1
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Dubois V, Lefebvre P, Staels B, Eeckhoute J. Nuclear receptors: pathophysiological mechanisms and drug targets in liver disease. Gut 2024:gutjnl-2023-331741. [PMID: 38862216 DOI: 10.1136/gutjnl-2023-331741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/18/2024] [Indexed: 06/13/2024]
Abstract
Nuclear receptors (NRs) are ligand-dependent transcription factors required for liver development and function. As a consequence, NRs have emerged as attractive drug targets in a wide range of liver diseases. However, liver dysfunction and failure are linked to loss of hepatocyte identity characterised by deficient NR expression and activities. This might at least partly explain why several pharmacological NR modulators have proven insufficiently efficient to improve liver functionality in advanced stages of diseases such as metabolic dysfunction-associated steatotic liver disease (MASLD). In this perspective, we review the most recent advances in the hepatic NR field and discuss the contribution of multiomic approaches to our understanding of their role in the molecular organisation of an intricated transcriptional regulatory network, as well as in liver intercellular dialogues and interorgan cross-talks. We discuss the potential benefit of novel therapeutic approaches simultaneously targeting multiple NRs, which would not only reactivate the hepatic NR network and restore hepatocyte identity but also impact intercellular and interorgan interplays whose importance to control liver functions is further defined. Finally, we highlight the need of considering individual parameters such as sex and disease stage in the development of NR-based clinical strategies.
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Affiliation(s)
- Vanessa Dubois
- Basic and Translational Endocrinology (BaTE), Department of Basic and Applied Medical Sciences, Ghent University, Gent, Belgium
| | - Philippe Lefebvre
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011-EGID, Lille, France
| | - Bart Staels
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011-EGID, Lille, France
| | - Jerome Eeckhoute
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011-EGID, Lille, France
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2
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Zou J, Li J, Zhong X, Tang D, Fan X, Chen R. Liver in infections: a single-cell and spatial transcriptomics perspective. J Biomed Sci 2023; 30:53. [PMID: 37430371 PMCID: PMC10332047 DOI: 10.1186/s12929-023-00945-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/27/2023] [Indexed: 07/12/2023] Open
Abstract
The liver is an immune organ that plays a vital role in the detection, capture, and clearance of pathogens and foreign antigens that invade the human body. During acute and chronic infections, the liver transforms from a tolerant to an active immune state. The defence mechanism of the liver mainly depends on a complicated network of intrahepatic and translocated immune cells and non-immune cells. Therefore, a comprehensive liver cell atlas in both healthy and diseased states is needed for new therapeutic target development and disease intervention improvement. With the development of high-throughput single-cell technology, we can now decipher heterogeneity, differentiation, and intercellular communication at the single-cell level in sophisticated organs and complicated diseases. In this concise review, we aimed to summarise the advancement of emerging high-throughput single-cell technologies and re-define our understanding of liver function towards infections, including hepatitis B virus, hepatitis C virus, Plasmodium, schistosomiasis, endotoxemia, and corona virus disease 2019 (COVID-19). We also unravel previously unknown pathogenic pathways and disease mechanisms for the development of new therapeutic targets. As high-throughput single-cell technologies mature, their integration into spatial transcriptomics, multiomics, and clinical data analysis will aid in patient stratification and in developing effective treatment plans for patients with or without liver injury due to infectious diseases.
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Affiliation(s)
- Ju Zou
- Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Jie Li
- Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Xiao Zhong
- Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Daolin Tang
- Department of Surgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Xuegong Fan
- Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Ruochan Chen
- Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
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3
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Dichamp J, Cellière G, Ghallab A, Hassan R, Boissier N, Hofmann U, Reinders J, Sezgin S, Zühlke S, Hengstler JG, Drasdo D. In vitro to in vivo acetaminophen hepatotoxicity extrapolation using classical schemes, pharmacodynamic models and a multiscale spatial-temporal liver twin. Front Bioeng Biotechnol 2023; 11:1049564. [PMID: 36815881 PMCID: PMC9932319 DOI: 10.3389/fbioe.2023.1049564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/10/2023] [Indexed: 02/05/2023] Open
Abstract
In vitro to in vivo extrapolation represents a critical challenge in toxicology. In this paper we explore extrapolation strategies for acetaminophen (APAP) based on mechanistic models, comparing classical (CL) homogeneous compartment pharmacodynamic (PD) models and a spatial-temporal (ST), multiscale digital twin model resolving liver microarchitecture at cellular resolution. The models integrate consensus detoxification reactions in each individual hepatocyte. We study the consequences of the two model types on the extrapolation and show in which cases these models perform better than the classical extrapolation strategy that is based either on the maximal drug concentration (Cmax) or the area under the pharmacokinetic curve (AUC) of the drug blood concentration. We find that an CL-model based on a well-mixed blood compartment is sufficient to correctly predict the in vivo toxicity from in vitro data. However, the ST-model that integrates more experimental information requires a change of at least one parameter to obtain the same prediction, indicating that spatial compartmentalization may indeed be an important factor.
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Affiliation(s)
- Jules Dichamp
- Group SIMBIOTX, INRIA Saclay-Île-de-France, Palaiseau, France,Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany,Group MAMBA, INRIA Paris, Paris, France
| | | | - Ahmed Ghallab
- Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany,Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena, Egypt
| | - Reham Hassan
- Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany,Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena, Egypt
| | - Noemie Boissier
- Group SIMBIOTX, INRIA Saclay-Île-de-France, Palaiseau, France
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology and University of Tübingen, Stuttgart, Germany
| | - Joerg Reinders
- Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
| | - Selahaddin Sezgin
- Faculty of Chemistry and Chemical Biology, TU Dortmund, Dortmund, Germany
| | - Sebastian Zühlke
- Center for Mass Spectrometry (CMS), Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
| | - Jan G. Hengstler
- Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
| | - Dirk Drasdo
- Group SIMBIOTX, INRIA Saclay-Île-de-France, Palaiseau, France,Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany,Group MAMBA, INRIA Paris, Paris, France,*Correspondence: Dirk Drasdo,
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4
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Hoehme S, Hammad S, Boettger J, Begher-Tibbe B, Bucur P, Vibert E, Gebhardt R, Hengstler JG, Drasdo D. Digital twin demonstrates significance of biomechanical growth control in liver regeneration after partial hepatectomy. iScience 2022; 26:105714. [PMID: 36691615 PMCID: PMC9860368 DOI: 10.1016/j.isci.2022.105714] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/23/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Partial liver removal is an important therapy option for liver cancer. In most patients within a few weeks, the liver is able to fully regenerate. In some patients, however, regeneration fails with often severe consequences. To better understand the control mechanisms of liver regeneration, experiments in mice were performed, guiding the creation of a spatiotemporal 3D model of the regenerating liver. The model represents cells and blood vessels within an entire liver lobe, a macroscopic liver subunit. The model could reproduce the experimental data only if a biomechanical growth control (BGC)-mechanism, inhibiting cell cycle entrance at high compression, was taken into account and predicted that BGC may act as a short-range growth inhibitor minimizing the number of proliferating neighbor cells of a proliferating cell, generating a checkerboard-like proliferation pattern. Model-predicted cell proliferation patterns in pigs and mice were found experimentally. The results underpin the importance of biomechanical aspects in liver growth control.
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Affiliation(s)
- Stefan Hoehme
- Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Haertelstraße 16-18, 04107 Leipzig, Germany,Institute of Computer Science, University of Leipzig, Haertelstraße 16-18, 04107 Leipzig, Germany,Saxonian Incubator for Clinical Research (SIKT), Philipp-Rosenthal-Straße 55, 04103 Leipzig, Germany
| | - Seddik Hammad
- Section Molecular Hepatology, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Germany,Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, 44139 Dortmund, Germany,Department of Forensic Medicine and Veterinary Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena, Egypt
| | - Jan Boettger
- Faculty of Medicine, Rudolf-Schoenheimer-Institute of Biochemistry, Leipzig University, 04103 Leipzig, Germany
| | - Brigitte Begher-Tibbe
- Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, 44139 Dortmund, Germany
| | - Petru Bucur
- Unité INSERM 1193, Centre Hépato-Biliaire, Villejuif, France,Service de Chirurgie Digestive, CHU Trousseau, Tours, France
| | - Eric Vibert
- Unité INSERM 1193, Centre Hépato-Biliaire, Villejuif, France
| | - Rolf Gebhardt
- Faculty of Medicine, Rudolf-Schoenheimer-Institute of Biochemistry, Leipzig University, 04103 Leipzig, Germany
| | - Jan G. Hengstler
- Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, 44139 Dortmund, Germany
| | - Dirk Drasdo
- Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Haertelstraße 16-18, 04107 Leipzig, Germany,Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, 44139 Dortmund, Germany,Inria Paris & Sorbonne Université LJLL, 75012 Paris, France,Correspondence:
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5
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Calzone L, Noël V, Barillot E, Kroemer G, Stoll G. Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells. Comput Struct Biotechnol J 2022; 20:5661-5671. [PMID: 36284705 PMCID: PMC9582792 DOI: 10.1016/j.csbj.2022.10.003] [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: 07/05/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 11/24/2022] Open
Abstract
As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell populations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by considering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribution (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response.
