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Computational Modeling in Biology and Medicine

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 20247

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Dr. Eunha Shim

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Guest Editor

Department of Mathematics, Soongsil University, 369 Sang-doro, Sangdo-dong, Dongjak-gu, Seoul 156-743, Korea
Interests: mathematical modeling of infectious diseases and intervention strategies; game theoretical application to epidemiological modeling; economic epidemiology

Special Issue Information

Dear Colleagues,

Mathematical and computational modeling is an important field in the understanding of biological processes at multiple scales. Mathematics and computational modeling can be used to create qualitative and quantitative predictions of biological phenomena, and/or applied to understand the complexity of population dynamics. This Special Issue is designed to focus on theoretical ideas and models associated with recent developments in biology and medicine. Papers are expected to present a major research finding of broad significance for biological and medical sciences, or mathematical/computational biology.

Topics of interest include but are not limited to:

- Mathematical, computational, biophysical and statistical modeling;
- Infectious and non-infectious diseases;
- Evolution;
- Immunology;
- Economic epidemiology;
- Population dynamics;
- Networks and complex systems.

Dr. Eunha Shim
Guest Editor

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Keywords

mathematical biology
computational biology
modeling
infectious diseases
medicine
statistical analysis
network
complex system
simulations

Published Papers (6 papers)

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Research

12 pages, 1363 KiB

Open AccessArticle

Computational Modeling of Therapy with the NMDA Antagonist in Neurodegenerative Disease: Information Theory in the Mechanism of Action of Memantine

by Dariusz Świetlik, Aida Kusiak and Agata Ossowska

Int. J. Environ. Res. Public Health 2022, 19(8), 4727; https://doi.org/10.3390/ijerph19084727 - 14 Apr 2022

Cited by 5 | Viewed by 1919

Abstract

(1) Background: in patients with neurodegenerative diseases, noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonists provide neuroprotective advantages. We performed memantine therapy and proved mathematical and computer modeling of neurodegenerative disease in this study. (2) Methods: a computer simulation environment of the N-methyl-D-aspartate receptor incorporating biological mechanisms of channel activation by high extracellular glutamic acid concentration. In comparison to controls, pathological models were essentially treated with doses of memantine 3–30 µM. (3) Results: the mean values and 95% CI for Shannon entropy in Alzheimer’s disease (AD) and memantine treatment models were 1.760 (95% CI, 1.704–1.818) vs. 2.385 (95% CI, 2.280–2.490). The Shannon entropy was significantly higher in the memantine treatment model relative to AD model (p = 0.0162). The mean values and 95% CI for the positive Lyapunov exponent in AD and memantine treatment models were 0.125 (95% CI, NE–NE) vs. 0.058 (95% CI, 0.044–0.073). The positive Lyapunov exponent was significantly higher in the AD model relative to the memantine treatment model (p = 0.0091). The mean values and 95% CI for transfer entropy in AD and memantine treatment models were 0.081 (95% CI, 0.048–0.114) vs. 0.040 (95% CI, 0.019–0.062). The transfer entropy was significantly higher in the AD model relative to the memantine treatment model (p = 0.0146). A correlation analysis showed positive and statistically significant correlations of the memantine concentrations and the positive Lyapunov exponent (correlation coefficient R = 0.87, p = 0.0023) and transfer entropy (TE) (correlation coefficient R = 0.99, p < 0.000001). (4) Conclusions: information theory results of simulation studies show that the NMDA antagonist, memantine, causes neuroprotective benefits in patients with AD. Our simulation study opens up remarkable new scenarios in which a medical product, drug, or device, can be developed and tested for efficacy based on parameters of information theory. Full article

(This article belongs to the Special Issue Computational Modeling in Biology and Medicine)

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11 pages, 2558 KiB

Open AccessArticle

Detection of Nail Oncometabolite SAICAR in Oral Cancer Patients and Its Molecular Interactions with PKM2 Enzyme

by Rushikesh Patel, Ajay Kumar Raj, Kiran Bharat Lokhande, Mazen A. Almasri, Khalid J. Alzahrani, Asma Saleh Almeslet, K. Venkateswara Swamy, Gargi S. Sarode, Sachin C. Sarode, Shankargouda Patil and Nilesh Kumar Sharma

Int. J. Environ. Res. Public Health 2021, 18(21), 11225; https://doi.org/10.3390/ijerph182111225 - 26 Oct 2021

