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Cadavid JL, Li NT, McGuigan AP. Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease. BIOPHYSICS REVIEWS 2024; 5:021301. [PMID: 38617201 PMCID: PMC11008916 DOI: 10.1063/5.0179125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.
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Deciphering signal transduction networks in the liver by mechanistic mathematical modelling. Biochem J 2022; 479:1361-1374. [PMID: 35748700 PMCID: PMC9246346 DOI: 10.1042/bcj20210548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/10/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.
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Mete B, Pekbilir E, Bilge BN, Georgiadou P, Çelik E, Sutlu T, Tabak F, Sahin U. Human immunodeficiency virus type 1 impairs sumoylation. Life Sci Alliance 2022; 5:5/6/e202101103. [PMID: 35181598 PMCID: PMC8860096 DOI: 10.26508/lsa.202101103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 12/18/2022] Open
Abstract
The HIV type 1 dampens host cell sumoylation in vitro and reduces the expression of UBA2 protein, a subunit of the SUMO E1–activating enzyme. In vivo, infection in patients is associated with diminished global leukocyte sumoylation activity. During infection, the human immunodeficiency virus type 1 (HIV-1) manipulates host cell mechanisms to its advantage, thereby controlling its replication or latency, and evading immune responses. Sumoylation is an essential post-translational modification that controls vital cellular activities including proliferation, stemness, or anti-viral immunity. SUMO peptides oppose pathogen replication and mediate interferon-dependent anti-viral activities. In turn, several viruses and bacteria attack sumoylation to disarm host immune responses. Here, we show that HIV-1 impairs cellular sumoylation and targets the host SUMO E1–activating enzyme. HIV-1 expression in cultured HEK293 cells or in CD4+ Jurkat T lymphocytes diminishes sumoylation by both SUMO paralogs, SUMO1 and SUMO2/3. HIV-1 causes a sharp and specific decline in UBA2 protein levels, a subunit of the heterodimeric SUMO E1 enzyme, which likely serves to reduce the efficiency of global protein sumoylation. Furthermore, HIV-1–infected individuals display a significant reduction in total leukocyte sumoylation that is uncoupled from HIV-induced cytopenia. Because sumoylation is vital for immune function, T-cell expansion and activity, loss of sumoylation during HIV disease may contribute to immune system deterioration in patients.
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Affiliation(s)
- Bilgül Mete
- Department of Infectious Diseases and Clinical Microbiology, Istanbul University-Cerrahpasa, Cerrahpasa School of Medicine, Istanbul, Turkey
| | - Emre Pekbilir
- Department of Molecular Biology and Genetics, Bogazici University, Center for Life Sciences and Technologies, Istanbul, Turkey
| | - Bilge Nur Bilge
- Department of Medical Biology, Istanbul University-Cerrahpasa, Cerrahpasa School of Medicine, Istanbul, Turkey
| | - Panagiota Georgiadou
- Department of Molecular Biology and Genetics, Bogazici University, Center for Life Sciences and Technologies, Istanbul, Turkey
| | - Elif Çelik
- Department of Molecular Biology and Genetics, Bogazici University, Center for Life Sciences and Technologies, Istanbul, Turkey
| | - Tolga Sutlu
- Department of Molecular Biology and Genetics, Bogazici University, Center for Life Sciences and Technologies, Istanbul, Turkey
| | - Fehmi Tabak
- Department of Infectious Diseases and Clinical Microbiology, Istanbul University-Cerrahpasa, Cerrahpasa School of Medicine, Istanbul, Turkey
| | - Umut Sahin
- Department of Molecular Biology and Genetics, Bogazici University, Center for Life Sciences and Technologies, Istanbul, Turkey
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Talaei K, Garan SA, Quintela BDM, Olufsen MS, Cho J, Jahansooz JR, Bhullar PK, Suen EK, Piszker WJ, Martins NRB, Moreira de Paula MA, Dos Santos RW, Lobosco M. A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus. Front Cell Infect Microbiol 2021; 11:711153. [PMID: 34869049 PMCID: PMC8633844 DOI: 10.3389/fcimb.2021.711153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022] Open
Abstract
Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.
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Affiliation(s)
- Kian Talaei
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Lawrence Berkeley National Laboratory, Berkeley, CA, United States.,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Steven A Garan
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | | | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, NC, United States
| | - Joshua Cho
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Lawrence Berkeley National Laboratory, Berkeley, CA, United States.,College of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Julia R Jahansooz
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Puneet K Bhullar
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Mayo Clinic Alix School of Medicine, Scottsdale, AZ, United States
| | - Elliott K Suen
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Walter J Piszker
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,College of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Nuno R B Martins
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States
| | | | | | - Marcelo Lobosco
- Department of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, Brazil
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Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response. Curr Opin Virol 2021; 50:103-109. [PMID: 34450519 PMCID: PMC8384423 DOI: 10.1016/j.coviro.2021.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 12/17/2022]
Abstract
The COVID-19 pandemic has highlighted a need for improved frameworks for drug discovery, repurposing, clinical trial design and therapy optimization and personalization. Mechanistic computational models can play an important role in developing these frameworks. We discuss how mechanistic models, which consider viral entry, replication in target cells, viral spread in the body, immune response, and the complex factors involved in tissue and organ damage and recovery, can clarify the mechanisms of humoral and cellular immune responses to the virus, viral distribution and replication in tissues, the origins of pathogenesis and patient-to-patient heterogeneity in responses. These models are already improving our understanding of the mechanisms of action of antivirals and immune modulators. We discuss how closer collaboration between the experimentalists, clinicians and modelers could result in more predictive models which may guide therapies for viral infections, improving survival and leading to faster and more complete recovery.