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Affiliation(s)
- Laurence Calzone
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France,Corresponding authors.
| | - Vincent Noël
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France,Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France,Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Europén Georges Pompidou, AP-HP, Paris, France
| | - Gautier Stoll
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France,Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France,Corresponding authors.
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6
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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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7
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Bi X, Liu Y, Li J, Du G, Lv X, Liu L. Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges. Biomolecules 2022; 12:biom12050721. [PMID: 35625648 PMCID: PMC9139095 DOI: 10.3390/biom12050721] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 12/04/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.
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Affiliation(s)
- Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Correspondence: ; Tel.: +86-0510-8591-8312; Fax: +86-0510-8591-8309
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8
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King J, Giselbrecht S, Truckenmüller R, Carlier A. Mechanistic Computational Models of Epithelial Cell Transporters-the Adorned Heroes of Pharmacokinetics. Front Pharmacol 2021; 12:780620. [PMID: 34803720 PMCID: PMC8599978 DOI: 10.3389/fphar.2021.780620] [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: 09/21/2021] [Accepted: 10/20/2021] [Indexed: 11/16/2022] Open
Abstract
Epithelial membrane transporter kinetics portray an irrefutable role in solute transport in and out of cells. Mechanistic models are used to investigate the transport of solutes at the organ, tissue, cell or membrane scale. Here, we review the recent advancements in using computational models to investigate epithelial transport kinetics on the cell membrane. Various methods have been employed to develop transport phenomena models of solute flux across the epithelial cell membrane. Interestingly, we noted that many models used lumped parameters, such as the Michaelis-Menten kinetics, to simplify the transporter-mediated reaction term. Unfortunately, this assumption neglects transporter numbers or the fact that transport across the membrane may be affected by external cues. In contrast, more recent mechanistic transporter kinetics models account for the transporter number. By creating models closer to reality researchers can investigate the downstream effects of physical or chemical disturbances on the system. Evidently, there is a need to increase the complexity of mechanistic models investigating the solute flux across a membrane to gain more knowledge of transporter-solute interactions by assigning individual parameter values to the transporter kinetics and capturing their dependence on each other. This change results in better pharmacokinetic predictions in larger scale platforms. More reliable and efficient model predictions can be made by creating mechanistic computational models coupled with dedicated in vitro experiments. It is also vital to foster collaborative efforts among transporter kinetics researchers in the modeling, material science and biological fields.
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Affiliation(s)
- Jasia King
- Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands.,Department of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands
| | - Stefan Giselbrecht
- Department of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands
| | - Roman Truckenmüller
- Department of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands
| | - Aurélie Carlier
- Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands
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9
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Schölzel C, Blesius V, Ernst G, Dominik A. Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective. NPJ Syst Biol Appl 2021; 7:27. [PMID: 34083542 PMCID: PMC8175692 DOI: 10.1038/s41540-021-00182-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/19/2021] [Indexed: 02/06/2023] Open
Abstract
Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.
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Affiliation(s)
- Christopher Schölzel
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany.
| | - Valeria Blesius
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - Gernot Ernst
- Vestre Viken Hospital Trust, Kongsberg, Norway
- University of Oslo, Oslo, Norway
| | - Andreas Dominik
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
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10
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Abstract
This book chapter is drafted for biologists with experimental experiences in ROS biology but being newcomers in the field of modeling. We start with a general introduction about computational modeling in biology and an overview of software tools suitable for beginners. This chapter encompasses an introduction to computational models with special focus on simulation of ROS dynamics. A step-by-step tutorial follows providing guidance for all relevant model development processes. This course of action gives a comprehensible way to understand the benefits of computational models and to gain the necessary knowledge to build own small equation-based models. Small models can be created without any special programming expertise or in-depth technical and mathematical knowledge. Afterward in the final section, a short overview of pitfalls, challenges, and limitations is provided, combined with suggestions for further reading to improve and expand modeling skills of biologists.
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Affiliation(s)
- Jana Schleicher
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany.
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11
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Donahue WP, Newhauser WD, Wong H, Moreno J, Dey J, Wilson VL. Computational feasibility of calculating the steady-state blood flow rate through the vasculature of the entire human body. Biomed Phys Eng Express 2020; 6:055026. [PMID: 33444257 DOI: 10.1088/2057-1976/abaf5d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The human body contains approximately 20 billion individual blood vessels that deliver nutrients and oxygen to tissues. While blood flow is a well-developed field of research, no previous studies have calculated the blood flow rates through more than 5 million connected vessels. The goal of this study was to test if it is computationally feasible to calculate the blood flow rates through a vasculature equal in size to that of the human body. We designed and implemented a two-step algorithm to calculate the blood flow rates using principles of steady-state fluid dynamics. Steady-state fluid dynamics is an accurate approximation for the microvascular and venous structures in the human body. To determine the computational feasibility, we measured and evaluated the execution time, scalability, and memory usage to quantify the computational requirements. We demonstrated that it is computationally feasible to calculate the blood flow rate through 17 billion vessels in 6.5 hours using 256 compute nodes. The computational modeling of blood flow rate in entire organisms may find application in research on drug delivery, treatment of cancer metastases, and modulation of physiological performance.
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Affiliation(s)
- William P Donahue
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United States of America
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12
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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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13
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Wu H, Xu C, Gu Y, Yang S, Wang Y, Wang C. An improved pseudotargeted GC-MS/MS-based metabolomics method and its application in radiation-induced hepatic injury in a rat model. J Chromatogr B Analyt Technol Biomed Life Sci 2020; 1152:122250. [PMID: 32619786 DOI: 10.1016/j.jchromb.2020.122250] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 06/09/2020] [Accepted: 06/14/2020] [Indexed: 12/12/2022]
Abstract
The liver is the pivotal metabolic organ primarily responsible for metabolic activities, detoxification and regulation of carbohydrate, protein, amino acid, and lipid metabolism. However, very little is known about the complicated pathophysiologic mechanisms of liver injury result from ionizing radiation exposure. Therefore, a pseudotargeted metabolomics approach based on gas chromatography-tandem mass spectrometry with selected reaction monitoring (GC-MS-SRM) was developed to study metabolic alterations of liver tissues in radiation-induced hepatic injury. The pseudotargeted GC-MS-SRM method was validated with satisfactory analytical characteristics in terms of precision, linearity, sensitivity and recovery. Compared to the SIM-based approach, the SRM scanning method had mildly better precision, higher sensitivity, and wider linear ranges. A total of 37 differential metabolites associated with radiation-induced hepatic injury were identified using the GC-MS-SRM metabolomics method. Global metabolic clustering analysis showed that amino acids, carbohydrates, unsaturated fatty acids, organic acids, metabolites associated with pyrimidine metabolism, ubiquinone biosynthesis and oxidative phosphorylation appeared significantly declined after high dose irradiation exposure, whereas metabolites related to lysine catabolism, glycerolipid metabolism and glutathione metabolism presented the opposite behavior. These changes indicate energy deficiency, antioxidant defense damage, accumulation of ammonia and lipid oxidation of liver tissues in response to radiation exposure. It is shown that the developed pseudotargeted method based on GC-MS-SRM is a useful tool for metabolomics study.
