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Krix S, Wilczynski E, Falgàs N, Sánchez-Valle R, Yoles E, Nevo U, Baruch K, Fröhlich H. Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches. Front Immunol 2024; 15:1343900. [PMID: 38720902 PMCID: PMC11078023 DOI: 10.3389/fimmu.2024.1343900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.
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Affiliation(s)
- Sophia Krix
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
| | - Ella Wilczynski
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Neus Falgàs
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Eti Yoles
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Uri Nevo
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Kuti Baruch
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
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2
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Gazo Hanna E, Younes K, Roufayel R, Khazaal M, Fajloun Z. Engineering innovations in medicine and biology: Revolutionizing patient care through mechanical solutions. Heliyon 2024; 10:e26154. [PMID: 38390063 PMCID: PMC10882044 DOI: 10.1016/j.heliyon.2024.e26154] [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: 06/02/2023] [Revised: 01/24/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
The overlap between mechanical engineering and medicine is expanding more and more over the years. Engineers are now using their expertise to design and create functional biomaterials and are continually collaborating with physicians to improve patient health. In this review, we explore the state of scientific knowledge in the areas of biomaterials, biomechanics, nanomechanics, and computational fluid dynamics (CFD) in relation to the pharmaceutical and medical industry. Focusing on current research and breakthroughs, we provide an overview of how these fields are being used to create new technologies for medical treatments of human patients. Barriers and constraints in these fields, as well as ways to overcome them, are also described in this review. Finally, the potential for future advances in biomaterials to fundamentally change the current approach to medicine and biology is also discussed.
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Affiliation(s)
- Eddie Gazo Hanna
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Khaled Younes
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Rabih Roufayel
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Mickael Khazaal
- École Supérieure des Techniques Aéronautiques et de Construction Automobile, ISAE-ESTACA, France
| | - Ziad Fajloun
- Faculty of Sciences 3, Department of Biology, Lebanese University, Campus Michel Slayman Ras Maska, 1352, Tripoli, Lebanon
- Laboratory of Applied Biotechnology (LBA3B), Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, 1300, Tripoli, Lebanon
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3
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Hulme PE, Beggs JR, Binny RN, Bray JP, Cogger N, Dhami MK, Finlay-Smits SC, French NP, Grant A, Hewitt CL, Jones EE, Lester PJ, Lockhart PJ. Emerging advances in biosecurity to underpin human, animal, plant, and ecosystem health. iScience 2023; 26:107462. [PMID: 37636074 PMCID: PMC10450416 DOI: 10.1016/j.isci.2023.107462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023] Open
Abstract
One Biosecurity is an interdisciplinary approach to policy and research that builds on the interconnections between human, animal, plant, and ecosystem health to effectively prevent and mitigate the impacts of invasive alien species. To support this approach requires that key cross-sectoral research innovations be identified and prioritized. Following an interdisciplinary horizon scan for emerging research that underpins One Biosecurity, four major interlinked advances were identified: implementation of new surveillance technologies adopting state-of-the-art sensors connected to the Internet of Things, deployable handheld molecular and genomic tracing tools, the incorporation of wellbeing and diverse human values into biosecurity decision-making, and sophisticated socio-environmental models and data capture. The relevance and applicability of these innovations to address threats from pathogens, pests, and weeds in both terrestrial and aquatic ecosystems emphasize the opportunity to build critical mass around interdisciplinary teams at a global scale that can rapidly advance science solutions targeting biosecurity threats.
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Affiliation(s)
- Philip E. Hulme
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
- Department of Pest Management and Conservation, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Jacqueline R. Beggs
- Centre for Biodiversity and Biosecurity, School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
| | - Rachelle N. Binny
- Manaaki Whenua - Landcare Research, PO Box 69040, Lincoln, New Zealand
| | - Jonathan P. Bray
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
- Department of Pest Management and Conservation, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Naomi Cogger
- Tāwharau Ora, School of Veterinary Science, Massey University, Palmerston North 4472, New Zealand
| | - Manpreet K. Dhami
- Manaaki Whenua - Landcare Research, PO Box 69040, Lincoln, New Zealand
| | | | - Nigel P. French
- Tāwharau Ora, School of Veterinary Science, Massey University, Palmerston North 4472, New Zealand
| | - Andrea Grant
- Scion, 10 Kyle Street, Riccarton, Christchurch 8011, New Zealand
| | - Chad L. Hewitt
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Eirian E. Jones
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
- Department of Pest Management and Conservation, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Phil J. Lester
- School of Biological Sciences, Victoria University of Wellington, PO Box 600, Wellington, New Zealand
| | - Peter J. Lockhart
- School of Natural Sciences, Massey University, Palmerston North 4472, New Zealand
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4
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Ghoreyshi ZS, George JT. Quantitative approaches for decoding the specificity of the human T cell repertoire. Front Immunol 2023; 14:1228873. [PMID: 37781387 PMCID: PMC10539903 DOI: 10.3389/fimmu.2023.1228873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023] Open
Abstract
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.
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Affiliation(s)
- Zahra S. Ghoreyshi
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Jason T. George
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Engineering Medicine Program, Texas A&M University, Houston, TX, United States
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
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5
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Konur S, Gheorghe M, Krasnogor N. Verifiable biology. J R Soc Interface 2023; 20:20230019. [PMID: 37160165 PMCID: PMC10169095 DOI: 10.1098/rsif.2023.0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] Open
Abstract
The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.
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Affiliation(s)
- Savas Konur
- Department of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK
| | - Marian Gheorghe
- Department of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK
| | - Natalio Krasnogor
- School of Computing Science, Newcastle University, Science Square, Newcastle upon Tyne NE4 5TG, UK
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6
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Hoerter A, Arnett E, Schlesinger LS, Pienaar E. Systems biology approaches to investigate the role of granulomas in TB-HIV coinfection. Front Immunol 2022; 13:1014515. [PMID: 36405707 PMCID: PMC9670175 DOI: 10.3389/fimmu.2022.1014515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/20/2022] [Indexed: 09/29/2023] Open
Abstract
The risk of active tuberculosis disease is 15-21 times higher in those coinfected with human immunodeficiency virus-1 (HIV) compared to tuberculosis alone, and tuberculosis is the leading cause of death in HIV+ individuals. Mechanisms driving synergy between Mycobacterium tuberculosis (Mtb) and HIV during coinfection include: disruption of cytokine balances, impairment of innate and adaptive immune cell functionality, and Mtb-induced increase in HIV viral loads. Tuberculosis granulomas are the interface of host-pathogen interactions. Thus, granuloma-based research elucidating the role and relative impact of coinfection mechanisms within Mtb granulomas could inform cohesive treatments that target both pathogens simultaneously. We review known interactions between Mtb and HIV, and discuss how the structure, function and development of the granuloma microenvironment create a positive feedback loop favoring pathogen expansion and interaction. We also identify key outstanding questions and highlight how coupling computational modeling with in vitro and in vivo efforts could accelerate Mtb-HIV coinfection discoveries.
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Affiliation(s)
- Alexis Hoerter
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Eusondia Arnett
- Host-Pathogen Interactions Program, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Larry S. Schlesinger
- Host-Pathogen Interactions Program, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Elsje Pienaar
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, United States
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7
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Marriott C, Bae P, Chebib J. Deterministic Response Threshold Models of Reproductive Division of Labor Are More Robust Than Probabilistic Models in Artificial Ants. ARTIFICIAL LIFE 2022; 28:264-286. [PMID: 35727996 DOI: 10.1162/artl_a_00369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We implement an agent-based simulation of the response threshold model of reproductive division of labor. Ants in our simulation must perform two tasks in their environment: forage and reproduce. The colony is capable of allocating ant resources to these roles using different division of labor strategies via genetic architectures and plasticity mechanisms. We find that the deterministic allocation strategy of the response threshold model is more robust than the probabilistic allocation strategy. The deterministic allocation strategy is also capable of evolving complex solutions to colony problems like niche construction and recovery from the loss of the breeding caste. In addition, plasticity mechanisms had both positive and negative influence on the emergence of reproductive division of labor. The combination of plasticity mechanisms has an additive and sometimes emergent impact.
