1
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Osborne JM. An adaptive numerical method for multi-cellular simulations of tissue development and maintenance. J Theor Biol 2024; 594:111922. [PMID: 39111542 DOI: 10.1016/j.jtbi.2024.111922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 08/22/2024]
Abstract
In recent years, multi-cellular models, where cells are represented as individual interacting entities, are becoming ever popular. This has led to a proliferation of novel methods and simulation tools. The first aim of this paper is to review the numerical methods utilised by multi-cellular modelling tools and to demonstrate which numerical methods are appropriate for simulations of tissue and organ development, maintenance, and disease. The second aim is to introduce an adaptive time-stepping algorithm and to demonstrate it's efficiency and accuracy. We focus on off-lattice, mechanics based, models where cell movement is defined by a series of first order ordinary differential equations, derived by assuming over-damped motion and balancing forces. We see that many numerical methods have been used, ranging from simple Forward Euler approaches through to higher order single-step methods like Runge-Kutta 4 and multi-step methods like Adams-Bashforth 2. Through a series of exemplar multi-cellular simulations, we see that if: care is taken to have events (births deaths and re-meshing/re-arrangements) occur on common time-steps; and boundaries are imposed on all sub-steps of numerical methods or implemented using forces, then all numerical methods can converge with the correct order. We introduce an adaptive time-stepping method and demonstrate that the best compromise between L∞ error and run-time is to use Runge-Kutta 4 with an increased time-step and moderate adaptivity. We see that a judicious choice of numerical method can speed the simulation up by a factor of 10-60 from the Forward Euler methods seen in Osborne et al. (2017), and a further speed up by a factor of 4 can be achieved by using an adaptive time-step.
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Affiliation(s)
- James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Victoria, Australia.
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2
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Otero AM, Connolly MG, Gonzalez-Ricon RJ, Wang SS, Allen JM, Antonson AM. Influenza A virus during pregnancy disrupts maternal intestinal immunity and fetal cortical development in a dose- and time-dependent manner. Mol Psychiatry 2024:10.1038/s41380-024-02648-9. [PMID: 38961232 DOI: 10.1038/s41380-024-02648-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/05/2024]
Abstract
Epidemiological studies link exposure to viral infection during pregnancy, including influenza A virus (IAV) infection, with increased incidence of neurodevelopmental disorders (NDDs) in offspring. Models of maternal immune activation (MIA) using viral mimetics demonstrate that activation of maternal intestinal T helper 17 (TH17) cells, which produce effector cytokine interleukin (IL)-17, leads to aberrant fetal brain development, such as neocortical malformations. Fetal microglia and border-associated macrophages (BAMs) also serve as potential cellular mediators of MIA-induced cortical abnormalities. However, neither the inflammation-induced TH17 cell pathway nor fetal brain-resident macrophages have been thoroughly examined in models of live viral infection during pregnancy. Here, we inoculated pregnant mice with two infectious doses of IAV and evaluated peak innate and adaptive immune responses in the dam and fetus. While respiratory IAV infection led to dose-dependent maternal colonic shortening and microbial dysregulation, there was no elevation in intestinal TH17 cells nor IL-17. Systemically, IAV resulted in consistent dose- and time-dependent increases in IL-6 and IFN-γ. Fetal cortical abnormalities and global changes in fetal brain transcripts were observable in the high-but not the moderate-dose IAV group. Profiling of fetal microglia and BAMs revealed dose- and time-dependent differences in the numbers of meningeal but not choroid plexus BAMs, while microglial numbers and proliferative capacity of Iba1+ cells remained constant. Fetal brain-resident macrophages increased phagocytic CD68 expression, also in a dose- and time-dependent fashion. Taken together, our findings indicate that certain features of MIA are conserved between mimetic and live virus models, while others are not. Overall, we provide consistent evidence of an infection severity threshold for downstream maternal inflammation and fetal cortical abnormalities, which recapitulates a key feature of the epidemiological data and further underscores the importance of using live pathogens in NDD modeling to better evaluate the complete immune response and to improve translation to the clinic.
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Affiliation(s)
- Ashley M Otero
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Meghan G Connolly
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | | | - Selena S Wang
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jacob M Allen
- Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Adrienne M Antonson
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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3
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Williams T, McCaw JM, Osborne JM. Spatial information allows inference of the prevalence of direct cell-to-cell viral infection. PLoS Comput Biol 2024; 20:e1012264. [PMID: 39042664 PMCID: PMC11296656 DOI: 10.1371/journal.pcbi.1012264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 08/02/2024] [Accepted: 06/19/2024] [Indexed: 07/25/2024] Open
Abstract
The role of direct cell-to-cell spread in viral infections-where virions spread between host and susceptible cells without needing to be secreted into the extracellular environment-has come to be understood as essential to the dynamics of medically significant viruses like hepatitis C and influenza. Recent work in both the experimental and mathematical modelling literature has attempted to quantify the prevalence of cell-to-cell infection compared to the conventional free virus route using a variety of methods and experimental data. However, estimates are subject to significant uncertainty and moreover rely on data collected by inhibiting one mode of infection by either chemical or physical factors, which may influence the other mode of infection to an extent which is difficult to quantify. In this work, we conduct a simulation-estimation study to probe the practical identifiability of the proportion of cell-to-cell infection, using two standard mathematical models and synthetic data that would likely be realistic to obtain in the laboratory. We show that this quantity cannot be estimated using non-spatial data alone, and that the collection of data which describes the spatial structure of the infection is necessary to infer the proportion of cell-to-cell infection. Our results provide guidance for the design of relevant experiments and mathematical tools for accurately inferring the prevalence of cell-to-cell infection in in vitro and in vivo contexts.
