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Puniya BL, Verma M, Damiani C, Bakr S, Dräger A. Perspectives on computational modeling of biological systems and the significance of the SysMod community. BIOINFORMATICS ADVANCES 2024; 4:vbae090. [PMID: 38948011 PMCID: PMC11213628 DOI: 10.1093/bioadv/vbae090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/12/2024] [Accepted: 06/14/2024] [Indexed: 07/02/2024]
Abstract
Motivation In recent years, applying computational modeling to systems biology has caused a substantial surge in both discovery and practical applications and a significant shift in our understanding of the complexity inherent in biological systems. Results In this perspective article, we briefly overview computational modeling in biology, highlighting recent advancements such as multi-scale modeling due to the omics revolution, single-cell technology, and integration of artificial intelligence and machine learning approaches. We also discuss the primary challenges faced: integration, standardization, model complexity, scalability, and interdisciplinary collaboration. Lastly, we highlight the contribution made by the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI) associated with the International Society of Computational Biology (ISCB) in driving progress within this rapidly evolving field through community engagement (via both in person and virtual meetings, social media interactions), webinars, and conferences. Availability and implementation Additional information about SysMod is available at https://sysmod.info.
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Affiliation(s)
- Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Meghna Verma
- Systems Medicine, Clinical Pharmacology and Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Gaithersburg, MD 20878, United States
| | - Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan 20126, Italy
| | - Shaimaa Bakr
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA 94305-5479, United States
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen 72076, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen 72076, Germany
- Quantitative Biology Center (QBiC), Eberhard Karl University of Tübingen, Tübingen 72076, Germany
- Data Analytics and Bioinformatics, Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale) 06120, Germany
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Aghamiri SS, Puniya BL, Amin R, Helikar T. A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery. Front Immunol 2023; 14:1112985. [PMID: 36993954 PMCID: PMC10040975 DOI: 10.3389/fimmu.2023.1112985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.
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Affiliation(s)
| | | | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
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Baker R, Hontecillas R, Tubau-Juni N, Leber AJ, Kale S, Bassaganya-Riera J. Computational modeling of complex bioenergetic mechanisms that modulate CD4+ T cell effector and regulatory functions. NPJ Syst Biol Appl 2022; 8:45. [DOI: 10.1038/s41540-022-00263-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 11/11/2022] [Indexed: 11/24/2022] Open
Abstract
AbstractWe built a computational model of complex mechanisms at the intersection of immunity and metabolism that regulate CD4+ T cell effector and regulatory functions by using coupled ordinary differential equations. The model provides an improved understanding of how CD4+ T cells are shaping the immune response during Clostridioides difficile infection (CDI), and how they may be targeted pharmacologically to produce a more robust regulatory (Treg) response, which is associated with improved disease outcomes during CDI and other diseases. LANCL2 activation during CDI decreased the effector response, increased regulatory response, and elicited metabolic changes that favored Treg. Interestingly, LANCL2 activation provided greater immune and metabolic modulation compared to the addition of exogenous IL-2. Additionally, we identified gluconeogenesis via PEPCK-M as potentially responsible for increased immunosuppressive behavior in Treg cells. The model can perturb immune signaling and metabolism within a CD4+ T cell and obtain clinically relevant outcomes that help identify novel drug targets for infectious, autoimmune, metabolic, and neurodegenerative diseases.
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Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA. Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med 2022; 5:64. [PMID: 35595830 PMCID: PMC9122990 DOI: 10.1038/s41746-022-00610-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - A Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France.,Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - T Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - G An
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - B Shapiro
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - R S Malik-Sheriff
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, Cambridge, UK
| | - T J Sego
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - A Knapp
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - P Macklin
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - J A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice. BIOLOGY 2022; 11:biology11020297. [PMID: 35205164 PMCID: PMC8869254 DOI: 10.3390/biology11020297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/08/2022] [Accepted: 02/09/2022] [Indexed: 11/29/2022]
Abstract
Simple Summary Computational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists. Abstract The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.