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Affiliation(s)
- Chris Marriott
- University of Washington, School of Engineering and Technology.
| | - Peter Bae
- University of Washington, School of Engineering and Technology
| | - Jobran Chebib
- University of Edinburgh, Institute of Evolutionary Biology
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8
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Bergman D, Sweis RF, Pearson AT, Nazari F, Jackson TL. A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues. iScience 2022; 25:104387. [PMID: 35637730 PMCID: PMC9142654 DOI: 10.1016/j.isci.2022.104387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/30/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Agent-based models (ABMs) are a natural platform for capturing the multiple time and spatial scales in biological processes. However, these models are computationally expensive, especially when including molecular-level effects. The traditional approach to simulating this type of multiscale ABM is to solve a system of ordinary differential equations for the molecular events per cell. This significantly adds to the computational cost of simulations as the number of agents grows, which contributes to many ABMs being limited to around10 5 cells. We propose an approach that requires the same computational time independent of the number of agents. This speeds up the entire simulation by orders of magnitude, allowing for more thorough explorations of ABMs with even larger numbers of agents. We use two systems to show that the new method strongly agrees with the traditionally used approach. This computational strategy can be applied to a wide range of biological investigations.
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Affiliation(s)
- Daniel Bergman
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Randy F. Sweis
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
| | - Alexander T. Pearson
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
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9
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An Agent-Based Interpretation of Leukocyte Chemotaxis in Cancer-on-Chip Experiments. MATHEMATICS 2022. [DOI: 10.3390/math10081338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The present paper was inspired by recent developments in laboratory experiments within the framework of cancer-on-chip technology, an immune-oncology microfluidic chip aiming at studying the fundamental mechanisms of immunocompetent behavior. We focus on the laboratory setting where cancer is treated with chemotherapy drugs, and in this case, the effects of the treatment administration hypothesized by biologists are: the absence of migration and proliferation of tumor cells, which are dying; the stimulation of the production of chemical substances (annexin); the migration of leukocytes in the direction of higher concentrations of chemicals. Here, following the physiological hypotheses made by biologists on the phenomena occurring in these experiments, we introduce an agent-based model reproducing the dynamics of two cell populations (agents), i.e., tumor cells and leukocytes living in the microfluidic chip environment. Our model aims at proof of concept, demonstrating that the observations of the biological phenomena can be obtained by the model on the basis of the explicit assumptions made. In this framework, close adherence of the computational model to the biological results, as shown in the section devoted to the first calibration of the model with respect to available observations, is successfully accomplished.
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10
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Pinton P. Computational models in inflammatory bowel disease. Clin Transl Sci 2022; 15:824-830. [PMID: 35122401 PMCID: PMC9010263 DOI: 10.1111/cts.13228] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/22/2021] [Accepted: 12/22/2021] [Indexed: 11/28/2022] Open
Abstract
Inflammatory bowel disease (IBD) is a chronic and relapsing disease with multiple underlying influences and notable heterogeneity among its clinical and response-to-treatment phenotypes. There is no cure for IBD, and none of the currently available therapies have demonstrated clinical efficacies beyond 40%-60%. Data collected about its omics, pathogenesis, and treatment strategies have grown exponentially with time making IBD a prime candidate for artificial intelligence (AI) mediated discovery support. AI can be leveraged to further understand or identify IBD features to improve clinical outcomes. Various treatment candidates are currently under evaluation in clinical trials, offering further approaches and opportunities for increasing the efficacies of treatments. However, currently, therapeutic plans are largely determined using clinical features due to the lack of specific biomarkers, and it has become necessary to step into precision medicine to predict therapeutic responses to guarantee optimal treatment efficacy. This is accompanied by the application of AI and the development of multiscale hybrid models combining mechanistic approaches and machine learning. These models ultimately lead to the creation of digital twins of given patients delivering on the promise of precision dosing and tailored treatment. Interleukin-6 (IL-6) is a prominent cytokine in cell-to-cell communication in the inflammatory responses' regulation. Dysregulated IL-6-induced signaling leads to severe immunological or proliferative pathologies, such as IBD and colon cancer. This mini-review explores multiscale models with the aim of predicting the response to therapy in IBD. Modeling IL-6 biology and generating digital twins enhance the credibility of their prediction.
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11
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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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12
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Getz WM, Salter R, Luisa Vissat L, Koopman JS, Simon CP. A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations. J R Soc Interface 2021; 18:20210648. [PMID: 34814729 PMCID: PMC8611333 DOI: 10.1098/rsif.2021.0648] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
We present methods for building a Java Runtime-Alterable-Model Platform (RAMP) of complex dynamical systems. We illustrate our methods by building a multivariant SEIR (epidemic) RAMP. Underlying our RAMP is an individual-based model that includes adaptive contact rates, pathogen genetic drift, waning and cross-immunity. Besides allowing parameter values, process descriptions and scriptable runtime drivers to be easily modified during simulations, our RAMP can used within R-Studio and other computational platforms. Process descriptions that can be runtime altered within our SEIR RAMP include pathogen variant-dependent host shedding, environmental persistence, host transmission and within-host pathogen mutation and replication. They also include adaptive social distancing and adaptive application of vaccination rates and variant-valency of vaccines. We present simulation results using parameter values and process descriptions relevant to the current COVID-19 pandemic. Our results suggest that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible variants, particularly if vaccine valencies are not adapted to deal with escape mutations. Our SEIR RAMP is designed for easy use by others. More generally, our RAMP concept facilitates construction of highly flexible complex systems models of all types, which can then be easily shared as stand-alone application programs.
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Affiliation(s)
- Wayne M Getz
- Department ESPM, UC Berkeley, Berkeley, CA 94720-3114, USA.,School of Mathematical Sciences, University of KwaZulu-Natal, Durban, South Africa.,Numerus, 850 Iron Point Rd., Folsom, CA 95630, USA
| | - Richard Salter
- Numerus, 850 Iron Point Rd., Folsom, CA 95630, USA.,Computer Science Department, Oberlin College, Oberlin, OH 44074, USA
| | | | - James S Koopman
- School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.,Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA
| | - Carl P Simon
- Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA.,Gerald R. Ford School of Public Policy, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
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13
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Getz M, Wang Y, An G, Asthana M, Becker A, Cockrell C, Collier N, Craig M, Davis CL, Faeder JR, Ford Versypt AN, Mapder T, Gianlupi JF, Glazier JA, Hamis S, Heiland R, Hillen T, Hou D, Islam MA, Jenner AL, Kurtoglu F, Larkin CI, Liu B, Macfarlane F, Maygrundter P, Morel PA, Narayanan A, Ozik J, Pienaar E, Rangamani P, Saglam AS, Shoemaker JE, Smith AM, Weaver JJA, Macklin P. Iterative community-driven development of a SARS-CoV-2 tissue simulator. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.04.02.019075. [PMID: 32511322 PMCID: PMC7239052 DOI: 10.1101/2020.04.02.019075] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.
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14
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Greaves RB, Chen D, Green EA. Thymic B Cells as a New Player in the Type 1 Diabetes Response. Front Immunol 2021; 12:772017. [PMID: 34745148 PMCID: PMC8566354 DOI: 10.3389/fimmu.2021.772017] [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: 09/07/2021] [Accepted: 10/01/2021] [Indexed: 12/27/2022] Open
Abstract
Type 1 diabetes (T1d) results from a sustained autoreactive T and B cell response towards insulin-producing β cells in the islets of Langerhans. The autoreactive nature of the condition has led to many investigations addressing the genetic or cellular changes in primary lymphoid tissues that impairs central tolerance- a key process in the deletion of autoreactive T and B cells during their development. For T cells, these studies have largely focused on medullary thymic epithelial cells (mTECs) critical for the effective negative selection of autoreactive T cells in the thymus. Recently, a new cellular player that impacts positively or negatively on the deletion of autoreactive T cells during their development has come to light, thymic B cells. Normally a small population within the thymus of mouse and man, thymic B cells expand in T1d as well as other autoimmune conditions, reside in thymic ectopic germinal centres and secrete autoantibodies that bind selective mTECs precipitating mTEC death. In this review we will discuss the ontogeny, characteristics and functionality of thymic B cells in healthy and autoimmune settings. Furthermore, we explore how in silico approaches may help decipher the complex cellular interplay of thymic B cells with other cells within the thymic microenvironment leading to new avenues for therapeutic intervention.
