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Lambers L, Waschinsky N, Schleicher J, König M, Tautenhahn HM, Albadry M, Dahmen U, Ricken T. Quantifying fat zonation in liver lobules: an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach. Biomech Model Mechanobiol 2024; 23:631-653. [PMID: 38402347 DOI: 10.1007/s10237-023-01797-0] [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: 09/12/2023] [Accepted: 11/22/2023] [Indexed: 02/26/2024]
Abstract
Metabolic zonation refers to the spatial separation of metabolic functions along the sinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepatic metabolism to physiological requirements in health and disease (e.g., metabolic dysfunction-associated steatotic liver disease/MASLD). Zonated metabolic functions are influenced by zonal morphological abnormalities in the liver, such as periportal fibrosis and pericentral steatosis. We aim to analyze the interplay between microperfusion, oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule. Therefore we developed a continuum biomechanical, tri-phasic, bi-scale, and multicomponent in silico model, which allows to numerically simulate coupled perfusion-function-growth interactions two-dimensionally in liver lobules. The developed homogenized model has the following specifications: (i) thermodynamically consistent, (ii) tri-phase model (tissue, fat, blood), (iii) penta-substances (glycogen, glucose, lactate, FFA, and oxygen), and (iv) bi-scale approach (lobule, cell). Our presented in silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion, metabolic pathways and fat accumulation. The model thus allows the prediction of fat development in the liver lobule, depending on perfusion, oxygen and plasma concentration of free fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs). The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus, we will investigate how changes at the cellular scale affect perfusion at the lobular scale and vice versa. This allows to predict the zonation of fat distribution (periportal or pericentral) depending on initial conditions, as well as external and internal boundary value conditions.
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Affiliation(s)
- Lena Lambers
- Institute of Structural Mechanics and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, Stuttgart, 70191, Germany
| | - Navina Waschinsky
- Institute of Structural Mechanics and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, Stuttgart, 70191, Germany
| | - Jana Schleicher
- Friedrich-Schiller-Universität Jena, Fürstengraben 27, Jena, 07743, Germany
| | - Matthias König
- Systems Medicine of Liver, Institute for Theoretical Biology, Institute for Biology, Humboldt-University Berlin, Philippstraße 13, 10115 Berlin, Germany
| | - Hans-Michael Tautenhahn
- Department of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, Liebigstraße 20, Leipzig, 04103, Germany
| | - Mohamed Albadry
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Drackendorfer Straße 1, Jena, 07747, Germany
- Department of Pathology, Faculty of Veterinary Medicine, Menoufia University, Shebin Elkom, Menoufia, Egypt
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Drackendorfer Straße 1, Jena, 07747, Germany
| | - Tim Ricken
- Institute of Structural Mechanics and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, Stuttgart, 70191, Germany.
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2
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Zhao J, Ghallab A, Hassan R, Dooley S, Hengstler JG, Drasdo D. A liver digital twin for in silico testing of cellular and inter-cellular mechanisms in regeneration after drug-induced damage. iScience 2024; 27:108077. [PMID: 38371522 PMCID: PMC10869925 DOI: 10.1016/j.isci.2023.108077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 02/22/2023] [Accepted: 09/25/2023] [Indexed: 02/20/2024] Open
Abstract
This communication presents a mathematical mechanism-based model of the regenerating liver after drug-induced pericentral lobule damage resolving tissue microarchitecture. The consequence of alternative hypotheses about the interplay of different cell types on regeneration was simulated. Regeneration dynamics has been quantified by the size of the damage-induced dead cell area, the hepatocyte density and the spatial-temporal profile of the different cell types. We use deviations of observed trajectories from the simulated system to identify branching points, at which the systems behavior cannot be explained by the underlying set of hypotheses anymore. Our procedure reflects a successful strategy for generating a fully digital liver twin that, among others, permits to test perturbations from the molecular up to the tissue scale. The model simulations are complementing current knowledge on liver regeneration by identifying gaps in mechanistic relationships and guiding the system toward the most informative (lacking) parameters that can be experimentally addressed.
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Affiliation(s)
- Jieling Zhao
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), 44139 Dortmund, Germany
- Group SIMBIOTX, INRIA Saclay, 91120 Palaiseau, France
| | - Ahmed Ghallab
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), 44139 Dortmund, Germany
- Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena 83523, Egypt
| | - Reham Hassan
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), 44139 Dortmund, Germany
- Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena 83523, Egypt
| | - Steven Dooley
- Molecular Hepatology Section, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Jan Georg Hengstler
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), 44139 Dortmund, Germany
| | - Dirk Drasdo
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund (IfADo), 44139 Dortmund, Germany
- Group SIMBIOTX, INRIA Saclay, 91120 Palaiseau, France
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3
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Sego TJ. SimService: a lightweight library for building simulation services in Python. Bioinformatics 2024; 40:btae009. [PMID: 38237907 PMCID: PMC10809901 DOI: 10.1093/bioinformatics/btae009] [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: 09/19/2023] [Revised: 11/27/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
SUMMARY Integrative biological modeling requires software infrastructure to launch, interconnect, and execute simulation software components without loss of functionality. SimService is a software library that enables deploying simulations in integrated applications as memory-isolated services with interactive proxy objects in the Python programming language. SimService supports customizing the interface of proxies so that simulation developers and users alike can tailor generated simulation instances according to model, method, and integrated application. AVAILABILITY AND IMPLEMENTATION SimService is written in Python, is freely available on GitHub under the MIT license at https://github.com/tjsego/simservice, and is available for download via the Python Package Index (package name "simservice") and conda (package name "simservice" on the conda-forge channel).
