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Krix S, Wilczynski E, Falgàs N, Sánchez-Valle R, Yoles E, Nevo U, Baruch K, Fröhlich H. Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches. Front Immunol 2024; 15:1343900. [PMID: 38720902 PMCID: PMC11078023 DOI: 10.3389/fimmu.2024.1343900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.
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Affiliation(s)
- Sophia Krix
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
| | - Ella Wilczynski
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Neus Falgàs
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Eti Yoles
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Uri Nevo
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Kuti Baruch
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
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Novel Ground-Up 3D Multicellular Simulators for Synthetic Biology CAD Integrating Stochastic Gillespie Simulations Benchmarked with Topologically Variable SBML Models. Genes (Basel) 2023; 14:genes14010154. [PMID: 36672895 PMCID: PMC9859520 DOI: 10.3390/genes14010154] [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: 12/02/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023] Open
Abstract
The elevation of Synthetic Biology from single cells to multicellular simulations would be a significant scale-up. The spatiotemporal behavior of cellular populations has the potential to be prototyped in silico for computer assisted design through ergonomic interfaces. Such a platform would have great practical potential across medicine, industry, research, education and accessible archiving in bioinformatics. Existing Synthetic Biology CAD systems are considered limited regarding population level behavior, and this work explored the in silico challenges posed from biological and computational perspectives. Retaining the connection to Synthetic Biology CAD, an extension of the Infobiotics Workbench Suite was considered, with potential for the integration of genetic regulatory models and/or chemical reaction networks through Next Generation Stochastic Simulator (NGSS) Gillespie algorithms. These were executed using SBML models generated by in-house SBML-Constructor over numerous topologies and benchmarked in association with multicellular simulation layers. Regarding multicellularity, two ground-up multicellular solutions were developed, including the use of Unreal Engine 4 contrasted with CPU multithreading and Blender visualization, resulting in a comparison of real-time versus batch-processed simulations. In conclusion, high-performance computing and client-server architectures could be considered for future works, along with the inclusion of numerous biologically and physically informed features, whilst still pursuing ergonomic solutions.
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Friedrich T, Henthorn N, Durante M. Modeling Radioimmune Response-Current Status and Perspectives. Front Oncol 2021; 11:647272. [PMID: 33796470 PMCID: PMC8008061 DOI: 10.3389/fonc.2021.647272] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/25/2021] [Indexed: 12/13/2022] Open
Abstract
The combination of immune therapy with radiation offers an exciting and promising treatment modality in cancer therapy. It has been hypothesized that radiation induces damage signals within the tumor, making it more detectable for the immune system. In combination with inhibiting immune checkpoints an effective anti-tumor immune response may be established. This inversion from tumor immune evasion raises numerous questions to be solved to support an effective clinical implementation: These include the optimum immune drug and radiation dose time courses, the amount of damage and associated doses required to stimulate an immune response, and the impact of lymphocyte status and dynamics. Biophysical modeling can offer unique insights, providing quantitative information addressing these factors and highlighting mechanisms of action. In this work we review the existing modeling approaches of combined ‘radioimmune’ response, as well as associated fields of study. We propose modeling attempts that appear relevant for an effective and predictive model. We emphasize the importance of the time course of drug and dose delivery in view to the time course of the triggered biological processes. Special attention is also paid to the dose distribution to circulating blood lymphocytes and the effect this has on immune competence.
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Affiliation(s)
- Thomas Friedrich
- Biophysics Department, GSI Helmholtz Center for Heavy Ion Research, Darmstadt, Germany
| | - Nicholas Henthorn
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Marco Durante
- Biophysics Department, GSI Helmholtz Center for Heavy Ion Research, Darmstadt, Germany.,Institute for Solid State Physics, Technical University Darmstadt, Darmstadt, Germany
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Liberman A, Mussel M, Kario D, Sprinzak D, Nevo U. Modelling cell surface dynamics and cell-cell interactions using Cell Studio: a three-dimensional visualization tool based on gaming technology. J R Soc Interface 2019; 16:20190264. [PMID: 31771451 DOI: 10.1098/rsif.2019.0264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Predictive modelling of complex biological systems and biophysical interactions requires the inclusion of multiple nano- and micro-scale events. In many scenarios, however, numerical solutions alone do not necessarily enhance the understanding of the system. Instead, this work explores the use of an agent-based model with visualization capabilities to elucidate interactions between single cells. We present a model of juxtacrine signalling, using Cell Studio, an agent-based modelling system, based on gaming and three-dimensional visualization tools. The main advantages of the system are its ability to apply any cell geometry and to dynamically visualize the diffusion and interactions of the molecules within the cells in real time. These provide an excellent tool for obtaining insight about different biological scenarios, as the user may view the dynamics of a system and observe its emergent behaviour as it unfolds. The agent-based model was validated against the results of a mean-field model of Notch receptors and ligands in two neighbouring cells. The conversion to an agent-based model is described in detail. To demonstrate the advantages of the model, we further created a filopodium-mediated signalling model. Our model revealed that diffusion and endocytosis alone are insufficient to produce significant signalling in a filopodia scenario. This is due to the bottleneck at the cell-filopodium contact region and the long distance to the end of the filopodium. However, allowing active transport of ligands into filopodia enhances the signalling significantly compared with a face-to-face scenario. We conclude that the agent-based approach can provide insights into mechanisms underlying cell signalling. The open-source model can be found in the Internet hosting service GitHub.
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Affiliation(s)
- Asaf Liberman
- The Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Matan Mussel
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Danny Kario
- The Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - David Sprinzak
- The Department of Biochemistry and Molecular Biology, Wise Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Uri Nevo
- The Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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