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van Oppen YB, Milias-Argeitis A. Gradient matching accelerates mixed-effects inference for biochemical networks. Bioinformatics 2025; 41:btaf154. [PMID: 40199819 PMCID: PMC12034378 DOI: 10.1093/bioinformatics/btaf154] [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: 07/24/2024] [Revised: 02/28/2025] [Accepted: 04/07/2025] [Indexed: 04/10/2025] Open
Abstract
MOTIVATION Single-cell time series data often exhibit significant variability within an isogenic cell population. When modeling intracellular processes, it is therefore more appropriate to infer parameter distributions that reflect this variability, rather than fitting the population average to obtain a single point estimate. The Global Two-Stage (GTS) approach for nonlinear mixed-effects (NLME) models is a simple and modular method commonly used for this purpose. However, this method is computationally intensive due to its repeated use of nonconvex optimization and numerical integration of the underlying system. RESULTS We propose the Gradient Matching GTS (GMGTS) method as an efficient alternative to GTS. Gradient matching offers an integration-free approach to parameter estimation that is particularly powerful for systems that are linear in the unknown parameters, such as biochemical networks modeled by mass action kinetics. By incorporating gradient matching into the GTS framework, we expand its capabilities through uncertainty propagation calculations and an iterative estimation scheme for partially observed systems. Comparisons between GMGTS and GTS across various inference setups show that our method significantly reduces computational demands, facilitating the application of complex NLME models in systems biology. AVAILABILITY AND IMPLEMENTATION A Matlab implementation of GMGTS is provided at https://github.com/yulanvanoppen/GMGTS (DOI: http://doi.org/10.5281/zenodo.14884457).
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Affiliation(s)
- Yulan B van Oppen
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen 9747 AG, The Netherlands
| | - Andreas Milias-Argeitis
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen 9747 AG, The Netherlands
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2
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Hermange G, Vainchenker W, Plo I, Cournède PH. Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against myeloproliferative neoplasms. MATHEMATICAL MEDICINE AND BIOLOGY : A JOURNAL OF THE IMA 2024; 41:110-134. [PMID: 38875109 DOI: 10.1093/imammb/dqae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 01/30/2024] [Accepted: 05/02/2024] [Indexed: 06/16/2024]
Abstract
Myeloproliferative neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFN$\alpha $ is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFN$\alpha $ acts on mutated stem cells by inducing their differentiation into progenitor cells; the higher the dose, the higher the effect. We found that the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured.
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Affiliation(s)
- Gurvan Hermange
- Université Paris-Saclay, CentraleSupélec, Laboratory of Mathematics and Informatics (MICS), Gif-sur-Yvette, France
| | - William Vainchenker
- INSERM U1287 (INSERM, Gustave Roussy, Université Paris-Saclay), Villejuif, France
- Gustave Roussy, Villejuif, France
- Université Paris-Saclay, Villejuif, France
| | - Isabelle Plo
- INSERM U1287 (INSERM, Gustave Roussy, Université Paris-Saclay), Villejuif, France
- Gustave Roussy, Villejuif, France
- Université Paris-Saclay, Villejuif, France
| | - Paul-Henry Cournède
- Université Paris-Saclay, CentraleSupélec, Laboratory of Mathematics and Informatics (MICS), Gif-sur-Yvette, France
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3
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Braam S, Tripodi F, Österberg L, Persson S, Welkenhuysen N, Coccetti P, Cvijovic M. Exploring carbon source related localization and phosphorylation in the Snf1/Mig1 network using population and single cell-based approaches. MICROBIAL CELL (GRAZ, AUSTRIA) 2024; 11:143-154. [PMID: 38756204 PMCID: PMC11097897 DOI: 10.15698/mic2024.05.822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 05/18/2024]
Abstract
The AMPK/SNF1 pathway governs energy balance in eukaryotic cells, notably influencing glucose de-repression. In S. cerevisiae, Snf1 is phosphorylated and hence activated upon glucose depletion. This activation is required but is not sufficient for mediating glucose de-repression, indicating further glucose-dependent regulation mechanisms. Employing fluorescence recovery after photobleaching (FRAP) in conjunction with non-linear mixed effects modelling, we explore the spatial dynamics of Snf1 as well as the relationship between Snf1 phosphorylation and its target Mig1 controlled by hexose sugars. Our results suggest that inactivation of Snf1 modulates Mig1 localization and that the kinetic of Snf1 localization to the nucleus is modulated by the presence of non-fermentable carbon sources. Our data offer insight into the true complexity of regulation of this central signaling pathway in orchestrating cellular responses to fluctuating environmental cues. These insights not only expand our understanding of glucose homeostasis but also pave the way for further studies evaluating the importance of Snf1 localization in relation to its phosphorylation state and regulation of downstream targets.