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Affiliation(s)
- Hanxu Wu
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Medical College of Soochow University, School for Radiological and Interdisciplinary Sciences (RAD-X), Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, PR China
| | - Chao Xu
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Medical College of Soochow University, School for Radiological and Interdisciplinary Sciences (RAD-X), Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, PR China
| | - Yifeng Gu
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Medical College of Soochow University, School for Radiological and Interdisciplinary Sciences (RAD-X), Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, PR China
| | - Shugao Yang
- Department of Biochemistry and Molecular Biology, Soochow University College of Medicine, Suzhou 215123, China
| | - Yarong Wang
- Experimental Center of Medical College, Soochow University, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, PR China
| | - Chang Wang
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Medical College of Soochow University, School for Radiological and Interdisciplinary Sciences (RAD-X), Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, PR China.
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14
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Stamataki Z, Swadling L. The liver as an immunological barrier redefined by single-cell analysis. Immunology 2020; 160:157-170. [PMID: 32176810 PMCID: PMC7218664 DOI: 10.1111/imm.13193] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 03/12/2020] [Accepted: 03/12/2020] [Indexed: 12/11/2022] Open
Abstract
The liver is a front-line immune tissue that plays a major role in the detection, capture and clearance of pathogens and foreign antigens entering the bloodstream, especially from the gut. Our largest internal organ maintains this immune barrier in the face of constant exposure to external but harmless antigens through a highly specialized network of liver-adapted immune cells. Mapping the immune resident compartment in the liver has been challenging because it requires multimodal single-cell deep phenotyping approaches of often rare cell populations in difficult to access samples. We can now measure the RNA transcripts present in a single cell (scRNA-seq), which is revolutionizing the way we characterize cell types. scRNA-seq has been applied to the diverse array of immune cells present in murine and human livers in health and disease. Here, we summarize how emerging single-cell technologies have advanced or redefined our understanding of the immunological barrier provided by the liver.
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Affiliation(s)
- Zania Stamataki
- Institute of Immunology and ImmunotherapyCentre for Liver and Gastrointestinal ResearchUniversity of BirminghamBirminghamUK
- NIHR Birmingham Liver Biomedical Research CentreUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
| | - Leo Swadling
- Division of Infection & ImmunityUniversity College LondonLondonUK
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15
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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16
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Ben-Moshe S, Shapira Y, Moor AE, Manco R, Veg T, Bahar Halpern K, Itzkovitz S. Spatial sorting enables comprehensive characterization of liver zonation. Nat Metab 2019; 1:899-911. [PMID: 31535084 PMCID: PMC6751089 DOI: 10.1038/s42255-019-0109-9] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The mammalian liver is composed of repeating hexagonal units termed lobules. Spatially resolved single-cell transcriptomics revealed that about half of hepatocyte genes are differentially expressed across the lobule, yet technical limitations impeded reconstructing similar global spatial maps of other hepatocyte features. Here, we show how zonated surface markers can be used to sort hepatocytes from defined lobule zones with high spatial resolution. We apply transcriptomics, miRNA array measurements and mass spectrometry proteomics to reconstruct spatial atlases of multiple zonated features. We demonstrate that protein zonation largely overlaps with mRNA zonation, with the periportal HNF4α as an exception. We identify zonation of miRNAs such as miR-122, and inverse zonation of miRNAs and their hepatocyte target genes, highlighting potential regulation of protein levels through zonated mRNA degradation. Among the targets we find the pericentral Wnt receptors Fzd7 and Fzd8 and the periportal Wnt inhibitors Tcf7l1 and Ctnnbip1. Our approach facilitates reconstructing spatial atlases of multiple cellular features in the liver and other structured tissues.
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Affiliation(s)
- Shani Ben-Moshe
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yonatan Shapira
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Andreas E Moor
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- Institute of Molecular Cancer Research, University of Zurich, Zurich, Switzerland
| | - Rita Manco
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Tamar Veg
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Keren Bahar Halpern
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Shalev Itzkovitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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17
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Abstract
Hepatocytes operate in highly structured repeating anatomical units termed liver lobules. Blood flow along the lobule radial axis creates gradients of oxygen, nutrients and hormones, which, together with morphogenetic fields, give rise to a highly variable microenvironment. In line with this spatial variability, key liver functions are expressed non-uniformly across the lobules, a phenomenon termed zonation. Technologies based on single-cell transcriptomics have constructed a global spatial map of hepatocyte gene expression in mice revealing that ~50% of hepatocyte genes are expressed in a zonated manner. This broad spatial heterogeneity suggests that hepatocytes in different lobule zones might have not only different gene expression profiles but also distinct epigenetic features, regenerative capacities, susceptibilities to damage and other functional aspects. Here, we present genomic approaches for studying liver zonation, describe the principles of liver zonation and discuss the intrinsic and extrinsic factors that dictate zonation patterns. We also explore the challenges and solutions for obtaining zonation maps of liver non-parenchymal cells. These approaches facilitate global characterization of liver function with high spatial resolution along physiological and pathological timescales.
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Affiliation(s)
- Shani Ben-Moshe
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Shalev Itzkovitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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18
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Stanford NJ, Scharm M, Dobson PD, Golebiewski M, Hucka M, Kothamachu VB, Nickerson D, Owen S, Pahle J, Wittig U, Waltemath D, Goble C, Mendes P, Snoep J. Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices. Methods Mol Biol 2019; 2049:285-314. [PMID: 31602618 DOI: 10.1007/978-1-4939-9736-7_17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR-findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.
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Affiliation(s)
| | - Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Paul D Dobson
- School of Computer Science, University of Manchester, Manchester, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Michael Hucka
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Stuart Owen
- School of Computer Science, University of Manchester, Manchester, UK
| | - Jürgen Pahle
- BIOMS/BioQuant, Heidelberg University, Heidelberg, Germany.
| | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Dagmar Waltemath
- Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Carole Goble
- School of Computer Science, University of Manchester, Manchester, UK
| | - Pedro Mendes
- Centre for Quantitative Medicine, University of Connecticut, Farmington, CT, USA
| | - Jacky Snoep
- School of Computer Science, University of Manchester, Manchester, UK.,Biochemistry, Stellenbosch University, Stellenbosch, South Africa
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19
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Jin W, Liang X, Brooks A, Futrega K, Liu X, Doran MR, Simpson MJ, Roberts MS, Wang H. Modelling of the SDF-1/CXCR4 regulated in vivo homing of therapeutic mesenchymal stem/stromal cells in mice. PeerJ 2018; 6:e6072. [PMID: 30564525 PMCID: PMC6286806 DOI: 10.7717/peerj.6072] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/05/2018] [Indexed: 01/12/2023] Open
Abstract
Background Mesenchymal stem/stromal cells (MSCs) are a promising tool for cell-based therapies in the treatment of tissue injury. The stromal cell-derived factor-1 (SDF-1)/CXC chemokine receptor 4 (CXCR4) axis plays a significant role in directing MSC homing to sites of injury. However in vivo MSC distribution following intravenous transplantation remains poorly understood, potentially hampering the precise prediction and evaluation of therapeutic efficacy. Methods A murine model of partial ischemia/reperfusion (I/R) is used to induce liver injury, increase the hepatic levels of SDF-1, and study in vivo MSC distribution. Hypoxia-preconditioning increases the expression of CXCR4 in human bone marrow-derived MSCs. Quantitative assays for human DNA using droplet digital PCR (ddPCR) allow us to examine the in vivo kinetics of intravenously infused human MSCs in mouse blood and liver. A mathematical model-based system is developed to characterize in vivo homing of human MSCs in mouse models with SDF-1 levels in liver and CXCR4 expression on the transfused MSCs. The model is calibrated to experimental data to provide novel estimates of relevant parameter values. Results Images of immunohistochemistry for SDF-1 in the mouse liver with I/R injury show a significantly higher SDF-1 level in the I/R injured liver than that in the control. Correspondingly, the ddPCR results illustrate a higher MSC concentration in the I/R injured liver than the normal liver. CXCR4 is overexpressed in hypoxia-preconditioned MSCs. An increased number of hypoxia-preconditioned MSCs in the I/R injured liver is observed from the ddPCR results. The model simulations align with the experimental data of control and hypoxia-preconditioned human MSC distribution in normal and injured mouse livers, and accurately predict the experimental outcomes with different MSC doses. Discussion The modelling results suggest that SDF-1 in organs is an effective in vivo attractant for MSCs through the SDF-1/CXCR4 axis and reveal the significance of the SDF-1/CXCR4 chemotaxis on in vivo homing of MSCs. This in vivo modelling approach allows qualitative characterization and prediction of the MSC homing to normal and injured organs on the basis of clinically accessible variables, such as the MSC dose and SDF-1 concentration in blood. This model could also be adapted to abnormal conditions and/or other types of circulating cells to predict in vivo homing patterns.
