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Fonseca LL, Böttcher L, Mehrad B, Laubenbacher RC. Surrogate modeling and control of medical digital twins. ARXIV 2024:arXiv:2402.05750v2. [PMID: 38827450 PMCID: PMC11142319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.
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Affiliation(s)
- Luis L. Fonseca
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Lucas Böttcher
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
- Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322 Frankfurt am Main, Germany
| | - Borna Mehrad
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Reinhard C. Laubenbacher
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
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Leonard-Duke J, Agro SMJ, Csordas DJ, Bruce AC, Eggertsen TG, Tavakol TN, Barker TH, Bonham CA, Saucerman JJ, Taite LJ, Peirce SM. Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585249. [PMID: 38559112 PMCID: PMC10979947 DOI: 10.1101/2024.03.15.585249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired due to loss of alveolar structures and lung function declines. Here, we integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (ECs) and pericytes, the cells that comprise microvessels. Nintedanib, an FDA-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition (PMT) in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can explain how cell decisions affect tissue changes during disease and in response to treatments.
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Affiliation(s)
- Julie Leonard-Duke
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
| | - Samuel M. J. Agro
- Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - David J. Csordas
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
| | - Anthony C. Bruce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Taylor G. Eggertsen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
| | - Tara N. Tavakol
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Thomas H. Barker
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
| | - Catherine A. Bonham
- Department of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Jeffery J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
| | - Lakeshia J. Taite
- Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Shayn M. Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA
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Shabman RS, Craig M, Laubenbacher R, Reeves D, Brown LL. NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases. Bull Math Biol 2024; 86:44. [PMID: 38512541 PMCID: PMC10957590 DOI: 10.1007/s11538-024-01276-2] [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: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/23/2024]
Abstract
On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.
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Affiliation(s)
- Reed S Shabman
- National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA.
| | - Morgan Craig
- Department of Mathematics and Statistics, Sainte-Justine University Hospital Research Centre, Université de Montréal, Montreal, Canada
| | | | - Daniel Reeves
- Department of Global Health, University of Washington, Seattle, WA, 98195, USA
| | - Liliana L Brown
- National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA.
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Böttcher L, Fonseca LL, Laubenbacher RC. Control of Medical Digital Twins with Artificial Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585589. [PMID: 38562787 PMCID: PMC10983973 DOI: 10.1101/2024.03.18.585589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.
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Laubenbacher R, Adler F, An G, Castiglione F, Eubank S, Fonseca LL, Glazier J, Helikar T, Jett-Tilton M, Kirschner D, Macklin P, Mehrad B, Moore B, Pasour V, Shmulevich I, Smith A, Voigt I, Yankeelov TE, Ziemssen T. Toward mechanistic medical digital twins: some use cases in immunology. Front Digit Health 2024; 6:1349595. [PMID: 38515550 PMCID: PMC10955144 DOI: 10.3389/fdgth.2024.1349595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.
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Affiliation(s)
| | - Fred Adler
- Department of Mathematics and School of Biological Sciences, University of Utah, Salt Lake, UT, United States
| | - Gary An
- Department of Surgery, University of Vermont, Burlington, VT, United States
| | - Filippo Castiglione
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates
| | - Stephen Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, United States
| | - Luis L. Fonseca
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - James Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
| | - Tomas Helikar
- Department of Biochemistry, University of Nebraska, Lincoln, NE, United States
| | - Marti Jett-Tilton
- U.S. Walter Reed Army Institute of Research, Silver Spring, MD, United States
| | - Denise Kirschner
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, United States
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
| | - Borna Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Beth Moore
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, United States
| | - Virginia Pasour
- U.S. Army Research Office, Research Triangle Park, NC, United States
| | | | - Amber Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Isabel Voigt
- Center for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences, Austin, TX, United States
- Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, The University of Texas, Austin, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Austin, TX, United States
| | - Tjalf Ziemssen
- Center for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany
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Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. NATURE COMPUTATIONAL SCIENCE 2024; 4:184-191. [PMID: 38532133 PMCID: PMC11102043 DOI: 10.1038/s43588-024-00607-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/12/2024] [Indexed: 03/28/2024]
Abstract
Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - B Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | | | - N Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Ribeiro HAL, Scindia Y, Mehrad B, Laubenbacher R. COVID-19-associated pulmonary aspergillosis in immunocompetent patients: a virtual patient cohort study. J Math Biol 2023; 87:6. [PMID: 37306747 DOI: 10.1007/s00285-023-01940-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 05/10/2023] [Accepted: 05/21/2023] [Indexed: 06/13/2023]
Abstract
The opportunistic fungus Aspergillus fumigatus infects the lungs of immunocompromised hosts, including patients undergoing chemotherapy or organ transplantation. More recently however, immunocompetent patients with severe SARS-CoV2 have been reported to be affected by COVID-19 Associated Pulmonary Aspergillosis (CAPA), in the absence of the conventional risk factors for invasive aspergillosis. This paper explores the hypothesis that contributing causes are the destruction of the lung epithelium permitting colonization by opportunistic pathogens. At the same time, the exhaustion of the immune system, characterized by cytokine storms, apoptosis, and depletion of leukocytes may hinder the response to A. fumigatus infection. The combination of these factors may explain the onset of invasive aspergillosis in immunocompetent patients. We used a previously published computational model of the innate immune response to infection with Aspergillus fumigatus. Variation of model parameters was used to create a virtual patient population. A simulation study of this virtual patient population to test potential causes for co-infection in immunocompetent patients. The two most important factors determining the likelihood of CAPA were the inherent virulence of the fungus and the effectiveness of the neutrophil population, as measured by granule half-life and ability to kill fungal cells. Varying these parameters across the virtual patient population generated a realistic distribution of CAPA phenotypes observed in the literature. Computational models are an effective tool for hypothesis generation. Varying model parameters can be used to create a virtual patient population for identifying candidate mechanisms for phenomena observed in actual patient populations.
