On the use of Gillespie stochastic simulation algorithm in a model of the human immune system response to the Yellow Fever vaccine

Xavier, Maicom Peters; Bonin, Carla Rezende Barbosa; Santos, Rodrigo Weber dos; Lobosco, Marcelo

doi:10.1109/bibm.2017.8217880

Cited by 8 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, stochastic modeling was previously used to model the human immune response to the yellow fever vaccine ( 9 ). Since COVID-19 is linked to immune response, modeling of the SARS-CoV-2 infection have been extensively published on different aspects of the disease, including the immune system using multiple ODEs to model immune cells, antibodies and cytokines ( 10 – 13 ), and on the clinical and radiological data ( 14 – 16 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Elemam¹,

Hammoudeh²,

Salameh³

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.

show abstract

Section: Introductionmentioning

confidence: 99%

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Elemam¹,

Hammoudeh²,

Salameh³

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…A large number of works use mathematical and computational tools to model the Human Immune System (HIS) using distinct techniques, such as ordinary differential equations (ODEs) ( Perelson, 1989 ; Baker et al, 1997 ; Chang et al, 2005 ; Vodovotz et al, 2006 ; Jarrett et al, 2015 ; Bonin et al, 2016 ), partial differential equations (PDEs) ( Pettet et al, 1996 ; Su et al, 2009 ; Flegg et al, 2012 ; Pigozzo et al, 2013 ; Quintela et al, 2014 ), stochastic methods ( Chao et al, 2004 ; Xavier et al, 2017 ), cellular automaton and agents ( Celada and Seiden, 1992 ; Morpurgo et al, 1995 ; Kohler et al, 2000 ; Bernaschi and Castiglione, 2001 ; Pappalardo et al, 2018 ). On the other hand, very few papers were published describing the dynamics of SARS-CoV-2 ( Almocera et al, 2020 ; Du and Yuan, 2020 ; Hernandez-Vargas and Velasco-Hernandez, 2020 ; Xavier et al, 2020 ), although some additional non-peer-reviewed papers can also be found on the internet.…”

Section: Related Workmentioning

confidence: 99%

A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19

Reis

Pigozzo

Bonin³

et al. 2021

Front. Mol. Biosci.

View full text Add to dashboard Cite

By June 2021, a new contagious disease, the Coronavirus disease 2019 (COVID-19), has infected more than 172 million people worldwide, causing more than 3.7 million deaths. Many aspects related to the interactions of the disease’s causative agent, SAR2-CoV-2, and the immune response are not well understood: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. Mathematical and computational tools can help researchers to answer these open questions about the disease. In this work, we present a system of fifteen ordinary differential equations that models the immune response to SARS-CoV-2. The model is used to investigate the hypothesis that the SARS-CoV-2 infects immune cells and, for this reason, induces high-level productions of inflammatory cytokines. Simulation results support this hypothesis and further explain why survivors have lower levels of cytokines levels than non-survivors.

show abstract

“…The technique used depends on some important factors, such as whether a deterministic or stochastic approach will be used, whether it will be a continuous or discrete model, in addition to defining the importance of temporal or spatial evolution. Although a CM based on ordinary differential equations (ODE) can be used to describe the behavior of the HIS and the interactions between its cells and molecules over time [9], [10], [11], [12], [13], [14], and partial differential equations (PDEs) can be used to analyze the immune system spatial evolution over time [15], [16], [17], [18], [19], other approaches can also be used, such as those based on stochastic methods [20], [21], cellular automaton and agents [22], [23], [24], [25], [26].…”

Section: Related Workmentioning

confidence: 99%

“…In one previous work, we have proposed a stochastic approach [21], based on Gillespie's algorithm [33], [34], to simulate the immune response to the Yellow Fever vaccine. The deterministic model is a system of five ODEs that represents the behavior of the following populations: Yellow Fever vaccine virus, generic antibodies, and three types of lymphocyte B cells (naive, active, and memory).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A simplified model of the Human Immune System response to the COVID-19

Xavier

Reis

Santos

et al. 2020

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

View full text Add to dashboard Cite

By November 2020, the Coronavirus disease 2019 has infected more than 50 million people worldwide, causing more than 1.2 million deaths. This new contagious disease is not well understood, and the scientific community is trying to comprehend better the interactions of the causative agent of the disease, SAR2-CoV-2, and the immune response to identify its weak points to develop new therapies to impair its lethal effects. Mathematical and computational tools can help in this task: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. In this work, we present a simple system of five ordinary differential equations that can be used to model the immune response to SARS-CoV-2. The model parameters and initial conditions were adjusted to cohort studies that collected viremia and antibody data. The results have shown that the model was able to reproduce both viremia and antibodies dynamics successfully.

show abstract

On the use of Gillespie stochastic simulation algorithm in a model of the human immune system response to the Yellow Fever vaccine

Cited by 8 publications

References 37 publications

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19

A simplified model of the Human Immune System response to the COVID-19

Contact Info

Product

Resources

About