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Affiliation(s)
- Thomas Williams
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - James M. McCaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - James M. Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
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4
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Gautam D, Sindhu A, Vats A, Rajput S, Roshan M, Pal H, De S. Characterization and expression profiling of buffalo IFN-lambda family. Vet Immunol Immunopathol 2024; 272:110770. [PMID: 38735115 DOI: 10.1016/j.vetimm.2024.110770] [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: 01/29/2024] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/14/2024]
Abstract
Interferon lambda (IFN-λ) is an important type III interferon triggered mainly by viral infection. IFN-λ binds to their heterodimeric receptors and signals through JAK-STAT pathways similar to type I IFN. In this study, we deduced the buffalo IFN-λ sequences through the polymerase chain reaction, and then studied IFN-λ's expression patterns in different tissues, and post induction with poly I:C and live MRSA using RT-qPCR. The full-length sequences of buffalo IFN-λ3, IFN-λ receptors, and a transcript variant of IFN-λ4 were determined. IFN-λ1 is identified as a pseudogene. Virus response elements and a recombination hotspot factor was observed in the regulatory region of IFN-λ. The IFN-λ3 expressed highest in lungs and monocytes but IFN-λ4 did not. The expression of Interferon Lambda Receptor 1 was tissue specific, while Interleukin 10 Receptor subunit beta was ubiquitous. Following poly I:C induction, IFN-λ3 expression was primarily observed in epithelial cells as opposed to fibroblasts, displaying cell type-dependent expression. The cytosolic RNA sensors were expressed highest in endometrial epithelial cells, whereas the endosomal receptor was higher in fibroblasts. 2',5'-oligoadenylate synthetase expressed higher in fibroblasts, myxoma resistance protein 1 and IFN-stimulated gene 56 in epithelial cells, displaying cell-specific antiviral response of the interferon stimulated genes (ISGs). The endometrial epithelial cells expressed IFN-λ3 after live S. aureus infection indicating its importance in bacterial infection. The induction of IFN-λ3 was S. aureus isolate specific at the same multiplicity of infection (MOI). This study elucidates the IFN-λ sequences, diverse expression patterns revealing tissue specificity, and specificity in response to poly I:C and bacterial stimuli, emphasising its crucial role in innate immune response modulation.
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Affiliation(s)
- Devika Gautam
- Animal Genomics Lab, Animal Biotechnology Centre, ICAR-National Dairy Research Institute (NDRI), Karnal, HR 132001, India; Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, HR 139031, India
| | - Anil Sindhu
- Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, HR 139031, India
| | - Ashutosh Vats
- Animal Genomics Lab, Animal Biotechnology Centre, ICAR-National Dairy Research Institute (NDRI), Karnal, HR 132001, India
| | - Shiveeli Rajput
- Animal Genomics Lab, Animal Biotechnology Centre, ICAR-National Dairy Research Institute (NDRI), Karnal, HR 132001, India
| | - Mayank Roshan
- Animal Genomics Lab, Animal Biotechnology Centre, ICAR-National Dairy Research Institute (NDRI), Karnal, HR 132001, India
| | - Hanshika Pal
- Animal Genomics Lab, Animal Biotechnology Centre, ICAR-National Dairy Research Institute (NDRI), Karnal, HR 132001, India
| | - Sachinandan De
- Animal Genomics Lab, Animal Biotechnology Centre, ICAR-National Dairy Research Institute (NDRI), Karnal, HR 132001, India.
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5
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Sego TJ. SimService: a lightweight library for building simulation services in Python. Bioinformatics 2024; 40:btae009. [PMID: 38237907 PMCID: PMC10809901 DOI: 10.1093/bioinformatics/btae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/27/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
SUMMARY Integrative biological modeling requires software infrastructure to launch, interconnect, and execute simulation software components without loss of functionality. SimService is a software library that enables deploying simulations in integrated applications as memory-isolated services with interactive proxy objects in the Python programming language. SimService supports customizing the interface of proxies so that simulation developers and users alike can tailor generated simulation instances according to model, method, and integrated application. AVAILABILITY AND IMPLEMENTATION SimService is written in Python, is freely available on GitHub under the MIT license at https://github.com/tjsego/simservice, and is available for download via the Python Package Index (package name "simservice") and conda (package name "simservice" on the conda-forge channel).
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Affiliation(s)
- T J Sego
- Department of Medicine, University of Florida, Gainesville, FL 32610-0225, United States
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Williams T, McCaw JM, Osborne JM. Choice of spatial discretisation influences the progression of viral infection within multicellular tissues. J Theor Biol 2023; 573:111592. [PMID: 37558160 DOI: 10.1016/j.jtbi.2023.111592] [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: 02/20/2023] [Revised: 06/16/2023] [Accepted: 08/02/2023] [Indexed: 08/11/2023]
Abstract
There has been an increasing recognition of the utility of models of the spatial dynamics of viral spread within tissues. Multicellular models, where cells are represented as discrete regions of space coupled to a virus density surface, are a popular approach to capture these dynamics. Conventionally, such models are simulated by discretising the viral surface and depending on the rate of viral diffusion and other considerations, a finer or coarser discretisation may be used. The impact that this choice may have on the behaviour of the system has not been studied. Here we demonstrate that under realistic parameter regimes - where viral diffusion is small enough to support the formation of familiar ring-shaped infection plaques - the choice of spatial discretisation of the viral surface can qualitatively change key model outcomes including the time scale of infection. Importantly, we show that the choice between implementing viral spread as a cell-scale process, or as a high-resolution converged PDE can generate distinct model outcomes, which raises important conceptual questions about the strength of assumptions underpinning the spatial structure of the model. We investigate the mechanisms driving these discretisation artefacts, the impacts they may have on model predictions, and provide guidance on the design and implementation of spatial and especially multicellular models of viral dynamics. We obtain our results using the simplest TIV construct for the viral dynamics, and therefore anticipate that the important effects we describe will also influence model predictions in more complex models of virus-cell-immune system interactions. This analysis will aid in the construction of models for robust and biologically realistic modelling and inference.