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Affiliation(s)
- Svenja Braam
- Department of Mathematical Sciences, Chalmers University of Technology, University of GothenburgSweden.
| | - Farida Tripodi
- Department of Biotechnology and Biosciences, University of MilanoBicoccaItaly.
| | - Linnea Österberg
- Department of Mathematical Sciences, Chalmers University of Technology, University of GothenburgSweden.
- Department of Biology and Biological Engineering, Department of Mathematical Sciences, Chalmers University of TechnologySweden.
- Department of Biotechnology and Biosciences, Chalmers University of Technology, University of GothenburgGothenburg, SE412 96Sweden.
- University of MilanoBicoccaMilano, 20126Italy.
| | - Sebastian Persson
- Department of Mathematical Sciences, Chalmers University of Technology, University of GothenburgSweden.
| | - Niek Welkenhuysen
- Department of Mathematical Sciences, Chalmers University of Technology, University of GothenburgSweden.
- Department of Biology and Biological Engineering, Department of Mathematical Sciences, Chalmers University of TechnologySweden.
- Department of Biotechnology and Biosciences, Chalmers University of Technology, University of GothenburgGothenburg, SE412 96Sweden.
- University of MilanoBicoccaMilano, 20126Italy.
| | - Paola Coccetti
- Department of Biotechnology and Biosciences, University of MilanoBicoccaItaly.
| | - Marija Cvijovic
- Department of Mathematical Sciences, Chalmers University of Technology, University of GothenburgSweden.
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Grima R, Esmenjaud PM. Quantifying and correcting bias in transcriptional parameter inference from single-cell data. Biophys J 2024; 123:4-30. [PMID: 37885177 PMCID: PMC10808030 DOI: 10.1016/j.bpj.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/12/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.
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Affiliation(s)
- Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
| | - Pierre-Marie Esmenjaud
- Biology Department, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
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Wang D, Rausch C, Buerger SA, Tschuri S, Rothenberg-Thurley M, Schulz M, Hasenauer J, Ziemann F, Metzeler KH, Marr C. Modeling early treatment response in AML from cell-free tumor DNA. iScience 2023; 26:108271. [PMID: 38047080 PMCID: PMC10690559 DOI: 10.1016/j.isci.2023.108271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/03/2023] [Accepted: 10/17/2023] [Indexed: 12/05/2023] Open
Abstract
Monitoring disease response after intensive chemotherapy for acute myeloid leukemia (AML) currently requires invasive bone marrow biopsies, imposing a significant burden on patients. In contrast, cell-free tumor DNA (ctDNA) in peripheral blood, carrying tumor-specific mutations, offers a less-invasive assessment of residual disease. However, the relationship between ctDNA levels and bone marrow blast kinetics remains unclear. We explored this in 10 AML patients with NPM1 and IDH2 mutations undergoing initial chemotherapy. Comparison of mathematical mixed-effect models showed that (1) inclusion of blast cell death in the bone marrow, (2) transition of ctDNA to peripheral blood, and (3) ctDNA decay in peripheral blood describes kinetics of blast cells and ctDNA best. The fitted model allows prediction of residual bone marrow blast content from ctDNA, and its scaling factor, representing clonal heterogeneity, correlates with relapse risk. Our study provides precise insights into blast and ctDNA kinetics, offering novel avenues for AML disease monitoring.
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Affiliation(s)
- Dantong Wang
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
| | - Christian Rausch
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital (LMU), Munich, Germany
- German Cancer Consortium (DKTK), partner sites Munich/Dresden, Germany
| | - Simon A. Buerger
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital (LMU), Munich, Germany
| | - Sebastian Tschuri
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital (LMU), Munich, Germany
| | - Maja Rothenberg-Thurley
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital (LMU), Munich, Germany
| | - Melanie Schulz
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
| | - Jan Hasenauer
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
- Computational Health Center, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
- Faculty of Mathematics and Natural Sciences, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Frank Ziemann
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital (LMU), Munich, Germany
- German Cancer Consortium (DKTK), partner sites Munich/Dresden, Germany
| | - Klaus H. Metzeler
- Department of Hematology and Cell Therapy, University Hospital Leipzig (UHL) 04103, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
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6
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Delvigne F, Martinez JA. Advances in automated and reactive flow cytometry for synthetic biotechnology. Curr Opin Biotechnol 2023; 83:102974. [PMID: 37515938 DOI: 10.1016/j.copbio.2023.102974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/20/2023] [Accepted: 07/03/2023] [Indexed: 07/31/2023]
Abstract
Automated flow cytometry (FC) has been initially considered for bioprocess monitoring and optimization. More recently, new physical and software interfaces have been made available, facilitating the access to this technology for labs and industries. It also comes with new capabilities, such as being able to act on the cultivation conditions based on population data. This approach, known as reactive FC, extended the range of applications of automated FC to bioprocess control and the stabilization of cocultures, but also to the broad field of synthetic and systems biology for the characterization of gene circuits. However, several issues must be addressed before automated and reactive FC can be considered standard and modular technologies.
