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Mamis K, Farazmand M. Modeling correlated uncertainties in stochastic compartmental models. Math Biosci 2024; 374:109226. [PMID: 38838933 DOI: 10.1016/j.mbs.2024.109226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 05/15/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
We consider compartmental models of communicable disease with uncertain contact rates. Stochastic fluctuations are often added to the contact rate to account for uncertainties. White noise, which is the typical choice for the fluctuations, leads to significant underestimation of the disease severity. Here, starting from reasonable assumptions on the social behavior of individuals, we model the contacts as a Markov process which takes into account the temporal correlations present in human social activities. Consequently, we show that the mean-reverting Ornstein-Uhlenbeck (OU) process is the correct model for the stochastic contact rate. We demonstrate the implication of our model on two examples: a Susceptibles-Infected-Susceptibles (SIS) model and a Susceptibles-Exposed-Infected-Removed (SEIR) model of the COVID-19 pandemic and compare the results to the available US data from the Johns Hopkins University database. In particular, we observe that both compartmental models with white noise uncertainties undergo transitions that lead to the systematic underestimation of the spread of the disease. In contrast, modeling the contact rate with the OU process significantly hinders such unrealistic noise-induced transitions. For the SIS model, we derive its stationary probability density analytically, for both white and correlated noise. This allows us to give a complete description of the model's asymptotic behavior as a function of its bifurcation parameters, i.e., the basic reproduction number, noise intensity, and correlation time. For the SEIR model, where the probability density is not available in closed form, we study the transitions using Monte Carlo simulations. Our modeling approach can be used to quantify uncertain parameters in a broad range of biological systems.
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Affiliation(s)
- Konstantinos Mamis
- Department of Applied Mathematics, University of Washington, Seattle, 98195-3925, WA, USA
| | - Mohammad Farazmand
- Department of Mathematics, North Carolina State University, 2311 Stinson Drive, Raleigh, 27695-8205, NC, USA.
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Liu X, Igarashi D, Hillmer RA, Stoddard T, Lu Y, Tsuda K, Myers CL, Katagiri F. Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations. PLANT COMMUNICATIONS 2024:100882. [PMID: 38486453 DOI: 10.1016/j.xplc.2024.100882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/25/2024] [Accepted: 03/13/2024] [Indexed: 05/02/2024]
Abstract
Rapid plant immune responses in the appropriate cells are needed for effective defense against pathogens. Although transcriptome analysis is often used to describe overall immune responses, collection of transcriptome data with sufficient resolution in both space and time is challenging. We reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2, which induces effector-triggered immunity in Arabidopsis. Double-peak time-course patterns are prevalent among thousands of upregulated genes. We implemented a multi-compartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each gene. The decomposed peaks reveal an "echoing" pattern: the peak times of the first and second peaks correlate well across most upregulated genes. We demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to AvrRpt2. Thus, the peak decomposition has extracted spatial information from the time-course data. The echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types. A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered immunity. Activation of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor types. We discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.
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Affiliation(s)
- Xiaotong Liu
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA
| | - Daisuke Igarashi
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Rachel A Hillmer
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA
| | - Thomas Stoddard
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA
| | - You Lu
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA
| | - Kenichi Tsuda
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Hubei Key Lab of Plant Pathology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA
| | - Fumiaki Katagiri
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA.
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Sharma D, Rawat P, Greiff V, Janakiraman V, Gromiha MM. Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166959. [PMID: 37967796 DOI: 10.1016/j.bbadis.2023.166959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/17/2023]
Abstract
COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.
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Affiliation(s)
- Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Puneet Rawat
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vani Janakiraman
- Infection Biology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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Yedomonhan E, Tovissodé CF, Kakaï RG. Modeling the effects of Prophylactic behaviors on the spread of SARS-CoV-2 in West Africa. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:12955-12989. [PMID: 37501474 DOI: 10.3934/mbe.2023578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Various general and individual measures have been implemented to limit the spread of SARS-CoV-2 since its emergence in China. Several phenomenological and mechanistic models have been developed to inform and guide health policy. Many of these models ignore opinions about certain control measures, although various opinions and attitudes can influence individual actions. To account for the effects of prophylactic opinions on disease dynamics and to avoid identifiability problems, we expand the SIR-Opinion model of Tyson et al. (2020) to take into account the partial detection of infected individuals in order to provide robust modeling of COVID-19 as well as degrees of adherence to prophylactic treatments, taking into account a hybrid modeling technique using Richard's model and the logistic model. Applying the approach to COVID-19 data from West Africa demonstrates that the more people with a strong prophylactic opinion, the smaller the final COVID-19 pandemic size. The influence of individuals on each other and from the media significantly influences the susceptible population and, thus, the dynamics of the disease. Thus, when considering the opinion of susceptible individuals to the disease, the view of the population at baseline influences its dynamics. The results are expected to inform public policy in the context of emerging and re-emerging infectious diseases.
