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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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Olivença DV, Davis JD, Kumbale CM, Zhao CY, Brown SP, McCarty NA, Voit EO. Mathematical models of cystic fibrosis as a systemic disease. WIREs Mech Dis 2023; 15:e1625. [PMID: 37544654 PMCID: PMC10843793 DOI: 10.1002/wsbm.1625] [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: 12/16/2022] [Revised: 06/22/2023] [Accepted: 07/06/2023] [Indexed: 08/08/2023]
Abstract
Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.
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Affiliation(s)
- Daniel V. Olivença
- Center for Engineering Innovation, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, Texas 75080, USA
| | - Jacob D. Davis
- Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia
| | - Carla M. Kumbale
- Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia
| | - Conan Y. Zhao
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Samuel P. Brown
- Department of Biological Sciences, Georgia Tech and Emory University, Atlanta, Georgia
| | - Nael A. McCarty
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia
| | - Eberhard O. Voit
- Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia
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Roy U. Insight into the structures of Interleukin-18 systems. Comput Biol Chem 2020; 88:107353. [PMID: 32769049 PMCID: PMC7392904 DOI: 10.1016/j.compbiolchem.2020.107353] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 07/01/2020] [Accepted: 07/28/2020] [Indexed: 02/08/2023]
Abstract
Structure-based molecular designs play a critical role in the context of next generation drug development. Besides their fundamental scientific aspects, the findings established in this approach have significant implications in the expansions of target-based therapies and vaccines. Interleukin-18 (IL-18), also known as interferon gamma (IFN-γ) inducing factor, is a pro-inflammatory cytokine. The IL-18 binds first to the IL-18α receptor and forms a lower affinity complex. Upon binding with IL-18β a hetero-trimeric complex with higher affinity is formed that initiates the signal transduction process. The present study, including structural and molecular dynamics simulations, takes a close look at the structural stabilities of IL-18 and IL-18 receptor-bound ligand structures as functions of time. The results help to identify the conformational changes of the ligand due to receptor binding, as well as the structural orders of the apo and holo IL-18 protein complexes.
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Affiliation(s)
- Urmi Roy
- Department of Chemistry & Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699-5820, United States.
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Olivença DV, Voit EO, Pinto FR. ENaC regulation by phospholipids and DGK explained through mathematical modeling. Sci Rep 2020; 10:13952. [PMID: 32811866 PMCID: PMC7435262 DOI: 10.1038/s41598-020-70630-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 07/21/2020] [Indexed: 01/16/2023] Open
Abstract
Cystic fibrosis is a condition caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR). It is also thought to increase the activity of epithelial sodium channels (ENaC). The altered function of these ion channels is one of the causes of the thick dehydrated mucus that characterizes the disease and is partially responsible for recurrent pulmonary infections and inflammation events that ultimately destroy the lungs of affected subjects. Phosphoinositides are signaling lipids that regulate numerous cellular processes and membrane proteins, including ENaC. Inhibition of diacylglycerol kinase (DGK), an enzyme of the phosphoinositide pathway, reduces ENaC function. We propose a computational analysis that is based on the combination of two existing mathematical models: one representing the dynamics of phosphoinositides and the other explaining how phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) influences ENaC activity and, consequently, airway surface liquid. This integrated model permits, for the first time, a detailed assessment of the intricate interactions between DGK and ENaC and is consistent with available literature data. In particular, the computational approach allows comparisons of two competing hypotheses regarding the regulation of ENaC. The results strongly suggest that the regulation of ENaC is primarily exerted through the control of PI(4,5)P2 production by type-I phosphatidylinositol-4-phosphate 5-kinase (PIP5KI), which in turn is controlled by phosphatidic acid (PA), the product of the DGK reaction.