Cited by 4 | Viewed by 2102

Abstract

Oncometabolites are known to drive metabolic adaptations in oral cancer. Several oncometabolites are known to be shared between cancer cells and non-cancer cells including microbiotas to modulate the tumor microenvironment. Among potential oncometabolites, succinylaminoimidazolecarboxamide ribose5′-phosphate (SAICAR) supports the growth and invasiveness of cancer cells by pyruvate kinase M2 (PKM2) enzyme in a glucose starved tumor microenvironment. There is a significant gap that shows the detection of SAICAR in biological samples including nails of oral cancer patients. Metabolite identification of SAICAR was investigated in the nails of oral cancer patients using novel vertical tube gel electrophoresis (VTGE) and LC-HRMS. Further molecular docking and molecular dynamics simulations (MDS) were employed to determine the nature of molecular interactions of SAICAR (CHEBI ID:18319) with PKM2 (PDB ID: 4G1N). Molecular docking of SAICAR (CHEBI ID:18319) was performed against pyruvate kinase M2 (PDB ID: 4G1N). Data suggest the presence of oncometabolite SAICAR in nails of oral cancer. Molecular docking of SAICAR with PKM2 showed appreciable binding affinity (−8.0 kcal/mol) with residues including ASP407, THR405, GLU410, ARG443, GLY321, ARG436, HIS439, LYS266, and TYR466. Furthermore, MDS confirmed the specific binding of SAICAR within the activator site of PKM2 and the stability of SAICAR and PKM2 molecular interactions. In conclusion, SAICAR is a promising oncometabolite biomarker present in the nails of oral cancer patients. A significant activation potential of SAICAR exists with the PKM2 enzyme. Full article

(This article belongs to the Special Issue Computational Modeling in Biology and Medicine)

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11 pages, 1525 KiB

Open AccessArticle

Projecting the Impact of SARS-CoV-2 Variants and the Vaccination Program on the Fourth Wave of the COVID-19 Pandemic in South Korea

by Eunha Shim

Int. J. Environ. Res. Public Health 2021, 18(14), 7578; https://doi.org/10.3390/ijerph18147578 - 16 Jul 2021

Cited by 32 | Viewed by 4301

Abstract

Vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are currently administered in South Korea; however, vaccine supply is limited. Considering constraints in vaccine supply and the emergence of variant strains, we evaluated the impact of coronavirus disease (COVID-19) vaccination program in reducing incidence, ICU hospitalization, and deaths in South Korea. We developed an age-structured model of SARS-CoV-2 transmission parameterized with Korean demographics and age-specific COVID-19 outcomes. Using our model, we analyzed the impact of the COVID-19 vaccination program during the fourth wave of the pandemic in South Korea in reducing disease burden. We projected that the vaccination program can reduce the overall attack rate to 3.9% from 6.9% without vaccination, over 150 days, starting from 5 July 2021. The highest relative reduction (50%) was observed among individuals aged 50–59 years. Vaccination markedly reduced adverse outcomes, such as ICU hospitalizations and deaths, decreasing them by 45% and 43%, respectively. In the presence of the Delta variant, vaccination is expected to reduce the overall attack rate to 11.9% from 26.9%. Our results indicate that the impact of vaccination can be substantially affected by the emergence of SARS-CoV-2 variants. Furthermore, herd immunity is unlikely to be achieved with the potential emergence of the Delta variant, inconsistent with the blueprint of the South Korean government. Full article

(This article belongs to the Special Issue Computational Modeling in Biology and Medicine)

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14 pages, 825 KiB

Open AccessArticle

COVID-19 Vaccine Priority Strategy Using a Heterogenous Transmission Model Based on Maximum Likelihood Estimation in the Republic of Korea

by Youngsuk Ko, Jacob Lee, Yeonju Kim, Donghyok Kwon and Eunok Jung

Int. J. Environ. Res. Public Health 2021, 18(12), 6469; https://doi.org/10.3390/ijerph18126469 - 15 Jun 2021