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Affiliation(s)
- Richard B Greaves
- Centre for Experimental Medicine and Biomedicine, Hull York Medical School, University of York, York, United Kingdom
| | - Dawei Chen
- Centre for Experimental Medicine and Biomedicine, Hull York Medical School, University of York, York, United Kingdom
| | - E Allison Green
- Centre for Experimental Medicine and Biomedicine, Hull York Medical School, University of York, York, United Kingdom
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15
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SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models. Processes (Basel) 2021. [DOI: 10.3390/pr9101830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.
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16
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Styles KM, Brown AT, Sagona AP. A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions. Front Microbiol 2021; 12:724767. [PMID: 34621252 PMCID: PMC8490754 DOI: 10.3389/fmicb.2021.724767] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/27/2021] [Indexed: 12/27/2022] Open
Abstract
Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the ‘post antibiotic era.’ Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in in vivo versus in vitro environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of in vitro studies for the in vivo environment, and to streamline lab work. Here, we introduce and review the current status of mathematical modeling and highlight that data on genetic heterogeneity and mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models in the future. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modelers, synergising skills and smoothing the road to regulatory approval and widespread use of phage therapy.
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Affiliation(s)
- Kathryn M Styles
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Aidan T Brown
- School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom
| | - Antonia P Sagona
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
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17
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Iranzo V, Pérez-González S. Epidemiological models and COVID-19: a comparative view. HISTORY AND PHILOSOPHY OF THE LIFE SCIENCES 2021; 43:104. [PMID: 34432152 PMCID: PMC8386152 DOI: 10.1007/s40656-021-00457-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/04/2021] [Indexed: 06/13/2023]
Abstract
Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials-some real examples are invoked-we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.
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Affiliation(s)
- Valeriano Iranzo
- Department of Philosophy, University of Valencia, Valencia, Spain
| | - Saúl Pérez-González
- Center for Logic, Language, and Cognition (LLC), Department of Philosophy and Education Sciences, University of Turin, Turin, Italy
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18
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Truong VT, Baverel PG, Lythe GD, Vicini P, Yates JWT, Dubois VFS. Step-by-step comparison of ordinary differential equation and agent-based approaches to pharmacokinetic-pharmacodynamic models. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 11:133-148. [PMID: 34399036 PMCID: PMC8846629 DOI: 10.1002/psp4.12703] [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] [Received: 02/01/2021] [Revised: 04/28/2021] [Accepted: 05/14/2021] [Indexed: 12/03/2022]
Abstract
Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach.
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Affiliation(s)
- Van Thuy Truong
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Department of Applied Mathematics, University of Leeds, Leeds, United Kingdom
| | - Paul G Baverel
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Roche Pharma Research and Early Development, Clinical Pharmacology, Pharmaceutical Sciences, Roche Innovation Center Basel F. Hoffmann-La Roche Ltd, Switzerland
| | - Grant D Lythe
- Department of Applied Mathematics, University of Leeds, Leeds, United Kingdom
| | - Paolo Vicini
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Confo Therapeutics, Technologiepark 94, 9052, Ghent (Zwijnaarde), Belgium
| | | | - Vincent F S Dubois
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK
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19
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Catching A, Capponi S, Yeh MT, Bianco S, Andino R. Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19. Sci Rep 2021; 11:15998. [PMID: 34362936 PMCID: PMC8346500 DOI: 10.1038/s41598-021-94960-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 06/23/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19's high virus transmission rates have caused a pandemic that is exacerbated by the high rates of asymptomatic and presymptomatic infections. These factors suggest that face masks and social distance could be paramount in containing the pandemic. We examined the efficacy of each measure and the combination of both measures using an agent-based model within a closed space that approximated real-life interactions. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face masks protection during inhaling and exhaling, our simulations demonstrate that a synergistic use of face masks and social distancing is the most effective intervention to curb the infection spread. To control the pandemic, our models suggest that high adherence to social distance is necessary to curb the spread of the disease, and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance. Finally, the face mask effectiveness in curbing the viral spread is not reduced if a large fraction of population is asymptomatic. Our findings have important implications for policies that dictate the reopening of social gatherings.
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Affiliation(s)
- Adam Catching
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, 94158, USA
- Graduate Program in Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Sara Capponi
- Functional Genomics and Cellular Engineering, AI and Cognitive Software, IBM Almaden Research Center, San Jose, CA, 95120, USA
- Center for Cellular Construction, San Francisco, CA, 94158, USA
| | - Ming Te Yeh
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Simone Bianco
- Functional Genomics and Cellular Engineering, AI and Cognitive Software, IBM Almaden Research Center, San Jose, CA, 95120, USA.
- Center for Cellular Construction, San Francisco, CA, 94158, USA.
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, 94158, USA.
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20
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Zhou Y, Li L, Ghasemi Y, Kallagudde R, Goyal K, Thakur D. An agent-based model for simulating COVID-19 transmissions on university campus and its implications on mitigation interventions: a case study. INFORMATION DISCOVERY AND DELIVERY 2021. [DOI: 10.1108/idd-12-2020-0154] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
Universities across the USA are facing challenging decision-making problems amid the COVID-19 pandemic. The purpose of this study is to facilitate universities in planning disease mitigation interventions as they respond to the pandemic.
Design/methodology/approach
An agent-based model is developed to mimic the virus transmission dynamics on campus. Scenario-based experiments are conducted to evaluate the effectiveness of various interventions including course modality shift (from face-to-face to online), social distancing, mask use and vaccination. A case study is performed for a typical US university.
Findings
With 10%, 30%, 50%, 70% and 90% course modality shift, the number of total cases can be reduced to 3.9%, 20.9%, 35.6%, 60.9% and 96.8%, respectively, comparing against the baseline scenario (no interventions). More than 99.9% of the total infections can be prevented when combined social distancing and mask use are implemented even without course modality shift. If vaccination is implemented without other interventions, the reductions are 57.1%, 90.6% and 99.6% with 80%, 85% and 90% vaccine efficacies, respectively. In contrast, more than 99% reductions are found with all three vaccine efficacies if mask use is combined.
Practical implications
This study provides useful implications for supporting universities in mitigating transmissions on campus and planning operations for the upcoming semesters.
Originality/value
An agent-based model is developed to investigate COVID-19 transmissions on campus and evaluate the effectiveness of various mitigation interventions.