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Affiliation(s)
- T J Sego
- Department of Medicine, University of Florida, Gainesville, FL 32610-0225, United States
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Ho H, Means S, Safaei S, Hunter PJ. In silico modeling for the hepatic circulation and transport: From the liver organ to lobules. WIREs Mech Dis 2023; 15:e1586. [PMID: 36131627 DOI: 10.1002/wsbm.1586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 08/08/2022] [Accepted: 08/15/2022] [Indexed: 11/12/2022]
Abstract
The function of the liver depends critically on its blood supply. Numerous in silico models have been developed to study various aspects of the hepatic circulation, including not only the macro-hemodynamics at the organ level, but also the microcirculation at the lobular level. In addition, computational models of blood flow and bile flow have been used to study the transport, metabolism, and clearance of drugs in pharmacokinetic studies. These in silico models aim to provide insights into the liver organ function under both healthy and diseased states, and to assist quantitative analysis for surgical planning and postsurgery treatment. The purpose of this review is to provide an update on state-of-the-art in silico models of the hepatic circulation and transport processes. We introduce the numerical methods and the physiological background of these models. We also discuss multiscale frameworks that have been proposed for the liver, and their linkage with the large context of systems biology, systems pharmacology, and the Physiome project. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering Cardiovascular Diseases > Computational Models.
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Affiliation(s)
- Harvey Ho
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Shawn Means
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Soroush Safaei
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Peter John Hunter
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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5
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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Fuji K, Tanida S, Sano M, Nonomura M, Riveline D, Honda H, Hiraiwa T. Computational approaches for simulating luminogenesis. Semin Cell Dev Biol 2022; 131:173-185. [PMID: 35773151 DOI: 10.1016/j.semcdb.2022.05.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 12/14/2022]
Abstract
Lumens, liquid-filled cavities surrounded by polarized tissue cells, are elementary units involved in the morphogenesis of organs. Theoretical modeling and computations, which can integrate various factors involved in biophysics of morphogenesis of cell assembly and lumens, may play significant roles to elucidate the mechanisms in formation of such complex tissue with lumens. However, up to present, it has not been documented well what computational approaches or frameworks can be applied for this purpose and how we can choose the appropriate approach for each problem. In this review, we report some typical lumen morphologies and basic mechanisms for the development of lumens, focusing on three keywords - mechanics, hydraulics and geometry - while outlining pros and cons of the current main computational strategies. We also describe brief guidance of readouts, i.e., what we should measure in experiments to make the comparison with the model's assumptions and predictions.
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Affiliation(s)
- Kana Fuji
- Universal Biology Institute, Graduate School of Science, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Sakurako Tanida
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan
| | - Masaki Sano
- Institute of Natural Sciences, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Makiko Nonomura
- Department of Mathematical Information Engineering, College of Industrial Technology, Nihon University, 1-2-1 Izumicho, Narashino-shi, Chiba 275-8575, Japan
| | - Daniel Riveline
- Laboratory of Cell Physics IGBMC, CNRS, INSERM and Université de Strasbourg, Strasbourg, France
| | - Hisao Honda
- Division of Cell Physiology, Department of Physiology and Cell Biology, Graduate School of Medicine Kobe University, Kobe, Hyogo, Japan
| | - Tetsuya Hiraiwa
- Mechanobiology Institute, Singapore, National University of Singapore, 117411, Singapore.
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7
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Unraveling the effect of intra- and intercellular processes on acetaminophen-induced liver injury. NPJ Syst Biol Appl 2022; 8:27. [PMID: 35933513 PMCID: PMC9357019 DOI: 10.1038/s41540-022-00238-5] [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: 02/17/2022] [Accepted: 07/20/2022] [Indexed: 11/09/2022] Open
Abstract
In high dosages, acetaminophen (APAP) can cause severe liver damage, but susceptibility to liver failure varies across individuals and is influenced by factors such as health status. Because APAP-induced liver injury and recovery is regulated by an intricate system of intra- and extracellular molecular signaling, we here aim to quantify the importance of specific modules in determining the outcome after an APAP insult and of potential targets for therapies that mitigate adversity. For this purpose, we integrated hepatocellular acetaminophen metabolism, DNA damage response induction and cell fate into a multiscale mechanistic liver lobule model which involves various cell types, such as hepatocytes, residential Kupffer cells and macrophages. Our model simulations show that zonal differences in metabolism and detoxification efficiency are essential determinants of necrotic damage. Moreover, the extent of senescence, which is regulated by intracellular processes and triggered by extracellular signaling, influences the potential to recover. In silico therapies at early and late time points after APAP insult indicated that prevention of necrotic damage is most beneficial for recovery, whereas interference with regulation of senescence promotes regeneration in a less pronounced way.
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8
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Verma A, Manchel A, Melunis J, Hengstler JG, Vadigepalli R. From Seeing to Simulating: A Survey of Imaging Techniques and Spatially-Resolved Data for Developing Multiscale Computational Models of Liver Regeneration. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:917191. [PMID: 37575468 PMCID: PMC10421626 DOI: 10.3389/fsysb.2022.917191] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Liver regeneration, which leads to the re-establishment of organ mass, follows a specifically organized set of biological processes acting on various time and length scales. Computational models of liver regeneration largely focused on incorporating molecular and signaling detail have been developed by multiple research groups in the recent years. These modeling efforts have supported a synthesis of disparate experimental results at the molecular scale. Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. Noninvasive imaging methods such as ultrasound, computed tomography and magnetic resonance imaging provide morphological and physiological features including volumetric measures over time. In this review, we discuss multiple imaging modalities capable of informing computational models of liver regeneration at the organ-, tissue- and cellular level. Additionally, we discuss available software and algorithms, which aid in the analysis and integration of imaging data into computational models. Such models can be generated or tuned for an individual patient with liver disease. Progress towards integrated multiscale models of liver regeneration can aid in prognostic tool development for treating liver disease.
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Affiliation(s)
- Aalap Verma
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Alexandra Manchel
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Justin Melunis
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jan G. Hengstler
- IfADo-Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
| | - Rajanikanth Vadigepalli
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
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9
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Christ B, Collatz M, Dahmen U, Herrmann KH, Höpfl S, König M, Lambers L, Marz M, Meyer D, Radde N, Reichenbach JR, Ricken T, Tautenhahn HM. Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function. Front Physiol 2021; 12:733868. [PMID: 34867441 PMCID: PMC8637208 DOI: 10.3389/fphys.2021.733868] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/26/2021] [Indexed: 01/17/2023] Open
Abstract
Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.
