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Wang Z, Hasenauer J, Schälte Y. Missing data in amortized simulation-based neural posterior estimation. PLoS Comput Biol 2024; 20:e1012184. [PMID: 38885265 PMCID: PMC11213359 DOI: 10.1371/journal.pcbi.1012184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 06/28/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates. In this work, we discuss various ways of encoding missing data and integrate them into the training and inference process. We implement the approaches in the BayesFlow methodology, an amortized estimation framework based on invertible neural networks, and evaluate their performance on multiple test problems. We find that an approach in which the data vector is augmented with binary indicators of presence or absence of values performs the most robustly. Indeed, it improved the performance also for the simpler problem of data sets with variable length. Accordingly, we demonstrate that amortized simulation-based inference approaches are applicable even with missing data, and we provide a guideline for their handling, which is relevant for a broad spectrum of applications.
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Affiliation(s)
- Zijian Wang
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
| | - Jan Hasenauer
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
- Helmholtz Center Munich, Computational Health Center, Neuherberg, Germany
| | - Yannik Schälte
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
- Helmholtz Center Munich, Computational Health Center, Neuherberg, Germany
- Technical University Munich, Center for Mathematics, Garching, Germany
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2
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Alamoudi E, Reck F, Bundgaard N, Graw F, Brusch L, Hasenauer J, Schälte Y. A wall-time minimizing parallelization strategy for approximate Bayesian computation. PLoS One 2024; 19:e0294015. [PMID: 38386671 PMCID: PMC10883530 DOI: 10.1371/journal.pone.0294015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/24/2023] [Indexed: 02/24/2024] Open
Abstract
Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often necessary. However, the existing parallelization strategies leave computing resources unused at times and thus do not optimally leverage them yet. We present look-ahead scheduling, a wall-time minimizing parallelization strategy for ABC Sequential Monte Carlo algorithms, which avoids idle times of computing units by preemptive sampling of subsequent generations. This allows to utilize all available resources. The strategy can be integrated with e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Our key contribution is the theoretical assessment of the strategy of preemptive sampling and the proof of unbiasedness. Complementary, we provide an implementation and evaluate the strategy on different problems and numbers of parallel cores, showing speed-ups of typically 10-20% and up to 50% compared to the best established approach, with some variability. Thus, the proposed strategy allows to improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.
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Affiliation(s)
- Emad Alamoudi
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Felipe Reck
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Nils Bundgaard
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Frederik Graw
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Department of Medicine 5, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Lutz Brusch
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden, Germany
| | - Jan Hasenauer
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Yannik Schälte
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
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3
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Wadkin LE, Makarenko I, Parker NG, Shukurov A, Figueiredo FC, Lako M. Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling. CURRENT STEM CELL REPORTS 2023; 9:57-66. [PMID: 38145008 PMCID: PMC10739444 DOI: 10.1007/s40778-023-00229-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/07/2023] [Indexed: 12/26/2023]
Abstract
Purpose of Review To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications. Recent Findings hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy. Summary Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research.
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Affiliation(s)
- L. E. Wadkin
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - I. Makarenko
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - N. G. Parker
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - A. Shukurov
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - F. C. Figueiredo
- Department of Ophthalmology, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - M. Lako
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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4
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Alamoudi E, Schälte Y, Müller R, Starruß J, Bundgaard N, Graw F, Brusch L, Hasenauer J. FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes. Bioinformatics 2023; 39:btad674. [PMID: 37947308 PMCID: PMC10666203 DOI: 10.1093/bioinformatics/btad674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023] Open
Abstract
MOTIVATION Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. RESULTS Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AVAILABILITY AND IMPLEMENTATION FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.
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Affiliation(s)
- Emad Alamoudi
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
| | - Yannik Schälte
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany
| | - Robert Müller
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Jörn Starruß
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Nils Bundgaard
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg 69120, Germany
| | - Frederik Graw
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg 69120, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg 69120, Germany
- Department of Medicine 5, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen 91054, Germany
| | - Lutz Brusch
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Jan Hasenauer
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany
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5
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Nanda P, Kirschner DE. Calibration methods to fit parameters within complex biological models. FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS 2023; 9:1256443. [PMID: 38222943 PMCID: PMC10785782 DOI: 10.3389/fams.2023.1256443] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.