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Affiliation(s)
- Wang Jin
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Xiaowen Liang
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, University of Queensland, Translational Research Institute, Brisbane, Australia
| | - Anastasia Brooks
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, University of Queensland, Translational Research Institute, Brisbane, Australia
| | - Kathryn Futrega
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Brisbane, Australia
| | - Xin Liu
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, University of Queensland, Translational Research Institute, Brisbane, Australia
| | - Michael R Doran
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Brisbane, Australia.,Mater Research Institute, University of Queensland, Translational Research Institute, Brisbane, Australia.,Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, Australia
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Michael S Roberts
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, University of Queensland, Translational Research Institute, Brisbane, Australia.,School of Pharmacy and Medical Science, University of South Australia, Adelaide, Australia
| | - Haolu Wang
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, University of Queensland, Translational Research Institute, Brisbane, Australia
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20
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Jin X, Zhao T, Shi D, Ye MB, Yi Q. Protective role of fucoxanthin in diethylnitrosamine-induced hepatocarcinogenesis in experimental adult rats. Drug Dev Res 2018; 80:209-217. [PMID: 30379338 DOI: 10.1002/ddr.21451] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 06/18/2018] [Accepted: 06/23/2018] [Indexed: 12/17/2022]
Abstract
Hepatocellular carcinoma (HCC) accounts for majority of cancer related deaths. Two major risk factors in induction of HCC are chemical and virus, however, the possible mechanisms of their differences remain indefinable. The current study focused on protective role of Fucoxanthin (Fx) in liver affected by diethylnitrosamine (DEN)-induced HCC. In this study, levels of liver enzymes, oxidative stressors, antioxidant status, and lipoproteins were compared both in tissue and blood. Tissues were also analyzed extensively by histological studies using H and E staining and transmission electron microscopy (TEM). Rats were clustered into four groups of six experimental animals. Group I: Control rats were administered isotonic saline intraperitoneal Group II: Animals received 0.01% DEN through drinking water to induce hepatocellular carcinoma. Group III: Animals received 0.01% DEN simultaneously oral supplementation of Fx (50 mg/kg b.w). Group IV: Rats were given Fx alone (50 mg/kg b.w) orally and the treatment is for 15 weeks. Results showed the decrease in body weight, serum albumin, antioxidant enzymes, and increased all the liver enzymes, serum bilirubin, and stress markers in DEN induced rats, where as the simultaneous supplementation of Fx reverted them to normal levels. Administration of only Fx did not show any change. Therefore, Fx may serve as a chemotherapeutic agent against liver cancer.
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Affiliation(s)
- Xin Jin
- Department of General Surgery, Ankang Central Hospital of Shaanxi, Shaanxi, China
| | - TingTing Zhao
- The Center of Experimental Teaching Management, Chongqing Medical University, Chongqing, China
| | - Dan Shi
- Surgical Operating Room, Chinese Medicine Hospital of Dianjiang County, Chongqing, China
| | - Ming Bao Ye
- Department of Urological Surgery, The First People's Hospital of Xianyang, Shaanxi Province, China
| | - Qiying Yi
- The Laboratory Animal Center of Chongqing Medical University, Chongqing, China
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21
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Cvitanović Tomaš T, Urlep Ž, Moškon M, Mraz M, Rozman D. LiverSex Computational Model: Sexual Aspects in Hepatic Metabolism and Abnormalities. Front Physiol 2018; 9:360. [PMID: 29706895 PMCID: PMC5907313 DOI: 10.3389/fphys.2018.00360] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/22/2018] [Indexed: 12/12/2022] Open
Abstract
The liver is to date the best example of a sexually dimorphic non-reproductive organ. Over 1,000 genes are differentially expressed between sexes indicating that female and male livers are two metabolically distinct organs. The spectrum of liver diseases is broad and is usually prevalent in one or the other sex, with different contributing genetic and environmental factors. It is thus difficult to predict individual's disease outcomes and treatment options. Systems approaches including mathematical modeling can aid importantly in understanding the multifactorial liver disease etiology leading toward tailored diagnostics, prognostics and therapy. The currently established computational models of hepatic metabolism that have proven to be essential for understanding of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are limited to the description of gender-independent response or reflect solely the response of the males. Herein we present LiverSex, the first sex-based multi-tissue and multi-level liver metabolic computational model. The model was constructed based on in silico liver model SteatoNet and the object-oriented modeling. The crucial factor in adaptation of liver metabolism to the sex is the inclusion of estrogen and androgen receptor responses to respective hormones and the link to sex-differences in growth hormone release. The model was extensively validated on literature data and experimental data obtained from wild type C57BL/6 mice fed with regular chow and western diet. These experimental results show extensive sex-dependent changes and could not be reproduced in silico with the uniform model SteatoNet. LiverSex represents the first large-scale liver metabolic model, which allows a detailed insight into the sex-dependent complex liver pathologies, and how the genetic and environmental factors interact with the sex in disease appearance and progression. We used the model to identify the most important sex-dependent metabolic pathways, which are involved in accumulation of triglycerides representing initial steps of NAFLD. We identified PGC1A, PPARα, FXR, and LXR as regulatory factors that could become important in sex-dependent personalized treatment of NAFLD.
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Affiliation(s)
- Tanja Cvitanović Tomaš
- Faculty of Medicine, Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, University of Ljubljana, Ljubljana, Slovenia
| | - Žiga Urlep
- Faculty of Medicine, Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Faculty of Medicine, Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, University of Ljubljana, Ljubljana, Slovenia
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22
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Model Prediction and Validation of an Order Mechanism Controlling the Spatiotemporal Phenotype of Early Hepatocellular Carcinoma. Bull Math Biol 2018; 80:1134-1171. [PMID: 29568983 DOI: 10.1007/s11538-017-0375-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 11/27/2017] [Indexed: 12/11/2022]
Abstract
Recently, hepatocyte-sinusoid alignment (HSA) has been identified as a mechanism that supports the coordination of hepatocytes during liver regeneration to reestablish a functional micro-architecture (Hoehme et al. in Proc Natl Acad Sci 107(23):10371-10376, 2010). HSA means that hepatocytes preferentially align along the closest micro-vessels. Here, we studied whether this mechanism is still active in early hepatocellular tumors. The same agent-based spatiotemporal model that previously correctly predicted HSA in liver regeneration was further developed to simulate scenarios in early tumor development, when individual initiated hepatocytes gain increased proliferation capacity. The model simulations were performed under conditions of realistic liver micro-architectures obtained from 3D reconstructions of confocal laser scanning micrographs. Interestingly, the established model predicted that initiated hepatocytes at first arrange in elongated patterns. Only when the tumor progresses to cell numbers of approximately 4000, does it adopt spherical structures. This prediction may have relevant consequences, since elongated tumors may reach critical structures faster, such as larger vessels, compared to a spherical tumor of similar cell number. Interestingly, this model prediction was confirmed by analysis of the spatial organization of initiated hepatocytes in a rat liver tumor initiation study using single doses of 250 mg/kg of the genotoxic carcinogen N-nitrosomorpholine (NNM). Indeed, small clusters of GST-P positive cells induced by NNM were elongated, almost columnar, while larger GDT-P positive foci of approximately the size of liver lobuli adopted spherical shapes. From simulations testing numerous possible mechanisms, only HSA could explain the experimentally observed initial deviation from spherical shape. The present study demonstrates that the architecture of small cell clusters of hepatocytes early after initiation is still controlled by physiological mechanisms. However, this coordinating influence is lost when the tumor grows to approximately 4000 cells, leading to further growth in spherical shape. Our findings stress the potential importance of organ micro-architecture in understanding tumor phenotypes.