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Affiliation(s)
- Bruno Christ
- Cell Transplantation/Molecular Hepatology Lab, Department of Visceral, Transplant, Thoracic and Vascular Surgery, University of Leipzig Medical Center, Leipzig, Germany
| | - Maximilian Collatz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
- Optisch-Molekulare Diagnostik und Systemtechnologié, Leibniz Institute of Photonic Technology (IPHT), Jena, Germany
- InfectoGnostics Research Campus Jena, Jena, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Sebastian Höpfl
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Matthias König
- Systems Medicine of the Liver Lab, Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Lena Lambers
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Daria Meyer
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Nicole Radde
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Jürgen R. Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Tim Ricken
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Hans-Michael Tautenhahn
- Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
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10
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Yang Y, Filipovic D, Bhattacharya S. A Negative Feedback Loop and Transcription Factor Cooperation Regulate Zonal Gene Induction by 2, 3, 7, 8-Tetrachlorodibenzo-p-Dioxin in the Mouse Liver. Hepatol Commun 2021; 6:750-764. [PMID: 34726355 PMCID: PMC8948569 DOI: 10.1002/hep4.1848] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 10/02/2021] [Accepted: 10/10/2021] [Indexed: 01/04/2023] Open
Abstract
The cytochrome P450 (Cyp) proteins Cyp1A1 and Cyp1A2 are strongly induced in the mouse liver by the potent environmental toxicant 2, 3, 7, 8‐tetrachlorodibenzo‐p‐dioxin (TCDD), acting through the aryl hydrocarbon receptor (AHR). The induction of Cyp1A1 is localized within the centrilobular regions of the mouse liver at low doses of TCDD, progressing to pan‐lobular induction at higher doses. Even without chemical perturbation, metabolic functions and associated genes are basally zonated in the liver lobule along the central‐to‐portal axis. To investigate the mechanistic basis of spatially restricted gene induction by TCDD, we have developed a multiscale computational model of the mouse liver lobule with single‐cell resolution. The spatial location of individual hepatocytes in the model was calibrated from previously published high‐resolution images. A systems biology model of the network of biochemical signaling pathways underlying Cyp1A1 and Cyp1A2 induction was then incorporated into each hepatocyte in the model. Model simulations showed that a negative feedback loop formed by binding of the induced Cyp1A2 protein to TCDD, together with cooperative gene induction by the β‐catenin/AHR/TCDD transcription factor complex and β‐catenin, help produce the spatially localized induction pattern of Cyp1A1. Although endogenous WNT regulates the metabolic zonation of many genes, it was not a driver of zonal Cyp1A1 induction in our model. Conclusion: In this work, we used data‐driven computational modeling to identify the mechanistic basis of zonally restricted gene expression induced by the potent and persistent environmental pollutant TCDD. The multiscale model and derived results clarify the mechanisms of dose‐dependent hepatic gene induction responses to TCDD. Additionally, this work contributes to our broader understanding of spatial gene regulation along the liver lobule.
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Affiliation(s)
- Yongliang Yang
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA.,Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI, USA
| | - David Filipovic
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA.,Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI, USA.,Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Sudin Bhattacharya
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA.,Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI, USA.,Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, USA.,Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, USA
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11
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Seyedpour SM, Nabati M, Lambers L, Nafisi S, Tautenhahn HM, Sack I, Reichenbach JR, Ricken T. Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review. Front Physiol 2021; 12:733393. [PMID: 34630152 PMCID: PMC8493836 DOI: 10.3389/fphys.2021.733393] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/25/2021] [Indexed: 12/15/2022] Open
Abstract
MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.
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Affiliation(s)
- Seyed M. Seyedpour
- Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
- Biomechanics Lab, Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
| | - Mehdi Nabati
- Department of Mechanical Engineering, Faculty of Engineering, Boğaziçi University, Istanbul, Turkey
| | - Lena Lambers
- Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
- Biomechanics Lab, Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
| | - Sara Nafisi
- Faculty of Pharmacy, Istinye University, Istanbul, Turkey
| | - Hans-Michael Tautenhahn
- Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
| | - Ingolf Sack
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Charité Mitte, Berlin, Germany
| | - Jürgen R. Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital-Friedrich Schiller University Jena, Jena, Germany
- Center of Medical Optics and Photonics, Friedrich Schiller University, Jena, Germany
- Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University, Jena, Germany
| | - Tim Ricken
- Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
- Biomechanics Lab, Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
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12
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Sego TJ, Aponte-Serrano JO, Gianlupi JF, Glazier JA. Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection. BMC Biol 2021; 19:196. [PMID: 34496857 PMCID: PMC8424622 DOI: 10.1186/s12915-021-01115-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 08/02/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND The biophysics of an organism span multiple scales from subcellular to organismal and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them and highlights the need for simpler methods able to model the spatial features of biological systems. RESULTS In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models, the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models. CONCLUSION We developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
| | - Josua O Aponte-Serrano
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Juliano F Gianlupi
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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13
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Zheng F, Xiao Y, Liu H, Fan Y, Dao M. Patient-Specific Organoid and Organ-on-a-Chip: 3D Cell-Culture Meets 3D Printing and Numerical Simulation. Adv Biol (Weinh) 2021; 5:e2000024. [PMID: 33856745 PMCID: PMC8243895 DOI: 10.1002/adbi.202000024] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 02/13/2021] [Indexed: 12/11/2022]
Abstract
The last few decades have witnessed diversified in vitro models to recapitulate the architecture and function of living organs or tissues and contribute immensely to advances in life science. Two novel 3D cell culture models: 1) Organoid, promoted mainly by the developments of stem cell biology and 2) Organ-on-a-chip, enhanced primarily due to microfluidic technology, have emerged as two promising approaches to advance the understanding of basic biological principles and clinical treatments. This review describes the comparable distinct differences between these two models and provides more insights into their complementarity and integration to recognize their merits and limitations for applicable fields. The convergence of the two approaches to produce multi-organoid-on-a-chip or human organoid-on-a-chip is emerging as a new approach for building 3D models with higher physiological relevance. Furthermore, rapid advancements in 3D printing and numerical simulations, which facilitate the design, manufacture, and results-translation of 3D cell culture models, can also serve as novel tools to promote the development and propagation of organoid and organ-on-a-chip systems. Current technological challenges and limitations, as well as expert recommendations and future solutions to address the promising combinations by incorporating organoids, organ-on-a-chip, 3D printing, and numerical simulation, are also summarized.