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Affiliation(s)
- Frank Delvigne
- Terra Research and Teaching Center, Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium.
| | - Juan A Martinez
- Terra Research and Teaching Center, Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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7
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Pouzet S, Cruz-Ramón J, Le Bec M, Cordier C, Banderas A, Barral S, Castaño-Cerezo S, Lautier T, Truan G, Hersen P. Optogenetic control of beta-carotene bioproduction in yeast across multiple lab-scales. Front Bioeng Biotechnol 2023; 11:1085268. [PMID: 36814715 PMCID: PMC9939774 DOI: 10.3389/fbioe.2023.1085268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/16/2023] [Indexed: 02/09/2023] Open
Abstract
Optogenetics arises as a valuable tool to precisely control genetic circuits in microbial cell factories. Light control holds the promise of optimizing bioproduction methods and maximizing yields, but its implementation at different steps of the strain development process and at different culture scales remains challenging. In this study, we aim to control beta-carotene bioproduction using optogenetics in Saccharomyces cerevisiae and investigate how its performance translates across culture scales. We built four lab-scale illumination devices, each handling different culture volumes, and each having specific illumination characteristics and cultivating conditions. We evaluated optogenetic activation and beta-carotene production across devices and optimized them both independently. Then, we combined optogenetic induction and beta-carotene production to make a light-inducible beta-carotene producer strain. This was achieved by placing the transcription of the bifunctional lycopene cyclase/phytoene synthase CrtYB under the control of the pC120 optogenetic promoter regulated by the EL222-VP16 light-activated transcription factor, while other carotenogenic enzymes (CrtI, CrtE, tHMG) were expressed constitutively. We show that illumination, culture volume and shaking impact differently optogenetic activation and beta-carotene production across devices. This enabled us to determine the best culture conditions to maximize light-induced beta-carotene production in each of the devices. Our study exemplifies the stakes of scaling up optogenetics in devices of different lab scales and sheds light on the interplays and potential conflicts between optogenetic control and metabolic pathway efficiency. As a general principle, we propose that it is important to first optimize both components of the system independently, before combining them into optogenetic producing strains to avoid extensive troubleshooting. We anticipate that our results can help designing both strains and devices that could eventually lead to larger scale systems in an effort to bring optogenetics to the industrial scale.
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Affiliation(s)
- Sylvain Pouzet
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Jessica Cruz-Ramón
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Matthias Le Bec
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Céline Cordier
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Alvaro Banderas
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Simon Barral
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Sara Castaño-Cerezo
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l′Agriculture, l′Alimentation et l′Environnement (INRAE), Institut National des Sciences Appliquées (INSA), Toulouse, France
| | - Thomas Lautier
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l′Agriculture, l′Alimentation et l′Environnement (INRAE), Institut National des Sciences Appliquées (INSA), Toulouse, France,CNRS@CREATE, Singapore Institute of Food and Biotechnology Innovation, Agency for Science Technology and Research, Singapore, Singapore
| | - Gilles Truan
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l′Agriculture, l′Alimentation et l′Environnement (INRAE), Institut National des Sciences Appliquées (INSA), Toulouse, France
| | - Pascal Hersen
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France,*Correspondence: Pascal Hersen,
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8
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Sarma U, Ripka L, Anyaegbunam UA, Legewie S. Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway. Methods Mol Biol 2023; 2634:215-251. [PMID: 37074581 DOI: 10.1007/978-1-0716-3008-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.
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Affiliation(s)
- Uddipan Sarma
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Lorenz Ripka
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Uchenna Alex Anyaegbunam
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Stefan Legewie
- Institute of Molecular Biology (IMB), Mainz, Germany.
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany.
- Stuttgart Research Center for Systems Biology, University of Stuttgart, Stuttgart, Germany.