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21
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Yu JS, Bagheri N. Agent-Based Modeling. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11509-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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22
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Sego TJ, Aponte-Serrano JO, Ferrari Gianlupi J, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness. PLoS Comput Biol 2020; 16:e1008451. [PMID: 33347439 PMCID: PMC7785254 DOI: 10.1371/journal.pcbi.1008451] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/05/2021] [Accepted: 10/20/2020] [Indexed: 12/23/2022] Open
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T. J. Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Josua O. Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Samuel R. Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Cognitive Science Program, Indiana University, Bloomington, Indiana, United States of America
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität, Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - James M. Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Ellen M. Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Richard K. Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, Georgia, United States of America
| | - James A. Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
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23
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Wallentin G, Kaziyeva D, Reibersdorfer-Adelsberger E. COVID-19 Intervention Scenarios for a Long-term Disease Management. Int J Health Policy Manag 2020; 9:508-516. [PMID: 32729281 PMCID: PMC7947653 DOI: 10.34172/ijhpm.2020.130] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 07/11/2020] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND The first outbreak of coronavirus disease 2019 (COVID-19) was successfully restrained in many countries around the world by means of a severe lockdown. Now, we are entering the second phase of the pandemics in which the spread of the virus needs to be contained within the limits that national health systems can cope with. This second phase of the epidemics is expected to last until a vaccination is available or herd immunity is reached. Long-term management strategies thus need to be developed. METHODS In this paper we present a new agent-based simulation model "COVID-19 ABM" with which we simulate 4 alternative scenarios for the second "new normality" phase that can help decision-makers to take adequate control and intervention measures. RESULTS The scenarios resulted in distinctly different outcomes. A continued lockdown could regionally eradicate the virus within a few months, whereas a relaxation back to 80% of former activity-levels was followed by a second outbreak. Contact-tracing as well as adaptive response strategies could keep COVID-19 within limits. CONCLUSION The main insights are that low-level voluntary use of tracing apps shows no relevant effects on containing the virus, whereas medium or high-level tracing allows maintaining a considerably higher level of social activity. Adaptive control strategies help in finding the level of least restrictions. A regional approach to adaptive management can further help in fine-tuning the response to regional dynamics and thus minimise negative economic effects.
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Affiliation(s)
- Gudrun Wallentin
- Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria
| | - Dana Kaziyeva
- Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria
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24
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Modeling the efficiency of filovirus entry into cells in vitro: Effects of SNP mutations in the receptor molecule. PLoS Comput Biol 2020; 16:e1007612. [PMID: 32986692 PMCID: PMC7544041 DOI: 10.1371/journal.pcbi.1007612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 10/08/2020] [Accepted: 08/03/2020] [Indexed: 11/27/2022] Open
Abstract
Interaction between filovirus glycoprotein (GP) and the Niemann-Pick C1 (NPC1) protein is essential for membrane fusion during virus entry. Some single-nucleotide polymorphism (SNPs) in two surface-exposed loops of NPC1 are known to reduce viral infectivity. However, the dependence of differences in entry efficiency on SNPs remains unclear. Using vesicular stomatitis virus pseudotyped with Ebola and Marburg virus GPs, we investigated the cell-to-cell spread of viruses in cultured cells expressing NPC1 or SNP derivatives. Eclipse and virus-producing phases were assessed by in vitro infection experiments, and we developed a mathematical model describing spatial-temporal virus spread. This mathematical model fit the plaque radius data well from day 2 to day 6. Based on the estimated parameters, we found that SNPs causing the P424A and D508N substitutions in NPC1 most effectively reduced the entry efficiency of Ebola and Marburg viruses, respectively. Our novel approach could be broadly applied to other virus plaque assays. Ebola (EBOV) and Marburg (MARV) viruses, which are included viruses of the family Filoviridae, cause severe hemorrhagic fever in humans. Filovirus particles is adsorbed to the cell through glycoprotein (GP), which is the only viral surface protein. Interaction between the filovirus sugar protein (GP) and the Niemann-Pick C1 (NPC1) protein plays a key role in membrane fusion during virus entry. Although some single-nucleotide polymorphism (SNPs) in two surface-exposed loops of NPC1 are known to reduce viral infectivity, the dependence of differences in entry efficiency on SNPs has not been studied. We therefore investigated the cell-to-cell spread of viruses in cultured cells expressing NPC1 or SNP derivatives. Using a mathematical model describing spatial-temporal virus spread, we quantitatively analyze viral entry efficiency and how this affected cell-to-cell spread. Our approach may be applied to not only understanding the roles of genetic polymorphisms in human susceptibility to filoviruses, but also other virus plaque assays.
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25
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Sego TJ, Aponte-Serrano JO, Gianlupi JF, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness: A multiscale model of viral infection in epithelial tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.04.27.064139. [PMID: 32511367 PMCID: PMC7263495 DOI: 10.1101/2020.04.27.064139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Josua O Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Samuel R Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Richard K Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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26
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Roychoudhury P, Swan DA, Duke E, Corey L, Zhu J, Davé V, Spuhler LR, Lund JM, Prlic M, Schiffer JT. Tissue-resident T cell-derived cytokines eliminate herpes simplex virus-2-infected cells. J Clin Invest 2020; 130:2903-2919. [PMID: 32125285 PMCID: PMC7260013 DOI: 10.1172/jci132583] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 02/11/2020] [Indexed: 01/19/2023] Open
Abstract
The mechanisms underlying rapid elimination of herpes simplex virus-2 (HSV-2) in the human genital tract despite low CD8+ and CD4+ tissue-resident T cell (Trm cell) density are unknown. We analyzed shedding episodes during chronic HSV-2 infection; viral clearance always predominated within 24 hours of detection even when viral load exceeded 1 × 107 HSV DNA copies, and surges in granzyme B and IFN-γ occurred within the early hours after reactivation and correlated with local viral load. We next developed an agent-based mathematical model of an HSV-2 genital ulcer to integrate mechanistic observations of Trm cells in in situ proliferation, trafficking, cytolytic effects, and cytokine alarm signaling from murine studies with viral kinetics, histopathology, and lesion size data from humans. A sufficiently high density of HSV-2-specific Trm cells predicted rapid elimination of infected cells, but our data suggest that such Trm cell densities are relatively uncommon in infected tissues. At lower, more commonly observed Trm cell densities, Trm cells must initiate a rapidly diffusing, polyfunctional cytokine response with activation of bystander T cells in order to eliminate a majority of infected cells and eradicate briskly spreading HSV-2 infection.
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Affiliation(s)
- Pavitra Roychoudhury
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Laboratory Medicine and
| | - David A. Swan
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Elizabeth Duke
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Lawrence Corey
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Laboratory Medicine and
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jia Zhu
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Laboratory Medicine and
| | - Veronica Davé
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Global Health and
| | - Laura Richert Spuhler
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jennifer M. Lund
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Global Health and
| | - Martin Prlic
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Global Health and
- Department of Immunology, University of Washington, Seattle, Washington, USA
| | - Joshua T. Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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Xiao Y, Xiang C, Cheke RA, Tang S. Coupling the Macroscale to the Microscale in a Spatiotemporal Context to Examine Effects of Spatial Diffusion on Disease Transmission. Bull Math Biol 2020; 82:58. [PMID: 32390107 PMCID: PMC7222150 DOI: 10.1007/s11538-020-00736-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 04/15/2020] [Indexed: 12/23/2022]
Abstract
There are many challenges to coupling the macroscale to the microscale in temporal or spatial contexts. In order to examine effects of an individual movement and spatial control measures on a disease outbreak, we developed a multiscale model and extended the semi-stochastic simulation method by linking individual movements to pathogen’s diffusion, linking the slow dynamics for disease transmission at the population level to the fast dynamics for pathogen shedding/excretion at the individual level. Numerical simulations indicate that during a disease outbreak individuals with the same infection status show the property of clustering and, in particular, individuals’ rapid movements lead to an increase in the average reproduction number \documentclass[12pt]{minimal}
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\begin{document}$$R_0$$\end{document}R0, the final size and the peak value of the outbreak. It is interesting that a high level of aggregation the individuals’ movement results in low new infections and a small final size of the infected population. Further, we obtained that either high diffusion rate of the pathogen or frequent environmental clearance lead to a decline in the total number of infected individuals, indicating the need for control measures such as improving air circulation or environmental hygiene. We found that the level of spatial heterogeneity when implementing control greatly affects the control efficacy, and in particular, an uniform isolation strategy leads to low a final size and small peak, compared with local measures, indicating that a large-scale isolation strategy with frequent clearance of the environment is beneficial for disease control.
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Affiliation(s)
- Yanni Xiao
- School of Mathematics and Stastics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Changcheng Xiang
- School of Science, Hubei University for Nationalities, Enshi, People's Republic of China
| | - Robert A Cheke
- Natural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Kent, ME4 4TB, UK
| | - Sanyi Tang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an, 710062, People's Republic of China.