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23
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Pedone E, Olteanu VA, Marucci L, Muñoz-Martin MI, Youssef SA, de Bruin A, Cosma MP. Modeling Dynamics and Function of Bone Marrow Cells in Mouse Liver Regeneration. Cell Rep 2017; 18:107-121. [PMID: 28052241 PMCID: PMC5236012 DOI: 10.1016/j.celrep.2016.12.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 07/15/2016] [Accepted: 12/01/2016] [Indexed: 12/13/2022] Open
Abstract
In rodents and humans, the liver can efficiently restore its mass after hepatectomy. This is largely attributed to the proliferation and cell cycle re-entry of hepatocytes. On the other hand, bone marrow cells (BMCs) migrate into the liver after resection. Here, we find that a block of BMC recruitment into the liver severely impairs its regeneration after the surgery. Mobilized hematopoietic stem and progenitor cells (HSPCs) in the resected liver can fuse with hepatocytes, and the hybrids proliferate earlier than the hepatocytes. Genetic ablation of the hybrids severely impairs hepatocyte proliferation and liver mass regeneration. Mathematical modeling reveals a key role of bone marrow (BM)-derived hybrids to drive proliferation in the regeneration process, and predicts regeneration efficiency in experimentally non-testable conditions. In conclusion, BM-derived hybrids are essential to trigger efficient liver regeneration after hepatectomy. Bone marrow cell migration after liver hepatectomy is key for liver regeneration Migrated bone marrow cells fuse with hepatocytes Hybrids are essential for liver regeneration Mathematical modeling unveils the hybrid function for liver regeneration
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Affiliation(s)
- Elisa Pedone
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), Dr Aiguader 88, 08003 Barcelona, Spain
| | - Vlad-Aris Olteanu
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK
| | - Lucia Marucci
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain; Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK.
| | - Maria Isabel Muñoz-Martin
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain
| | - Sameh A Youssef
- Dutch Molecular Pathology Center, Department of Pathobiology, Faculty of Veterinary Medicine, Utrecht University, 3584 Utrecht, the Netherlands; Department of Pathology, Alexandria Veterinary College, University of Alexandria-Egypt, 21612 Alexandria, Egypt
| | - Alain de Bruin
- Dutch Molecular Pathology Center, Department of Pathobiology, Faculty of Veterinary Medicine, Utrecht University, 3584 Utrecht, the Netherlands; University Medical Center Groningen, Department of Pediatrics, University of Groningen, 9713 Groningen, the Netherlands
| | - Maria Pia Cosma
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), Dr Aiguader 88, 08003 Barcelona, Spain; ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain.
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Bardini R, Politano G, Benso A, Di Carlo S. Multi-level and hybrid modelling approaches for systems biology. Comput Struct Biotechnol J 2017; 15:396-402. [PMID: 28855977 PMCID: PMC5565741 DOI: 10.1016/j.csbj.2017.07.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 06/28/2017] [Accepted: 07/31/2017] [Indexed: 01/27/2023] Open
Abstract
During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.
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Affiliation(s)
| | | | | | - S. Di Carlo
- Politecnico di Torino, Department of Control and Computer Engineering, 10129 Torino, Italy
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Xiao X, Hu M, Zhang X, Hu JZ. NMR-based Metabolomics Analysis of Liver from C57BL/6 Mouse Exposed to Ionizing Radiation. Radiat Res 2017; 188:44-55. [PMID: 28463589 PMCID: PMC5564182 DOI: 10.1667/rr14602.1] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The effects of ionizing radiation to human health are of great concern in the field of space exploration and for patients considering radiotherapy. However, to date, the effect of high-dose radiation on metabolism in the liver has not been clearly defined. In this study, 1H nuclear magnetic resonance (NMR)-based metabolomics combined with multivariate data analysis was applied to study the changes of metabolism in the liver of C57BL/6 mouse after whole-body gamma (3.0 and 7.8 Gy) or proton (3.0 Gy) irradiation. Principal component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS) were used for classification and identification of potential biomarkers associated with exposure to gamma and proton radiation. The results show that the radiation exposed groups can be well separated from the control group. Where the same dose was received, the proton exposed group was nevertheless well separated from the gamma-exposed group, indicating that different radiation sources induce different alterations in the metabolic profile. Common among all high-dose gamma and proton exposed groups were the statistically decreased concentrations of choline, O-phosphocholine and trimethylamine N-oxide, while the concentrations of glutamine, glutathione, malate, creatinine, phosphate, betaine and 4-hydroxyphenylacetate were statistically and significantly elevated. Since these altered metabolites are associated with multiple biological pathways, the results suggest that radiation induces abnormality in multiple biological pathways. In particular, metabolites such as 4-hydroxyphenylacetate, betaine, glutamine, choline and trimethylamine N-oxide may be prediagnostic biomarkers candidates for ionizing exposure of the liver.
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Affiliation(s)
- Xiongjie Xiao
- Pacific Northwest National Laboratory, Richland, Washington 99352
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430071, PR China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mary Hu
- Pacific Northwest National Laboratory, Richland, Washington 99352
| | - Xu Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430071, PR China
| | - Jian Zhi Hu
- Pacific Northwest National Laboratory, Richland, Washington 99352
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Moor AE, Itzkovitz S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr Opin Biotechnol 2017; 46:126-133. [PMID: 28346891 DOI: 10.1016/j.copbio.2017.02.004] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 02/08/2017] [Accepted: 02/10/2017] [Indexed: 02/07/2023]
Abstract
The tissues in our bodies are complex systems composed of diverse cell types that often interact in highly structured repeating anatomical units. External gradients of morphogens, directional blood flow, as well as the secretion and absorption of materials by cells generate distinct microenvironments at different tissue coordinates. Such spatial heterogeneity enables optimized function through division of labor among cells. Unraveling the design principles that govern this spatial division of labor requires techniques to quantify the entire transcriptomes of cells while accounting for their spatial coordinates. In this review we describe how recent advances in spatial transcriptomics open the way for tissue-level systems biology.
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Affiliation(s)
- Andreas E Moor
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Shalev Itzkovitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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Graepel R, Lamon L, Asturiol D, Berggren E, Joossens E, Paini A, Prieto P, Whelan M, Worth A. The virtual cell based assay: Current status and future perspectives. Toxicol In Vitro 2017; 45:258-267. [PMID: 28108195 PMCID: PMC5742635 DOI: 10.1016/j.tiv.2017.01.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 12/15/2016] [Accepted: 01/15/2017] [Indexed: 12/22/2022]
Abstract
In order to replace the use of animals in toxicity testing, there is a need to predict in vivo toxic doses from concentrations that cause toxicological effects in relevant in vitro systems. The Virtual Cell Based Assay (VCBA) estimates time-dependent concentration of a test chemical in the cell and cell culture for a given in vitro system. The concentrations in the different compartments of the cell and test system are derived from ordinary differential equations, physicochemical parameters of the test chemical and properties of the cell line. The VCBA has been developed for a range of cell lines including BALB/c 3T3 cells, HepG2, HepaRG, lung A459 cells, and cardiomyocytes. The model can be used to design and refine in vitro experiments and extrapolate in vitro effective concentrations to in vivo doses that can be applied in risk assessment. In this paper, we first discuss potential applications of the VCBA: i) design of in vitro High Throughput Screening (HTS) experiments; ii) hazard identification (based on acute systemic toxicity); and iii) risk assessment. Further extension of the VCBA is discussed in the second part, exploring potential application to i) manufactured nanomaterials, ii) additional cell lines and endpoints, and considering iii) other opportunities. VCBA as an alternative approach can be applied in the domain of nanotoxicology. VCBA can support better testing strategies in acute toxicity. Refinement of the VCBA taking into account biological oscillators could improve toxicity prediction. Extensions of the VCBA can capture effects related to additional subcellular compartments.
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Affiliation(s)
- Rabea Graepel
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
| | - Lara Lamon
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy.
| | - David Asturiol
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
| | - Elisabet Berggren
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
| | - Elisabeth Joossens
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
| | - Alicia Paini
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
| | - Pilar Prieto
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
| | - Maurice Whelan
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
| | - Andrew Worth
- Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Directorate Health, Consumers and Reference Materials, European Commission, Joint Research Centre, Ispra, Italy
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Aznar JMG, Valero C, Borau C, Garijo N. Computational mechano-chemo-biology: a tool for the design of tissue scaffolds. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s40898-016-0002-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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A Liver-Centric Multiscale Modeling Framework for Xenobiotics. PLoS One 2016; 11:e0162428. [PMID: 27636091 PMCID: PMC5026379 DOI: 10.1371/journal.pone.0162428] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 07/27/2016] [Indexed: 01/12/2023] Open
Abstract
We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.