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Affiliation(s)
- Fuyin Zheng
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yuminghao Xiao
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hui Liu
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Yubo Fan
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Ming Dao
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore
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14
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Physiologically based pharmacokinetic (PBPK) modeling of RNAi therapeutics: Opportunities and challenges. Biochem Pharmacol 2021; 189:114468. [PMID: 33577889 DOI: 10.1016/j.bcp.2021.114468] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool with many demonstrated applications in various phases of drug development and regulatory review. RNA interference (RNAi)-based therapeutics are a class of drugs that have unique pharmacokinetic properties and mechanisms of action. With an increasing number of RNAi therapeutics in the pipeline and reaching the market, there is a considerable amount of active research in this area requiring a multidisciplinary approach. The application of PBPK models for RNAi therapeutics is in its infancy and its utility to facilitate the development of this new class of drugs is yet to be fully evaluated. From this perspective, we briefly discuss some of the current computational modeling approaches used in support of efficient development and approval of RNAi therapeutics. Considerations for PBPK model development are highlighted both in a relative context between small molecules and large molecules such as monoclonal antibodies and as it applies to RNAi therapeutics. In addition, the prospects for drawing upon other recognized avenues of PBPK modeling and some of the foreseeable challenges in PBPK model development for these chemical modalities are briefly discussed. Finally, an exploration of the potential application of PBPK model development for RNAi therapeutics is provided. We hope these preliminary thoughts will help initiate a dialogue between scientists in the relevant sectors to examine the value of PBPK modeling for RNAi therapeutics. Such evaluations could help standardize the practice in the future and support appropriate guidance development for strengthening the RNAi therapeutics development program.
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15
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Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes. iScience 2021; 24:102101. [PMID: 33615200 PMCID: PMC7878987 DOI: 10.1016/j.isci.2021.102101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/21/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
A virtual metabolic human model is a valuable complement to experimental biology and clinical studies, because in vivo research involves serious ethical and technical problems. I have proposed a multi-organ and multi-scale kinetic model that formulates the reactions of enzymes and transporters with the regulation of hormonal actions at postprandial and postabsorptive states. The computational model consists of 202 ordinary differential equations for metabolites with 217 reaction rates and 1,140 kinetic parameter constants. It is the most comprehensive, largest, and highly predictive model of the whole-body metabolism. Use of the model revealed the mechanisms by which individual disorders, such as steatosis, β cell dysfunction, and insulin resistance, were combined to cause diabetes. The model predicted a glycerol kinase inhibitor to be an effective medicine for type 2 diabetes, which not only decreased hepatic triglyceride but also reduced plasma glucose. The model also enabled us to rationally design combination therapy. A standard of virtual metabolic human dynamic models is proposed It integrates the three scales of molecules, organs, and whole body It gets insight into pathological mechanisms of type 1 and type 2 diabetes It enables the computer-aided design of medication treatment for diabetes
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16
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Wang Y, Brodin E, Nishii K, Frieboes HB, Mumenthaler SM, Sparks JL, Macklin P. Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach. Sci Rep 2021; 11:1710. [PMID: 33462259 PMCID: PMC7813881 DOI: 10.1038/s41598-020-78780-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could "reawaken" after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer.
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Affiliation(s)
- Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Erik Brodin
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Kenichiro Nishii
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica L Sparks
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA.
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
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17
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Ho H, Zhang E. Virtual Lobule Models Are the Key for Multiscale Biomechanical and Pharmacological Modeling for the Liver. Front Physiol 2020; 11:1061. [PMID: 32982791 PMCID: PMC7492636 DOI: 10.3389/fphys.2020.01061] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 07/31/2020] [Indexed: 12/18/2022] Open
Affiliation(s)
- Harvey Ho
- Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - En Zhang
- Chongqing Institute for Food and Drug Control, Chongqing City, China
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18
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Frieboes HB, Raghavan S, Godin B. Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis. Front Bioeng Biotechnol 2020; 8:1011. [PMID: 32974325 PMCID: PMC7466654 DOI: 10.3389/fbioe.2020.01011] [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: 06/16/2020] [Accepted: 08/03/2020] [Indexed: 12/13/2022] Open
Abstract
The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.
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Affiliation(s)
- Hermann B. Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, United States
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, United States
- Center for Predictive Medicine, University of Louisville, Louisville, KY, United States
| | - Shreya Raghavan
- Department of Biomedical Engineering, College of Engineering, Texas A&M University, College Station, TX, United States
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
| | - Biana Godin
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
- Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, TX, United States
- Developmental Therapeutics Program, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, United States
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19
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Herland A, Maoz BM, Das D, Somayaji MR, Prantil-Baun R, Novak R, Cronce M, Huffstater T, Jeanty SSF, Ingram M, Chalkiadaki A, Benson Chou D, Marquez S, Delahanty A, Jalili-Firoozinezhad S, Milton Y, Sontheimer-Phelps A, Swenor B, Levy O, Parker KK, Przekwas A, Ingber DE. Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips. Nat Biomed Eng 2020; 4:421-436. [PMID: 31988459 PMCID: PMC8011576 DOI: 10.1038/s41551-019-0498-9] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 11/25/2019] [Indexed: 01/15/2023]
Abstract
Analyses of drug pharmacokinetics (PKs) and pharmacodynamics (PDs) performed in animals are often not predictive of drug PKs and PDs in humans, and in vitro PK and PD modelling does not provide quantitative PK parameters. Here, we show that physiological PK modelling of first-pass drug absorption, metabolism and excretion in humans-using computationally scaled data from multiple fluidically linked two-channel organ chips-predicts PK parameters for orally administered nicotine (using gut, liver and kidney chips) and for intravenously injected cisplatin (using coupled bone marrow, liver and kidney chips). The chips are linked through sequential robotic liquid transfers of a common blood substitute by their endothelium-lined channels (as reported by Novak et al. in an associated Article) and share an arteriovenous fluid-mixing reservoir. We also show that predictions of cisplatin PDs match previously reported patient data. The quantitative in-vitro-to-in-vivo translation of PK and PD parameters and the prediction of drug absorption, distribution, metabolism, excretion and toxicity through fluidically coupled organ chips may improve the design of drug-administration regimens for phase-I clinical trials.