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Wertheim KY, Puniya BL, La Fleur A, Shah AR, Barberis M, Helikar T. A multi-approach and multi-scale platform to model CD4+ T cells responding to infections. PLoS Comput Biol 2021; 17:e1009209. [PMID: 34343169 PMCID: PMC8376204 DOI: 10.1371/journal.pcbi.1009209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/19/2021] [Accepted: 06/23/2021] [Indexed: 12/24/2022] Open
Abstract
Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology. CD4+ T cells are a key part of the adaptive immune system. They differentiate into different phenotypes to carry out different functions. They do so by secreting molecules called cytokines to regulate other immune cells. Multi-scale modeling can potentially explain their emergent behaviors by integrating biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). We built a computational platform by combining disparate modeling frameworks (compartmental ordinary differential equations, agent-based modeling, Boolean network modeling, and constraint-based modeling). We validated the platform’s ability to predict CD4+ T cells’ emergent behaviors by reproducing their differentiation patterns, metabolic regulation, and population dynamics in response to influenza infection. We then used it to predict and explain novel switch-like and oscillatory behaviors for CD4+ T cells. On the basis of these results, we believe that our multi-approach and multi-scale platform will be a valuable addition to the systems immunology toolkit. In addition to its immediate relevance to CD4+ T cells, it also has the potential to become the foundation of a virtual immune system.
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Affiliation(s)
- Kenneth Y. Wertheim
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- Department of Computer Science and Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Alyssa La Fleur
- Department of Biochemistry, Department of Mathematics and Computer Science, Whitworth University, Spokane, Washington, United States of America
| | - Ab Rauf Shah
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- * E-mail: , (MB); (TH)
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- * E-mail: , (MB); (TH)
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Prediction of Long-Term Stroke Recurrence Using Machine Learning Models. J Clin Med 2021; 10:jcm10061286. [PMID: 33804724 PMCID: PMC8003970 DOI: 10.3390/jcm10061286] [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: 01/30/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 01/01/2023] Open
Abstract
Background: The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether performance metrics can be optimized. Methods: We used patient-level data from electronic health records, six interpretable algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), four feature selection strategies, five prediction windows, and two sampling strategies to develop 288 models for up to 5-year stroke recurrence prediction. We further identified important clinical features and different optimization strategies. Results: We included 2091 ischemic stroke patients. Model area under the receiver operating characteristic (AUROC) curve was stable for prediction windows of 1, 2, 3, 4, and 5 years, with the highest score for the 1-year (0.79) and the lowest score for the 5-year prediction window (0.69). A total of 21 (7%) models reached an AUROC above 0.73 while 110 (38%) models reached an AUROC greater than 0.7. Among the 53 features analyzed, age, body mass index, and laboratory-based features (such as high-density lipoprotein, hemoglobin A1c, and creatinine) had the highest overall importance scores. The balance between specificity and sensitivity improved through sampling strategies. Conclusion: All of the selected six algorithms could be trained to predict the long-term stroke recurrence and laboratory-based variables were highly associated with stroke recurrence. The latter could be targeted for personalized interventions. Model performance metrics could be optimized, and models can be implemented in the same healthcare system as intelligent decision support for targeted intervention.