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Environmental Restrictions: A New Concept Governing HIV-1 Spread Emerging from Integrated Experimental-Computational Analysis of Tissue-Like 3D Cultures. Cells 2020; 9:cells9051112. [PMID: 32365826 PMCID: PMC7291240 DOI: 10.3390/cells9051112] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 04/27/2020] [Accepted: 04/28/2020] [Indexed: 12/22/2022] Open
Abstract
HIV-1 can use cell-free and cell-associated transmission modes to infect new target cells, but how the virus spreads in the infected host remains to be determined. We recently established 3D collagen cultures to study HIV-1 spread in tissue-like environments and applied iterative cycles of experimentation and computation to develop a first in silico model to describe the dynamics of HIV-1 spread in complex tissue. These analyses (i) revealed that 3D collagen environments restrict cell-free HIV-1 infection but promote cell-associated virus transmission and (ii) defined that cell densities in tissue dictate the efficacy of these transmission modes for virus spread. In this review, we discuss, in the context of the current literature, the implications of this study for our understanding of HIV-1 spread in vivo, which aspects of in vivo physiology this integrated experimental-computational analysis takes into account, and how it can be further improved experimentally and in silico.
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29
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Quirouette C, Younis NP, Reddy MB, Beauchemin CAA. A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract. PLoS Comput Biol 2020; 16:e1007705. [PMID: 32282797 PMCID: PMC7179943 DOI: 10.1371/journal.pcbi.1007705] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 04/23/2020] [Accepted: 01/31/2020] [Indexed: 12/20/2022] Open
Abstract
Within the human respiratory tract (HRT), virus diffuses through the periciliary fluid (PCF) bathing the epithelium. But virus also undergoes advection: as the mucus layer sitting atop the PCF is pushed along by the ciliated cell's beating cilia, the PCF and its virus content are also pushed along, upwards towards the nose and mouth. While many mathematical models (MMs) have described the course of influenza A virus (IAV) infections in vivo, none have considered the impact of both diffusion and advection on the kinetics and localization of the infection. The MM herein represents the HRT as a one-dimensional track extending from the nose down towards the lower HRT, wherein stationary cells interact with IAV which moves within (diffusion) and along with (advection) the PCF. Diffusion was found to be negligible in the presence of advection which effectively sweeps away IAV, preventing infection from disseminating below the depth at which virus first deposits. Higher virus production rates (10-fold) are required at higher advection speeds (40 μm/s) to maintain equivalent infection severity and timing. Because virus is entrained upwards, upper parts of the HRT see more virus than lower parts. As such, infection peaks and resolves faster in the upper than in the lower HRT, making it appear as though infection progresses from the upper towards the lower HRT, as reported in mice. When the spatial MM is expanded to include cellular regeneration and an immune response, it reproduces tissue damage levels reported in patients. It also captures the kinetics of seasonal and avian IAV infections, via parameter changes consistent with reported differences between these strains, enabling comparison of their treatment with antivirals. This new MM offers a convenient and unique platform from which to study the localization and spread of respiratory viral infections within the HRT.
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Affiliation(s)
| | - Nada P. Younis
- Department of Physics, Ryerson University, Toronto, Ontario, Canada
| | - Micaela B. Reddy
- Array BioPharma Inc., Boulder, Colorado, United States of America
| | - Catherine A. A. Beauchemin
- Department of Physics, Ryerson University, Toronto, Ontario, Canada
- Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), RIKEN, Wako, Japan
- * E-mail:
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30
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Fribourg M. A case for the reuse and adaptation of mechanistic computational models to study transplant immunology. Am J Transplant 2020; 20:355-361. [PMID: 31562790 PMCID: PMC6984985 DOI: 10.1111/ajt.15623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 02/06/2023]
Abstract
Computational mechanistic models constitute powerful tools for summarizing our knowledge in quantitative terms, providing mechanistic understanding, and generating new hypotheses. The present review emphasizes the advantages of reusing publicly available computational models as a way to capitalize on existing knowledge, reduce the number of parameters that need to be adjusted to experimental data, and facilitate hypothesis generation. Finally, it includes a step-by-step example of the reuse and adaptation of an existing model of immune responses to tuberculosis, tumor growth, and blood pathogens, to study donor-specific antibody (DSA) responses. This review aims to illustrate the benefit of leveraging the currently available computational models in immunology to accelerate the study of alloimmune responses, and to encourage modelers to share their models to further advance our understanding of transplant immunology.
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Affiliation(s)
- Miguel Fribourg
- Translational Transplant Research Center, Department of Medicine, and Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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31
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Hao H, Silvestre D, Silvestre C. A microscopic-view Infection model based on linear systems. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.09.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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32
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Ewald J, Sieber P, Garde R, Lang SN, Schuster S, Ibrahim B. Trends in mathematical modeling of host-pathogen interactions. Cell Mol Life Sci 2020; 77:467-480. [PMID: 31776589 PMCID: PMC7010650 DOI: 10.1007/s00018-019-03382-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/05/2019] [Accepted: 11/12/2019] [Indexed: 12/18/2022]
Abstract
Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.
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Affiliation(s)
- Jan Ewald
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Patricia Sieber
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Ravindra Garde
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
- Max Planck Institute for Chemical Ecology, Hans-Knöll-Str. 8, 07745, Jena, Germany
| | - Stefan N Lang
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Stefan Schuster
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
| | - Bashar Ibrahim
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
- Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, 32093, Hawally, Kuwait.
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33
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A personalized computational model of edema formation in myocarditis based on long-axis biventricular MRI images. BMC Bioinformatics 2019; 20:532. [PMID: 31822264 PMCID: PMC6905016 DOI: 10.1186/s12859-019-3139-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 10/09/2019] [Indexed: 12/25/2022] Open
Abstract
Background Myocarditis is defined as the inflammation of the myocardium, i.e. the cardiac muscle. Among the reasons that lead to this disease, we may include infections caused by a virus, bacteria, protozoa, fungus, and others. One of the signs of the inflammation is the formation of edema, which may be a consequence of the interaction between interstitial fluid dynamics and immune response. This complex physiological process was mathematically modeled using a nonlinear system of partial differential equations (PDE) based on porous media approach. By combing a model based on Biot’s poroelasticity theory with a model for the immune response we developed a new hydro-mechanical model for inflammatory edema. To verify this new computational model, T2 parametric mapping obtained by Magnetic Resonance (MR) imaging was used to identify the region of edema in a patient diagnosed with unspecific myocarditis. Results A patient-specific geometrical model was created using MRI images from the patient with myocarditis. With this model, edema formation was simulated using the proposed hydro-mechanical mathematical model in a two-dimensional domain. The computer simulations allowed us to correlate spatiotemporal dynamics of representative cells of the immune systems, such as leucocytes and the pathogen, with fluid accumulation and cardiac tissue deformation. Conclusions This study demonstrates that the proposed mathematical model is a very promising tool to better understand edema formation in myocarditis. Simulations obtained from a patient-specific model reproduced important aspects related to the formation of cardiac edema, its area, position, and shape, and how these features are related to immune response.
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34
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35
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Liberman A, Mussel M, Kario D, Sprinzak D, Nevo U. Modelling cell surface dynamics and cell-cell interactions using Cell Studio: a three-dimensional visualization tool based on gaming technology. J R Soc Interface 2019; 16:20190264. [PMID: 31771451 DOI: 10.1098/rsif.2019.0264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Predictive modelling of complex biological systems and biophysical interactions requires the inclusion of multiple nano- and micro-scale events. In many scenarios, however, numerical solutions alone do not necessarily enhance the understanding of the system. Instead, this work explores the use of an agent-based model with visualization capabilities to elucidate interactions between single cells. We present a model of juxtacrine signalling, using Cell Studio, an agent-based modelling system, based on gaming and three-dimensional visualization tools. The main advantages of the system are its ability to apply any cell geometry and to dynamically visualize the diffusion and interactions of the molecules within the cells in real time. These provide an excellent tool for obtaining insight about different biological scenarios, as the user may view the dynamics of a system and observe its emergent behaviour as it unfolds. The agent-based model was validated against the results of a mean-field model of Notch receptors and ligands in two neighbouring cells. The conversion to an agent-based model is described in detail. To demonstrate the advantages of the model, we further created a filopodium-mediated signalling model. Our model revealed that diffusion and endocytosis alone are insufficient to produce significant signalling in a filopodia scenario. This is due to the bottleneck at the cell-filopodium contact region and the long distance to the end of the filopodium. However, allowing active transport of ligands into filopodia enhances the signalling significantly compared with a face-to-face scenario. We conclude that the agent-based approach can provide insights into mechanisms underlying cell signalling. The open-source model can be found in the Internet hosting service GitHub.