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Affiliation(s)
- Anna Herland
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- Division of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden
- AIMES, Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Ben M Maoz
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Debarun Das
- CFD Research Corporation, Huntsville, AL, USA
| | | | - Rachelle Prantil-Baun
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Richard Novak
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Michael Cronce
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Tessa Huffstater
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Sauveur S F Jeanty
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Miles Ingram
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Angeliki Chalkiadaki
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - David Benson Chou
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Susan Marquez
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Aaron Delahanty
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Sasan Jalili-Firoozinezhad
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- Department of Bioengineering and Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Portugal Graduate Program, Universidade de Lisboa, Lisbon, Portugal
| | - Yuka Milton
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Alexandra Sontheimer-Phelps
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Ben Swenor
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Oren Levy
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Kevin K Parker
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | | | - Donald E Ingber
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
- Division of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden.
- Vascular Biology Program and Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
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20
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Liver Bioreactor Design Issues of Fluid Flow and Zonation, Fibrosis, and Mechanics: A Computational Perspective. J Funct Biomater 2020; 11:jfb11010013. [PMID: 32121053 PMCID: PMC7151609 DOI: 10.3390/jfb11010013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 01/27/2020] [Accepted: 02/18/2020] [Indexed: 02/06/2023] Open
Abstract
Tissue engineering, with the goal of repairing or replacing damaged tissue and organs, has continued to make dramatic science-based advances since its origins in the late 1980’s and early 1990’s. Such advances are always multi-disciplinary in nature, from basic biology and chemistry through physics and mathematics to various engineering and computer fields. This review will focus its attention on two topics critical for tissue engineering liver development: (a) fluid flow, zonation, and drug screening, and (b) biomechanics, tissue stiffness, and fibrosis, all within the context of 3D structures. First, a general overview of various bioreactor designs developed to investigate fluid transport and tissue biomechanics is given. This includes a mention of computational fluid dynamic methods used to optimize and validate these designs. Thereafter, the perspective provided by computer simulations of flow, reactive transport, and biomechanics responses at the scale of the liver lobule and liver tissue is outlined, in addition to how bioreactor-measured properties can be utilized in these models. Here, the fundamental issues of tortuosity and upscaling are highlighted, as well as the role of disease and fibrosis in these issues. Some idealized simulations of the effects of fibrosis on lobule drug transport and mechanics responses are provided to further illustrate these concepts. This review concludes with an outline of some practical applications of tissue engineering advances and how efficient computational upscaling techniques, such as dual continuum modeling, might be used to quantify the transition of bioreactor results to the full liver scale.
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Chowkwale M, Mahler GJ, Huang P, Murray BT. A multiscale in silico model of endothelial to mesenchymal transformation in a tumor microenvironment. J Theor Biol 2019; 480:229-240. [PMID: 31430445 DOI: 10.1016/j.jtbi.2019.08.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 08/01/2019] [Accepted: 08/16/2019] [Indexed: 12/12/2022]
Abstract
Endothelial to mesenchymal transformation (EndMT) is a process in which endothelial cells gain a mesenchymal-like phenotype in response to mechanobiological signals that results in the remodeling or repair of underlying tissue. While initially associated with embryonic development, this process has since been shown to occur in adult tissue remodeling including wound healing, fibrosis, and cancer. In an attempt to understand the role of EndMT in cancer progression and metastasis, we present a multiscale, three-dimensional, in silico model. The model couples tissue level phenomena such as extracellular matrix remodeling, cellular level phenomena such as migration and proliferation, and chemical transport in the tumor microenvironment to mimic in vitro tissue models of the cancer microenvironment. The model is used to study the presence of EndMT-derived activated fibroblasts (EDAFs) and varying substrate stiffness on tumor cell migration and proliferation. The simulations accurately model the behavior of tumor cells under given conditions. The presence of EDAFs and/or an increase in substrate stiffness resulted in an increase in tumor cell activity. This model lays the foundation of further studies of EDAFs in a tumor microenvironment on a cellular and subcellular physiological level.
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Affiliation(s)
- M Chowkwale
- Department of Biomedical Engineering, Binghamton University, PO Box 6000, Binghamton, NY 13902, USA
| | - G J Mahler
- Department of Biomedical Engineering, Binghamton University, PO Box 6000, Binghamton, NY 13902, USA
| | - P Huang
- Department of Mechanical Engineering, Binghamton University, PO Box 6000, Binghamton, NY 13902, USA
| | - B T Murray
- Department of Mechanical Engineering, Binghamton University, PO Box 6000, Binghamton, NY 13902, USA.