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Affiliation(s)
- Daniel V. Olivença
- Faculty of Sciences, BioISI – Biosystems and Integrative Sciences Institute, University of Lisboa, 1749-016 Lisbon, Portugal
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Drive, Atlanta, GA 30332-2000 USA
| | - Eberhard O. Voit
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Drive, Atlanta, GA 30332-2000 USA
| | - Francisco R. Pinto
- Faculty of Sciences, BioISI – Biosystems and Integrative Sciences Institute, University of Lisboa, 1749-016 Lisbon, Portugal
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Blickensdorf M, Timme S, Figge MT. Hybrid Agent-Based Modeling of Aspergillus fumigatus Infection to Quantitatively Investigate the Role of Pores of Kohn in Human Alveoli. Front Microbiol 2020; 11:1951. [PMID: 32903715 PMCID: PMC7438790 DOI: 10.3389/fmicb.2020.01951] [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: 05/19/2020] [Accepted: 07/24/2020] [Indexed: 12/31/2022] Open
Abstract
The healthy state of an organism is constantly threatened by external cues. Due to the daily inhalation of hundreds of particles and pathogens, the immune system needs to constantly accomplish the task of pathogen clearance in order to maintain this healthy state. However, infection dynamics are highly influenced by the peculiar anatomy of the human lung. Lung alveoli that are packed in alveolar sacs are interconnected by so called Pores of Kohn. Mainly due to the lack of in vivo methods, the role of Pores of Kohn in the mammalian lung is still under debate and partly contradicting hypotheses remain to be investigated. Although it was shown by electron microscopy that Pores of Kohn may serve as passageways for immune cells, their impact on the infection dynamics in the lung is still unknown under in vivo conditions. In the present study, we apply a hybrid agent-based infection model to quantitatively compare three different scenarios and discuss the importance of Pores of Kohn during infections of Aspergillus fumigatus. A. fumigatus is an airborne opportunistic fungus with rising incidences causing severe infections in immunocompromised patients that are associated with high mortality rates. Our hybrid agent-based model incorporates immune cell dynamics of alveolar macrophages – the resident phagocytes in the lung – as well as molecular dynamics of diffusing chemokines that attract alveolar macrophages to the site of infection. Consequently, this model allows a quantitative comparison of three different scenarios and to study the importance of Pores of Kohn. This enables us to demonstrate how passaging of alveolar macrophages and chemokine diffusion affect A. fumigatus infection dynamics. We show that Pores of Kohn alter important infection clearance mechanisms, such as the spatial distribution of macrophages and the effect of chemokine signaling. However, despite these differences, a lack of passageways for alveolar macrophages does impede infection clearance only to a minor extend. Furthermore, we quantify the importance of recruited macrophages in comparison to resident macrophages.
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Affiliation(s)
- Marco Blickensdorf
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany.,Faculty of Biological Sciences, Institute of Microbiology, Friedrich Schiller University Jena, Jena, Germany
| | - Sandra Timme
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany
| | - Marc Thilo Figge
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany.,Faculty of Biological Sciences, Institute of Microbiology, Friedrich Schiller University Jena, Jena, Germany
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Perspective: The Fundamental Value of Engineering Pedagogy for Realizing Personalized Medicine. REGENERATIVE ENGINEERING AND TRANSLATIONAL MEDICINE 2017. [DOI: 10.1007/s40883-017-0039-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Hannan RT, Peirce SM, Barker TH. Fibroblasts: Diverse Cells Critical to Biomaterials Integration. ACS Biomater Sci Eng 2017; 4:1223-1232. [PMID: 31440581 DOI: 10.1021/acsbiomaterials.7b00244] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Fibroblasts are key participants in wound healing and inflammation, and are capable of driving the progression of tissue repair to fully functional tissue or pathologic scar, or fibrosis, depending on the specific mechanical and biochemical cues with which they are presented. Thus, understanding and modulating the fibroblastic response to implanted materials is paramount to achieving desirable outcomes, such as long-term implant function or tissue regeneration. However, fibroblasts are remarkably heterogeneous and can differ vastly in their contributions to regeneration and fibrosis. This heterogeneity exists between tissues and within tissues, down to the level of individual cells. This review will discuss the role of fibroblasts, the pitfalls of describing them as a collective, the specifics of their function, and potential future directions to better understand and organize their highly variable biology.