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Affiliation(s)
- Asaf Liberman
- The Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Matan Mussel
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Danny Kario
- The Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - David Sprinzak
- The Department of Biochemistry and Molecular Biology, Wise Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Uri Nevo
- The Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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36
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Metzner C. On the efficiency of chemotactic pursuit - Comparing blind search with temporal and spatial gradient sensing. Sci Rep 2019; 9:14091. [PMID: 31575917 PMCID: PMC6773759 DOI: 10.1038/s41598-019-50514-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 09/13/2019] [Indexed: 02/04/2023] Open
Abstract
In chemotaxis, cells are modulating their migration patterns in response to concentration gradients of a guiding substance. Immune cells are believed to use such chemotactic sensing for remotely detecting and homing in on pathogens. Considering that immune cells may encounter a multitude of targets with vastly different migration properties, ranging from immobile to highly mobile, it is not clear which strategies of chemotactic pursuit are simultaneously efficient and versatile. We tackle this problem theoretically and define a tunable response function that maps temporal or spatial concentration gradients to migration behavior. The seven free parameters of this response function are optimized numerically with the objective of maximizing search efficiency against a wide spectrum of target cell properties. Finally, we reverse-engineer the best-performing parameter sets to uncover strategies of chemotactic pursuit that are efficient under different biologically realistic boundary conditions. Although strategies based on the temporal or spatial sensing of chemotactic gradients are significantly more efficient than unguided migration, such ‘blind search’ turns out to work surprisingly well, in particular if the immune cells are fast and directionally persistent. The resulting simulated data can be used for the design of chemotaxis experiments and for the development of algorithms that automatically detect and quantify goal oriented behavior in measured immune cell trajectories.
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Affiliation(s)
- Claus Metzner
- Biophysics Group, Department of Physics, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.
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37
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Das J, Lanier LL. Data analysis to modeling to building theory in NK cell biology and beyond: How can computational modeling contribute? J Leukoc Biol 2019; 105:1305-1317. [PMID: 31063614 DOI: 10.1002/jlb.6mr1218-505r] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 03/25/2019] [Accepted: 04/03/2019] [Indexed: 12/31/2022] Open
Abstract
The use of mathematical and computational tools in investigating Natural Killer (NK) cell biology and in general the immune system has increased steadily in the last few decades. However, unlike the physical sciences, there is a persistent ambivalence, which however is increasingly diminishing, in the biology community toward appreciating the utility of quantitative tools in addressing questions of biological importance. We survey some of the recent developments in the application of quantitative approaches for investigating different problems in NK cell biology and evaluate opportunities and challenges of using quantitative methods in providing biological insights in NK cell biology.
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Affiliation(s)
- Jayajit Das
- Battelle Center for Mathematical Medicine, Research Institute at the Nationwide Children's Hospital, Columbus, Ohio, USA.,Department of Pediatrics, The Ohio State University, Columbus, Ohio, USA.,Department of Physics, The Ohio State University, Columbus, Ohio, USA.,Biophysics Program, The Ohio State University, Columbus, Ohio, USA
| | - Lewis L Lanier
- Department of Microbiology and Immunology and the Parker Institute for Cancer Immunotherapy, University of California, San Francisco, California, USA
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38
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Liang Q, Zhuang H, Lu M, Wang Q, Attalage D, Hsu SC, Chen WH, Xing D, Lee PH. Multi-agent simulation regulated by microbe-oriented thermodynamics and kinetics equations for exploiting interspecies dynamics and evolution between methanogenesis, sulfidogenesis, hydrogenesis and exoelectrogenesis. JOURNAL OF HAZARDOUS MATERIALS 2019; 366:573-581. [PMID: 30572297 DOI: 10.1016/j.jhazmat.2018.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 11/10/2018] [Accepted: 12/05/2018] [Indexed: 06/09/2023]
Abstract
Multi-agent simulation (MAS) regulated by microbe-oriented thermodynamics and kinetics equations were performed for exploiting the interspecies dynamics and evolution in anaerobic respiration and bioelectrochemical systems. A newly-defined kinetically thermodynamic parameter is recognized microbes as agents in various conditions, including electron donors and acceptors, temperature, pH, etc. For verification of the MAS, the treatment of synthetic wastewater containing glucose and acetate was evaluated in four 25°C laboratory-scale reactors with different electron acceptors and cathode materials that had potential for methanogenesis, hydrogenesis, sulfidogenesis and exoelectrogenesis. Within 1000 h operation, the reactors performance and microbial structures using 16S rRNA sequencing matched with the MAS, suggesting acetoclastic exoelectrogenesis predominance (Geobacter). After 2400 h, MAS observed the co-existence of acetoclastic methanogenesis and acetoclastic and propionate exoelectrogenesis, as was reported previously. Such microbial evolution from the short-term to long-term operation likely resulted from the glucose-driven propionate. The MAS developed is applicable in a wide range of complex engineering and natural ecosystems.
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Affiliation(s)
- Qing Liang
- School of Environment, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Environment, Harbin Institute of Technology, P.O. Box 2614, 73 Huanghe Road, Nangang District, Harbin, Heilongjiang Province 150090, China; Department. of Civil and Environmental Engineering, Hong Kong Polytechnic University, Office ZS919, Phase 8 Development, Hong Kong
| | - Huichuan Zhuang
- Department. of Civil and Environmental Engineering, Hong Kong Polytechnic University, Office ZS919, Phase 8 Development, Hong Kong
| | - Miaojia Lu
- Department. of Civil and Environmental Engineering, Hong Kong Polytechnic University, Office ZS919, Phase 8 Development, Hong Kong
| | - Qian Wang
- Department. of Civil and Environmental Engineering, Hong Kong Polytechnic University, Office ZS919, Phase 8 Development, Hong Kong
| | - Dinu Attalage
- Department. of Civil and Environmental Engineering, Hong Kong Polytechnic University, Office ZS919, Phase 8 Development, Hong Kong
| | - Shu-Chien Hsu
- Department. of Civil and Environmental Engineering, Hong Kong Polytechnic University, Office ZS919, Phase 8 Development, Hong Kong
| | - Wen-Hsing Chen
- Department of Environmental Engineering, National Ilan University, Yilan 260, Taiwan
| | - Defeng Xing
- School of Environment, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Environment, Harbin Institute of Technology, P.O. Box 2614, 73 Huanghe Road, Nangang District, Harbin, Heilongjiang Province 150090, China.
| | - Po-Heng Lee
- Department. of Civil and Environmental Engineering, Hong Kong Polytechnic University, Office ZS919, Phase 8 Development, Hong Kong.
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Shinde SB, Kurhekar MP. Review of the systems biology of the immune system using agent-based models. IET Syst Biol 2019; 12:83-92. [PMID: 29745901 DOI: 10.1049/iet-syb.2017.0073] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.