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22
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Franiatte S, Clarke R, Ho H. A computational model for hepatotoxicity by coupling drug transport and acetaminophen metabolism equations. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3234. [PMID: 31254976 DOI: 10.1002/cnm.3234] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 06/02/2019] [Accepted: 06/25/2019] [Indexed: 06/09/2023]
Abstract
The spatial distributions of cytochrome P450 (CYP450) and glutathione (GSH) in liver lobules determine the heterogeneous hepatotoxicity of acetaminophen (APAP). Their interplay in conjunction with blood flow is not well understood. In this paper, we integrate a cellular APAP metabolism model with a sinusoidal blood flow to simulate the temporal-spatial patterns of APAP-induced hepatotoxicity. The heterogeneous distribution of CYP450 and GSH is modeled by linearly varying their reaction rates along the portal triad to the central vein axis of a sinusoid. We found that the spatial distribution of GSH, glutathione S-transferases (GSTs), and CYP450 all contributes to the high acetaminophen protein adduct formation at zone 3 of the lobules. The reversed spatial gradients of CYP450 and GSH cause quick depletion of GSH, which is further accelerated by the distribution of GST. The hepatic flow congestion and hyperperfusion however do not seem to play a significant role in the zonal hepatotoxicity. The simulation results may be useful for understanding the APAP-induced hepatotoxicity and associated pharmaceutical treatment.
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Affiliation(s)
- Sylvain Franiatte
- ENSEEIHT, National Polytechnic Institute of Toulouse, Toulouse, France
| | - Richard Clarke
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Harvey Ho
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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Galic N, Hindle AG, DeLong JP, Watanabe K, Forbes V, Buck CL. Modeling genomes to phenomes to populations in a changing climate: The need for collaborative networks. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.05.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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24
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Wolff HB, Davidson LA, Merks RMH. Adapting a Plant Tissue Model to Animal Development: Introducing Cell Sliding into VirtualLeaf. Bull Math Biol 2019; 81:3322-3341. [PMID: 30927191 PMCID: PMC6677868 DOI: 10.1007/s11538-019-00599-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 03/11/2019] [Indexed: 11/16/2022]
Abstract
Cell-based, mathematical modeling of collective cell behavior has become a prominent tool in developmental biology. Cell-based models represent individual cells as single particles or as sets of interconnected particles and predict the collective cell behavior that follows from a set of interaction rules. In particular, vertex-based models are a popular tool for studying the mechanics of confluent, epithelial cell layers. They represent the junctions between three (or sometimes more) cells in confluent tissues as point particles, connected using structural elements that represent the cell boundaries. A disadvantage of these models is that cell-cell interfaces are represented as straight lines. This is a suitable simplification for epithelial tissues, where the interfaces are typically under tension, but this simplification may not be appropriate for mesenchymal tissues or tissues that are under compression, such that the cell-cell boundaries can buckle. In this paper, we introduce a variant of VMs in which this and two other limitations of VMs have been resolved. The new model can also be seen as on off-the-lattice generalization of the Cellular Potts Model. It is an extension of the open-source package VirtualLeaf, which was initially developed to simulate plant tissue morphogenesis where cells do not move relative to one another. The present extension of VirtualLeaf introduces a new rule for cell-cell shear or sliding, from which cell rearrangement (T1) and cell extrusion (T2) transitions emerge naturally, allowing the application of VirtualLeaf to problems of animal development. We show that the updated VirtualLeaf yields different results than the traditional vertex-based models for differential adhesion-driven cell sorting and for the neighborhood topology of soft cellular networks.
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Affiliation(s)
- Henri B Wolff
- Centrum Wiskunde and Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands
- Departments of Bioengineering, Developmental Biology, and Computational and Systems Biology, University of Pittsburgh, Bioscience Tower 3-5059 3501 Fifth Avenue, Pittsburgh, PA, USA
- Department of Epidemiology and Biostatistics, Decision Modeling Center VUmc, Amsterdam UMC location VUmc, PO Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Lance A Davidson
- Departments of Bioengineering, Developmental Biology, and Computational and Systems Biology, University of Pittsburgh, Bioscience Tower 3-5059 3501 Fifth Avenue, Pittsburgh, PA, USA.
| | - Roeland M H Merks
- Centrum Wiskunde and Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands.
- Mathematical Institute, University Leiden, P.O. Box 9512, 2300 RA, Leiden, The Netherlands.
- Mathematical Institute and Institute of Biology, Leiden University, P.O. Box 9505, 2300 RA, Leiden, The Netherlands.
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25
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Verma BK, Subramaniam P, Vadigepalli R. Model-based virtual patient analysis of human liver regeneration predicts critical perioperative factors controlling the dynamic mode of response to resection. BMC SYSTEMS BIOLOGY 2019; 13:9. [PMID: 30651095 PMCID: PMC6335689 DOI: 10.1186/s12918-019-0678-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 01/02/2019] [Indexed: 11/10/2022]
Abstract
BACKGROUND Liver has the unique ability to regenerate following injury, with a wide range of variability of the regenerative response across individuals. Existing computational models of the liver regeneration are largely tuned based on rodent data and hence it is not clear how well these models capture the dynamics of human liver regeneration. Recent availability of human liver volumetry time series data has enabled new opportunities to tune the computational models for human-relevant time scales, and to predict factors that can significantly alter the dynamics of liver regeneration following a resection. METHODS We utilized a mathematical model that integrates signaling mechanisms and cellular functional state transitions. We tuned the model parameters to match the time scale of human liver regeneration using an elastic net based regularization approach for identifying optimal parameter values. We initially examined the effect of each parameter individually on the response mode (normal, suppressed, failure) and extent of recovery to identify critical parameters. We employed phase plane analysis to compute the threshold of resection. We mapped the distribution of the response modes and threshold of resection in a virtual patient cohort generated in silico via simultaneous variations in two most critical parameters. RESULTS Analysis of the responses to resection with individual parameter variations showed that the response mode and extent of recovery following resection were most sensitive to variations in two perioperative factors, metabolic load and cell death post partial hepatectomy. Phase plane analysis identified two steady states corresponding to recovery and failure, with a threshold of resection separating the two basins of attraction. The size of the basin of attraction for the recovery mode varied as a function of metabolic load and cell death sensitivity, leading to a change in the multiplicity of the system in response to changes in these two parameters. CONCLUSIONS Our results suggest that the response mode and threshold of failure are critically dependent on the metabolic load and cell death sensitivity parameters that are likely to be patient-specific. Interventions that modulate these critical perioperative factors may be helpful to drive the liver regenerative response process towards a complete recovery mode.
