020119

2018 Impact Factor: 1.313

Metrics

  • PDF downloads (60)
  • HTML views (0)
  • Cited by (5)

[Back to Top]