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9
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Konrath F, Loewer A, Wolf J. Resolving Crosstalk Between Signaling Pathways Using Mathematical Modeling and Time-Resolved Single Cell Data. Methods Mol Biol 2023; 2634:267-284. [PMID: 37074583 DOI: 10.1007/978-1-0716-3008-2_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Crosstalk between signaling pathways can modulate the cellular response to stimuli and is therefore an important part of signal transduction. For a comprehensive understanding of cellular responses, identifying points of interaction between the underlying molecular networks is essential. Here, we present an approach that allows the systematic prediction of such interactions by perturbing one pathway and quantifying the concomitant alterations in the response of a second pathway. As the observed alterations contain information about the crosstalk, we use an ordinary differential equation-based model to extract this information by linking altered dynamics to individual processes. Consequently, we can predict the interaction points between two pathways. As an example, we employed our approach to investigate the crosstalk between the NF-κB and p53 signaling pathway. We monitored the response of p53 to genotoxic stress using time-resolved single cell data and perturbed NF-κB signaling by inhibiting the kinase IKK2. Employing a subpopulation-based modeling approach enabled us to identify multiple interaction points that are simultaneously affected by perturbation of NF-κB signaling. Hence, our approach can be used to analyze crosstalk between two signaling pathways in a systematic manner.
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Affiliation(s)
- Fabian Konrath
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Alexander Loewer
- Systems Biology of the Stress Response, Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Jana Wolf
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine, Berlin, Germany.
- Mathematical Modelling of Cellular Processes, Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany.
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10
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CyberSco.Py an open-source software for event-based, conditional microscopy. Sci Rep 2022; 12:11579. [PMID: 35803978 PMCID: PMC9270370 DOI: 10.1038/s41598-022-15207-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/20/2022] [Indexed: 12/01/2022] Open
Abstract
Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes could become much more powerful investigation systems if they were endowed with simple unsupervised decision-making algorithms to transform them into fully responsive and automated measurement devices. Here, we report CyberSco.Py, Python software for advanced automated timelapse experiments. We provide proof-of-principle of a user-friendly framework that increases the tunability and flexibility when setting up and running fluorescence timelapse microscopy experiments. Importantly, CyberSco.Py combines real-time image analysis with automation capability, which allows users to create conditional, event-based experiments in which the imaging acquisition parameters and the status of various devices can be changed automatically based on the image analysis. We exemplify the relevance of CyberSco.Py to cell biology using several use case experiments with budding yeast. We anticipate that CyberSco.Py could be used to address the growing need for smart microscopy systems to implement more informative quantitative cell biology experiments.
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11
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Tanwar S, Kim JH, Bulte JWM, Barman I. Surface-enhanced Raman scattering: An emerging tool for sensing cellular function. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2022; 14:e1802. [PMID: 35510405 PMCID: PMC9302385 DOI: 10.1002/wnan.1802] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 03/05/2022] [Accepted: 03/27/2022] [Indexed: 12/18/2022]
Abstract
Continuous long-term intracellular imaging and multiplexed monitoring of biomolecular changes associated with key cellular processes remains a challenge for the scientific community. Recently, surface-enhanced Raman scattering (SERS) has been demonstrated as a powerful spectroscopic tool in the field of biology owing to its significant advantages. Some of these include the ability to provide molecule-specific information with exquisite sensitivity, working with small volumes of precious samples, real-time monitoring, and optimal optical contrast. More importantly, the availability of a large number of novel Raman reporters with narrower full width at half maximum (FWHM) of spectral peaks/vibrational modes than conventional fluorophores has created a versatile palette of SERS-based probes that allow targeted multiplex sensing surpassing the detection sensitivity of even fluorescent probes. Due to its nondestructive nature, its applicability has been recognized for biological sensing, molecular imaging, and dynamic monitoring of complex intracellular processes. We critically discuss recent developments in this area with a focus on different applications where SERS has been used for obtaining information that remains elusive for conventional imaging methods. Current reports indicate that SERS has made significant inroads in the field of biology and has the potential to be used for in vivo human applications. This article is categorized under: Diagnostic Tools > In Vitro Nanoparticle-Based Sensing Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Biosensing Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.
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Affiliation(s)
- Swati Tanwar
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeong Hee Kim
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeff W M Bulte
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.,Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.,Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
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12
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Grujcic V, Taylor GT, Foster RA. One Cell at a Time: Advances in Single-Cell Methods and Instrumentation for Discovery in Aquatic Microbiology. Front Microbiol 2022; 13:881018. [PMID: 35677911 PMCID: PMC9169044 DOI: 10.3389/fmicb.2022.881018] [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: 02/22/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Studying microbes from a single-cell perspective has become a major theme and interest within the field of aquatic microbiology. One emerging trend is the unfailing observation of heterogeneity in activity levels within microbial populations. Wherever researchers have looked, intra-population variability in biochemical composition, growth rates, and responses to varying environmental conditions has been evident and probably reflect coexisting genetically distinct strains of the same species. Such observations of heterogeneity require a shift away from bulk analytical approaches and development of new methods or adaptation of existing techniques, many of which were first pioneered in other, unrelated fields, e.g., material, physical, and biomedical sciences. Many co-opted approaches were initially optimized using model organisms. In a field with so few cultivable models, method development has been challenging but has also contributed tremendous insights, breakthroughs, and stimulated curiosity. In this perspective, we present a subset of methods that have been effectively applied to study aquatic microbes at the single-cell level. Opportunities and challenges for innovation are also discussed. We suggest future directions for aquatic microbiological research that will benefit from open access to sophisticated instruments and highly interdisciplinary collaborations.