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Affiliation(s)
- Babita K Verma
- Daniel Baugh Institute for Functional Genomics/Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Chemical Engineering, Indian Institute of Technology-Madras, Chennai, India
| | | | - Rajanikanth Vadigepalli
- Daniel Baugh Institute for Functional Genomics/Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA.
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26
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Cohen Hubal EA, Wetmore BA, Wambaugh JF, El-Masri H, Sobus JR, Bahadori T. Advancing internal exposure and physiologically-based toxicokinetic modeling for 21st-century risk assessments. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2019; 29:11-20. [PMID: 30116055 PMCID: PMC6760598 DOI: 10.1038/s41370-018-0046-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 03/15/2018] [Accepted: 03/19/2018] [Indexed: 05/22/2023]
Abstract
Scientifically sound, risk-informed evaluation of chemicals is essential to protecting public health. Systematically leveraging information from exposure, toxicology, and epidemiology studies can provide a holistic understanding of how real-world exposure to chemicals may impact the health of populations, including sensitive and vulnerable individuals and life-stages. Increasingly, public health policy makers are employing toxicokinetic (TK) modeling tools to integrate these data streams and predict potential human health impact. Development of a suite of tools for predicting internal exposure, including physiologically-based toxicokinetic (PBTK) models, is being driven by needs to address large numbers of data-poor chemicals efficiently, translate bioactivity, and mechanistic information from new in vitro test systems, and integrate multiple lines of evidence to enable scientifically sound, risk-informed decisions. New modeling approaches are being designed "fit for purpose" to inform specific decision contexts, with applications ranging from rapid screening of hundreds of chemicals, to improved prediction of risks during sensitive stages of development. New data are being generated experimentally and computationally to support these models. Progress to meet the demand for internal exposure and PBTK modeling tools will require transparent publication of models and data to build credibility in results, as well as opportunities to partner with decision makers to evaluate and build confidence in use of these for improved decisions that promote safe use of chemicals.
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Affiliation(s)
| | - Barbara A Wetmore
- National Exposure Research Laboratory (NERL), US EPA, Washington, USA
| | - John F Wambaugh
- National Center for Computational Toxicology (NCCT), US EPA, Washington, USA
| | - Hisham El-Masri
- National Health and Environmental Effects Laboratory (NHEERL), US EPA, Washington, USA
| | - Jon R Sobus
- National Exposure Research Laboratory (NERL), US EPA, Washington, USA
| | - Tina Bahadori
- National Center for Environmental Assessment (NCEA), US EPA, Washington, USA
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27
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Pletz J, Enoch SJ, Jais DM, Mellor CL, Pawar G, Firman JW, Madden JC, Webb SD, Tagliati CA, Cronin MTD. A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction. Expert Opin Drug Metab Toxicol 2018; 14:1225-1253. [PMID: 30345815 DOI: 10.1080/17425255.2018.1539076] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilize multiscale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. Aims covered: The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled. Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo, and human data that may assist in the development of in silico models which in turn may shed light on the interrelationships between nephrotoxicity mechanisms.
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Affiliation(s)
- Julia Pletz
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Steven J Enoch
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Diviya M Jais
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Claire L Mellor
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Gopal Pawar
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - James W Firman
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Judith C Madden
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Steven D Webb
- b Department of Applied Mathematics , Liverpool John Moores University , Liverpool , UK
| | - Carlos A Tagliati
- c Departamento de Análises Clínicas e Toxicológicas , Universidade Federal de Minas Gerais , Belo Horizonte , Brazil
| | - Mark T D Cronin
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
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28
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Means SA, Ho H. A spatial-temporal model for zonal hepatotoxicity of acetaminophen. Drug Metab Pharmacokinet 2018; 34:71-77. [PMID: 30377056 DOI: 10.1016/j.dmpk.2018.09.266] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 09/11/2018] [Accepted: 09/28/2018] [Indexed: 12/19/2022]
Abstract
The metabolism zonation in liver lobules is well known yet its incorporation into the mathematical models of acetaminophen (APAP) metabolism is still primitive - only the oxidation pathway via reaction with the cytochrome P450 (CYP450) has been considered, yet the zonal heterogeneity exhibits in all three pathways including sulphation, glucuronidation and oxidation. In this paper we present a novel computational method where an intracellular APAP metabolism model is integrated into a Finite Element Model (FEM) of sinusoids, and the zonal heterogeneity in three metabolism pathways are all incorporated. We demonstrate that the degradation of APAP, detoxification via glutathione (GSH) and the formation of hepatotoxicity, are all affected profoundly by the zonal difference. Specifically, glucuronidation plays a major role in the degradation of APAP. Generation of GSH, its conjugation with the toxic NAPQI and the spatial distribution of CYP450 combined together determine the toxicity of APAP. We suggest that the current platform be used for further hepatotoxicity study of APAP by incorporating other heterogeneity factors.
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Affiliation(s)
- Shawn A Means
- Auckland Bioengineering Institute, The University of Auckland, New Zealand
| | - Harvey Ho
- Auckland Bioengineering Institute, The University of Auckland, New Zealand.
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29
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Fu X, Sluka JP, Clendenon SG, Dunn KW, Wang Z, Klaunig JE, Glazier JA. Modeling of xenobiotic transport and metabolism in virtual hepatic lobule models. PLoS One 2018; 13:e0198060. [PMID: 30212461 PMCID: PMC6136710 DOI: 10.1371/journal.pone.0198060] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 08/23/2018] [Indexed: 12/29/2022] Open
Abstract
Computational models of normal liver function and xenobiotic induced liver damage are increasingly being used to interpret in vitro and in vivo data and as an approach to the de novo prediction of the liver’s response to xenobiotics. The microdosimetry (dose at the level of individual cells) of xenobiotics vary spatially within the liver because of both compound-independent and compound-dependent factors. In this paper, we build model liver lobules to investigate the interplay between vascular structure, blood flow and cellular transport that lead to regional variations in microdosimetry. We then compared simulation results obtained using this complex spatial model with a simpler linear pipe model of a sinusoid and a very simple single box model. We found that variations in diffusive transport, transporter-mediated transport and metabolism, coupled with complex liver sinusoid architecture and blood flow distribution, led to three essential patterns of xenobiotic exposure within the virtual liver lobule: (1) lobular-wise uniform, (2) radially varying and (3) both radially and azimuthally varying. We propose to use these essential patterns of exposure as a reference for selection of model representations when a computational study involves modeling detailed hepatic responses to xenobiotics.