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Jeanquartier F, Jean-Quartier C, Cemernek D, Holzinger A. In silico modeling for tumor growth visualization. BMC SYSTEMS BIOLOGY 2016; 10:59. [PMID: 27503052 PMCID: PMC4977902 DOI: 10.1186/s12918-016-0318-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 07/12/2016] [Indexed: 12/18/2022]
Abstract
BACKGROUND Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research. RESULTS We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ . CONCLUSION In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.
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Affiliation(s)
- Fleur Jeanquartier
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - Claire Jean-Quartier
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - David Cemernek
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - Andreas Holzinger
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria. .,Institute of Information Systems and Computer Media, Graz University of Technology, Inffeldgasse 16c, Graz, 8010, AT, Austria.
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31
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Muller A, Clarke R, Ho H. Fast blood-flow simulation for large arterial trees containing thousands of vessels. Comput Methods Biomech Biomed Engin 2016; 20:160-170. [PMID: 27376402 DOI: 10.1080/10255842.2016.1207170] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Blood flow modelling has previously been successfully carried out in arterial trees to study pulse wave propagation using nonlinear or linear flow solvers. However, the number of vessels used in the simulations seldom grows over a few hundred. The aim of this work is to present a computationally efficient solver coupled with highly detailed arterial trees containing thousands of vessels. The core of the solver is based on a modified transmission line method, which exploits the analogy between electrical current in finite-length conductors and blood flow in vessels. The viscoelastic behaviour of the arterial-wall is taken into account using a complex elastic modulus. The flow is solved vessel by vessel in the frequency domain and the calculated output pressure is then used as an input boundary condition for daughter vessels. The computational results yield pulsatile blood pressure and flow rate for every segment in the tree. This solver is coupled with large arterial trees generated from a three-dimensional constrained constructive optimisation algorithm. The tree contains thousands of blood vessels with radii spanning ~1 mm in the root artery to ~30 μm in leaf vessels. The computation takes seconds to complete for a vasculature of 2048 vessels and less than 2 min for a vasculature of 4096 vessels on a desktop computer.
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Affiliation(s)
- Alexandre Muller
- a Bioengineering Institute, University of Auckland , Auckland , New Zealand.,b ENSEEIHT , National Polytechnic Institute of Toulouse , Toulouse , France
| | - Richard Clarke
- c Department of Engineering Science , University of Auckland , Auckland , New Zealand
| | - Harvey Ho
- a Bioengineering Institute, University of Auckland , Auckland , New Zealand
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32
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Bois FY, Ochoa JGD, Gajewska M, Kovarich S, Mauch K, Paini A, Péry A, Benito JVS, Teng S, Worth A. Multiscale modelling approaches for assessing cosmetic ingredients safety. Toxicology 2016; 392:130-139. [PMID: 27267299 DOI: 10.1016/j.tox.2016.05.026] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 11/30/2015] [Accepted: 05/31/2016] [Indexed: 12/27/2022]
Abstract
The European Union's ban on animal testing for cosmetic ingredients and products has generated a strong momentum for the development of in silico and in vitro alternative methods. One of the focus of the COSMOS project was ab initio prediction of kinetics and toxic effects through multiscale pharmacokinetic modeling and in vitro data integration. In our experience, mathematical or computer modeling and in vitro experiments are complementary. We present here a summary of the main models and results obtained within the framework of the project on these topics. A first section presents our work at the organelle and cellular level. We then go toward modeling cell levels effects (monitored continuously), multiscale physiologically based pharmacokinetic and effect models, and route to route extrapolation. We follow with a short presentation of the automated KNIME workflows developed for dissemination and easy use of the models. We end with a discussion of two challenges to the field: our limited ability to deal with massive data and complex computations.
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Affiliation(s)
- Frédéric Y Bois
- INERIS, DRC/VIVA/METO, Parc ALATA, BP2, 60550 Verneuil-en-Halatte, France.
| | - Juan G Diaz Ochoa
- Insilico Biotechnology AG, Meitnerstrasse 8, 70563 Stuttgart, Germany
| | - Monika Gajewska
- European Commission Joint Research Centre, Institute for Health and Consumer Protection, Systems Toxicology Unit, Via Enrico Fermi 2749, Ispra, VA, Italy
| | - Simona Kovarich
- S-IN Soluzioni Informatiche, via G. Ferrari 14, 36100 Vicenza, Italy
| | - Klaus Mauch
- Insilico Biotechnology AG, Meitnerstrasse 8, 70563 Stuttgart, Germany
| | - Alicia Paini
- European Commission Joint Research Centre, Institute for Health and Consumer Protection, Systems Toxicology Unit, Via Enrico Fermi 2749, Ispra, VA, Italy
| | - Alexandre Péry
- INERIS, DRC/VIVA/METO, Parc ALATA, BP2, 60550 Verneuil-en-Halatte, France
| | - Jose Vicente Sala Benito
- European Commission Joint Research Centre, Institute for Health and Consumer Protection, Systems Toxicology Unit, Via Enrico Fermi 2749, Ispra, VA, Italy
| | - Sophie Teng
- INERIS, DRC/VIVA/METO, Parc ALATA, BP2, 60550 Verneuil-en-Halatte, France
| | - Andrew Worth
- European Commission Joint Research Centre, Institute for Health and Consumer Protection, Systems Toxicology Unit, Via Enrico Fermi 2749, Ispra, VA, Italy
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Pancreatic Beta Cell G-Protein Coupled Receptors and Second Messenger Interactions: A Systems Biology Computational Analysis. PLoS One 2016; 11:e0152869. [PMID: 27138453 PMCID: PMC4854486 DOI: 10.1371/journal.pone.0152869] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/21/2016] [Indexed: 12/17/2022] Open
Abstract
Insulin secretory in pancreatic beta-cells responses to nutrient stimuli and hormonal modulators include multiple messengers and signaling pathways with complex interdependencies. Here we present a computational model that incorporates recent data on glucose metabolism, plasma membrane potential, G-protein-coupled-receptors (GPCR), cytoplasmic and endoplasmic reticulum calcium dynamics, cAMP and phospholipase C pathways that regulate interactions between second messengers in pancreatic beta-cells. The values of key model parameters were inferred from published experimental data. The model gives a reasonable fit to important aspects of experimentally measured metabolic and second messenger concentrations and provides a framework for analyzing the role of metabolic, hormones and neurotransmitters changes on insulin secretion. Our analysis of the dynamic data provides support for the hypothesis that activation of Ca2+-dependent adenylyl cyclases play a critical role in modulating the effects of glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and catecholamines. The regulatory properties of adenylyl cyclase isoforms determine fluctuations in cytoplasmic cAMP concentration and reveal a synergistic action of glucose, GLP-1 and GIP on insulin secretion. On the other hand, the regulatory properties of phospholipase C isoforms determine the interaction of glucose, acetylcholine and free fatty acids (FFA) (that act through the FFA receptors) on insulin secretion. We found that a combination of GPCR agonists activating different messenger pathways can stimulate insulin secretion more effectively than a combination of GPCR agonists for a single pathway. This analysis also suggests that the activators of GLP-1, GIP and FFA receptors may have a relatively low risk of hypoglycemia in fasting conditions whereas an activator of muscarinic receptors can increase this risk. This computational analysis demonstrates that study of second messenger pathway interactions will improve understanding of critical regulatory sites, how different GPCRs interact and pharmacological targets for modulating insulin secretion in type 2 diabetes.