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Affiliation(s)
- Vesna Grujcic
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Gordon T Taylor
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, United States
| | - Rachel A Foster
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
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13
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Persson S, Welkenhuysen N, Shashkova S, Wiqvist S, Reith P, Schmidt GW, Picchini U, Cvijovic M. Scalable and flexible inference framework for stochastic dynamic single-cell models. PLoS Comput Biol 2022; 18:e1010082. [PMID: 35588132 PMCID: PMC9159578 DOI: 10.1371/journal.pcbi.1010082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 06/01/2022] [Accepted: 04/05/2022] [Indexed: 01/22/2023] Open
Abstract
Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.
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Affiliation(s)
- Sebastian Persson
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Niek Welkenhuysen
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Sviatlana Shashkova
- Department of Microbiology and Immunology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Samuel Wiqvist
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Patrick Reith
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Gregor W. Schmidt
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Umberto Picchini
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Marija Cvijovic
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
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14
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Davidović A, Chait R, Batt G, Ruess J. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Comput Biol 2022; 18:e1009950. [PMID: 35303737 PMCID: PMC8967023 DOI: 10.1371/journal.pcbi.1009950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 03/30/2022] [Accepted: 02/21/2022] [Indexed: 01/30/2023] Open
Abstract
Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system.
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Affiliation(s)
- Anđela Davidović
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Remy Chait
- Biosciences, Living Systems Institute, University of Exeter, Exeter, The United Kingdom
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Gregory Batt
- Department of Computational Biology, Institut Pasteur, Paris, France
- Inria Paris, Paris, France
| | - Jakob Ruess
- Department of Computational Biology, Institut Pasteur, Paris, France
- Inria Paris, Paris, France
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15
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Yu C, Liu Q, Wang J. A physical mechanism of heterogeneity and micro-metastasis in stem cell, cancer and cancer stem cell. J Chem Phys 2022; 156:075103. [DOI: 10.1063/5.0078196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Chong Yu
- Changchun Institute of Applied Chemistry Chinese Academy of Sciences, China
| | - Qiong Liu
- Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, China
| | - Jin Wang
- Chemistry, Physics and Astronomy, Stony Brook University, United States of America
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16
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Chaves M, Gomes-Pereira LC, Roux J. Two-level modeling approach to identify the regulatory dynamics capturing drug response heterogeneity in single-cells. Sci Rep 2021; 11:20809. [PMID: 34675364 PMCID: PMC8531316 DOI: 10.1038/s41598-021-99943-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 09/27/2021] [Indexed: 11/09/2022] Open
Abstract
Single-cell multimodal technologies reveal the scales of cellular heterogeneity impairing cancer treatment, yet cell response dynamics remain largely underused to decipher the mechanisms of drug resistance they take part in. As the phenotypic heterogeneity of a clonal cell population informs on the capacity of each single-cell to recapitulate the whole range of observed behaviors, we developed a modeling approach utilizing single-cell response data to identify regulatory reactions driving population heterogeneity in drug response. Dynamic data of hundreds of HeLa cells treated with TNF-related apoptosis-inducing ligand (TRAIL) were used to characterize the fate-determining kinetic parameters of an apoptosis receptor reaction model. Selected reactions sets were augmented to incorporate a mechanism that leads to the separation of the opposing response phenotypes. Using a positive feedback loop motif to identify the reaction set, we show that caspase-8 is able to encapsulate high levels of heterogeneity by introducing a response delay and amplifying the initial differences arising from natural protein expression variability. Our approach enables the identification of fate-determining reactions that drive the population response heterogeneity, providing regulatory targets to curb the cell dynamics of drug resistance.
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Affiliation(s)
- Madalena Chaves
- Université Côte d'Azur, Inria, INRAE, CNRS, Sorbonne Université, Biocore Team, Sophia Antipolis, France
| | - Luis C Gomes-Pereira
- Université Côte d'Azur, Inria, INRAE, CNRS, Sorbonne Université, Biocore Team, Sophia Antipolis, France.,Université Côte d'Azur, CNRS UMR 7284, Inserm U 1081, Institut de Recherche sur le Cancer et le Vieillissement de Nice, Centre Antoine Lacassagne, 06107, Nice, France
| | - Jérémie Roux
- Université Côte d'Azur, CNRS UMR 7284, Inserm U 1081, Institut de Recherche sur le Cancer et le Vieillissement de Nice, Centre Antoine Lacassagne, 06107, Nice, France.