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Affiliation(s)
- Pariksheet Nanda
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Denise E. Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
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6
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Germano DPJ, Zanca A, Johnston ST, Flegg JA, Osborne JM. Free and Interfacial Boundaries in Individual-Based Models of Multicellular Biological systems. Bull Math Biol 2023; 85:111. [PMID: 37805982 PMCID: PMC10560655 DOI: 10.1007/s11538-023-01214-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023]
Abstract
Coordination of cell behaviour is key to a myriad of biological processes including tissue morphogenesis, wound healing, and tumour growth. As such, individual-based computational models, which explicitly describe inter-cellular interactions, are commonly used to model collective cell dynamics. However, when using individual-based models, it is unclear how descriptions of cell boundaries affect overall population dynamics. In order to investigate this we define three cell boundary descriptions of varying complexities for each of three widely used off-lattice individual-based models: overlapping spheres, Voronoi tessellation, and vertex models. We apply our models to multiple biological scenarios to investigate how cell boundary description can influence tissue-scale behaviour. We find that the Voronoi tessellation model is most sensitive to changes in the cell boundary description with basic models being inappropriate in many cases. The timescale of tissue evolution when using an overlapping spheres model is coupled to the boundary description. The vertex model is demonstrated to be the most stable to changes in boundary description, though still exhibits timescale sensitivity. When using individual-based computational models one should carefully consider how cell boundaries are defined. To inform future work, we provide an exploration of common individual-based models and cell boundary descriptions in frequently studied biological scenarios and discuss their benefits and disadvantages.
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Affiliation(s)
- Domenic P. J. Germano
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - Adriana Zanca
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - Stuart T. Johnston
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - Jennifer A. Flegg
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - James M. Osborne
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
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7
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Jørgensen ACS, Hill CS, Sturrock M, Tang W, Karamched SR, Gorup D, Lythgoe MF, Parrinello S, Marguerat S, Shahrezaei V. Data-driven spatio-temporal modelling of glioblastoma. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221444. [PMID: 36968241 PMCID: PMC10031411 DOI: 10.1098/rsos.221444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
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Affiliation(s)
| | - Ciaran Scott Hill
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin D02 YN77, Ireland
| | - Wenhao Tang
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
| | - Saketh R. Karamched
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Dunja Gorup
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Mark F. Lythgoe
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Simona Parrinello
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Samuel Marguerat
- Genomics Translational Technology Platform, UCL Cancer Institute, University College London, London WC1E 6DD, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
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8
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Dynamic Mechanism and Decision Analysis of Urban Exhibition Tourism Development Based on High-Performance Computing. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3806752. [PMID: 35432521 PMCID: PMC9007668 DOI: 10.1155/2022/3806752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 11/17/2022]
Abstract
MICE tourism has received the attention and support from relevant departments and tourism industry with the growth rate of recent years. In this paper, the dynamics mechanism of urban exhibition tourism development is studied by using high-performance computing, and the dynamics mechanism model of exhibition tourism is constructed. To improve the performance, the solution constructed in the adaptive joint algorithm is used as the initial solution, and then the initial solution is improved using the method proposed by the new neighborhood strategy. It promotes the development of MICE tourism and urban MICE tourism by combining research on theoretical aspects related to tourism development, such as the relationship between tourism development and exhibition, and the development dynamics and stages of MICE tourism. The research results show that, in the assessment of the development mechanism of urban MICE tourism, tourism resources, economic, and policy factors have a relatively large weight, among which the weight coefficient of tourism resources factor is 0.2411. It can be seen that the development power of MICE tourism should start from tourism resources, so that the development power mechanism of urban MICE tourism can be optimized and improved, which not only provides new methods and new ideas for the transformation and upgrading of tourism industry but also promotes tourism enterprises and related departments to creatively develop tourism resources and design competitive tourism products, so as to improve the supply quality of MICE tourism, and then continuously promote the process of supply-side reform of MICE tourism.
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9
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Wen J, Xie P. Innovative Research on Urban Community Governance Decision-Making Relying on Distributed High-Performance Computing Blockchain Key Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4078014. [PMID: 35330600 PMCID: PMC8940554 DOI: 10.1155/2022/4078014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 02/16/2022] [Indexed: 11/17/2022]
Abstract
Since the country began to go global, the country's economy has developed rapidly, cultural exchanges between countries have become more and more frequent, and foreign cultures have begun to gradually spread to the country. At the same time, through the absorption of foreign community governance experience, domestic research on community governance has also begun to be put on the agenda. This paper aims to study the innovative exploration of urban community governance mechanisms through key high-performance computing algorithms. To this end, this paper proposes a combination of the Bayesian algorithm and distributed high-performance computing to analyze and explore the governance and management methods of the community through its efficient and stable computing power, and derive the most suitable community governance mechanism. An experiment was also set up for comparative analysis. The experimental results show that the community governance mechanism derived from the key distributed high-performance computing algorithm improves the community governance capability by 19.4%, effectively improving the community governance and management issues.