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Affiliation(s)
- Elodie Yedomonhan
- Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, Benin
| | - Chénangnon Frédéric Tovissodé
- Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, Benin
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, United States
| | - Romain Glèlè Kakaï
- Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, Benin
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Hincapie R, Munoz DA, Ortega N, Isfeld-Kiely HK, Shaw SY, Keynan Y, Rueda ZV. Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories. J Travel Med 2022; 29:6679266. [PMID: 36041018 PMCID: PMC9452173 DOI: 10.1093/jtm/taac100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND The COVID-19 pandemic has challenged health services and governments in Canada and around the world. Our research aims to evaluate the effect of domestic and international air travel patterns on the COVID-19 pandemic in Canadian provinces and territories. METHODS Air travel data were obtained through licensed access to the 'BlueDot Intelligence Platform', BlueDot Inc. Daily provincial and territorial COVID-19 cases for Canada and global figures, including mortality, cases recovered and population data were downloaded from public datasets. The effects of domestic and international air travel and passenger volume on the number of local and non-local infected people in each Canadian province and territory were evaluated with a semi-Markov model. Provinces and territories are grouped into large (>100 000 confirmed COVID-19 cases and >1 000 000 inhabitants) and small jurisdictions (≤100 000 confirmed COVID-19 cases and ≤1 000 000 inhabitants). RESULTS Our results show a clear decline in passenger volumes from March 2020 due to public health policies, interventions and other measures taken to limit or control the spread of COVID-19. As the measures were eased, some provinces and territories saw small increases in passenger volumes, although travel remained below pre-pandemic levels. During the early phase of disease introduction, the burden of illness is determined by the connectivity of jurisdictions. In provinces with a larger population and greater connectivity, the burden of illness is driven by case importation, although local transmission rapidly replaces imported cases as the most important driver of increasing new infections. In smaller jurisdictions, a steep increase in cases is seen after importation, leading to outbreaks within the community. CONCLUSIONS Historical travel volumes, combined with data on an emerging infection, are useful to understand the behaviour of an infectious agent in regions of Canada with different connectivity and population size. Historical travel information is important for public health planning and pandemic resource allocation.
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Affiliation(s)
- Roberto Hincapie
- Escuela de Ingenierias, Universidad Pontificia Bolivariana, Medellin, Colombia
| | - Diego A Munoz
- Escuela de Matemáticas, Universidad Nacional de Colombia, Medellin, Colombia
| | - Nathalia Ortega
- Escuela de Ingenierias, Universidad Pontificia Bolivariana, Medellin, Colombia
| | | | - Souradet Y Shaw
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.,Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
| | - Yoav Keynan
- National Collaborating Centre for Infectious Diseases, Winnipeg, Canada.,Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.,Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada.,Department of Internal Medicine, University of Manitoba, Winnipeg, Canada
| | - Zulma Vanessa Rueda
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada.,Facultad de Medicina, Universidad Pontificia Bolivariana, Medellin, Colombia
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Understanding the mutational frequency in SARS-CoV-2 proteome using structural features. Comput Biol Med 2022; 147:105708. [PMID: 35714506 PMCID: PMC9173821 DOI: 10.1016/j.compbiomed.2022.105708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/26/2022] [Accepted: 06/04/2022] [Indexed: 01/18/2023]
Abstract
The prolonged transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in the human population has led to demographic divergence and the emergence of several location-specific clusters of viral strains. Although the effect of mutation(s) on severity and survival of the virus is still unclear, it is evident that certain sites in the viral proteome are more/less prone to mutations. In fact, millions of SARS-CoV-2 sequences collected all over the world have provided us a unique opportunity to understand viral protein mutations and develop novel computational approaches to predict mutational patterns. In this study, we have classified the mutation sites into low and high mutability classes based on viral isolates count containing mutations. The physicochemical features and structural analysis of the SARS-CoV-2 proteins showed that features including residue type, surface accessibility, residue bulkiness, stability and sequence conservation at the mutation site were able to classify the low and high mutability sites. We further developed machine learning models using above-mentioned features, to predict low and high mutability sites at different selection thresholds (ranging 5-30% of topmost and bottommost mutated sites) and observed the improvement in performance as the selection threshold is reduced (prediction accuracy ranging from 65 to 77%). The analysis will be useful for early detection of variants of concern for the SARS-CoV-2, which can also be applied to other existing and emerging viruses for another pandemic prevention.
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