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Bayani A, Dunster JL, Crofts JJ, Nelson MR. Spatial considerations in the resolution of inflammation: Elucidating leukocyte interactions via an experimentally-calibrated agent-based model. PLoS Comput Biol 2020; 16:e1008413. [PMID: 33137107 PMCID: PMC7660912 DOI: 10.1371/journal.pcbi.1008413] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 11/12/2020] [Accepted: 10/01/2020] [Indexed: 01/13/2023] Open
Abstract
Many common medical conditions (such as cancer, arthritis, chronic obstructive pulmonary disease (COPD), and others) are associated with inflammation, and even more so when combined with the effects of ageing and multimorbidity. While the inflammatory response varies in different tissue types, under disease and in response to therapeutic interventions, it has common interactions that occur between immune cells and inflammatory mediators. Understanding these underlying inflammatory mechanisms is key in progressing treatments and therapies for numerous inflammatory conditions. It is now considered that constituent mechanisms of the inflammatory response can be actively manipulated in order to drive resolution of inflammatory damage; particularly, those mechanisms related to the pro-inflammatory role of neutrophils and the anti-inflammatory role of macrophages. In this article, we describe the assembly of a hybrid mathematical model in which the spatial spread of inflammatory mediators is described through partial differential equations, and immune cells (neutrophils and macrophages) are described individually via an agent-based modelling approach. We pay close attention to how immune cells chemotax toward pro-inflammatory mediators, presenting a model for cell chemotaxis that is calibrated against experimentally observed cell trajectories in healthy and COPD-affected scenarios. We illustrate how variations in key model parameters can drive the switch from resolution of inflammation to chronic outcomes, and show that aberrant neutrophil chemotaxis can move an otherwise healthy outcome to one of chronicity. Finally, we reflect on our results in the context of the on-going hunt for new therapeutic interventions. Inflammation is the body’s primary defence to harmful stimuli such as infections, toxins and tissue strain but also underlies a much broader range of conditions, including asthma, arthritis and cancer. The inflammatory response is key in resolving injury to facilitate recovery, and involves a range of interactions between immune cells (leukocytes, neutrophils and macrophages in particular) and inflammatory mediators. Immune cells are recruited from the blood stream in response to injury. Once in tissue, neutrophils release toxins to kill invading agents and resolve damage; however, if not carefully managed by other immune cells (mainly macrophages), their responses can increase inflammation instead of helping to resolve it. We model these interactions in response to damage using a spatial model, examining how a healthy response can prevent localised inflammation from spreading. We pay close attention to how cells migrate toward the damaged area, as many inflammatory conditions are associated with impairment of this process. We calibrate our model against experimentally-observed cell trajectories from healthy patients and patients with chronic obstructive pulmonary disease. We illustrate that a healthy outcome depends strongly upon efficient cell migration and a delicate balance between the pro- and anti-inflammatory effects of neutrophils and macrophages.
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Affiliation(s)
- Anahita Bayani
- Department of Physics & Mathematics, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, United Kingdom
| | - Joanne L. Dunster
- Institute for Cardiovascular and Metabolic Research, University of Reading, Reading, RG6 6AS, United Kingdom
| | - Jonathan J. Crofts
- Department of Physics & Mathematics, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, United Kingdom
| | - Martin R. Nelson
- Department of Physics & Mathematics, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, United Kingdom
- * E-mail:
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Sharma V, Sharma V, Khan A, Wassmer DJ, Schoenholtz MD, Hontecillas R, Bassaganya-Riera J, Zand R, Abedi V. Malnutrition, Health and the Role of Machine Learning in Clinical Setting. Front Nutr 2020; 7:44. [PMID: 32351968 PMCID: PMC7174626 DOI: 10.3389/fnut.2020.00044] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 03/23/2020] [Indexed: 12/16/2022] Open
Abstract
Nutrition plays a vital role in health and the recovery process. Deficiencies in macronutrients and micronutrients can impact the development and progression of various disorders. However, malnutrition screening tools and their utility in the clinical setting remain largely understudied. In this study, we summarize the importance of nutritional adequacy and its association with neurological, cardiovascular, and immune-related disorders. We also examine general and specific malnutrition assessment tools utilized in healthcare settings. Since the implementation of the screening process in 2016, malnutrition data from hospitalized patients in the Geisinger Health System is presented and discussed as a case study. Clinical data from five Geisinger hospitals shows that ~10% of all admitted patients are acknowledged for having some form of nutritional deficiency, from which about 60-80% of the patients are targeted for a more comprehensive assessment. Finally, we conclude that with a reflection on how technological advances, specifically machine learning-based algorithms, can be integrated into electronic health records to provide decision support system to care providers in the identification and management of patients at higher risk of malnutrition.