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Affiliation(s)
- Thomas Williams
- School of Mathematics and Statistics, University of Melbourne, Australia
| | - James M McCaw
- School of Mathematics and Statistics, University of Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Australia.
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7
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McMahon‑Cole H, Johnson A, Sadat Aghamiri S, Helikar T, Crawford LB. Modeling and Remodeling the Cell: How Digital Twins and HCMV Can Elucidate the Complex Interactions of Viral Latency, Epigenetic Regulation, and Immune Responses. CURRENT CLINICAL MICROBIOLOGY REPORTS 2023; 10:141-151. [PMID: 37901689 PMCID: PMC10601359 DOI: 10.1007/s40588-023-00201-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 10/31/2023]
Abstract
Purpose of Review Human cytomegalovirus (HCMV), while asymptomatic in most, causes significant complications during fetal development, following transplant or in immunosuppressed individuals. The host-virus interactions regulating viral latency and reactivation and viral control of the cellular environment (immune regulation, differentiation, epigenetics) are highly complex. Understanding these processes is essential to controlling infection and can be leveraged as a novel approach for understanding basic cell biology. Recent Findings Immune digital twins (IDTs) are digital simulations integrating knowledge of human immunology, physiology, and patient-specific clinical data to predict individualized immune responses and targeted treatments. Recent studies used IDTs to elucidate mechanisms of T cells, dendritic cells, and epigenetic control-all key to HCMV biology. Summary Here, we discuss how leveraging the unique biology of HCMV and IDTs will clarify immune response dynamics, host-virus interactions, and viral latency and reactivation and serve as a powerful IDT-validation platform for individualized and holistic health management.
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Affiliation(s)
- Hana McMahon‑Cole
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Alicia Johnson
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Lindsey B. Crawford
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
- Nebraska Center for Virology, Lincoln, NE, USA
- Nebraska Center for Integrated Biomolecular Communication, Lincoln, NE, USA
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8
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Raach B, Bundgaard N, Haase MJ, Starruß J, Sotillo R, Stanifer ML, Graw F. Influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics within human airway epithelium. PLoS Comput Biol 2023; 19:e1011356. [PMID: 37566610 PMCID: PMC10446191 DOI: 10.1371/journal.pcbi.1011356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/23/2023] [Accepted: 07/13/2023] [Indexed: 08/13/2023] Open
Abstract
Human airway epithelium (HAE) represents the primary site of viral infection for SARS-CoV-2. Comprising different cell populations, a lot of research has been aimed at deciphering the major cell types and infection dynamics that determine disease progression and severity. However, the cell type-specific replication kinetics, as well as the contribution of cellular composition of the respiratory epithelium to infection and pathology are still not fully understood. Although experimental advances, including Air-liquid interface (ALI) cultures of reconstituted pseudostratified HAE, as well as lung organoid systems, allow the observation of infection dynamics under physiological conditions in unprecedented level of detail, disentangling and quantifying the contribution of individual processes and cells to these dynamics remains challenging. Here, we present how a combination of experimental data and mathematical modelling can be used to infer and address the influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics. Using a stepwise approach that integrates various experimental data on HAE culture systems with regard to tissue differentiation and infection dynamics, we develop an individual cell-based model that enables investigation of infection and regeneration dynamics within pseudostratified HAE. In addition, we present a novel method to quantify tissue integrity based on image data related to the standard measures of transepithelial electrical resistance measurements. Our analysis provides a first aim of quantitatively assessing cell type specific infection kinetics and shows how tissue composition and changes in regeneration capacity, as e.g. in smokers, can influence disease progression and pathology. Furthermore, we identified key measurements that still need to be assessed in order to improve inference of cell type specific infection kinetics and disease progression. Our approach provides a method that, in combination with additional experimental data, can be used to disentangle the complex dynamics of viral infection and immunity within human airway epithelial culture systems.
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Affiliation(s)
- Benjamin Raach
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Nils Bundgaard
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Marika J. Haase
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Jörn Starruß
- Center for Information Services and High Performance Computing, TU Dresden, Dresden, Germany
| | - Rocio Sotillo
- Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Megan L. Stanifer
- Department of Infectious Diseases, Molecular Virology, University Hospital Heidelberg, Heidelberg, Germany
- University of Florida, College of Medicine, Dept. of Molecular Genetics and Microbiology, Gainesville, Florida, United States of America
| | - Frederik Graw
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Medicine 5, Erlangen, Germany
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9
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Jiang B, Li Q, Zhang Z, Huang Y, Wu Y, Li X, Huang M, Huang Y, Jian J. Involvement of CD27 in innate and adaptive immunities of Nile tilapia (Oreochromis niloticus). FISH & SHELLFISH IMMUNOLOGY 2023; 139:108923. [PMID: 37394017 DOI: 10.1016/j.fsi.2023.108923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/15/2023] [Accepted: 06/29/2023] [Indexed: 07/04/2023]
Abstract
CD27 is a member of the TNF-receptor superfamily and plays various roles in immunities. However, the detailed information and mechanism of CD27 in bony fish immunity remain unclear. Therefore, in this research, certain interesting roles of CD27 in Nile tilapia (On-CD27) were determined. On-CD27 was largely expressed in the immune organs, head kidney, and spleen, and was sharply induced during bacterial infection. The in vitro tests suggested On-CD27 was involved in regulating inflammatory responses, activating immune-related signal pathways, and inducing apoptosis and pyroptosis progress. The scRNA data and in vivo experiments indicated that On-CD27 is mainly expressed in CD4+ T cells and involved in both innate and adaptive immunities. The present data provide a theoretical principle for further research on the mechanisms of CD27 in the innate and adaptive immunities of fish.
