Analysis of Practical Identifiability of a Viral Infection Model

Nguyen, Van Kính; Klawonn, Frank; Mikolajczyk, Rafael; Hernandez‐Vargas, Esteban A.

doi:10.1371/journal.pone.0167568

Cited by 40 publications

(35 citation statements)

References 59 publications

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“…When designing an experiment using ddPCR noise, it would benefit the researcher to optimize the sampling schedule to obtain as much information as possible. Our results fit well in the literature on pratical identifiability for biomedical systems [25]. …”

Section: Discussionsupporting

confidence: 89%

Implications of measurement assay type in design of HIV experiments

Cannon

Jagarapu

Vargas-García

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Time series measurements of circular viral episome (2-LTR) concentrations enable indirect quantification of persistent low-level Human Immunodeficiency Virus (HIV) replication in patients on Integrase-Inhibitor intensified Combined Antiretroviral Therapy (cART). In order to determine the magnitude of these low level infection events, blood has to be drawn from a patients at a frequency and volume that is strictly regulated by the Institutional Review Board (IRB). Once the blood is drawn, the 2-LTR concentration is determined by quantifying the amount of HIV DNA present in the sample via a PCR (Polymerase Chain Reaction) assay. Real time quantitative Polymerase Chain Reaction (qPCR) is a widely used method of performing PCR; however, a newer droplet digital Polymerase Chain Reaction (ddPCR) method has been shown to provide more accurate quantification of DNA. Using a validated model of HIV viral replication, this paper demonstrates the importance of considering DNA quantification assay type when optimizing experiment design conditions. Experiments are optimized using a Genetic Algorithm (GA) to locate a family of suboptimal sample schedules which yield the highest fitness. Fitness is defined as the expected information gained in the experiment, measured by the Kullback-Leibler Divergence (KLD) between the prior and posterior distributions of the model parameters. We compare the information content of the optimized schedules to uniform schedules as well as two clinical schedules implemented by researchers at UCSF and the University of Melbourne. This work shows that there is a significantly greater gain information in experiments using a ddPCR assay vs. a qPCR assay and that certain experiment design considerations should be taken when using either assay.

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Section: Discussionsupporting

confidence: 89%

Implications of measurement assay type in design of HIV experiments

Cannon

Jagarapu

Vargas-García

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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“…This value was arbitrarily chosen at a value below the detection level of 50 TCID 50 . To our knowledge, there is not any conclusive method to define this number while its value can affect the parameter accuracy (24). Smoothing and extrapolating approaches have been used (15) and seem to provide a reasonable estimate of the initial viral titers (24).…”

Section: If You See An Error That Saysmentioning

confidence: 99%

Parameter estimation in mathematical models of viral infections using R

Nguyen

Vargas

2017

Preprint

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In recent years, mathematical modeling approaches have played a central role to understand and to quantify mechanisms in different viral infectious diseases. In this approach, biologicalbased hypotheses are expressed via mathematical relations and then tested based on empirical data. The simulation results can be used to either identify underlying mechanisms, provide predictions on infection outcomes, or evaluate the efficacy of a treatment.Conducting parameter estimation for mathematical models is not an easy task. Here we detail an approach to conduct parameter estimation and to evaluate the results using the free software R. The method is applicable to influenza virus dynamics at different complexity levels, widening experimentalists capabilities in understanding their data. The parameter estimation approach presented here can be also applied to other viral infections or biological applications.Key Wo rds: viral infectio n, mathematic a l modelin g, parameter estimatio n, influen za not peer-reviewed)

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“…These previous approaches are based on differential equations constructed based on biological reasoning. While they are suitable tools to test different hypothesis, and have helped elucidate many of the details of the mechanisms of these intricate systems, these models are susceptible to bias by the designer and model complexity rapidly limits the reliability in the parameter fitting procedures [24] . In contrast, TDA is a tool that detects true patterns in the data, without imposing artificial assumptions.…”

Section: Introductionmentioning

confidence: 99%

Topological data analysis to model the shape of immune responses during co-infections

Sasaki

Bruder

Hernandez‐Vargas

2020

Communications in Nonlinear Science and Numerical Simulation

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a b s t r a c tCo-infections by multiple pathogens have important implications in many aspects of health, epidemiology and evolution. However, how to disentangle the non-linear dynamics of the immune response when two infections take place at the same time is largely unexplored. Using data sets of the immune response during influenza-pneumococcal coinfection in mice, we employ here topological data analysis to simplify and visualise high dimensional data sets.We identified persistent shapes of the simplicial complexes of the data in the three infection scenarios: single viral infection, single bacterial infection, and co-infection. The immune response was found to be distinct for each of the infection scenarios and we uncovered that the immune response during the co-infection has three phases and two transition points. During the first phase, its dynamics is inherited from its response to the primary (viral) infection. The immune response has an early shift (few hours post coinfection) and then modulates its response to react against the secondary (bacterial) infection. Between 18 and 26 h post co-infection the nature of the immune response changes again and does no longer resembles either of the single infection scenarios.

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Analysis of Practical Identifiability of a Viral Infection Model

Cited by 40 publications

References 59 publications

Implications of measurement assay type in design of HIV experiments

Implications of measurement assay type in design of HIV experiments

Parameter estimation in mathematical models of viral infections using R

Topological data analysis to model the shape of immune responses during co-infections

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