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Affiliation(s)
- Jun Wen
- School of Cultural Creative Industries Management, Shanghai Institute of Visual Arts, Shanghai 201620, China
| | - Peihong Xie
- School of Management, Shanghai University of International Business and Economics, Shanghai 201620, China
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10
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Akasiadis C, Ponce‐de‐Leon M, Montagud A, Michelioudakis E, Atsidakou A, Alevizos E, Artikis A, Valencia A, Paliouras G. Parallel model exploration for tumor treatment simulations. Comput Intell 2022. [DOI: 10.1111/coin.12515] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Charilaos Akasiadis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | | | - Arnau Montagud
- Life Sciences Department Barcelona Supercomputing Center Barcelona Spain
| | - Evangelos Michelioudakis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
- Department of Informatics and Telecommunications University of Athens Athens Greece
| | - Alexia Atsidakou
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Elias Alevizos
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Alexander Artikis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Alfonso Valencia
- Life Sciences Department Barcelona Supercomputing Center Barcelona Spain
| | - Georgios Paliouras
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
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11
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Inferring the effect of interventions on COVID-19 transmission networks. Sci Rep 2021; 11:21913. [PMID: 34754025 PMCID: PMC8578219 DOI: 10.1038/s41598-021-01407-y] [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: 05/31/2021] [Accepted: 10/12/2021] [Indexed: 12/11/2022] Open
Abstract
Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts–Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a previously unappreciated transition from an exponential to a constant regime of new cases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory.
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12
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HCV Spread Kinetics Reveal Varying Contributions of Transmission Modes to Infection Dynamics. Viruses 2021; 13:v13071308. [PMID: 34372514 PMCID: PMC8310333 DOI: 10.3390/v13071308] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/13/2021] [Accepted: 06/29/2021] [Indexed: 01/04/2023] Open
Abstract
The hepatitis C virus (HCV) is capable of spreading within a host by two different transmission modes: cell-free and cell-to-cell. However, the contribution of each of these transmission mechanisms to HCV spread is unknown. To dissect the contribution of these different transmission modes to HCV spread, we measured HCV lifecycle kinetics and used an in vitro spread assay to monitor HCV spread kinetics after a low multiplicity of infection in the absence and presence of a neutralizing antibody that blocks cell-free spread. By analyzing these data with a spatially explicit mathematical model that describes viral spread on a single-cell level, we quantified the contribution of cell-free, and cell-to-cell spread to the overall infection dynamics and show that both transmission modes act synergistically to enhance the spread of infection. Thus, the simultaneous occurrence of both transmission modes represents an advantage for HCV that may contribute to viral persistence. Notably, the relative contribution of each viral transmission mode appeared to vary dependent on different experimental conditions and suggests that viral spread is optimized according to the environment. Together, our analyses provide insight into the spread dynamics of HCV and reveal how different transmission modes impact each other.
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13
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Hashemi M, Vattikonda AN, Sip V, Diaz-Pier S, Peyser A, Wang H, Guye M, Bartolomei F, Woodman MM, Jirsa VK. On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread. PLoS Comput Biol 2021; 17:e1009129. [PMID: 34260596 PMCID: PMC8312957 DOI: 10.1371/journal.pcbi.1009129] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 07/26/2021] [Accepted: 05/29/2021] [Indexed: 11/18/2022] Open
Abstract
Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.
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Affiliation(s)
- Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Sandra Diaz-Pier
- SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Alexander Peyser
- SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Google, München, Germany
| | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | | | - Viktor K. Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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14
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Matthews ML, Marshall-Colón A. Multiscale plant modeling: from genome to phenome and beyond. Emerg Top Life Sci 2021; 5:231-237. [PMID: 33543231 PMCID: PMC8166335 DOI: 10.1042/etls20200276] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 01/08/2023]
Abstract
Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.