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Frisch HP, Sprau A, McElroy VF, Turner JD, Becher LRE, Nevala WK, Leontovich AA, Markovic SN. Cancer immune control dynamics: a clinical data driven model of systemic immunity in patients with metastatic melanoma. BMC Bioinformatics 2021; 22:197. [PMID: 33863290 PMCID: PMC8052714 DOI: 10.1186/s12859-021-04025-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 02/15/2021] [Indexed: 11/10/2022] Open
Abstract
Background Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This interdisciplinary effort exploits engineering analysis methods utilized to investigate anomalous physical system behaviors to explore immune system behaviors. Cancer Immune Control Dynamics (CICD), a systems analysis approach, attempts to identify differences between systemic immune homeostasis of 27 healthy volunteers versus 14 patients with metastatic malignant melanoma based on daily serial measurements of conventional peripheral blood biomarkers (15 cell subsets, 35 cytokines). The modeling strategy applies engineering control theory to analyze an individual’s immune system based on the biomarkers’ dynamic non-linear oscillatory behaviors. The reverse engineering analysis uses a Singular Value Decomposition (SVD) algorithm to solve the inverse problem and identify a solution profile of the active biomarker relationships. Herein, 28,605 biologically possible biomarker interactions are modeled by a set of matrix equations creating a system interaction model. CICD quantifies the model with a participant’s biomarker data then computationally solves it to measure each relationship’s activity allowing a visualization of the individual’s current state of immunity. Results CICD results provide initial evidence that this model-based analysis is consistent with identified roles of biomarkers in systemic immunity of cancer patients versus that of healthy volunteers. The mathematical computations alone identified a plausible network of immune cells, including T cells, natural killer (NK) cells, monocytes, and dendritic cells (DC) with cytokines MCP-1 [CXCL2], IP-10 [CXCL10], and IL-8 that play a role in sustaining the state of immunity in advanced cancer. Conclusions With CICD modeling capabilities, the complexity of the immune system is mathematically quantified through thousands of possible interactions between multiple biomarkers. Therefore, the overall state of an individual’s immune system regardless of clinical status, is modeled as reflected in their blood samples. It is anticipated that CICD-based capabilities will provide tools to specifically address cancer and treatment modulated (immune checkpoint inhibitors) parameters of human immunity, revealing clinically relevant biological interactions. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04025-7.
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Affiliation(s)
- Harold P Frisch
- Payload Systems Engineering Branch, Emeritus, NASA, Annapolis, MD, USA
| | | | | | - James D Turner
- Retired Aerospace Consultant, Texas A&M University, College Station, TX, USA
| | - Laura R E Becher
- Department of Medical Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Wendy K Nevala
- Department of Oncology Research, Mayo Clinic, Rochester, MN, USA
| | - Alexey A Leontovich
- Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Svetomir N Markovic
- Department of Medical Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
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Johnson ME, Chen A, Faeder JR, Henning P, Moraru II, Meier-Schellersheim M, Murphy RF, Prüstel T, Theriot JA, Uhrmacher AM. Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry. Mol Biol Cell 2021; 32:186-210. [PMID: 33237849 PMCID: PMC8120688 DOI: 10.1091/mbc.e20-08-0530] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/13/2020] [Accepted: 11/17/2020] [Indexed: 12/29/2022] Open
Abstract
Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.
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Affiliation(s)
- M. E. Johnson
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - A. Chen
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - J. R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260
| | - P. Henning
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
| | - I. I. Moraru
- Department of Cell Biology, Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030
| | - M. Meier-Schellersheim
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - R. F. Murphy
- Computational Biology Department, Department of Biological Sciences, Department of Biomedical Engineering, Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15289
| | - T. Prüstel
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - J. A. Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195
| | - A. M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
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Joshi A, Wang DH, Watterson S, McClean PL, Behera CK, Sharp T, Wong-Lin K. Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease. Neuropharmacology 2020; 174:108118. [PMID: 32380022 PMCID: PMC7322519 DOI: 10.1016/j.neuropharm.2020.108118] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 04/13/2020] [Accepted: 04/27/2020] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aβ) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aβ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets. Alzheimer's disease (AD) drug treatment is limited, and alternatives are needed. Serotonin (5-HT) mediated signalling pathways may regulate Aβ and tau levels. 5-HT based drugs have the potential to provide as novel therapeutics for AD. Complex 5-HT signalling mechanisms for AD and related drugs hinder understanding. Multiscale models may offer insights into mechanisms and therapeutic targets.
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Affiliation(s)
- Alok Joshi
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK.
| | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; School of System Science, Beijing Normal University, Beijing, China
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Paula L McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Chandan K Behera
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK.
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