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Artcibasova A, Wang L, Anchisi S, Yamauchi Y, Schmolke M, Matthias P, Stelling J. A quantitative model for virus uncoating predicts influenza A infectivity. Cell Rep 2023; 42:113558. [PMID: 38103200 DOI: 10.1016/j.celrep.2023.113558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/13/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
For virus infection of new host cells, the disassembly of the protective outer protein shell (capsid) is a critical step, but the mechanisms and host-virus interactions underlying the dynamic, active, and regulated uncoating process are largely unknown. Here, we develop an experimentally supported, multiscale kinetics model that elucidates mechanisms of influenza A virus (IAV) uncoating in cells. Biophysical modeling demonstrates that interactions between capsid M1 proteins, host histone deacetylase 6 (HDAC6), and molecular motors can physically break the capsid in a tug-of-war mechanism. Biochemical analysis and biochemical-biophysical modeling identify unanchored ubiquitin chains as essential and allow robust prediction of uncoating efficiency in cells. Remarkably, the different infectivity of two clinical strains can be ascribed to a single amino acid variation in M1 that affects binding to HDAC6. By identifying crucial modules of viral infection kinetics, the mechanisms and models presented here could help formulate novel strategies for broad-range antiviral treatment.
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Affiliation(s)
- Alina Artcibasova
- Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland
| | - Longlong Wang
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland; Faculty of Sciences, University of Basel, 4031 Basel, Switzerland
| | - Stephanie Anchisi
- Department of Microbiology and Molecular Medicine and Geneva Center of Inflammation Research, Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland
| | - Yohei Yamauchi
- Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Mirco Schmolke
- Department of Microbiology and Molecular Medicine and Geneva Center of Inflammation Research, Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland
| | - Patrick Matthias
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland; Faculty of Sciences, University of Basel, 4031 Basel, Switzerland.
| | - Jörg Stelling
- Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland.
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2
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Mathematical Models for FDG Kinetics in Cancer: A Review. Metabolites 2021; 11:metabo11080519. [PMID: 34436460 PMCID: PMC8398381 DOI: 10.3390/metabo11080519] [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: 06/27/2021] [Revised: 07/28/2021] [Accepted: 08/02/2021] [Indexed: 11/21/2022] Open
Abstract
Compartmental analysis is the mathematical framework for the modelling of tracer kinetics in dynamical Positron Emission Tomography. This paper provides a review of how compartmental models are constructed and numerically optimized. Specific focus is given on the identifiability and sensitivity issues and on the impact of complex physiological conditions on the mathematical properties of the models.
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3
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A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics. Cell Syst 2019; 8:15-26.e11. [PMID: 30638813 DOI: 10.1016/j.cels.2018.12.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 10/16/2018] [Accepted: 12/11/2018] [Indexed: 01/26/2023]
Abstract
Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.
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4
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Fröhlich F, Loos C, Hasenauer J. Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes. Methods Mol Biol 2019; 1883:385-422. [PMID: 30547409 DOI: 10.1007/978-1-4939-8882-2_16] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.
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Affiliation(s)
- Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Carolin Loos
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- Center for Mathematics, Technische Universität München, Garching, Germany.