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Affiliation(s)
- Henrique A L Ribeiro
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, Gainesville, 32610, FL, USA
| | - Yogesh Scindia
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, Gainesville, 32610, FL, USA
| | - Borna Mehrad
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, Gainesville, 32610, FL, USA
| | - Reinhard Laubenbacher
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, Gainesville, 32610, FL, USA.
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Ribeiro HAL, Scindia Y, Mehrad B, Laubenbacher R. COVID-19-associated pulmonary aspergillosis in immunocompetent patients: A virtual patient cohort study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2022.07.18.500514. [PMID: 35898340 PMCID: PMC9327627 DOI: 10.1101/2022.07.18.500514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Purpose The opportunistic fungus Aspergillus fumigatus infects the lungs of immunocompromised hosts, including patients undergoing chemotherapy or organ transplantation. More recently however, immunocompetent patients with severe SARS-CoV2 have been reported to be affected by COVID-19 Associated Pulmonary Aspergillosis (CAPA), in the absence of the conventional risk factors for invasive aspergillosis. This paper explores the hypothesis that contributing causes are the destruction of the lung epithelium permitting colonization by opportunistic pathogens. At the same time, the exhaustion of the immune system, characterized by cytokine storms, apoptosis, and depletion of leukocytes may hinder the response to A. fumigatus infection. The combination of these factors may explain the onset of invasive aspergillosis in immunocompetent patients. Methods We used a previously published computational model of the innate immune response to infection with Aspergillus fumigatus . Variation of model parameters was used to create a virtual patient population. A simulation study of this virtual patient population to test potential causes for co-infection in immunocompetent patients. Results The two most important factors determining the likelihood of CAPA were the inherent virulence of the fungus and the effectiveness of the neutrophil population, as measured by granule half-life and ability to kill fungal cells. Varying these parameters across the virtual patient population generated a realistic distribution of CAPA phenotypes observed in the literature. Conclusions Computational models are an effective tool for hypothesis generation. Varying model parameters can be used to create a virtual patient population for identifying candidate mechanisms for phenomena observed in actual patient populations.
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Abstract
PURPOSE OF REVIEW Invasive fungal diseases (IFDs) such as invasive aspergillosis continue to be associated with high morbidity and mortality while presenting significant diagnostic challenges. Siderophores are high-affinity Fe 3+ chelators produced by Aspergillus spp. and other fungi capable of causing IFD. Previously evaluated as a treatment target in mucormycosis, siderophores have recently emerged as new diagnostic targets for invasive aspergillosis and scedosporiosis. Here, we review the diagnostic potential of siderophores for diagnosing IFD, with a particular focus on invasive aspergillosis. RECENT FINDINGS The major secreted siderophore of A. fumigatus , triacetylfusarinine C (TAFC), has been successfully detected by mass spectrometry in serum, BALF and urine of patients with invasive aspergillosis, with promising sensitivities and specificities in single-centre studies. Intracellular uptake of siderophores has also been utilized for imaging, wherein fungal siderophores have been conjugated with the easy-to-produce radioactive isotope gallium-68 ( 68 Ga) to visualize infected body sites in PET. For the Scedosporium apiospermum complex, another siderophore N(α)-methyl coprogen B has been shown promising as a marker for airway colonization in early studies. SUMMARY Siderophores and particular TAFC have the potential to revolutionize diagnostic pathways for invasive aspergillosis and other mould infections. However, larger multicentre studies are needed to confirm these promising performances. Methods that allow rapid and cost-effective measurements in routine clinical practice need to be developed, particularly when TAFC is used as a biomarker in patient specimens.
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