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Thomas S, Wolstencroft K, de Bono B, Hunter PJ. A physiome interoperability roadmap for personalized drug development. Interface Focus 2016; 6:20150094. [PMID: 27051513 DOI: 10.1098/rsfs.2015.0094] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The goal of developing therapies and dosage regimes for characterized subgroups of the general population can be facilitated by the use of simulation models able to incorporate information about inter-individual variability in drug disposition (pharmacokinetics), toxicity and response effect (pharmacodynamics). Such observed variability can have multiple causes at various scales, ranging from gross anatomical differences to differences in genome sequence. Relevant data for many of these aspects, particularly related to molecular assays (known as '-omics'), are available in online resources, but identification and assignment to appropriate model variables and parameters is a significant bottleneck in the model development process. Through its efforts to standardize annotation with consequent increase in data usability, the human physiome project has a vital role in improving productivity in model development and, thus, the development of personalized therapy regimes. Here, we review the current status of personalized medicine in clinical practice, outline some of the challenges that must be overcome in order to expand its applicability, and discuss the relevance of personalized medicine to the more widespread challenges being faced in drug discovery and development. We then review some of (i) the key data resources available for use in model development and (ii) the potential areas where advances made within the physiome modelling community could contribute to physiologically based pharmacokinetic and physiologically based pharmacokinetic/pharmacodynamic modelling in support of personalized drug development. We conclude by proposing a roadmap to further guide the physiome community in its on-going efforts to improve data usability, and integration with modelling efforts in the support of personalized medicine development.
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Affiliation(s)
- Simon Thomas
- Cyprotex Discovery Ltd , 15 Beech Lane, Macclesfield SK10 2DR , UK
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science , Leiden University , 111 Snellius, Niels Bohrweg 1, 2333 CA Leiden , The Netherlands
| | - Bernard de Bono
- Farr Institute, University College London, London NW1 2DA, UK; Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand
| | - Peter J Hunter
- Auckland Bioengineering Institute , The University of Auckland , Auckland 1010 , New Zealand
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Kolczyk K, Conradi C. Challenges in horizontal model integration. BMC SYSTEMS BIOLOGY 2016; 10:28. [PMID: 26968798 PMCID: PMC4788958 DOI: 10.1186/s12918-016-0266-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 02/09/2016] [Indexed: 11/30/2022]
Abstract
Background Systems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field. Results Obtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model. Conclusions The reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0266-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Katrin Kolczyk
- Max-Planck-Institute Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106, Magdeburg, Germany
| | - Carsten Conradi
- Max-Planck-Institute Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106, Magdeburg, Germany.
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Abstract
To understand the meaning of the term Systems Medicine and to distinguish it from seemingly related other expressions currently in use, such as precision, personalized, -omics, or big data medicine, its underlying history and development into present time needs to be highlighted. Having this development in mind, it becomes evident that Systems Medicine is a genuine concept as well as a novel way of tackling the manifold complexity that occurs in nowadays clinical medicine-and not just a rebranding of what has previously been done in the past. So looking back it seems clear to many in the field that Systems Medicine has its origin in an integrative method to unravel biocomplexity, namely, Systems Biology. Here scientist by now gained useful experience that is on the verge toward implementation in clinical research and practice.Systems Medicine and Systems Biology have the same underlying theoretical principle in systems-based thinking-a methodology to understand complexity that can be traced back to ancient Greece. During the last decade, however, and due to a rapid methodological development in the life sciences and computing/IT technologies, Systems Biology has evolved from a scientific concept into an independent discipline most competent to tackle key questions of biocomplexity-with the potential to transform medicine and how it will be practiced in the future. To understand this process in more detail, the following section will thus give a short summary of the foundation of systems-based thinking and the different developmental stages including systems theory, the development of modern Systems Biology, and its transition into clinical practice. These are the components to pave the way toward Systems Medicine.
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Affiliation(s)
- Marc Kirschner
- Forschungszentrum Jülich GmbH, Projektträger Jülich, Molekulare Lebenswissenschaften, Jülich, 52425, Germany.
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Quantitative analysis of drug effects at the whole-body level: a case study for glucose metabolism in malaria patients. Biochem Soc Trans 2015; 43:1157-63. [PMID: 26614654 DOI: 10.1042/bst20150145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
We propose a hierarchical modelling approach to construct models for disease states at the whole-body level. Such models can simulate effects of drug-induced inhibition of reaction steps on the whole-body physiology. We illustrate the approach for glucose metabolism in malaria patients, by merging two detailed kinetic models for glucose metabolism in the parasite Plasmodium falciparum and the human red blood cell with a coarse-grained model for whole-body glucose metabolism. In addition we use a genome-scale metabolic model for the parasite to predict amino acid production profiles by the malaria parasite that can be used as a complex biomarker.
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From within host dynamics to the epidemiology of infectious disease: Scientific overview and challenges. Math Biosci 2015; 270:143-55. [PMID: 26474512 DOI: 10.1016/j.mbs.2015.10.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Since their earliest days, humans have been struggling with infectious diseases. Caused by viruses, bacteria, protozoa, or even higher organisms like worms, these diseases depend critically on numerous intricate interactions between parasites and hosts, and while we have learned much about these interactions, many details are still obscure. It is evident that the combined host-parasite dynamics constitutes a complex system that involves components and processes at multiple scales of time, space, and biological organization. At one end of this hierarchy we know of individual molecules that play crucial roles for the survival of a parasite or for the response and survival of its host. At the other end, one realizes that the spread of infectious diseases by far exceeds specific locales and, due to today's easy travel of hosts carrying a multitude of organisms, can quickly reach global proportions. The community of mathematical modelers has been addressing specific aspects of infectious diseases for a long time. Most of these efforts have focused on one or two select scales of a multi-level disease and used quite different computational approaches. This restriction to a molecular, physiological, or epidemiological level was prudent, as it has produced solid pillars of a foundation from which it might eventually be possible to launch comprehensive, multi-scale modeling efforts that make full use of the recent advances in biology and, in particular, the various high-throughput methodologies accompanying the emerging -omics revolution. This special issue contains contributions from biologists and modelers, most of whom presented and discussed their work at the workshop From within Host Dynamics to the Epidemiology of Infectious Disease, which was held at the Mathematical Biosciences Institute at Ohio State University in April 2014. These contributions highlight some of the forays into a deeper understanding of the dynamics between parasites and their hosts, and the consequences of this dynamics for the spread and treatment of infectious diseases.
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Raoof AA, Aerssens J. Patient-centered drug discovery as the means to improved R&D productivity. Drug Discov Today 2015; 20:1044-8. [DOI: 10.1016/j.drudis.2015.04.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Revised: 03/23/2015] [Accepted: 04/14/2015] [Indexed: 01/06/2023]
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Schwen LO, Schenk A, Kreutz C, Timmer J, Bartolomé Rodríguez MM, Kuepfer L, Preusser T. Representative Sinusoids for Hepatic Four-Scale Pharmacokinetics Simulations. PLoS One 2015. [PMID: 26222615 PMCID: PMC4519332 DOI: 10.1371/journal.pone.0133653] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The mammalian liver plays a key role for metabolism and detoxification of xenobiotics in the body. The corresponding biochemical processes are typically subject to spatial variations at different length scales. Zonal enzyme expression along sinusoids leads to zonated metabolization already in the healthy state. Pathological states of the liver may involve liver cells affected in a zonated manner or heterogeneously across the whole organ. This spatial heterogeneity, however, cannot be described by most computational models which usually consider the liver as a homogeneous, well-stirred organ. The goal of this article is to present a methodology to extend whole-body pharmacokinetics models by a detailed liver model, combining different modeling approaches from the literature. This approach results in an integrated four-scale model, from single cells via sinusoids and the organ to the whole organism, capable of mechanistically representing metabolization inhomogeneity in livers at different spatial scales. Moreover, the model shows circulatory mixing effects due to a delayed recirculation through the surrounding organism. To show that this approach is generally applicable for different physiological processes, we show three applications as proofs of concept, covering a range of species, compounds, and diseased states: clearance of midazolam in steatotic human livers, clearance of caffeine in mouse livers regenerating from necrosis, and a parameter study on the impact of different cell entities on insulin uptake in mouse livers. The examples illustrate how variations only discernible at the local scale influence substance distribution in the plasma at the whole-body level. In particular, our results show that simultaneously considering variations at all relevant spatial scales may be necessary to understand their impact on observations at the organism scale.