Cited by 14 | Viewed by 3048

Abstract

(1) Background: The vaccine supply is likely to be limited in 2021 due to constraints in manufacturing. To maximize the benefit from the rollout phase, an optimal strategy of vaccine allocation is necessary based on each country’s epidemic status. (2) Methods: We first developed a heterogeneous population model considering the transmission matrix using maximum likelihood estimation based on the epidemiological records of individual COVID-19 cases in the Republic of Korea. Using this model, the vaccine priorities for minimizing mortality or incidence were investigated. (3) Results: The simulation results showed that the optimal vaccine allocation strategy to minimize the mortality (or incidence) was to prioritize elderly and healthcare workers (or adults) as long as the reproductive number was below

1.2

(or over

0.9

). (4) Conclusion: Our simulation results support the current Korean government vaccination priority strategy, which prioritizes healthcare workers and senior groups to minimize mortality, under the condition that the reproductive number remains below

1.2

. This study revealed that, in order to maintain the current vaccine priority policy, it is important to ensure that the reproductive number does not exceed the threshold by concurrently implementing nonpharmaceutical interventions. Full article

(This article belongs to the Special Issue Computational Modeling in Biology and Medicine)

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19 pages, 6266 KiB

Open AccessArticle

Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach

by Yongin Choi, James Slghee Kim, Jung Eun Kim, Heejin Choi and Chang Hyeong Lee

Int. J. Environ. Res. Public Health 2021, 18(8), 4240; https://doi.org/10.3390/ijerph18084240 - 16 Apr 2021

Cited by 36 | Viewed by 4232

Abstract

Coronavirus disease 2019 (COVID-19) vaccination has recently started worldwide. As the vaccine supply will be limited for a considerable period of time in many countries, it is important to devise the effective vaccination strategies that reduce the number of deaths and incidence of infection. One of the characteristics of COVID-19 is that the symptom, severity, and mortality of the disease differ by age. Thus, when the vaccination supply is limited, age-dependent vaccination priority strategy should be implemented to minimize the incidences and mortalities. In this study, we developed an age-structured model for describing the transmission dynamics of COVID-19, including vaccination. Using the model and actual epidemiological data in Korea, we estimated the infection probability for each age group under different levels of social distancing implemented in Korea and investigated the effective age-dependent vaccination strategies to reduce the confirmed cases and fatalities of COVID-19. We found that, in a lower level of social distancing, vaccination priority for the age groups with the highest transmission rates will reduce the incidence mostly, but, in higher levels of social distancing, prioritizing vaccination for the elderly age group reduces the infection incidences more effectively. To reduce mortalities, vaccination priority for the elderly age group is the best strategy in all scenarios of levels of social distancing. Furthermore, we investigated the effect of vaccine supply and efficacy on the reduction in incidence and mortality. Full article

(This article belongs to the Special Issue Computational Modeling in Biology and Medicine)

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12 pages, 2039 KiB

Open AccessArticle

Multimorbidity Patterns of Chronic Diseases among Indonesians: Insights from Indonesian National Health Insurance (INHI) Sample Data

by Atina Husnayain, Nopryan Ekadinata, Dedik Sulistiawan and Emily Chia-Yu Su

Int. J. Environ. Res. Public Health 2020, 17(23), 8900; https://doi.org/10.3390/ijerph17238900 - 30 Nov 2020

Cited by 8 | Viewed by 3346

Abstract

Given the increasing burden of chronic diseases in Indonesia, characteristics of chronic multimorbidities have not been comprehensively explored. Therefore, this research evaluated chronic multimorbidity patterns among Indonesians using Indonesian National Health Insurance (INHI) sample data. We included 46 chronic diseases and analyzed their distributions using population-weighted variables provided in the datasets. Results showed that chronic disease patients accounted for 39.7% of total patients who attended secondary health care in 2015–2016. In addition, 43.1% of those were identified as having chronic multimorbidities. Findings also showed that multimorbidities were strongly correlated with an advanced age, with large numbers of patients and visits in all provinces, beyond those on Java island. Furthermore, hypertension was the leading disease, and the most common comorbidities were diabetes mellitus, cerebral ischemia/chronic stroke, and chronic ischemic heart disease. In addition, disease proportions for certain disease dyads differed according to age group and gender. Compared to survey methods, claims data are more economically efficient and are not influenced by recall bias. Claims data can be a promising data source in the next few years as increasing percentages of Indonesians utilize health insurance coverage. Nevertheless, some adjustments in the data structure are accordingly needed to utilize claims data for disease control and surveillance purposes. Full article

(This article belongs to the Special Issue Computational Modeling in Biology and Medicine)

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