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Affiliation(s)
- Baijian Jiang
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China
| | - Qi Li
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China
| | - Zhiqiang Zhang
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China
| | - Yongxiong Huang
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China
| | - Yiqin Wu
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China
| | - Xing Li
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China
| | - Meiling Huang
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China
| | - Yu Huang
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China; Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China; Guangdong Provincial Engineering Research Center for Aquatic Animal Health Assessment, Shenzhen, China.
| | - Jichang Jian
- College of Fishery, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, China; Guangdong Provincial Key Laboratory of Control for Disease of Aquatic Animals of Guangdong Higher Education Institutes, Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, China; Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China; Guangdong Provincial Engineering Research Center for Aquatic Animal Health Assessment, Shenzhen, China.
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10
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Beahm DR, Deng Y, DeAngelo TM, Sarpeshkar R. Drug Cocktail Formulation via Circuit Design. IEEE TRANSACTIONS ON MOLECULAR, BIOLOGICAL, AND MULTI-SCALE COMMUNICATIONS 2023; 9:28-48. [PMID: 37397625 PMCID: PMC10312325 DOI: 10.1109/tmbmc.2023.3246928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Drug cocktail therapies are a powerful tool against diseases that exhibit such dynamics. We show that just six key states, which are represented in a feedback circuit, enable drug-cocktail formulation: 1) healthy cell number; 2) infected cell number; 3) extracellular pathogen number; 4) intracellular pathogenic molecule number; 5) innate immune system strength; and 6) adaptive immune system strength. To enable drug cocktail formulation, the model represents the effects of the drugs in the circuit. For example, a nonlinear feedback circuit model fits measured clinical data, represents cytokine storm and adaptive autoimmune behavior, and accounts for age, sex, and variant effects for SARS-CoV-2 with few free parameters. The latter circuit model provided three quantitative insights on the optimal timing and dosage of drug components in a cocktail: 1) antipathogenic drugs should be given early in the infection, but immunosuppressant timing involves a tradeoff between controlling pathogen load and mitigating inflammation; 2) both within and across-class combinations of drugs have synergistic effects; 3) if they are administered sufficiently early in the infection, anti-pathogenic drugs are more effective at mitigating autoimmune behavior than immunosuppressant drugs.
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Affiliation(s)
| | - Yijie Deng
- Thayer School or Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Thomas M DeAngelo
- Thayer School or Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Rahul Sarpeshkar
- Departments of Engineering, Physics, Microbiology & Immunobiology, and Molecular & Systems Biology, Dartmouth College, Hanover, NH 03755 USA
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11
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Gazeau S, Deng X, Ooi HK, Mostefai F, Hussin J, Heffernan J, Jenner AL, Craig M. The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2023; 9:100021. [PMID: 36643886 PMCID: PMC9826539 DOI: 10.1016/j.immuno.2023.100021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
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Affiliation(s)
- Sonia Gazeau
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Xiaoyan Deng
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada
| | - Fatima Mostefai
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Julie Hussin
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Jane Heffernan
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane Australia
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
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12
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Caputo V, Libera M, Sisti S, Giuliani B, Diotti RA, Criscuolo E. The initial interplay between HIV and mucosal innate immunity. Front Immunol 2023; 14:1104423. [PMID: 36798134 PMCID: PMC9927018 DOI: 10.3389/fimmu.2023.1104423] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/17/2023] [Indexed: 02/01/2023] Open
Abstract
Human Immunodeficiency Virus (HIV) is still one of the major global health issues, and despite significant efforts that have been put into studying the pathogenesis of HIV infection, several aspects need to be clarified, including how innate immunity acts in different anatomical compartments. Given the nature of HIV as a sexually transmitted disease, one of the aspects that demands particular attention is the mucosal innate immune response. Given this scenario, we focused our attention on the interplay between HIV and mucosal innate response: the different mucosae act as a physical barrier, whose integrity can be compromised by the infection, and the virus-cell interaction induces the innate immune response. In addition, we explored the role of the mucosal microbiota in facilitating or preventing HIV infection and highlighted how its changes could influence the development of several opportunistic infections. Although recent progress, a proper characterization of mucosal innate immune response and microbiota is still missing, and further studies are needed to understand how they can be helpful for the formulation of an effective vaccine.
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13
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Cockerell A, Wright L, Dattani A, Guo G, Smith A, Tsaneva-Atanasova K, Richards DM. Biophysical models of early mammalian embryogenesis. Stem Cell Reports 2023; 18:26-46. [PMID: 36630902 PMCID: PMC9860129 DOI: 10.1016/j.stemcr.2022.11.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 11/02/2022] [Accepted: 11/24/2022] [Indexed: 01/12/2023] Open
Abstract
Embryo development is a critical and fascinating stage in the life cycle of many organisms. Despite decades of research, the earliest stages of mammalian embryogenesis are still poorly understood, caused by a scarcity of high-resolution spatial and temporal data, the use of only a few model organisms, and a paucity of truly multidisciplinary approaches that combine biological research with biophysical modeling and computational simulation. Here, we explain the theoretical frameworks and biophysical processes that are best suited to modeling the early mammalian embryo, review a comprehensive list of previous models, and discuss the most promising avenues for future work.
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Affiliation(s)
- Alaina Cockerell
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
| | - Liam Wright
- Department of Mathematics, University of Exeter, North Park Road, Exeter EX4 4QF, UK
| | - Anish Dattani
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
| | - Ge Guo
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
| | - Austin Smith
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
| | - Krasimira Tsaneva-Atanasova
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK; Department of Mathematics, University of Exeter, North Park Road, Exeter EX4 4QF, UK; EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter EX4 4QJ, UK; Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Street, 1113 Sofia, Bulgaria
| | - David M Richards
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK; Department of Physics and Astronomy, University of Exeter, North Park Road, Exeter EX4 4QL, UK.