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Affiliation(s)
- Snehal B Shinde
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India.
| | - Manish P Kurhekar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
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40
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Petersen BK, Yang J, Grathwohl WS, Cockrell C, Santiago C, An G, Faissol DM. Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. J Comput Biol 2019; 26:597-604. [PMID: 30681362 DOI: 10.1089/cmb.2018.0168] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Traditionally, precision medicine involves classifying patients to identify subpopulations that respond favorably to specific therapeutics. We pose precision medicine as a dynamic feedback control problem, where treatment administered to a patient is guided by measurements taken during the course of treatment. We consider sepsis, a life-threatening condition in which dysregulation of the immune system causes tissue damage. We leverage an existing simulation of the innate immune response to infection and apply deep reinforcement learning (DRL) to discover an adaptive personalized treatment policy that specifies effective multicytokine therapy to simulated sepsis patients based on systemic measurements. The learned policy achieves a dramatic reduction in mortality rate over a set of 500 simulated patients relative to standalone antibiotic therapy. Advantages of our approach are threefold: (1) the use of simulation allows exploring therapeutic strategies beyond clinical practice and available data, (2) advances in DRL accommodate learning complex therapeutic strategies for complex biological systems, and (3) optimized treatments respond to a patient's individual disease progression over time, therefore, capturing both differences across patients and the inherent randomness of disease progression within a single patient. We hope that this work motivates both considering adaptive personalized multicytokine mediation therapy for sepsis and exploiting simulation with DRL for precision medicine more broadly.
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Affiliation(s)
- Brenden K Petersen
- 1 Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California
| | - Jiachen Yang
- 1 Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California
| | - Will S Grathwohl
- 1 Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California
| | - Chase Cockrell
- 2 Department of Surgery, University of Vermont, Burlington, Vermont
| | - Claudio Santiago
- 1 Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California
| | - Gary An
- 2 Department of Surgery, University of Vermont, Burlington, Vermont
| | - Daniel M Faissol
- 1 Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California
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41
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Gallagher ME, Brooke CB, Ke R, Koelle K. Causes and Consequences of Spatial Within-Host Viral Spread. Viruses 2018; 10:E627. [PMID: 30428545 PMCID: PMC6267451 DOI: 10.3390/v10110627] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/08/2018] [Accepted: 11/10/2018] [Indexed: 02/07/2023] Open
Abstract
The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.
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Affiliation(s)
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
| | - Ruian Ke
- T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Katia Koelle
- Department of Biology, Emory University, Atlanta, GA 30322, USA.
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42
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Physiological factors leading to a successful vaccination: A computational approach. J Theor Biol 2018; 454:215-230. [PMID: 29894721 DOI: 10.1016/j.jtbi.2018.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 06/01/2018] [Accepted: 06/06/2018] [Indexed: 11/23/2022]
Abstract
The immune system mounts a response to an infection by activating T cells. T cell activation occurs when dendritic cells, which have already interacted with the pathogen, scan a T cell that is cognate for (responsive to) the pathogen. This often occurs inside lymph nodes. The time it takes for this scanning event to occur, indeed the probability that it will occur at all, depends on many factors, including the rate that T cells and dendritic cells enter and leave the lymph node as well as the geometry of the lymph node and of course other cellular and molecular parameters. In this paper, we develop a hybrid stochastic-deterministic mathematical model at the tissue scale of the lymph node and simulate dendritic cells and cognate T cells to investigate the most important physiological factors leading to a successful and timely immune response after a vaccination. We use an agent-based model to describe the small population of cognate naive T cells and a partial differential equation description for the concentration of mature dendritic cells. We estimate the model parameters based on the known literature and measurements previously taken in our lab. We perform a parameter sensitivity analysis to quantify the sensitivity of the model results to the parameters. The results show that increasing T cell inflow through high endothelial venules, restricting cellular egress via the efferent lymph and increasing the total dendritic cell count by improving vaccinations are the among the most important physiological factors leading to an improved immune response. We also find that increasing the physical size of lymph nodes improves the overall likelihood that an immune response will take place but has a fairly weak effect on the response rate. The nature of dendritic cell trafficking through the LN (either passive or active transport) seems to have little effect on the overall immune response except if a change in overall egress time is observed.
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Bai F, Huff KES, Allen LJS. The effect of delay in viral production in within-host models during early infection. JOURNAL OF BIOLOGICAL DYNAMICS 2018; 13:47-73. [PMID: 30021482 DOI: 10.1080/17513758.2018.1498984] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 06/29/2018] [Indexed: 06/08/2023]
Abstract
Delay in viral production may have a significant impact on the early stages of infection. During the eclipse phase, the time from viral entry until active production of viral particles, no viruses are produced. This delay affects the probability that a viral infection becomes established and timing of the peak viral load. Deterministic and stochastic models are formulated with either multiple latent stages or a fixed delay for the eclipse phase. The deterministic model with multiple latent stages approaches in the limit the model with a fixed delay as the number of stages approaches infinity. The deterministic model framework is used to formulate continuous-time Markov chain and stochastic differential equation models. The probability of a minor infection with rapid viral clearance as opposed to a major full-blown infection with a high viral load is estimated from a branching process approximation of the Markov chain model and the results are confirmed through numerical simulations. In addition, parameter values for influenza A are used to numerically estimate the time to peak viral infection and peak viral load for the deterministic and stochastic models. Although the average length of the eclipse phase is the same in each of the models, as the number of latent stages increases, the numerical results show that the time to viral peak and the peak viral load increase.
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Affiliation(s)
- Fan Bai
- a Department of Mathematics and Statistics, Texas Tech University , Lubbock , TX , USA
| | - Krystin E S Huff
- a Department of Mathematics and Statistics, Texas Tech University , Lubbock , TX , USA
| | - Linda J S Allen
- a Department of Mathematics and Statistics, Texas Tech University , Lubbock , TX , USA
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Getz WM, Salter R, Muellerklein O, Yoon HS, Tallam K. Modeling epidemics: A primer and Numerus Model Builder implementation. Epidemics 2018; 25:9-19. [PMID: 30017895 DOI: 10.1016/j.epidem.2018.06.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 05/30/2018] [Accepted: 06/01/2018] [Indexed: 01/09/2023] Open
Abstract
Epidemiological models are dominated by compartmental models, of which SIR formulations are the most commonly used. These formulations can be continuous or discrete (in either the state-variable values or time), deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SIR dynamical systems models, and we outline how they can be easily and rapidly constructed using Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using NMB network and mapping tools.
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Affiliation(s)
- Wayne M Getz
- Dept. ESPM, UC Berkeley, CA 94720-3114, USA; School of Mathematical Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa; Numerus, 850 Iron Point Rd., Folsom, CA 95630, USA.
| | - Richard Salter
- Numerus, 850 Iron Point Rd., Folsom, CA 95630, USA; Computer Science Dept., Oberlin College, Oberlin, OH 44074, USA
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45
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Zitzmann C, Kaderali L. Mathematical Analysis of Viral Replication Dynamics and Antiviral Treatment Strategies: From Basic Models to Age-Based Multi-Scale Modeling. Front Microbiol 2018; 9:1546. [PMID: 30050523 PMCID: PMC6050366 DOI: 10.3389/fmicb.2018.01546] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 06/21/2018] [Indexed: 12/14/2022] Open
Abstract
Viral infectious diseases are a global health concern, as is evident by recent outbreaks of the middle east respiratory syndrome, Ebola virus disease, and re-emerging zika, dengue, and chikungunya fevers. Viral epidemics are a socio-economic burden that causes short- and long-term costs for disease diagnosis and treatment as well as a loss in productivity by absenteeism. These outbreaks and their socio-economic costs underline the necessity for a precise analysis of virus-host interactions, which would help to understand disease mechanisms and to develop therapeutic interventions. The combination of quantitative measurements and dynamic mathematical modeling has increased our understanding of the within-host infection dynamics and has led to important insights into viral pathogenesis, transmission, and disease progression. Furthermore, virus-host models helped to identify drug targets, to predict the treatment duration to achieve cure, and to reduce treatment costs. In this article, we review important achievements made by mathematical modeling of viral kinetics on the extracellular, intracellular, and multi-scale level for Human Immunodeficiency Virus, Hepatitis C Virus, Influenza A Virus, Ebola Virus, Dengue Virus, and Zika Virus. Herein, we focus on basic mathematical models on the population scale (so-called target cell-limited models), detailed models regarding the most important steps in the viral life cycle, and the combination of both. For this purpose, we review how mathematical modeling of viral dynamics helped to understand the virus-host interactions and disease progression or clearance. Additionally, we review different types and effects of therapeutic strategies and how mathematical modeling has been used to predict new treatment regimens.