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Affiliation(s)
- Megan L Matthews
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | - Amy Marshall-Colón
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
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15
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Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns. PLoS Biol 2020; 18:e3000708. [PMID: 33290409 PMCID: PMC7748264 DOI: 10.1371/journal.pbio.3000708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 12/18/2020] [Accepted: 11/17/2020] [Indexed: 12/28/2022] Open
Abstract
Regulation of quiescence and cell cycle entry is pivotal for the maintenance of stem cell populations. Regulatory mechanisms, however, are poorly understood. In particular, it is unclear how the activity of single stem cells is coordinated within the population or if cells divide in a purely random fashion. We addressed this issue by analyzing division events in an adult neural stem cell (NSC) population of the zebrafish telencephalon. Spatial statistics and mathematical modeling of over 80,000 NSCs in 36 brain hemispheres revealed weakly aggregated, nonrandom division patterns in space and time. Analyzing divisions at 2 time points allowed us to infer cell cycle and S-phase lengths computationally. Interestingly, we observed rapid cell cycle reentries in roughly 15% of newly born NSCs. In agent-based simulations of NSC populations, this redividing activity sufficed to induce aggregated spatiotemporal division patterns that matched the ones observed experimentally. In contrast, omitting redivisions leads to a random spatiotemporal distribution of dividing cells. Spatiotemporal aggregation of dividing stem cells can thus emerge solely from the cells’ history. An interdisciplinary study of the rules governing cell divisions in a population of neural stem cells in the zebrafish brain reveals the existence of aggregated spatio-temporal division patterns of rapid cell cycles in stem cells, and shows that these patterns can be explained by a simple agent-based model relying solely on the cells‘ division history.
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16
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Mikelson J, Khammash M. Likelihood-free nested sampling for parameter inference of biochemical reaction networks. PLoS Comput Biol 2020; 16:e1008264. [PMID: 33035218 PMCID: PMC7577508 DOI: 10.1371/journal.pcbi.1008264] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 10/21/2020] [Accepted: 08/16/2020] [Indexed: 12/03/2022] Open
Abstract
The development of mechanistic models of biological systems is a central part of Systems Biology. One major challenge in developing these models is the accurate inference of model parameters. In recent years, nested sampling methods have gained increased attention in the Systems Biology community due to the fact that they are parallelizable and provide error estimates with no additional computations. One drawback that severely limits the usability of these methods, however, is that they require the likelihood function to be available, and thus cannot be applied to systems with intractable likelihoods, such as stochastic models. Here we present a likelihood-free nested sampling method for parameter inference which overcomes these drawbacks. This method gives an unbiased estimator of the Bayesian evidence as well as samples from the posterior. We derive a lower bound on the estimators variance which we use to formulate a novel termination criterion for nested sampling. The presented method enables not only the reliable inference of the posterior of parameters for stochastic systems of a size and complexity that is challenging for traditional methods, but it also provides an estimate of the obtained variance. We illustrate our approach by applying it to several realistically sized models with simulated data as well as recently published biological data. We also compare our developed method with the two most popular other likeliood-free approaches: pMCMC and ABC-SMC. The C++ code of the proposed methods, together with test data, is available at the github web page https://github.com/Mijan/LFNS_paper. The behaviour of mathematical models of biochemical reactions is governed by model parameters encoding for various reaction rates, molecule concentrations and other biochemical quantities. As the general purpose of these models is to reproduce and predict the true biological response to different stimuli, the inference of these parameters, given experimental observations, is a crucial part of Systems Biology. While plenty of methods have been published for the inference of model parameters, most of them require the availability of the likelihood function and thus cannot be applied to models that do not allow for the computation of the likelihood. Further, most established methods do not provide an estimate of the variance of the obtained estimator. In this paper, we present a novel inference method that accurately approximates the posterior distribution of parameters and does not require the evaluation of the likelihood function. Our method is based on the nested sampling algorithm and approximates the likelihood with a particle filter. We show that the resulting posterior estimates are unbiased and provide a way to estimate not just the posterior distribution, but also an error estimate of the final estimator. We illustrate our method on several stochastic models with simulated data as well as one model of transcription with real biological data.