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14
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Cockrell C, Larie D, An G. Preparing for the next pandemic: Simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents. Front Immunol 2022; 13:995395. [PMID: 36479109 PMCID: PMC9720328 DOI: 10.3389/fimmu.2022.995395] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 11/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background Preparation to address the critical gap in a future pandemic between non-pharmacological measures and the deployment of new drugs/vaccines requires addressing two factors: 1) finding virus/pathogen-agnostic pathophysiological targets to mitigate disease severity and 2) finding a more rational approach to repurposing existing drugs. It is increasingly recognized that acute viral disease severity is heavily driven by the immune response to the infection ("cytokine storm" or "cytokine release syndrome"). There exist numerous clinically available biologics that suppress various pro-inflammatory cytokines/mediators, but it is extremely difficult to identify clinically effective treatment regimens with these agents. We propose that this is a complex control problem that resists standard methods of developing treatment regimens and accomplishing this goal requires the application of simulation-based, model-free deep reinforcement learning (DRL) in a fashion akin to training successful game-playing artificial intelligences (AIs). This proof-of-concept study determines if simulated sepsis (e.g. infection-driven cytokine storm) can be controlled in the absence of effective antimicrobial agents by targeting cytokines for which FDA-approved biologics currently exist. Methods We use a previously validated agent-based model, the Innate Immune Response Agent-based Model (IIRABM), for control discovery using DRL. DRL training used a Deep Deterministic Policy Gradient (DDPG) approach with a clinically plausible control interval of 6 hours with manipulation of six cytokines for which there are existing drugs: Tumor Necrosis Factor (TNF), Interleukin-1 (IL-1), Interleukin-4 (IL-4), Interleukin-8 (IL-8), Interleukin-12 (IL-12) and Interferon-γ(IFNg). Results DRL trained an AI policy that could improve outcomes from a baseline Recovered Rate of 61% to one with a Recovered Rate of 90% over ~21 days simulated time. This DRL policy was then tested on four different parameterizations not seen in training representing a range of host and microbe characteristics, demonstrating a range of improvement in Recovered Rate by +33% to +56. Discussion The current proof-of-concept study demonstrates that significant disease severity mitigation can potentially be accomplished with existing anti-mediator drugs, but only through a multi-modal, adaptive treatment policy requiring implementation with an AI. While the actual clinical implementation of this approach is a projection for the future, the current goal of this work is to inspire the development of a research ecosystem that marries what is needed to improve the simulation models with the development of the sensing/assay technologies to collect the data needed to iteratively refine those models.
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15
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Miller C, Crampin E, Osborne JM. Multiscale modelling of desquamation in the interfollicular epidermis. PLoS Comput Biol 2022; 18:e1010368. [PMID: 36037236 PMCID: PMC9462764 DOI: 10.1371/journal.pcbi.1010368] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/09/2022] [Accepted: 07/08/2022] [Indexed: 11/19/2022] Open
Abstract
Maintenance of epidermal thickness is critical to the barrier function of the skin. Decreased tissue thickness, specifically in the stratum corneum (the outermost layer of the tissue), causes discomfort and inflammation, and is related to several severe diseases of the tissue. In order to maintain both stratum corneum thickness and overall tissue thickness it is necessary for the system to balance cell proliferation and cell loss. Cell proliferation in the epidermis occurs in the basal layer and causes constant upwards movement in the tissue. Cell loss occurs when dead cells at the top of the tissue are lost to the environment through a process called desquamation. Desquamation is thought to occur through a gradual reduction in adhesion between cells, due to the cleaving of adhesion proteins by enzymes, in the stratum corneum.
In this paper we will investigate combining a (mass action) subcellular model of desquamation with a three dimensional (cell centre based) multicellular model of the interfollicular epidermis to better understand maintenance of epidermal thickness. Specifically, our aim is to determine if a hypothesised biological model for the degradation of cell-cell adhesion, from the literature, is sufficient to maintain a steady state tissue thickness. These investigations show the model is able to provide a consistent rate of cell loss in the multicellular model. This loss balances proliferation, and hence maintains a homeostatic tissue thickness. Moreover, we find that multiple proliferative cell populations in the basal layer can be represented by a single proliferative cell population, simplifying investigations with this model.
The model is used to investigate a disorder (Netherton Syndrome) which disrupts desquamation. The model shows how biochemical changes can cause disruptions to the tissue, resulting in a reduced tissue thickness and consequently diminishing the protective role of the tissue. A hypothetical treatment result is also investigated: we compare the cases of a partially effective homogeneous treatment (where all cells partially recover) and a totally effective heterogeneous treatment (in which a proportion of the cells totally recover) with the aim to determine the difference in the response of the tissue to these different scenarios. Results show an increased benefit to corneum thickness from the heterogeneous treatment over the homogeneous treatment.
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Affiliation(s)
- Claire Miller
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- Computational Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Edmund Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- School of Medicine, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
| | - James M. Osborne
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
- * E-mail:
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16
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Mochan E, Sego TJ, Ermentrout B. Age-Related Changes to the Immune System Exacerbate the Inflammatory Response to Pandemic H1N1 Infection. Bull Math Biol 2022; 84:88. [PMID: 35829841 PMCID: PMC9278316 DOI: 10.1007/s11538-022-01045-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 06/22/2022] [Indexed: 11/30/2022]
Abstract
Age-induced dysregulation of the immune response is a major contributor to the morbidity and mortality related to influenza a virus infections. Experimental data have shown substantial changes to the activation and maintenance of the immune response will occur with age, but it remains unclear which of these many interrelated changes are most critical to controlling the survival of the host during infection. To ascertain which mechanisms are predominantly responsible for the increased morbidity in elderly hosts, we developed an ordinary differential equation model to simulate the immune response to pandemic H1N1 infection. We fit this model to experimental data measured in young and old macaques. We determined that the severity of the infection in the elderly hosts is caused by a dysregulation in the innate immune response. We also simulated CD8+ T cell exhaustion, a common consequence of chronic and extensive infections. Our simulations indicate that while T cell exhaustion is possible in both age groups, its effects are more severe in the elderly population, as their dysregulated immune response cannot easily compensate for the exhausted T cells. Finally, we explore a therapeutic approach to reversing T cell exhaustion through an inflammatory stimulus. A controlled increase in inflammatory signals can lead to a higher chance of surviving the infection, but excess inflammation will likely lead to septic death. These results indicate that our model captures distinctions in the predominant mechanisms controlling the immune response in younger and older hosts and allows for simulations of clinically relevant therapeutic strategies post-infection.