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5
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Fröhlich F, Theis FJ, Rädler JO, Hasenauer J. Parameter estimation for dynamical systems with discrete events and logical operations. Bioinformatics 2017; 33:1049-1056. [PMID: 28040696 DOI: 10.1093/bioinformatics/btw764] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 11/25/2016] [Indexed: 11/14/2022] Open
Abstract
Motivation Ordinary differential equation (ODE) models are frequently used to describe the dynamic behaviour of biochemical processes. Such ODE models are often extended by events to describe the effect of fast latent processes on the process dynamics. To exploit the predictive power of ODE models, their parameters have to be inferred from experimental data. For models without events, gradient based optimization schemes perform well for parameter estimation, when sensitivity equations are used for gradient computation. Yet, sensitivity equations for models with parameter- and state-dependent events and event-triggered observations are not supported by existing toolboxes. Results In this manuscript, we describe the sensitivity equations for differential equation models with events and demonstrate how to estimate parameters from event-resolved data using event-triggered observations in parameter estimation. We consider a model for GFP expression after transfection and a model for spiking neurons and demonstrate that we can improve computational efficiency and robustness of parameter estimation by using sensitivity equations for systems with events. Moreover, we demonstrate that, by using event-outputs, it is possible to consider event-resolved data, such as time-to-event data, for parameter estimation with ODE models. By providing a user-friendly, modular implementation in the toolbox AMICI, the developed methods are made publicly available and can be integrated in other systems biology toolboxes. Availability and Implementation We implement the methods in the open-source toolbox Advanced MATLAB Interface for CVODES and IDAS (AMICI, https://github.com/ICB-DCM/AMICI ). Contact jan.hasenauer@helmholtz-muenchen.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.,Center for Mathematics, Technische Universität München, Garching 85748, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.,Center for Mathematics, Technische Universität München, Garching 85748, Germany
| | - Joachim O Rädler
- Faculty of Physics, Ludwig-Maximilians-Universität, München 80539, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.,Center for Mathematics, Technische Universität München, Garching 85748, Germany
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6
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Fröhlich F, Kaltenbacher B, Theis FJ, Hasenauer J. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks. PLoS Comput Biol 2017; 13:e1005331. [PMID: 28114351 PMCID: PMC5256869 DOI: 10.1371/journal.pcbi.1005331] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 12/20/2016] [Indexed: 01/06/2023] Open
Abstract
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. In this manuscript, we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks. Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters. Standard methods for parameter estimation are usually computationally intractable at these scales. Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking. We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models. We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model. This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model. The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM.github.io/AMICI/.
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Affiliation(s)
- Fabian Fröhlich
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | | | - Fabian J. Theis
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
- * E-mail:
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7
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Gnügge R, Dharmarajan L, Lang M, Stelling J. An Orthogonal Permease-Inducer-Repressor Feedback Loop Shows Bistability. ACS Synth Biol 2016; 5:1098-1107. [PMID: 27148753 DOI: 10.1021/acssynbio.6b00013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Feedback loops in biological networks, among others, enable differentiation and cell cycle progression, and increase robustness in signal transduction. In natural networks, feedback loops are often complex and intertwined, making it challenging to identify which loops are mainly responsible for an observed behavior. However, minimal synthetic replicas could allow for such identification. Here, we engineered a synthetic permease-inducer-repressor system in Saccharomyces cerevisiae to analyze if a transport-mediated positive feedback loop could be a core mechanism for the switch-like behavior in the regulation of metabolic gene networks such as the S. cerevisiae GAL system or the Escherichia coli lac operon. We characterized the synthetic circuit using deterministic and stochastic mathematical models. Similar to its natural counterparts, our synthetic system shows bistable and hysteretic behavior, and the inducer concentration range for bistability as well as the switching rates between the two stable states depend on the repressor concentration. Our results indicate that a generic permease-inducer-repressor circuit with a single feedback loop is sufficient to explain the experimentally observed bistable behavior of the natural systems. We anticipate that the approach of reimplementing natural systems with orthogonal parts to identify crucial network components is applicable to other natural systems such as signaling pathways.