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17
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Schälte Y, Hasenauer J. Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation. Bioinformatics 2020; 36:i551-i559. [PMID: 32657404 PMCID: PMC7355286 DOI: 10.1093/bioinformatics/btaa397] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, as it allows analyzing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, as ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC. RESULTS We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling-based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, Markov jump processes and stochastically interacting agents, and noise models including normal, Laplace and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications. AVAILABILITY AND IMPLEMENTATION The developed algorithms are made publicly available as part of the open-source python toolbox pyABC (https://github.com/icb-dcm/pyabc). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yannik Schälte
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg 85764, Germany
- Department of Mathematics, Chair of Mathematical Modeling of Biological Systems, Technical University Munich, Garching 85748, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg 85764, Germany
- Department of Mathematics, Chair of Mathematical Modeling of Biological Systems, Technical University Munich, Garching 85748, Germany
- Research Unit Biomathematics, University of Bonn, Bonn 53113, Germany
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18
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Van Liedekerke P, Neitsch J, Johann T, Warmt E, Gonzàlez-Valverde I, Hoehme S, Grosser S, Kaes J, Drasdo D. A quantitative high-resolution computational mechanics cell model for growing and regenerating tissues. Biomech Model Mechanobiol 2019; 19:189-220. [PMID: 31749071 PMCID: PMC7005086 DOI: 10.1007/s10237-019-01204-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 07/16/2019] [Indexed: 12/19/2022]
Abstract
Mathematical models are increasingly designed to guide experiments in biology, biotechnology, as well as to assist in medical decision making. They are in particular important to understand emergent collective cell behavior. For this purpose, the models, despite still abstractions of reality, need to be quantitative in all aspects relevant for the question of interest. This paper considers as showcase example the regeneration of liver after drug-induced depletion of hepatocytes, in which the surviving and dividing hepatocytes must squeeze in between the blood vessels of a network to refill the emerged lesions. Here, the cells' response to mechanical stress might significantly impact the regeneration process. We present a 3D high-resolution cell-based model integrating information from measurements in order to obtain a refined and quantitative understanding of the impact of cell-biomechanical effects on the closure of drug-induced lesions in liver. Our model represents each cell individually and is constructed by a discrete, physically scalable network of viscoelastic elements, capable of mimicking realistic cell deformation and supplying information at subcellular scales. The cells have the capability to migrate, grow, and divide, and the nature and parameters of their mechanical elements can be inferred from comparisons with optical stretcher experiments. Due to triangulation of the cell surface, interactions of cells with arbitrarily shaped (triangulated) structures such as blood vessels can be captured naturally. Comparing our simulations with those of so-called center-based models, in which cells have a largely rigid shape and forces are exerted between cell centers, we find that the migration forces a cell needs to exert on its environment to close a tissue lesion, is much smaller than predicted by center-based models. To stress generality of the approach, the liver simulations were complemented by monolayer and multicellular spheroid growth simulations. In summary, our model can give quantitative insight in many tissue organization processes, permits hypothesis testing in silico, and guide experiments in situations in which cell mechanics is considered important.
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Affiliation(s)
- Paul Van Liedekerke
- Inria Paris & Sorbonne Université LJLL, 2 Rue Simone IFF, 75012, Paris, France. .,IfADo - Leibniz Research Centre for Working Environment and Human Factors, Ardeystrasse 67, Dortmund, Germany.
| | - Johannes Neitsch
- Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstr. 16-18, 04107, Leipzig, Germany
| | - Tim Johann
- IfADo - Leibniz Research Centre for Working Environment and Human Factors, Ardeystrasse 67, Dortmund, Germany
| | - Enrico Warmt
- Faculty of Physics and Earth Science, Peter Debye Institute for Soft Matter Physics, Leipzig University, Linnéstraße 5, 04103, Leipzig, Germany
| | | | - Stefan Hoehme
- Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstr. 16-18, 04107, Leipzig, Germany.,Institute for Computer Science, Leipzig University, Härtelstr. 16-18, 04107, Leipzig, Germany
| | - Steffen Grosser
- Faculty of Physics and Earth Science, Peter Debye Institute for Soft Matter Physics, Leipzig University, Linnéstraße 5, 04103, Leipzig, Germany
| | - Josef Kaes
- Faculty of Physics and Earth Science, Peter Debye Institute for Soft Matter Physics, Leipzig University, Linnéstraße 5, 04103, Leipzig, Germany
| | - Dirk Drasdo
- Inria Paris & Sorbonne Université LJLL, 2 Rue Simone IFF, 75012, Paris, France. .,IfADo - Leibniz Research Centre for Working Environment and Human Factors, Ardeystrasse 67, Dortmund, Germany. .,Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstr. 16-18, 04107, Leipzig, Germany.