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Affiliation(s)
- Ericka Mochan
- Department of Analytical, Physical, and Social Sciences, Carlow University, Pittsburgh, PA, 15213, USA.
| | - T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
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17
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Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA. Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med 2022; 5:64. [PMID: 35595830 PMCID: PMC9122990 DOI: 10.1038/s41746-022-00610-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - A Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France.,Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - T Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - G An
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - B Shapiro
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - R S Malik-Sheriff
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, Cambridge, UK
| | - T J Sego
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - A Knapp
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - P Macklin
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - J A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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18
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Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2. Viruses 2022; 14:v14030605. [PMID: 35337012 PMCID: PMC8953050 DOI: 10.3390/v14030605] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/06/2023] Open
Abstract
We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.
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19
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Sego TJ, Mochan ED, Ermentrout GB, Glazier JA. A multiscale multicellular spatiotemporal model of local influenza infection and immune response. J Theor Biol 2022; 532:110918. [PMID: 34592264 PMCID: PMC8478073 DOI: 10.1016/j.jtbi.2021.110918] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 12/11/2022]
Abstract
Respiratory viral infections pose a
serious public health concern, from mild seasonal influenza to pandemics
like those of SARS-CoV-2. Spatiotemporal dynamics of viral infection
impact nearly all aspects of the progression of a viral infection, like
the dependence of viral replication rates on the type of cell and
pathogen, the strength of the immune response and localization of
infection. Mathematical modeling is often used to describe respiratory
viral infections and the immune response to them using ordinary
differential equation (ODE) models. However, ODE models neglect
spatially-resolved biophysical mechanisms like lesion shape and the
details of viral transport, and so cannot model spatial effects of a
viral infection and immune response. In this work, we develop a
multiscale, multicellular spatiotemporal model of influenza infection and
immune response by combining non-spatial ODE modeling and spatial,
cell-based modeling. We employ cellularization, a recently developed
method for generating spatial, cell-based, stochastic models from
non-spatial ODE models, to generate much of our model from a calibrated
ODE model that describes infection, death and recovery of susceptible
cells and innate and adaptive responses during influenza infection, and
develop models of cell migration and other mechanisms not explicitly
described by the ODE model. We determine new model parameters to generate
agreement between the spatial and original ODE models under certain
conditions, where simulation replicas using our model serve as
microconfigurations of the ODE model, and compare results between the
models to investigate the nature of viral exposure and impact of
heterogeneous infection on the time-evolution of the viral infection. We
found that using spatially homogeneous initial exposure conditions
consistently with those employed during calibration of the ODE model
generates far less severe infection, and that local exposure to virus
must be multiple orders of magnitude greater than a uniformly applied
exposure to all available susceptible cells. This strongly suggests a
prominent role of localization of exposure in influenza A infection. We
propose that the particularities of the microenvironment to which a virus
is introduced plays a dominant role in disease onset and progression, and
that spatially resolved models like ours may be important to better
understand and more reliably predict future health states based on
susceptibility of potential lesion sites using spatially resolved patient
data of the state of an infection. We can readily integrate the immune
response components of our model into other modeling and simulation
frameworks of viral infection dynamics that do detailed modeling of other
mechanisms like viral internalization and intracellular viral replication
dynamics, which are not explicitly represented in the ODE model. We can
also combine our model with available experimental data and modeling of
exposure scenarios and spatiotemporal aspects of mechanisms like
mucociliary clearance that are only implicitly described by the ODE
model, which would significantly improve the ability of our model to
present spatially resolved predictions about the progression of influenza
infection and immune response.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
| | - Ericka D Mochan
- Department of Analytical, Physical, and Social Sciences, Carlow University, Pittsburgh, PA, USA
| | - G Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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20
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Zhang L, Li R, Song G, Scholes GD, She ZS. Impairment of T cells' antiviral and anti-inflammation immunities may be critical to death from COVID-19. ROYAL SOCIETY OPEN SCIENCE 2021; 8:211606. [PMID: 34950497 PMCID: PMC8692966 DOI: 10.1098/rsos.211606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/25/2021] [Indexed: 05/02/2023]
Abstract
Clarifying dominant factors determining the immune heterogeneity from non-survivors to survivors is crucial for developing therapeutics and vaccines against COVID-19. The main difficulty is quantitatively analysing the multi-level clinical data, including viral dynamics, immune response and tissue damages. Here, we adopt a top-down modelling approach to quantify key functional aspects and their dynamical interplay in the battle between the virus and the immune system, yielding an accurate description of real-time clinical data involving hundreds of patients for the first time. The quantification of antiviral responses gives that, compared to antibodies, T cells play a more dominant role in virus clearance, especially for mild patients (96.5%). Moreover, the anti-inflammatory responses, namely the cytokine inhibition and tissue repair rates, also positively correlate with T cell number and are significantly suppressed in non-survivors. Simulations show that the lack of T cells can lead to more significant inflammation, proposing an explanation for the monotonic increase of COVID-19 mortality with age and higher mortality for males. We propose that T cells play a crucial role in the immunity against COVID-19, which provides a new direction-improvement of T cell number for advancing current prevention and treatment.
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Affiliation(s)
- Luhao Zhang
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Department of Chemistry, Princeton University, Princeton, NJ 08540, USA
| | - Rong Li
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing 100871, People's Republic of China
| | - Gang Song
- Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, People's Republic of China
| | | | - Zhen-Su She
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing 100871, People's Republic of China
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21
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Moses ME, Hofmeyr S, Cannon JL, Andrews A, Gridley R, Hinga M, Leyba K, Pribisova A, Surjadidjaja V, Tasnim H, Forrest S. Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection. PLoS Comput Biol 2021; 17:e1009735. [PMID: 34941862 PMCID: PMC8740970 DOI: 10.1371/journal.pcbi.1009735] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/07/2022] [Accepted: 12/09/2021] [Indexed: 01/03/2023] Open
Abstract
A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.