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Affiliation(s)
| | - Arne Schenk
- Computational Systems Biology, Bayer Technology Services, Leverkusen, Germany
- Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen University, Aachen, Germany
| | - Clemens Kreutz
- Freiburg Center for Data Analysis and Modeling (FDM), Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Jens Timmer
- Freiburg Center for Data Analysis and Modeling (FDM), Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | | | - Lars Kuepfer
- Computational Systems Biology, Bayer Technology Services, Leverkusen, Germany
- Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany
| | - Tobias Preusser
- Fraunhofer MEVIS, Bremen, Germany
- Jacobs University, Bremen, Germany
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Karr JR, Takahashi K, Funahashi A. The principles of whole-cell modeling. Curr Opin Microbiol 2015; 27:18-24. [PMID: 26115539 DOI: 10.1016/j.mib.2015.06.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 05/25/2015] [Accepted: 06/05/2015] [Indexed: 11/17/2022]
Abstract
Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.
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Affiliation(s)
- Jonathan R Karr
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Koichi Takahashi
- RIKEN Quantitative Biology Center, RIKEN, Osaka 565-0874, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan
| | - Akira Funahashi
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
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Gutierrez JB, Galinski MR, Cantrell S, Voit EO. WITHDRAWN: From within host dynamics to the epidemiology of infectious disease: Scientific overview and challenges. Math Biosci 2015:S0025-5564(15)00085-1. [PMID: 25890102 DOI: 10.1016/j.mbs.2015.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Juan B Gutierrez
- Department of Mathematics, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, United States .
| | - Mary R Galinski
- Emory University School of Medicine, Division of Infectious Diseases, Emory Vaccine Center, Yerkes National Primate Research Center, Emory University, 954 Gatewood Road, Atlanta, GA 30329, United States .
| | - Stephen Cantrell
- Department of Mathematics, University of Miami, Coral Gables, FL 33124, United States .
| | - Eberhard O Voit
- Department of Biomedical Engineering, Georgia Institute of Technology, 313 Ferst Drive, Suite 4103, Atlanta, GA 30332-0535, United States .
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Papatheodorou I, Oellrich A, Smedley D. Linking gene expression to phenotypes via pathway information. J Biomed Semantics 2015; 6:17. [PMID: 25901272 PMCID: PMC4404592 DOI: 10.1186/s13326-015-0013-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Accepted: 03/19/2015] [Indexed: 11/10/2022] Open
Abstract
Establishing robust links among gene expression, pathways and phenotypes is critical for understanding diseases and developing treatments. In recent years there have been many efforts to develop the computational means to traverse from genes to gene expression, model pathways and classify phenotypes. Numerous ontologies and other controlled vocabularies have been developed, as well as computational methods to combine and mine these data sets and establish connections. Here we discuss these efforts and identify areas of future work that could lead to a better integration of genes, pathways and phenotypes to provide insights into the mechanisms under which gene mutations affect expression and pathways and how these effects are manifested onto the phenotype.
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Affiliation(s)
- Irene Papatheodorou
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Anika Oellrich
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Damian Smedley
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
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Regulatory toxicology in the twenty-first century: challenges, perspectives and possible solutions. Arch Toxicol 2015; 89:823-50. [DOI: 10.1007/s00204-015-1510-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 03/17/2015] [Indexed: 10/23/2022]
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Schleicher J, Tokarski C, Marbach E, Matz-Soja M, Zellmer S, Gebhardt R, Schuster S. Zonation of hepatic fatty acid metabolism - The diversity of its regulation and the benefit of modeling. Biochim Biophys Acta Mol Cell Biol Lipids 2015; 1851:641-56. [PMID: 25677822 DOI: 10.1016/j.bbalip.2015.02.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Revised: 01/26/2015] [Accepted: 02/03/2015] [Indexed: 02/07/2023]
Abstract
A pronounced heterogeneity between hepatocytes in subcellular structure and enzyme activities was discovered more than 50years ago and initiated the idea of metabolic zonation. In the last decades zonation patterns of liver metabolism were extensively investigated for carbohydrate, nitrogen and lipid metabolism. The present review focuses on zonation patterns of the latter. We review recent findings regarding the zonation of fatty acid uptake and oxidation, ketogenesis, triglyceride synthesis and secretion, de novo lipogenesis, as well as bile acid and cholesterol metabolism. In doing so, we expose knowledge gaps and discuss contradictory experimental results, for example on the zonation pattern of fatty acid oxidation and de novo lipogenesis. Thus, possible rewarding directions of further research are identified. Furthermore, recent findings about the regulation of metabolic zonation are summarized, especially regarding the role of hormones, nerve innervation, morphogens, gender differences and the influence of the circadian clock. In the last part of the review, a short collection of models considering hepatic lipid metabolism is provided. We conclude that modeling, despite its proven benefit for understanding of hepatic carbohydrate and ammonia metabolisms, has so far been largely disregarded in the study of lipid metabolism; therefore some possible fields of modeling interest are presented.
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Affiliation(s)
- J Schleicher
- Department of Bioinformatics, University of Jena, Jena, Germany.
| | - C Tokarski
- Department of Bioinformatics, University of Jena, Jena, Germany
| | - E Marbach
- Institute of Biochemistry, Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - M Matz-Soja
- Institute of Biochemistry, Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - S Zellmer
- Department of Chemicals and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - R Gebhardt
- Institute of Biochemistry, Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - S Schuster
- Department of Bioinformatics, University of Jena, Jena, Germany
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Budd A, Corpas M, Brazas MD, Fuller JC, Goecks J, Mulder NJ, Michaut M, Ouellette BFF, Pawlik A, Blomberg N. A quick guide for building a successful bioinformatics community. PLoS Comput Biol 2015; 11:e1003972. [PMID: 25654371 PMCID: PMC4318577 DOI: 10.1371/journal.pcbi.1003972] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop "The 'How To Guide' for Establishing a Successful Bioinformatics Network" at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB).
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Affiliation(s)
- Aidan Budd
- Structural and Computational Biology (SCB) Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Manuel Corpas
- The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich, United Kingdom
| | - Michelle D. Brazas
- Ontario Institute for Cancer Research, MaRS Centre, West Tower, Toronto, Ontario, Canada
| | - Jonathan C. Fuller
- Heidelberg Institute for Theoretical Studies (HITS) gGmbH, Heidelberg, Germany
| | - Jeremy Goecks
- The Computational Biology Institute, George Washington University, Innovation Hall, Virginia, United States of America
| | - Nicola J. Mulder
- Computational Biology Group, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town Faculty of Health Sciences, Cape Town, South Africa
| | - Magali Michaut
- Computational Cancer Biology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - B. F. Francis Ouellette
- Ontario Institute for Cancer Research, MaRS Centre, West Tower, Toronto, Ontario, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
| | - Aleksandra Pawlik
- The Software Sustainability Institute, School of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Niklas Blomberg
- ELIXIR Hub, Wellcome Trust Genome Campus, Cambridge, United Kingdom
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Bauch A, Superti-Furga G. Systems biology. Rheumatology (Oxford) 2015. [DOI: 10.1016/b978-0-323-09138-1.00017-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies. PLoS Comput Biol 2014; 10:e1003993. [PMID: 25500563 PMCID: PMC4263370 DOI: 10.1371/journal.pcbi.1003993] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Accepted: 10/15/2014] [Indexed: 12/15/2022] Open
Abstract
Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.
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Dräger A, Palsson BØ. Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol 2014; 2:61. [PMID: 25538939 PMCID: PMC4259112 DOI: 10.3389/fbioe.2014.00061] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 11/14/2014] [Indexed: 11/17/2022] Open
Abstract
Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.
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Affiliation(s)
- Andreas Dräger
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Cognitive Systems, Center for Bioinformatics Tübingen (ZBIT), Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Bernhard Ø. Palsson
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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Pritchard L, Birch PRJ. The zigzag model of plant-microbe interactions: is it time to move on? MOLECULAR PLANT PATHOLOGY 2014; 15:865-70. [PMID: 25382065 PMCID: PMC6638871 DOI: 10.1111/mpp.12210] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Affiliation(s)
- Leighton Pritchard
- Information and Computational Sciences, University of Dundee, Errol Rd, Invergowrie, Dundee, DD2 5DA, UK; Dundee Effector Consortium, University of Dundee, Errol Rd, Invergowrie, Dundee, DD2 5DA, UK; Centre for Human and Animal Pathogens in the Environment, University of Dundee, Errol Rd, Invergowrie, Dundee, DD2 5DA, UK
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