1
|
Bouguéon M, Legagneux V, Hazard O, Bomo J, Siegel A, Feret J, Théret N. A rule-based multiscale model of hepatic stellate cell plasticity: Critical role of the inactivation loop in fibrosis progression. PLoS Comput Biol 2024; 20:e1011858. [PMID: 39074160 PMCID: PMC11309422 DOI: 10.1371/journal.pcbi.1011858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 08/08/2024] [Accepted: 07/05/2024] [Indexed: 07/31/2024] Open
Abstract
Hepatic stellate cells (HSC) are the source of extracellular matrix (ECM) whose overproduction leads to fibrosis, a condition that impairs liver functions in chronic liver diseases. Understanding the dynamics of HSCs will provide insights needed to develop new therapeutic approaches. Few models of hepatic fibrosis have been proposed, and none of them include the heterogeneity of HSC phenotypes recently highlighted by single-cell RNA sequencing analyses. Here, we developed rule-based models to study HSC dynamics during fibrosis progression and reversion. We used the Kappa graph rewriting language, for which we used tokens and counters to overcome temporal explosion. HSCs are modeled as agents that present seven physiological cellular states and that interact with (TGFβ1) molecules which regulate HSC activation and the secretion of type I collagen, the main component of the ECM. Simulation studies revealed the critical role of the HSC inactivation process during fibrosis progression and reversion. While inactivation allows elimination of activated HSCs during reversion steps, reactivation loops of inactivated HSCs (iHSCs) are required to sustain fibrosis. Furthermore, we demonstrated the model's sensitivity to (TGFβ1) parameters, suggesting its adaptability to a variety of pathophysiological conditions for which levels of (TGFβ1) production associated with the inflammatory response differ. Using new experimental data from a mouse model of CCl4-induced liver fibrosis, we validated the predicted ECM dynamics. Our model also predicts the accumulation of iHSCs during chronic liver disease. By analyzing RNA sequencing data from patients with non-alcoholic steatohepatitis (NASH) associated with liver fibrosis, we confirmed this accumulation, identifying iHSCs as novel markers of fibrosis progression. Overall, our study provides the first model of HSC dynamics in chronic liver disease that can be used to explore the regulatory role of iHSCs in liver homeostasis. Moreover, our model can also be generalized to fibroblasts during repair and fibrosis in other tissues.
Collapse
Affiliation(s)
- Matthieu Bouguéon
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
| | | | - Octave Hazard
- École Polytechnique, Palaiseau, France
- DI-ENS (Inria, ÉNS, CNRS, PSL University), École normale supérieure, Paris, France
| | - Jérémy Bomo
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jérôme Feret
- DI-ENS (Inria, ÉNS, CNRS, PSL University), École normale supérieure, Paris, France
- Team Antique, Inria, Paris, France
| | - Nathalie Théret
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
| |
Collapse
|
2
|
Doran JWG, Thompson RN, Yates CA, Bowness R. Mathematical methods for scaling from within-host to population-scale in infectious disease systems. Epidemics 2023; 45:100724. [PMID: 37976680 DOI: 10.1016/j.epidem.2023.100724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/29/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases.
Collapse
Affiliation(s)
- James W G Doran
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom.
| | - Robin N Thompson
- Mathematics Institute, Zeeman Building, University of Warwick, Coventry, CV4 7AL, United Kingdom; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, CV4 7AL, United Kingdom; Mathematical Institute, University of Oxford, Oxford, OX2 6GG, United Kingdom
| | - Christian A Yates
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom
| | - Ruth Bowness
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom
| |
Collapse
|
3
|
Costa B, Vale N. Modulating Immune Response in Viral Infection for Quantitative Forecasts of Drug Efficacy. Pharmaceutics 2023; 15:pharmaceutics15010167. [PMID: 36678799 PMCID: PMC9867121 DOI: 10.3390/pharmaceutics15010167] [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] [Received: 11/10/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 01/05/2023] Open
Abstract
The antiretroviral drug, the total level of viral production, and the effectiveness of immune responses are the main topics of this review because they are all dynamically interrelated. Immunological and viral processes interact in extremely complex and non-linear ways. For reliable analysis and quantitative forecasts that may be used to follow the immune system and create a disease profile for each patient, mathematical models are helpful in characterizing these non-linear interactions. To increase our ability to treat patients and identify individual differences in disease development, immune response profiling might be useful. Identifying which patients are moving from mild to severe disease would be more beneficial using immune system parameters. Prioritize treatments based on their inability to control the immune response and prevent T cell exhaustion. To increase treatment efficacy and spur additional research in this field, this review intends to provide examples of the effects of modelling immune response in viral infections, as well as the impact of pharmaceuticals on immune response.