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Affiliation(s)
- Melanie E. Moses
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Steven Hofmeyr
- Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Judy L. Cannon
- Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
| | - Akil Andrews
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Rebekah Gridley
- Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
| | - Monica Hinga
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Kirtus Leyba
- Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
| | - Abigail Pribisova
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Vanessa Surjadidjaja
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Humayra Tasnim
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Stephanie Forrest
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
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22
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Day JD, Park S, Ranard BL, Singh H, Chow CC, Vodovotz Y. Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model. Front Immunol 2021; 12:754127. [PMID: 34777366 PMCID: PMC8582279 DOI: 10.3389/fimmu.2021.754127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/04/2021] [Indexed: 01/08/2023] Open
Abstract
COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.
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Affiliation(s)
- Judy D Day
- Department of Mathematics, University of Tennessee, Knoxville, TN, United States.,Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville, TN, United States
| | - Soojin Park
- Department of Neurology & Division of Critical Care and Hospital Neurology, Columbia University College of Physicians and Surgeons, New York Presbyterian Hospital - Columbia University Irving Medical Center, New York, NY, United States.,Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, United States
| | - Benjamin L Ranard
- Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, United States.,Division of Pulmonary, Allergy & Critical Care Medicine, Department of Medicine, Columbia University College of Physicians and Surgeons, New York Presbyterian Hospital - Columbia University Irving Medical Center, New York, NY, United States
| | - Harinder Singh
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, United States.,Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Carson C Chow
- Mathematical Biology Section, Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, United States
| | - Yoram Vodovotz
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Surgery, University of Pittsburgh, Pittsburgh, PA, United States.,Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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23
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Marzban S, Han R, Juhász N, Röst G. A hybrid PDE-ABM model for viral dynamics with application to SARS-CoV-2 and influenza. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210787. [PMID: 34737874 PMCID: PMC8564626 DOI: 10.1098/rsos.210787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 10/11/2021] [Indexed: 05/08/2023]
Abstract
We propose a hybrid partial differential equation-agent-based (PDE-ABM) model to describe the spatio-temporal viral dynamics in a cell population. The virus concentration is considered as a continuous variable and virus movement is modelled by diffusion, while changes in the states of cells (i.e. healthy, infected, dead) are represented by a stochastic ABM. The two subsystems are intertwined: the probability of an agent getting infected in the ABM depends on the local viral concentration, and the source term of viral production in the PDE is determined by the cells that are infected. We develop a computational tool that allows us to study the hybrid system and the generated spatial patterns in detail. We systematically compare the outputs with a classical ODE system of viral dynamics, and find that the ODE model is a good approximation only if the diffusion coefficient is large. We demonstrate that the model is able to predict SARS-CoV-2 infection dynamics, and replicate the output of in vitro experiments. Applying the model to influenza as well, we can gain insight into why the outcomes of these two infections are different.
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Affiliation(s)
- Sadegh Marzban
- Bolyai Institute, University of Szeged, Szeged 6720, Hungary
| | - Renji Han
- Bolyai Institute, University of Szeged, Szeged 6720, Hungary
| | - Nóra Juhász
- Bolyai Institute, University of Szeged, Szeged 6720, Hungary
| | - Gergely Röst
- Bolyai Institute, University of Szeged, Szeged 6720, Hungary
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Kontsevaya GV, Gerlinskaya LA, Moshkin YM, Anisimova MV, Stanova AK, Babochkina TI, Moshkin MP. The Effects of Sperm and Seminal Fluid of Immunized Male Mice on In Vitro Fertilization and Surrogate Mother-Embryo Interaction. Int J Mol Sci 2021; 22:ijms221910650. [PMID: 34638989 PMCID: PMC8508670 DOI: 10.3390/ijms221910650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/22/2021] [Accepted: 09/27/2021] [Indexed: 11/16/2022] Open
Abstract
The latest vaccination campaign has actualized the potential impact of antigenic stimuli on reproductive functions. To address this, we mimicked vaccination’s effects by administering keyhole limpet hemocyanin (KLH ) to CD1 male mice and used their sperm for in vitro fertilization (IVF). Two-cell embryos after IVF with spermatozoa from control (C) or KLH-treated (Im) male mice were transferred to surrogate mothers mated with vasectomized control (C) or KLH-treated (Im) male mice, resulting in four experimental groups: C–C, Im–C, C–Im, and Im–Im. The pre-implantation losses were significantly lower in the Im–C group than in the C–Im group. At the same time, the resorption rates reduced markedly in the C–Im compared to the Im–C group. Embryo and placenta weights were significantly higher in the Im–Im group. Although the GM-CSF levels were lower in the amniotic fluid of the gestating surrogate mothers in the Im–Im group, they were strongly correlated with embryo mass. The number–size trade-off was only significant in the Im–Im group. This suggests a positive, cooperative effect of spermatozoa and seminal fluid from immune-primed males on embryo growth and the optimal distribution of surrogate mother maternal resources despite the negative impact of males’ antigenic challenge on the IVF success rate.
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Affiliation(s)
- Galina Vladimirovna Kontsevaya
- Federal Research Center Institute of Cytology and Genetics, Siberian Branch of RAS, 630090 Novosibirsk, Russia; (G.V.K.); (Y.M.M.); (M.V.A.); (A.K.S.); (M.P.M.)
| | - Ludmila Alekseevna Gerlinskaya
- Federal Research Center Institute of Cytology and Genetics, Siberian Branch of RAS, 630090 Novosibirsk, Russia; (G.V.K.); (Y.M.M.); (M.V.A.); (A.K.S.); (M.P.M.)