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Affiliation(s)
- Robert Gnügge
- Life
Science Zurich Ph.D. Program on Molecular and Translational Biomedicine, and Competence Centre for Personalized Medicine, ETH Zurich, 8093 Zurich, Switzerland
- D-BSSE, ETH Zurich and Swiss Institute of Bioinformatics, Mattenstrasse
26, 4058 Basel, Switzerland
| | - Lekshmi Dharmarajan
- D-BSSE, ETH Zurich and Swiss Institute of Bioinformatics, Mattenstrasse
26, 4058 Basel, Switzerland
| | - Moritz Lang
- D-BSSE, ETH Zurich and Swiss Institute of Bioinformatics, Mattenstrasse
26, 4058 Basel, Switzerland
| | - Jörg Stelling
- D-BSSE, ETH Zurich and Swiss Institute of Bioinformatics, Mattenstrasse
26, 4058 Basel, Switzerland
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8
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Lewis J, Breeze CE, Charlesworth J, Maclaren OJ, Cooper J. Where next for the reproducibility agenda in computational biology? BMC SYSTEMS BIOLOGY 2016; 10:52. [PMID: 27422148 PMCID: PMC4946111 DOI: 10.1186/s12918-016-0288-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 06/08/2016] [Indexed: 11/24/2022]
Abstract
Background The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has been addressed mainly in terms of exact replicability or numerical equivalence, ignoring the wider issue of the reproducibility of conclusions through equivalent, extended or alternative methods. Results We use case studies from our own research experience to illustrate how concepts of reproducibility might be applied in computational biology. Several fields have developed ‘minimum information’ checklists to support the full reporting of computational simulations, analyses and results, and standardised data formats and model description languages can facilitate the use of multiple systems to address the same research question. We note the importance of defining the key features of a result to be reproduced, and the expected agreement between original and subsequent results. Dynamic, updatable tools for publishing methods and results are becoming increasingly common, but sometimes come at the cost of clear communication. In general, the reproducibility of computational research is improving but would benefit from additional resources and incentives. Conclusions We conclude with a series of linked recommendations for improving reproducibility in computational biology through communication, policy, education and research practice. More reproducible research will lead to higher quality conclusions, deeper understanding and more valuable knowledge.
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Affiliation(s)
- Joanna Lewis
- Centre for Maths and Physics in the Life Sciences and Experimental Biology, University College London, Physics Building, Gower Place, London, WC1E 6BT, UK. .,NIHR Health Protection Research Unit in Modelling Methodology, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
| | - Charles E Breeze
- UCL Cancer Institute, University College London, 72 Huntley St, London, WC1E 6DD, UK
| | - Jane Charlesworth
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
| | - Oliver J Maclaren
- Department of Mathematics, University of Auckland, Auckland, 1142, New Zealand.,Department of Engineering Science, University of Auckland, Auckland, 1142, New Zealand
| | - Jonathan Cooper
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK
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9
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Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks. PLoS Comput Biol 2015; 11:e1004457. [PMID: 26317784 PMCID: PMC4552555 DOI: 10.1371/journal.pcbi.1004457] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Accepted: 07/20/2015] [Indexed: 12/22/2022] Open
Abstract
Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is “non-intrusive” and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design. In various scientific domains, in particular in systems biology, dynamic mathematical models of increasing complexity are being developed and analyzed to study biochemical reaction networks. A major challenge in dealing with such models is the uncertainty in parameters such as kinetic constants; how to efficiently and precisely quantify the effects of parametric uncertainties on systems behavior remains a question. Addressing this computational challenge for large systems, with good scaling up to hundreds of species and kinetic parameters, is important for many forward (e.g., uncertainty quantification) and inverse (e.g., system identification) problems. Here, we propose a sparse, deterministic adaptive interpolation method tailored to high-dimensional parametric problems that allows for fast, deterministic computational analysis of large biochemical reaction networks. The method is based on adaptive Smolyak interpolation of the parametric solution at judiciously chosen points in high-dimensional parameter space, combined with adaptive time-stepping for the actual numerical simulation of the network dynamics. It is “non-intrusive” and well-suited both for massively parallel implementation and for use in standard (systems biology) toolboxes.
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Taylor B, Lee TJ, Weitz JS. A guide to sensitivity analysis of quantitative models of gene expression dynamics. Methods 2013; 62:109-20. [DOI: 10.1016/j.ymeth.2013.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Accepted: 03/08/2013] [Indexed: 11/30/2022] Open
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