Collapse
Affiliation(s)
- Bárbara Costa
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal
- CINTESIS@RISE, Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal
| | - Nuno Vale
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal
- CINTESIS@RISE, Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal
- Correspondence: ; Tel.: +351-220426537
| |
Collapse
|
4
|
Sanz-Leon P, Hamilton LHW, Raison SJ, Pan AJX, Stevenson NJ, Stuart RM, Abeysuriya RG, Kerr CC, Lambert SB, Roberts JA. Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210311. [PMID: 35965469 PMCID: PMC9376720 DOI: 10.1098/rsta.2021.0311] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/13/2022] [Indexed: 05/21/2023]
Abstract
Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Collapse
Affiliation(s)
- Paula Sanz-Leon
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Lachlan H W Hamilton
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Sebastian J Raison
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Anna J X Pan
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Robyn M Stuart
- Department of Mathematical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | | | - Cliff C Kerr
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA 98109, USA
| | - Stephen B Lambert
- National Centre for Immunisation Research and Surveillance for Vaccine Preventable Diseases, Westmead, NSW 2145, Australia
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| |
Collapse
|
5
|
Panovska-Griffiths J, Waites W, Ackland GJ. Technical challenges of modelling real-life epidemics and examples of overcoming these. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20220179. [PMID: 35965472 PMCID: PMC9376714 DOI: 10.1098/rsta.2022.0179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Collapse
Affiliation(s)
- J. Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen’s College, University of Oxford, Oxford, UK
| | - W. Waites
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK
| | - G. J. Ackland
- Institute of Condensed Matter and Complex Systems, School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, UK
| |
Collapse
|
6
|
Dykes J, Abdul-Rahman A, Archambault D, Bach B, Borgo R, Chen M, Enright J, Fang H, Firat EE, Freeman E, Gönen T, Harris C, Jianu R, John NW, Khan S, Lahiff A, Laramee RS, Matthews L, Mohr S, Nguyen PH, Rahat AAM, Reeve R, Ritsos PD, Roberts JC, Slingsby A, Swallow B, Torsney-Weir T, Turkay C, Turner R, Vidal FP, Wang Q, Wood J, Xu K. Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210299. [PMID: 35965467 PMCID: PMC9376715 DOI: 10.1098/rsta.2021.0299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Collapse
Affiliation(s)
| | | | | | | | | | - Min Chen
- University of Oxford, Oxford, UK
| | | | - Hui Fang
- Loughborough University, Loughborough, UK
| | | | | | | | - Claire Harris
- Biomathematics and Statistics Scotland, Edinburgh, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Qiru Wang
- University of Nottingham, Nottingham, UK
| | - Jo Wood
- City, University of London, London, UK
| | - Kai Xu
- Middlesex University, London, UK
| |
Collapse
|
7
|
Ackland GJ, Panovska-Griffiths J, Waites W, Cates ME. The Royal Society RAMP modelling initiative. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210316. [PMID: 35965460 PMCID: PMC9376713 DOI: 10.1098/rsta.2021.0316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 05/07/2023]
Abstract
Normally, science proceeds following a well-established set of principles. Studies are done with an emphasis on correctness, are submitted to a journal editor who evaluates their relevance, and then undergo anonymous peer review by experts before publication in a journal and acceptance by the scientific community via the open literature. This process is slow, but its accuracy has served all fields of science well. In an emergency situation, different priorities come to the fore. Research and review need to be conducted quickly, and the target audience consists of policymakers. Scientists must jostle for the attention of non-specialists without sacrificing rigour, and must deal not only with peer assessment but also with media scrutiny by journalists who may have agendas other than ensuring scientific correctness. Here, we describe how the Royal Society coordinated efforts of diverse scientists to help model the coronavirus epidemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Collapse
Affiliation(s)
- G. J. Ackland
- Institute of Condensed Matter and Complex Systems, School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, UK
| | - J. Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX1 4AW, UK
- The Queen’s College, University of Oxford, Oxford OX1 4AW, UK
| | - W. Waites
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK
| | - M. E. Cates
- DAMTP, University of Cambridge, Cambridge CB3 0WA, UK
| |
Collapse
|
8
|
Libkind S, Baas A, Halter M, Patterson E, Fairbanks JP. An algebraic framework for structured epidemic modelling. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210309. [PMID: 35965465 PMCID: PMC9376710 DOI: 10.1098/rsta.2021.0309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 06/07/2022] [Indexed: 05/07/2023]
Abstract
Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model-its submodels and their interactions-from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Collapse
Affiliation(s)
- Sophie Libkind
- Department of Mathematics, Stanford University, Stanford, CA, USA
| | - Andrew Baas
- Georgia Tech Research Institute, Atlanta, GA, USA
| | - Micah Halter
- Georgia Tech Research Institute, Atlanta, GA, USA
| | | | - James P. Fairbanks
- Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA
| |
Collapse
|
9
|
Dykes J, Abdul-Rahman A, Archambault D, Bach B, Borgo R, Chen M, Enright J, Fang H, Firat EE, Freeman E, Gönen T, Harris C, Jianu R, John NW, Khan S, Lahiff A, Laramee RS, Matthews L, Mohr S, Nguyen PH, Rahat AAM, Reeve R, Ritsos PD, Roberts JC, Slingsby A, Swallow B, Torsney-Weir T, Turkay C, Turner R, Vidal FP, Wang Q, Wood J, Xu K. Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022. [PMID: 35965467 DOI: 10.6084/m9.figshare.c.6080807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Collapse
Affiliation(s)
| | | | | | | | | | - Min Chen
- University of Oxford, Oxford, UK
| | | | - Hui Fang
- Loughborough University, Loughborough, UK
| | | | | | | | - Claire Harris
- Biomathematics and Statistics Scotland, Edinburgh, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Qiru Wang
- University of Nottingham, Nottingham, UK
| | - Jo Wood
- City, University of London, London, UK
| | - Kai Xu
- Middlesex University, London, UK
| |
Collapse
|