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19
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de Oliveira LR, Jaqaman K. FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data. Biophys J 2019; 117:1012-1028. [PMID: 31443908 DOI: 10.1016/j.bpj.2019.07.050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 06/27/2019] [Accepted: 07/22/2019] [Indexed: 12/12/2022] Open
Abstract
Recent experimental and computational developments have been pushing the limits of live-cell single-molecule imaging, enabling the monitoring of intermolecular interactions in their native environment with high spatiotemporal resolution. However, interactions are captured only for the labeled subset of molecules, which tends to be a small fraction. As a result, it has remained a challenge to calculate molecular interaction kinetics, in particular association rates, from live-cell single-molecule tracking data. To overcome this challenge, we developed a mathematical modeling-based Framework for the Inference of in Situ Interaction Kinetics (FISIK) from single-molecule imaging data with substoichiometric labeling. FISIK consists of (I) devising a mathematical model of molecular movement and interactions, mimicking the biological system and data-acquisition setup, and (II) estimating the unknown model parameters, including molecular association and dissociation rates, by fitting the model to experimental single-molecule data. Due to the stochastic nature of the model and data, we adapted the method of indirect inference for model calibration. We validated FISIK using a series of tests in which we simulated trajectories of diffusing molecules that interact with each other, considering a wide range of model parameters, and including resolution limitations, tracking errors, and mismatches between the model and the biological system it mimics. We found that FISIK has the sensitivity to determine association and dissociation rates, with accuracy and precision depending on the labeled fraction of molecules and the extent of molecule tracking errors. For cases where the labeled fraction is too low (e.g., to afford accurate tracking), combining dynamic but sparse single-molecule imaging data with almost-whole population oligomer distribution data improves FISIK's performance. All in all, FISIK is a promising approach for the derivation of molecular interaction kinetics in their native environment from single-molecule imaging data with substoichiometric labeling.
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Affiliation(s)
| | - Khuloud Jaqaman
- Department of Biophysics, UT Southwestern Medical Center, Dallas, Texas; Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas.
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20
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Loskot P, Atitey K, Mihaylova L. Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks. Front Genet 2019; 10:549. [PMID: 31258548 PMCID: PMC6588029 DOI: 10.3389/fgene.2019.00549] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/24/2019] [Indexed: 01/30/2023] Open
Abstract
The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered-perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.
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Affiliation(s)
- Pavel Loskot
- College of Engineering, Swansea University, Swansea, United Kingdom
| | - Komlan Atitey
- College of Engineering, Swansea University, Swansea, United Kingdom
| | - Lyudmila Mihaylova
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
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21
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Imle A, Kumberger P, Schnellbächer ND, Fehr J, Carrillo-Bustamante P, Ales J, Schmidt P, Ritter C, Godinez WJ, Müller B, Rohr K, Hamprecht FA, Schwarz US, Graw F, Fackler OT. Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures. Nat Commun 2019; 10:2144. [PMID: 31086185 PMCID: PMC6514199 DOI: 10.1038/s41467-019-09879-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 04/04/2019] [Indexed: 11/09/2022] Open
Abstract
Pathogens face varying microenvironments in vivo, but suitable experimental systems and analysis tools to dissect how three-dimensional (3D) tissue environments impact pathogen spread are lacking. Here we develop an Integrative method to Study Pathogen spread by Experiment and Computation within Tissue-like 3D cultures (INSPECT-3D), combining quantification of pathogen replication with imaging to study single-cell and cell population dynamics. We apply INSPECT-3D to analyze HIV-1 spread between primary human CD4 T-lymphocytes using collagen as tissue-like 3D-scaffold. Measurements of virus replication, infectivity, diffusion, cellular motility and interactions are combined by mathematical analyses into an integrated spatial infection model to estimate parameters governing HIV-1 spread. This reveals that environmental restrictions limit infection by cell-free virions but promote cell-associated HIV-1 transmission. Experimental validation identifies cell motility and density as essential determinants of efficacy and mode of HIV-1 spread in 3D. INSPECT-3D represents an adaptable method for quantitative time-resolved analyses of 3D pathogen spread. Here, using an integrative experimental and computational approach, Imle et al. show how cell motility and density affect HIV cell-associated transmission in a three-dimensional tissue-like culture system of CD4+ T cells and collagen, and how different collagen matrices restrict infection by cell-free virions.