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Affiliation(s)
- Vaibhav Sharma
- Geisinger Commonwealth School of Medicine, Scranton, PA, United States
| | - Vishakha Sharma
- Geisinger Commonwealth School of Medicine, Scranton, PA, United States
| | - Ayesha Khan
- Neuroscience Institute, Geisinger Health System, Danville, PA, United States
| | - David J. Wassmer
- Neuroscience Institute, Geisinger Health System, Danville, PA, United States
| | | | | | | | - Ramin Zand
- Neuroscience Institute, Geisinger Health System, Danville, PA, United States
| | - Vida Abedi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, United States
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Bayani A, Dunster JL, Crofts JJ, Nelson MR. Mechanisms and Points of Control in the Spread of Inflammation: A Mathematical Investigation. Bull Math Biol 2020; 82:45. [PMID: 32222839 PMCID: PMC7103018 DOI: 10.1007/s11538-020-00709-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/14/2020] [Indexed: 02/07/2023]
Abstract
Understanding the mechanisms that control the body’s response to inflammation is of key importance, due to its involvement in myriad medical conditions, including cancer, arthritis, Alzheimer’s disease and asthma. While resolving inflammation has historically been considered a passive process, since the turn of the century the hunt for novel therapeutic interventions has begun to focus upon active manipulation of constituent mechanisms, particularly involving the roles of apoptosing neutrophils, phagocytosing macrophages and anti-inflammatory mediators. Moreover, there is growing interest in how inflammatory damage can spread spatially due to the motility of inflammatory mediators and immune cells. For example, impaired neutrophil chemotaxis is implicated in causing chronic inflammation under trauma and in ageing, while neutrophil migration is an attractive therapeutic target in ailments such as chronic obstructive pulmonary disease. We extend an existing homogeneous model that captures interactions between inflammatory mediators, neutrophils and macrophages to incorporate spatial behaviour. Through bifurcation analysis and numerical simulation, we show that spatially inhomogeneous outcomes can present close to the switch from bistability to guaranteed resolution in the corresponding homogeneous model. Finally, we show how aberrant spatial mechanisms can play a role in the failure of inflammation to resolve and discuss our results within the broader context of seeking novel inflammatory treatments.
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Affiliation(s)
- A Bayani
- Department of Physics and Mathematics, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK
| | - J L Dunster
- Institute for Cardiovascular and Metabolic Research, University of Reading, Reading, RG6 6AS, UK
| | - J J Crofts
- Department of Physics and Mathematics, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK
| | - M R Nelson
- Department of Physics and Mathematics, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK.
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Verma M, Bassaganya-Riera J, Leber A, Tubau-Juni N, Hoops S, Abedi V, Chen X, Hontecillas R. High-resolution computational modeling of immune responses in the gut. Gigascience 2020; 8:5513894. [PMID: 31185494 PMCID: PMC6559340 DOI: 10.1093/gigascience/giz062] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 01/19/2019] [Accepted: 05/05/2019] [Indexed: 02/07/2023] Open
Abstract
Background Helicobacter pylori causes gastric cancer in 1–2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori–colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing–driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations. Results The outputs were analyzed using 2 sequential stages: the first used a partial rank correlation coefficient regression–based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells. Conclusions The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.
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Affiliation(s)
- Meghna Verma
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.,Graduate Program in Translational Biology, Medicine and Health, Virginia Tech, Blacksburg, 1 Riverside Circle, Roanoke, VA 24016, USA
| | - Josep Bassaganya-Riera
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Andrew Leber
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Nuria Tubau-Juni
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Stefan Hoops
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Vida Abedi
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Xi Chen
- Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Perry St, Blacksburg, VA 24061, USA
| | - Raquel Hontecillas
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
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