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Affiliation(s)
- Riley T Hannan
- Department of Pathology, University of Virginia, 415 Lane Road, Charlottesville, Virginia 22903, United States.,Department of Biomedical Engineering, University of Virginia, 415 Lane Road, Charlottesville, Virginia 22903, United States
| | - Shayn M Peirce
- Department of Pathology, University of Virginia, 415 Lane Road, Charlottesville, Virginia 22903, United States.,Department of Biomedical Engineering, University of Virginia, 415 Lane Road, Charlottesville, Virginia 22903, United States
| | - Thomas H Barker
- Department of Biomedical Engineering, University of Virginia, 415 Lane Road, Charlottesville, Virginia 22903, United States
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Zeigler AC, Richardson WJ, Holmes JW, Saucerman JJ. Computational modeling of cardiac fibroblasts and fibrosis. J Mol Cell Cardiol 2016; 93:73-83. [PMID: 26608708 PMCID: PMC4846515 DOI: 10.1016/j.yjmcc.2015.11.020] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 11/18/2015] [Accepted: 11/18/2015] [Indexed: 12/31/2022]
Abstract
Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.
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Affiliation(s)
- Angela C Zeigler
- University of Virginia, Biomedical Engineering Department, 415 Lane Road, Charlottesville, VA 22903, USA.
| | - William J Richardson
- University of Virginia, Biomedical Engineering Department, 415 Lane Road, Charlottesville, VA 22903, USA.
| | - Jeffrey W Holmes
- University of Virginia, Biomedical Engineering Department, 415 Lane Road, Charlottesville, VA 22903, USA.
| | - Jeffrey J Saucerman
- University of Virginia, Biomedical Engineering Department, 415 Lane Road, Charlottesville, VA 22903, USA.
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Stochastic tracking of infection in a CF lung. PLoS One 2014; 9:e111245. [PMID: 25360611 PMCID: PMC4216002 DOI: 10.1371/journal.pone.0111245] [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: 06/16/2014] [Accepted: 09/22/2014] [Indexed: 11/19/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan are the two ubiquitous imaging sources that physicians use to diagnose patients with Cystic Fibrosis (CF) or any other Chronic Obstructive Pulmonary Disease (COPD). Unfortunately the cost constraints limit the frequent usage of these medical imaging procedures. In addition, even though both CT scan and MRI provide mesoscopic details of a lung, in order to obtain microscopic information a very high resolution is required. Neither MRI nor CT scans provide micro level information about the location of infection in a binary tree structure the binary tree structure of the human lung. In this paper we present an algorithm that enhances the current imaging results by providing estimated micro level information concerning the location of the infection. The estimate is based on a calculation of the distribution of possible mucus blockages consistent with available information using an offline Metropolis-Hastings algorithm in combination with a real-time interpolation scheme. When supplemented with growth rates for the pockets of mucus, the algorithm can also be used to estimate how lung functionality as manifested in spirometric tests will change in patients with CF or COPD.
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Abstract
Background Substance dependence poses a critical health problem. Sadly, its neurobiological mechanisms are still unclear, and this lack of real understanding is reflected in insufficient treatment options. It has been hypothesized that alcohol effects are due to an imbalance between neuroexcitatory and neuroinhibitory amino acids. However, glutamate and GABA interact with other neurotransmitters, which form a complicated network whose functioning evades intuition and should be investigated systemically with methods of biomedical systems analysis. Methods and Results We present a heuristic model of neurotransmitters that combines a neurochemical interaction matrix at the biochemical level with a mobile describing the balances between pairs of neurotransmitters at the physiological and behavioral level. We investigate the effects of alcohol on the integrated neurotransmitter systems at both levels. The model simulation results are consistent with clinical and experimental observations. The model demonstrates that the drug diazepam for symptoms of alcohol withdrawal effectively reduces the imbalances between neurotransmitters. Moreover, the acetylcholine signal is suggested as a novel target for treatment of symptoms associated with alcohol withdrawal. Conclusions Efficient means of integrating clinical symptoms across multiple levels are still scarce and difficult to establish. We present a heuristic model of systemic neurotransmitter functionality that permits the assessment of genetic, biochemical, and pharmacological perturbations. The model can serve as a tool to represent clinical and biological observations and explore various scenarios associated with alcohol dependence and its treatments. It also is very well suited for educational purposes.
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Affiliation(s)
- Zhen Qi
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Medical School, Atlanta, Georgia, United States of America
- Integrative BioSystems Institute, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Felix Tretter
- Isar-Amper-Klinikum gemeinnützige GmbH, Klinikum München-Ost, Haar, Landkreis München, Germany
| | - Eberhard O. Voit
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Medical School, Atlanta, Georgia, United States of America
- Integrative BioSystems Institute, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail:
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