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Affiliation(s)
- Andrea Imle
- Department of Infectious Diseases, Centre for Integrative Infectious Disease Research (CIID), Integrative Virology, University Hospital Heidelberg, 69120, Heidelberg, Germany.,Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany
| | - Peter Kumberger
- Centre for Modelling and Simulation in the Biosciences, BioQuant, Heidelberg University, 69120, Heidelberg, Germany
| | - Nikolas D Schnellbächer
- Institute for Theoretical Physics and BioQuant, Heidelberg University, 69120, Heidelberg, Germany
| | - Jana Fehr
- Centre for Modelling and Simulation in the Biosciences, BioQuant, Heidelberg University, 69120, Heidelberg, Germany.,Digital Health & Machine Learning, Hasso-Plattner Institute, 14482, Potsdam, Germany
| | - Paola Carrillo-Bustamante
- Centre for Modelling and Simulation in the Biosciences, BioQuant, Heidelberg University, 69120, Heidelberg, Germany.,Vector Biology Unit, Max-Planck Institute for Infection Biology, 10117, Berlin, Germany
| | - Janez Ales
- HCI/IWR, Heidelberg University, 69120, Heidelberg, Germany
| | - Philip Schmidt
- HCI/IWR, Heidelberg University, 69120, Heidelberg, Germany
| | - Christian Ritter
- Biomedical Computer Vision Group, BioQuant, IPMB, and DKFZ, Heidelberg University, 69120, Heidelberg, Germany
| | - William J Godinez
- Biomedical Computer Vision Group, BioQuant, IPMB, and DKFZ, Heidelberg University, 69120, Heidelberg, Germany
| | - Barbara Müller
- Department of Infectious Diseases, Centre for Integrative Infectious Disease Research (CIID), Virology, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, and DKFZ, Heidelberg University, 69120, Heidelberg, Germany
| | | | - Ulrich S Schwarz
- Institute for Theoretical Physics and BioQuant, Heidelberg University, 69120, Heidelberg, Germany
| | - Frederik Graw
- Centre for Modelling and Simulation in the Biosciences, BioQuant, Heidelberg University, 69120, Heidelberg, Germany
| | - Oliver T Fackler
- Department of Infectious Diseases, Centre for Integrative Infectious Disease Research (CIID), Integrative Virology, University Hospital Heidelberg, 69120, Heidelberg, Germany. .,German Centre for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany.
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22
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An energetic reformulation of kinetic rate laws enables scalable parameter estimation for biochemical networks. J Theor Biol 2019; 461:145-156. [DOI: 10.1016/j.jtbi.2018.10.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 09/20/2018] [Accepted: 10/19/2018] [Indexed: 11/18/2022]
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23
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Hross S, Theis FJ, Sixt M, Hasenauer J. Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data. J R Soc Interface 2018; 15:20180600. [PMID: 30958238 PMCID: PMC6303801 DOI: 10.1098/rsif.2018.0600] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 11/19/2018] [Indexed: 02/06/2023] Open
Abstract
Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems; however, mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal mathematical modelling has helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum-likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parametrization and subsequent refinement of the model using additional mechanisms. Among other results, model-based hypothesis testing predicted lymphatic vessel-dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.
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Affiliation(s)
- Sabrina Hross
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, München, Germany
- Department of Mathematics, Technische Universität München, München, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, München, Germany
- Department of Mathematics, Technische Universität München, München, Germany
| | - Michael Sixt
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, München, Germany
- Department of Mathematics, Technische Universität München, München, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
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24
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Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model. Cell Syst 2018; 7:567-579.e6. [DOI: 10.1016/j.cels.2018.10.013] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 09/07/2018] [Accepted: 10/29/2018] [Indexed: 12/25/2022]
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25
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Engblom S, Wilson DB, Baker RE. Scalable population-level modelling of biological cells incorporating mechanics and kinetics in continuous time. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180379. [PMID: 30225024 PMCID: PMC6124129 DOI: 10.1098/rsos.180379] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 06/18/2018] [Indexed: 06/08/2023]
Abstract
The processes taking place inside the living cell are now understood to the point where predictive computational models can be used to gain detailed understanding of important biological phenomena. A key challenge is to extrapolate this detailed knowledge of the individual cell to be able to explain at the population level how cells interact and respond with each other and their environment. In particular, the goal is to understand how organisms develop, maintain and repair functional tissues and organs. In this paper, we propose a novel computational framework for modelling populations of interacting cells. Our framework incorporates mechanistic, constitutive descriptions of biomechanical properties of the cell population, and uses a coarse-graining approach to derive individual rate laws that enable propagation of the population through time. Thanks to its multiscale nature, the resulting simulation algorithm is extremely scalable and highly efficient. As highlighted in our computational examples, the framework is also very flexible and may straightforwardly be coupled with continuous-time descriptions of biochemical signalling within, and between, individual cells.