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17
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Kurdyaeva T, Milias-Argeitis A. Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter. J R Soc Interface 2021; 18:20210331. [PMID: 34343452 DOI: 10.1098/rsif.2021.0331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Differential equation models of biochemical networks are frequently associated with a large degree of uncertainty in parameters and/or initial conditions. However, estimating the impact of this uncertainty on model predictions via Monte Carlo simulation is computationally demanding. A more efficient approach could be to track a system of low-order statistical moments of the state. Unfortunately, when the underlying model is nonlinear, the system of moment equations is infinite-dimensional and cannot be solved without a moment closure approximation which may introduce bias in the moment dynamics. Here, we present a new method to study the time evolution of the desired moments for nonlinear systems with polynomial rate laws. Our approach is based on solving a system of low-order moment equations by substituting the higher-order moments with Monte Carlo-based estimates from a small number of simulations, and using an extended Kalman filter to counteract Monte Carlo noise. Our algorithm provides more accurate and robust results compared to traditional Monte Carlo and moment closure techniques, and we expect that it will be widely useful for the quantification of uncertainty in biochemical model predictions.
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Affiliation(s)
- Tamara Kurdyaeva
- Molecular Systems Biology, Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Andreas Milias-Argeitis
- Molecular Systems Biology, Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, Groningen, Netherlands
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18
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Phillips NE, Hugues A, Yeung J, Durandau E, Nicolas D, Naef F. The circadian oscillator analysed at the single-transcript level. Mol Syst Biol 2021; 17:e10135. [PMID: 33719202 PMCID: PMC7957410 DOI: 10.15252/msb.202010135] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/05/2021] [Accepted: 01/19/2021] [Indexed: 12/31/2022] Open
Abstract
The circadian clock is an endogenous and self-sustained oscillator that anticipates daily environmental cycles. While rhythmic gene expression of circadian genes is well-described in populations of cells, the single-cell mRNA dynamics of multiple core clock genes remain largely unknown. Here we use single-molecule fluorescence in situ hybridisation (smFISH) at multiple time points to measure pairs of core clock transcripts, Rev-erbα (Nr1d1), Cry1 and Bmal1, in mouse fibroblasts. The mean mRNA level oscillates over 24 h for all three genes, but mRNA numbers show considerable spread between cells. We develop a probabilistic model for multivariate mRNA counts using mixtures of negative binomials, which accounts for transcriptional bursting, circadian time and cell-to-cell heterogeneity, notably in cell size. Decomposing the mRNA variability into distinct noise sources shows that clock time contributes a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single-cell mRNA counts and suggest that circadian phase in single cells is encoded post-transcriptionally.
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Affiliation(s)
- Nicholas E Phillips
- Institute of BioengineeringSchool of Life SciencesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Alice Hugues
- Institute of BioengineeringSchool of Life SciencesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Master de BiologieÉcole Normale Supérieure de LyonUniversité Claude Bernard Lyon IUniversité de LyonLyonFrance
| | - Jake Yeung
- Institute of BioengineeringSchool of Life SciencesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Eric Durandau
- Institute of BioengineeringSchool of Life SciencesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Damien Nicolas
- Institute of BioengineeringSchool of Life SciencesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Felix Naef
- Institute of BioengineeringSchool of Life SciencesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
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19
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Hsu IS, Moses AM. Stochastic models for single-cell data: Current challenges and the way forward. FEBS J 2021; 289:647-658. [PMID: 33570798 DOI: 10.1111/febs.15760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/22/2020] [Accepted: 02/10/2021] [Indexed: 11/28/2022]
Abstract
Although the quantity and quality of single-cell data have progressed rapidly, making quantitative predictions with single-cell stochastic models remains challenging. The stochastic nature of cellular processes leads to at least three challenges in building models with single-cell data: (a) because variability in single-cell data can be attributed to multiple different sources, it is difficult to rule out conflicting mechanistic models that explain the same data equally well; (b) the distinction between interesting biological variability and experimental variability is sometimes ambiguous; (c) the nonstandard distributions of single-cell data can lead to violations of the assumption of symmetric errors in least-squares fitting. In this review, we first discuss recent studies that overcome some of the challenges or set up a promising direction and then introduce some powerful statistical approaches utilized in these studies. We conclude that applying and developing statistical approaches could lead to further progress in building stochastic models for single-cell data.