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Affiliation(s)
- Xiao Fu
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Physics, Indiana University, Bloomington, IN, United States of America
| | - James P. Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
- * E-mail:
| | - Sherry G. Clendenon
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
| | - Kenneth W. Dunn
- School of Medicine, Indiana University, Indianapolis, IN, United States of America
| | - Zemin Wang
- School of Public Health, Indiana University, Bloomington, IN, United States of America
| | - James E. Klaunig
- School of Public Health, Indiana University, Bloomington, IN, United States of America
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
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30
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Piñero J, Furlong LI, Sanz F. In silico models in drug development: where we are. Curr Opin Pharmacol 2018; 42:111-121. [PMID: 30205360 DOI: 10.1016/j.coph.2018.08.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 07/30/2018] [Accepted: 08/13/2018] [Indexed: 02/07/2023]
Abstract
The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.
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Affiliation(s)
- Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.
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31
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Christ B, Dahmen U, Herrmann KH, König M, Reichenbach JR, Ricken T, Schleicher J, Ole Schwen L, Vlaic S, Waschinsky N. Computational Modeling in Liver Surgery. Front Physiol 2017; 8:906. [PMID: 29249974 PMCID: PMC5715340 DOI: 10.3389/fphys.2017.00906] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 10/25/2017] [Indexed: 12/13/2022] Open
Abstract
The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.
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Affiliation(s)
- Bruno Christ
- Molecular Hepatology Lab, Clinics of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Matthias König
- Department of Biology, Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Tim Ricken
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
| | - Jana Schleicher
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany.,Department of Bioinformatics, Friedrich Schiller University Jena, Jena, Germany
| | | | - Sebastian Vlaic
- Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
| | - Navina Waschinsky
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
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32
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Kotsampasakou E, Ecker GF. Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters-An in Silico Modeling Approach. J Chem Inf Model 2017; 57:608-615. [PMID: 28166633 PMCID: PMC5411109 DOI: 10.1021/acs.jcim.6b00518] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Cholestasis represents one out of three types of drug induced liver injury (DILI), which comprises a major challenge in drug development. In this study we applied a two-class classification scheme based on k-nearest neighbors in order to predict cholestasis, using a set of 93 two-dimensional (2D) physicochemical descriptors and predictions of selected hepatic transporters' inhibition (BSEP, BCRP, P-gp, OATP1B1, and OATP1B3). In order to assess the potential contribution of transporter inhibition, we compared whether the inclusion of the transporters' inhibition predictions contributes to a significant increase in model performance in comparison to the plain use of the 93 2D physicochemical descriptors. Our findings were in agreement with literature findings, indicating a contribution not only from BSEP inhibition but a rather synergistic effect deriving from the whole set of transporters. The final optimal model was validated via both 10-fold cross validation and external validation. It performs quite satisfactorily resulting in 0.686 ± 0.013 for accuracy and 0.722 ± 0.014 for area under the receiver operating characteristic curve (AUC) for 10-fold cross-validation (mean ± standard deviation from 50 iterations).
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Affiliation(s)
- Eleni Kotsampasakou
- University of Vienna , Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
| | - Gerhard F Ecker
- University of Vienna , Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
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33
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Woodhead JL, Watkins PB, Howell BA, Siler SQ, Shoda LKM. The role of quantitative systems pharmacology modeling in the prediction and explanation of idiosyncratic drug-induced liver injury. Drug Metab Pharmacokinet 2016; 32:40-45. [PMID: 28129975 DOI: 10.1016/j.dmpk.2016.11.008] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 10/14/2016] [Accepted: 11/15/2016] [Indexed: 01/12/2023]
Abstract
Idiosyncratic drug-induced liver injury (iDILI) is a serious concern in drug development. The rarity and multifactorial nature of iDILI makes it difficult to predict and explain. Recently, human leukocyte antigen (HLA) allele associations have provided strong support for a role of an adaptive immune response in the pathogenesis of many iDILI cases; however, it is likely that an adaptive immune attack requires several preceding events. Quantitative systems pharmacology (QSP), an in silico modeling technique that leverages known physiology and the results of in vitro experiments in order to make predictions about how drugs affect biological processes, is proposed as a potentially useful tool for predicting and explaining critical events that likely precede immune-mediated iDILI, as well as the immune attack itself. DILIsym, a QSP platform for drug-induced liver injury, has demonstrated success in predicting the presence of delayed hepatocellular stress events that likely precede the iDILI cascade, and has successfully predicted hepatocellular stress likely underlying iDILI attributed to troglitazone and tolvaptan. The incorporation of a model of the adaptive immune system into DILIsym would represent and important advance. In summary, QSP methods can play a key role in the future prediction and understanding of both immune-mediated and non-immune-mediated iDILI.
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Affiliation(s)
- Jeffrey L Woodhead
- DILIsym Services, Inc., 6 Davis Drive, Research Triangle Park, NC 27709, USA.
| | - Paul B Watkins
- Institute for Drug Safety Sciences, UNC-Eshelman School of Pharmacy, 6 Davis Drive, Research Triangle Park, NC 27709, USA
| | - Brett A Howell
- DILIsym Services, Inc., 6 Davis Drive, Research Triangle Park, NC 27709, USA
| | - Scott Q Siler
- DILIsym Services, Inc., 6 Davis Drive, Research Triangle Park, NC 27709, USA
| | - Lisl K M Shoda
- DILIsym Services, Inc., 6 Davis Drive, Research Triangle Park, NC 27709, USA
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