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Affiliation(s)
- Stefan Engblom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden
| | - Daniel B. Wilson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK
| | - Ruth E. Baker
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK
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26
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Ballnus B, Schaper S, Theis FJ, Hasenauer J. Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering. Bioinformatics 2018; 34:i494-i501. [PMID: 29949983 PMCID: PMC6022572 DOI: 10.1093/bioinformatics/bty229] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Motivation Mathematical models have become standard tools for the investigation of cellular processes and the unraveling of signal processing mechanisms. The parameters of these models are usually derived from the available data using optimization and sampling methods. However, the efficiency of these methods is limited by the properties of the mathematical model, e.g. non-identifiabilities, and the resulting posterior distribution. In particular, multi-modal distributions with long valleys or pronounced tails are difficult to optimize and sample. Thus, the developement or improvement of optimization and sampling methods is subject to ongoing research. Results We suggest a region-based adaptive parallel tempering algorithm which adapts to the problem-specific posterior distributions, i.e. modes and valleys. The algorithm combines several established algorithms to overcome their individual shortcomings and to improve sampling efficiency. We assessed its properties for established benchmark problems and two ordinary differential equation models of biochemical reaction networks. The proposed algorithm outperformed state-of-the-art methods in terms of calculation efficiency and mixing. Since the algorithm does not rely on a specific problem structure, but adapts to the posterior distribution, it is suitable for a variety of model classes. Availability and implementation The code is available both as Supplementary Material and in a Git repository written in MATLAB. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Benjamin Ballnus
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | - Steffen Schaper
- Bayer AG, Engineering and Technologies, Applied Mathematics, Leverkusen, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
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Accounting for Space—Quantification of Cell-To-Cell Transmission Kinetics Using Virus Dynamics Models. Viruses 2018; 10:v10040200. [PMID: 29673154 PMCID: PMC5923494 DOI: 10.3390/v10040200] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/11/2018] [Accepted: 04/12/2018] [Indexed: 12/12/2022] Open
Abstract
Mathematical models based on ordinary differential equations (ODE) that describe the population dynamics of viruses and infected cells have been an essential tool to characterize and quantify viral infection dynamics. Although an important aspect of viral infection is the dynamics of viral spread, which includes transmission by cell-free virions and direct cell-to-cell transmission, models used so far ignored cell-to-cell transmission completely, or accounted for this process by simple mass-action kinetics between infected and uninfected cells. In this study, we show that the simple mass-action approach falls short when describing viral spread in a spatially-defined environment. Using simulated data, we present a model extension that allows correct quantification of cell-to-cell transmission dynamics within a monolayer of cells. By considering the decreasing proportion of cells that can contribute to cell-to-cell spread with progressing infection, our extension accounts for the transmission dynamics on a single cell level while still remaining applicable to standard population-based experimental measurements. While the ability to infer the proportion of cells infected by either of the transmission modes depends on the viral diffusion rate, the improved estimates obtained using our novel approach emphasize the need to correctly account for spatial aspects when analyzing viral spread.
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Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues. J Theor Biol 2018; 443:66-81. [PMID: 29391171 DOI: 10.1016/j.jtbi.2018.01.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 01/15/2018] [Accepted: 01/17/2018] [Indexed: 11/22/2022]
Abstract
The growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.
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Babtie AC, Stumpf MPH. How to deal with parameters for whole-cell modelling. J R Soc Interface 2017; 14:20170237. [PMID: 28768879 PMCID: PMC5582120 DOI: 10.1098/rsif.2017.0237] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 06/22/2017] [Indexed: 11/12/2022] Open
Abstract
Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.
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Affiliation(s)
- Ann C Babtie
- Department of Life Sciences, Imperial College London, London, UK
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Klinger E, Hasenauer J. A Scheme for Adaptive Selection of Population Sizes in Approximate Bayesian Computation - Sequential Monte Carlo. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY 2017. [DOI: 10.1007/978-3-319-67471-1_8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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