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Affiliation(s)
- Ian S Hsu
- Department of Cell & Systems Biology, University of Toronto, ON, Canada
| | - Alan M Moses
- Department of Cell & Systems Biology, University of Toronto, ON, Canada
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20
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Audebert C, Laubreton D, Arpin C, Gandrillon O, Marvel J, Crauste F. Modeling and characterization of inter-individual variability in CD8 T cell responses in mice. In Silico Biol 2021; 14:13-39. [PMID: 33554899 PMCID: PMC8203221 DOI: 10.3233/isb-200205] [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] [Indexed: 11/24/2022]
Abstract
To develop vaccines it is mandatory yet challenging to account for inter-individual variability during immune responses. Even in laboratory mice, T cell responses of single individuals exhibit a high heterogeneity that may come from genetic backgrounds, intra-specific processes (e.g. antigen-processing and presentation) and immunization protocols. To account for inter-individual variability in CD8 T cell responses in mice, we propose a dynamical model coupled to a statistical, nonlinear mixed effects model. Average and individual dynamics during a CD8 T cell response are characterized in different immunization contexts (vaccinia virus and tumor). On one hand, we identify biological processes that generate inter-individual variability (activation rate of naive cells, the mortality rate of effector cells, and dynamics of the immunogen). On the other hand, introducing categorical covariates to analyze two different immunization regimens, we highlight the steps of the response impacted by immunogens (priming, differentiation of naive cells, expansion of effector cells and generation of memory cells). The robustness of the model is assessed by confrontation to new experimental data. Our approach allows to investigate immune responses in various immunization contexts, when measurements are scarce or missing, and contributes to a better understanding of inter-individual variability in CD8 T cell immune responses.
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Affiliation(s)
- Chloe Audebert
- Inria Dracula, Villeurbanne, France.,Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions UMR 7598, F-75005 Paris, France.,Sorbonne Université, CNRS, Institut de biologie Paris-Seine (IBPS), Laboratoire de Biologie Computationnelle et Quantitative UMR 7238, F-75005 Paris, France
| | - Daphné Laubreton
- Centre International de recherche en Infectiologie, Université de Lyon, INSERM U1111, CNRS UMR 5308, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, 69007 Lyon, France
| | - Christophe Arpin
- Centre International de recherche en Infectiologie, Université de Lyon, INSERM U1111, CNRS UMR 5308, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, 69007 Lyon, France
| | - Olivier Gandrillon
- Inria Dracula, Villeurbanne, France.,Laboratory of Biology and Modelling of the Cell, Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5239, INSERM U1210, 69007 Lyon, France
| | - Jacqueline Marvel
- Centre International de recherche en Infectiologie, Université de Lyon, INSERM U1111, CNRS UMR 5308, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, 69007 Lyon, France
| | - Fabien Crauste
- Inria Dracula, Villeurbanne, France.,Université de Paris, MAP5, CNRS, F-75006, France
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21
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Persson S, Welkenhuysen N, Shashkova S, Cvijovic M. Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop. Front Physiol 2020; 11:954. [PMID: 32922308 PMCID: PMC7456839 DOI: 10.3389/fphys.2020.00954] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/15/2020] [Indexed: 11/22/2022] Open
Abstract
Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast, Saccharomyces cerevisiae, the sucrose non-fermenting protein kinase complex SNF1 is a master regulator of energy homeostasis. It is affected by multiple inputs, among which energy levels is the most prominent. Cells which are exposed to a switch in carbon source availability display a change in the gene expression machinery. It has been shown that the magnitude of the change varies from cell to cell. In a glucose rich environment Snf1/Mig1 pathway represses the expression of its downstream target, such as SUC2. However, upon glucose depletion SNF1 is activated which leads to an increase in SUC2 expression. Our single cell experiments indicate that upon starvation, gene expression pattern of SUC2 shows rapid increase followed by a decrease to initial state with high cell-to-cell variability. The mechanism behind this behavior is currently unknown. In this work we study the long-term behavior of the Snf1/Mig1 pathway upon glucose starvation with a microfluidics and non-linear mixed effect modeling approach. We show a negative feedback mechanism, involving Snf1 and Reg1, which reduces SUC2 expression after the initial strong activation. Snf1 kinase activity plays a key role in this feedback mechanism. Our systems biology approach proposes a negative feedback mechanism that works through the SNF1 complex and is controlled by energy levels. We further show that Reg1 likely is involved in the negative feedback mechanism.