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Affiliation(s)
- Carolin Zitzmann
- Institute of Bioinformatics and Center for Functional Genomics of Microbes, University Medicine Greifswald, Greifswald, Germany
| | - Lars Kaderali
- Institute of Bioinformatics and Center for Functional Genomics of Microbes, University Medicine Greifswald, Greifswald, Germany
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46
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Carruthers J, López-García M, Gillard JJ, Laws TR, Lythe G, Molina-París C. A Novel Stochastic Multi-Scale Model of Francisella tularensis Infection to Predict Risk of Infection in a Laboratory. Front Microbiol 2018; 9:1165. [PMID: 30034369 PMCID: PMC6043654 DOI: 10.3389/fmicb.2018.01165] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 05/14/2018] [Indexed: 12/30/2022] Open
Abstract
We present a multi-scale model of the within-phagocyte, within-host and population-level infection dynamics of Francisella tularensis, which extends the mechanistic one proposed by Wood et al. (2014). Our multi-scale model incorporates key aspects of the interaction between host phagocytes and extracellular bacteria, accounts for inter-phagocyte variability in the number of bacteria released upon phagocyte rupture, and allows one to compute the probability of response, and mean time until response, of an infected individual as a function of the initial infection dose. A Bayesian approach is applied to parameterize both the within-phagocyte and within-host models using infection data. Finally, we show how dose response probabilities at the individual level can be used to estimate the airborne propagation of Francisella tularensis in indoor settings (such as a microbiology laboratory) at the population level, by means of a deterministic zonal ventilation model.
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Affiliation(s)
- Jonathan Carruthers
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, United Kingdom
| | - Martín López-García
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, United Kingdom
| | - Joseph J. Gillard
- Defence Science and Technology Laboratory, Salisbury, United Kingdom
| | - Thomas R. Laws
- Defence Science and Technology Laboratory, Salisbury, United Kingdom
| | - Grant Lythe
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, United Kingdom
| | - Carmen Molina-París
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, United Kingdom
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47
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Castro C, Flores DL, Cervantes-Vásquez D, Vargas-Viveros E, Gutiérrez-López E, Muñoz-Muñoz F. An agent-based model of the fission yeast cell cycle. Curr Genet 2018; 65:193-200. [PMID: 29916047 DOI: 10.1007/s00294-018-0859-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 06/13/2018] [Accepted: 06/15/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Carlos Castro
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Dora-Luz Flores
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico.
| | - David Cervantes-Vásquez
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Eunice Vargas-Viveros
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Everardo Gutiérrez-López
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Franklin Muñoz-Muñoz
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
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Liberman A, Kario D, Mussel M, Brill J, Buetow K, Efroni S, Nevo U. Cell studio: A platform for interactive, 3D graphical simulation of immunological processes. APL Bioeng 2018; 2:026107. [PMID: 31069304 PMCID: PMC6481718 DOI: 10.1063/1.5039473] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 05/04/2018] [Indexed: 12/27/2022] Open
Abstract
The field of computer modeling and simulation of biological systems is rapidly advancing, backed by significant progress in the fields of experimentation techniques, computer hardware, and programming software. The result of a simulation may be delivered in several ways, from numerical results, through graphs of the simulated run, to a visualization of the simulation. The vision of an in-silico experiment mimicking an in-vitro or in-vivo experiment as it is viewed under a microscope is appealing but technically demanding and computationally intensive. Here, we report “Cell Studio,” a generic, hybrid platform to simulate an immune microenvironment with biological and biophysical rules. We use game engines—generic programs for game creation which offer ready-made assets and tools—to create a visualized, interactive 3D simulation. We also utilize a scalable architecture that delegates the computational load to a server. The user may view the simulation, move the “camera” around, stop, fast-forward, and rewind it and inject soluble molecules into the extracellular medium at any point in time. During simulation, graphs are created in real time for a broad view of system-wide processes. The model is parametrized using a user-friendly Graphical User Interface (GUI). We show a simple validation simulation and compare its results with those from a “classical” simulation, validated against a “wet” experiment. We believe that interactive, real-time 3D visualization may aid in generating insights from the model and encourage intuition about the immunological scenario.
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Affiliation(s)
- Asaf Liberman
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | | | - Matan Mussel
- Physics Department, TU Dortmund University, Dortmund 44227, Germany
| | - Jacob Brill
- Arizona State University, Tempe, Arizona 85281, USA
| | | | - Sol Efroni
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan 52900, Israel
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49
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Madonia A, Melchiorri C, Bonamano S, Marcelli M, Bulfon C, Castiglione F, Galeotti M, Volpatti D, Mosca F, Tiscar PG, Romano N. Computational modeling of immune system of the fish for a more effective vaccination in aquaculture. Bioinformatics 2018; 33:3065-3071. [PMID: 28549079 DOI: 10.1093/bioinformatics/btx341] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2017] [Accepted: 05/24/2017] [Indexed: 01/06/2023] Open
Abstract
Motivation A computational model equipped with the main immunological features of the sea bass (Dicentrarchus labrax L.) immune system was used to predict more effective vaccination in fish. The performance of the model was evaluated by using the results of two in vivo vaccinations trials against L. anguillarum and P. damselae. Results Tests were performed to select the appropriate doses of vaccine and infectious bacteria to set up the model. Simulation outputs were compared with the specific antibody production and the expression of BcR and TcR gene transcripts in spleen. The model has shown a good ability to be used in sea bass and could be implemented for different routes of vaccine administration even with more than two pathogens. The model confirms the suitability of in silico methods to optimize vaccine doses and the immune response to them. This model could be applied to other species to optimize the design of new vaccination treatments of fish in aquaculture. Availability and implementation The method is available at http://www.iac.cnr.it/∼filippo/c-immsim/. Contact nromano@unitus.it. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alice Madonia
- Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy
| | - Cristiano Melchiorri
- Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy
| | - Simone Bonamano
- Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy
| | - Marco Marcelli
- Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy
| | - Chiara Bulfon
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), Section of Animal and Veterinary Sciences, University of Udine, 33100, Italy
| | | | - Marco Galeotti
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), Section of Animal and Veterinary Sciences, University of Udine, 33100, Italy
| | - Donatella Volpatti
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), Section of Animal and Veterinary Sciences, University of Udine, 33100, Italy
| | - Francesco Mosca
- Institute of Applied Computing "M.Picone", CNR, 00185, Rome, Italy
| | | | - Nicla Romano
- Department of Ecological and Biological Sciences, Tuscia University, 01100, Viterbo, Italy
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50
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Abstract
When a virus infects a host cell, it hijacks the biosynthetic capacity of the cell to produce virus progeny, a process that may take less than an hour or more than a week. The overall time required for a virus to reproduce depends collectively on the rates of multiple steps in the infection process, including initial binding of the virus particle to the surface of the cell, virus internalization and release of the viral genome within the cell, decoding of the genome to make viral proteins, replication of the genome, assembly of progeny virus particles, and release of these particles into the extracellular environment. For a large number of virus types, much has been learned about the molecular mechanisms and rates of the various steps. However, in only relatively few cases during the last 50 years has an attempt been made-using mathematical modeling-to account for how the different steps contribute to the overall timing and productivity of the infection cycle in a cell. Here we review the initial case studies, which include studies of the one-step growth behavior of viruses that infect bacteria (Qβ, T7, and M13), human immunodeficiency virus, influenza A virus, poliovirus, vesicular stomatitis virus, baculovirus, hepatitis B and C viruses, and herpes simplex virus. Further, we consider how such models enable one to explore how cellular resources are utilized and how antiviral strategies might be designed to resist escape. Finally, we highlight challenges and opportunities at the frontiers of cell-level modeling of virus infections.
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Affiliation(s)
- John Yin
- Department of Chemical and Biological Engineering, Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jacob Redovich
- Department of Chemical and Biological Engineering, Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA
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