- Correspondence: (L.A.G.); (T.I.B.)
| | - Yury Mikhailovich Moshkin
- Federal Research Center Institute of Cytology and Genetics, Siberian Branch of RAS, 630090 Novosibirsk, Russia; (G.V.K.); (Y.M.M.); (M.V.A.); (A.K.S.); (M.P.M.)
| | - Margarita Vladimirovna Anisimova
- Federal Research Center Institute of Cytology and Genetics, Siberian Branch of RAS, 630090 Novosibirsk, Russia; (G.V.K.); (Y.M.M.); (M.V.A.); (A.K.S.); (M.P.M.)
| | - Aliya Konstantinovna Stanova
- Federal Research Center Institute of Cytology and Genetics, Siberian Branch of RAS, 630090 Novosibirsk, Russia; (G.V.K.); (Y.M.M.); (M.V.A.); (A.K.S.); (M.P.M.)
| | - Tatyana Ivanovna Babochkina
- Federal Research Center Institute of Cytology and Genetics, Siberian Branch of RAS, 630090 Novosibirsk, Russia; (G.V.K.); (Y.M.M.); (M.V.A.); (A.K.S.); (M.P.M.)
- Correspondence: (L.A.G.); (T.I.B.)
| | - Mikhail Pavlovich Moshkin
- Federal Research Center Institute of Cytology and Genetics, Siberian Branch of RAS, 630090 Novosibirsk, Russia; (G.V.K.); (Y.M.M.); (M.V.A.); (A.K.S.); (M.P.M.)
- Biological Institute at Tomsk State University, 634050 Tomsk, Russia
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Sego TJ, Aponte-Serrano JO, Gianlupi JF, Glazier JA. Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection. BMC Biol 2021; 19:196. [PMID: 34496857 PMCID: PMC8424622 DOI: 10.1186/s12915-021-01115-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 08/02/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND The biophysics of an organism span multiple scales from subcellular to organismal and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them and highlights the need for simpler methods able to model the spatial features of biological systems. RESULTS In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models, the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models. CONCLUSION We developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
| | - Josua O Aponte-Serrano
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Juliano F Gianlupi
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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Abstract
Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.
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Grebennikov D, Kholodareva E, Sazonov I, Karsonova A, Meyerhans A, Bocharov G. Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling. Viruses 2021; 13:1735. [PMID: 34578317 PMCID: PMC8473439 DOI: 10.3390/v13091735] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 12/15/2022] Open
Abstract
SARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contributing to the observed disease phenotypes. In our study here, we formulate and calibrate a deterministic model of the SARS-CoV-2 life cycle. It provides a kinetic description of the major replication stages of SARS-CoV-2. Sensitivity analysis of the net viral progeny with respect to model parameters enables the identification of the life cycle stages that have the strongest impact on viral replication. These three most influential parameters are (i) degradation rate of positive sense vRNAs in cytoplasm (negative effect), (ii) threshold number of non-structural proteins enhancing vRNA transcription (negative effect), and (iii) translation rate of non-structural proteins (positive effect). The results of our analysis could be used for guiding the search for antiviral drug targets to combat SARS-CoV-2 infection.
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Affiliation(s)
- Dmitry Grebennikov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, Russia;
- Moscow Center for Fundamental and Applied Mathematics at INM RAS, 119333 Moscow, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Ekaterina Kholodareva
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, Russia;
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, 141701 Moscow Oblast, Russia
| | - Igor Sazonov
- College of Engineering, Swansea University, Bay Campus, Fabian Way, Swansea SA1 8EN, UK;
| | - Antonina Karsonova
- Department of Clinical Immunology and Allergology, Sechenov First Moscow State Medical University, 119991 Moscow, Russia;
| | - Andreas Meyerhans
- Infection Biology Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain;
- ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain
| | - Gennady Bocharov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, Russia;
- Moscow Center for Fundamental and Applied Mathematics at INM RAS, 119333 Moscow, Russia
- Institute of Computer Science and Mathematical Modelling, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
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Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response. Curr Opin Virol 2021; 50:103-109. [PMID: 34450519 PMCID: PMC8384423 DOI: 10.1016/j.coviro.2021.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 12/17/2022]
Abstract
The COVID-19 pandemic has highlighted a need for improved frameworks for drug discovery, repurposing, clinical trial design and therapy optimization and personalization. Mechanistic computational models can play an important role in developing these frameworks. We discuss how mechanistic models, which consider viral entry, replication in target cells, viral spread in the body, immune response, and the complex factors involved in tissue and organ damage and recovery, can clarify the mechanisms of humoral and cellular immune responses to the virus, viral distribution and replication in tissues, the origins of pathogenesis and patient-to-patient heterogeneity in responses. These models are already improving our understanding of the mechanisms of action of antivirals and immune modulators. We discuss how closer collaboration between the experimentalists, clinicians and modelers could result in more predictive models which may guide therapies for viral infections, improving survival and leading to faster and more complete recovery.
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Hwang W, Lei W, Katritsis NM, MacMahon M, Chapman K, Han N. Current and prospective computational approaches and challenges for developing COVID-19 vaccines. Adv Drug Deliv Rev 2021; 172:249-274. [PMID: 33561453 PMCID: PMC7871111 DOI: 10.1016/j.addr.2021.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.
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Affiliation(s)
- Woochang Hwang
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Winnie Lei
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Nicholas M Katritsis
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Méabh MacMahon
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Centre for Therapeutics Discovery, LifeArc, Stevenage, UK
| | - Kathryn Chapman
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Namshik Han
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
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Affiliation(s)
| | - James P Sluka
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
| | - James A Glazier
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
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31
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Perelson AS, Ke R. Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics. Clin Pharmacol Ther 2021; 109:829-840. [PMID: 33410134 DOI: 10.1002/cpt.2160] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022]
Abstract
Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.
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Affiliation(s)
- Alan S Perelson
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
| | - Ruian Ke
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
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