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Affiliation(s)
- Sebastian Persson
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Niek Welkenhuysen
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Sviatlana Shashkova
- Department of Microbiology and Immunology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Marija Cvijovic
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
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22
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Konrath F, Mittermeier A, Cristiano E, Wolf J, Loewer A. A systematic approach to decipher crosstalk in the p53 signaling pathway using single cell dynamics. PLoS Comput Biol 2020; 16:e1007901. [PMID: 32589666 PMCID: PMC7319280 DOI: 10.1371/journal.pcbi.1007901] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 04/22/2020] [Indexed: 01/15/2023] Open
Abstract
The transcription factors NF-κB and p53 are key regulators in the genotoxic stress response and are critical for tumor development. Although there is ample evidence for interactions between both networks, a comprehensive understanding of the crosstalk is lacking. Here, we developed a systematic approach to identify potential interactions between the pathways. We perturbed NF-κB signaling by inhibiting IKK2, a critical regulator of NF-κB activity, and monitored the altered response of p53 to genotoxic stress using single cell time lapse microscopy. Fitting subpopulation-specific computational p53 models to this time-resolved single cell data allowed to reproduce in a quantitative manner signaling dynamics and cellular heterogeneity for the unperturbed and perturbed conditions. The approach enabled us to untangle the integrated effects of IKK/ NF-κB perturbation on p53 dynamics and thereby derive potential interactions between both networks. Intriguingly, we find that a simultaneous perturbation of multiple processes is necessary to explain the observed changes in the p53 response. Specifically, we show interference with the activation and degradation of p53 as well as the degradation of Mdm2. Our results highlight the importance of the crosstalk and its potential implications in p53-dependent cellular functions. Cells can respond to external and internal inputs by transducing information to the nucleus where transcription factors initiate corresponding cellular responses. Cellular signaling is mediated by several pathways; molecular networks that can interact with each other, which alters signal processing and modulates cellular responses. As deregulated signaling can lead to the development of tumors it is important to understand not only how signaling pathways function but also the contribution of their interaction on the signaling dynamics. Here, we analyzed the interplay of the IKK/ NF-κB and p53 pathway, which are both critical for the cellular response to DNA damage and have been implicated in tumor development. To systematically identify interaction points between both pathways, we perturbed IKK/ NF-κB signaling and tracked the changes in the response of p53 to DNA damage. Using computational methods, we show that several reactions in the p53 pathway are simultaneously affected by NF-κB signaling and that this combined action is necessary to explain altered behaviour of the p53 pathway. Hence, our results provide new insights into the interplay between the NF-κB and p53 pathway and help to gain a more comprehensive understanding of the crosstalk.
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Affiliation(s)
- Fabian Konrath
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Anna Mittermeier
- Systems Biology of the Stress Response, Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Elena Cristiano
- Signaling Dynamics in Single Cells, Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Jana Wolf
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
- * E-mail: (JW); (AL)
| | - Alexander Loewer
- Systems Biology of the Stress Response, Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
- Signaling Dynamics in Single Cells, Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
- * E-mail: (JW); (AL)
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23
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Katebi A, Kohar V, Lu M. Random Parametric Perturbations of Gene Regulatory Circuit Uncover State Transitions in Cell Cycle. iScience 2020; 23:101150. [PMID: 32450514 PMCID: PMC7251928 DOI: 10.1016/j.isci.2020.101150] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/05/2020] [Accepted: 05/05/2020] [Indexed: 02/03/2023] Open
Abstract
Many biological processes involve precise cellular state transitions controlled by complex gene regulation. Here, we use budding yeast cell cycle as a model system and explore how a gene regulatory circuit encodes essential information of state transitions. We present a generalized random circuit perturbation method for circuits containing heterogeneous regulation types and its usage to analyze both steady and oscillatory states from an ensemble of circuit models with random kinetic parameters. The stable steady states form robust clusters with a circular structure that are associated with cell cycle phases. This circular structure in the clusters is consistent with single-cell RNA sequencing data. The oscillatory states specify the irreversible state transitions along cell cycle progression. Furthermore, we identify possible mechanisms to understand the irreversible state transitions from the steady states. We expect this approach to be robust and generally applicable to unbiasedly predict dynamical transitions of a gene regulatory circuit.
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Affiliation(s)
- Ataur Katebi
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Vivek Kohar
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Mingyang Lu
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.
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24
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Marguet A, Lavielle M, Cinquemani E. Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data. Bioinformatics 2020; 35:i586-i595. [PMID: 31510690 PMCID: PMC6612834 DOI: 10.1093/bioinformatics/btz378] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Motivation Modern experimental technologies enable monitoring of gene expression dynamics in individual cells and quantification of its variability in isogenic microbial populations. Among the sources of this variability is the randomness that affects inheritance of gene expression factors at cell division. Known parental relationships among individually observed cells provide invaluable information for the characterization of this extrinsic source of gene expression noise. Despite this fact, most existing methods to infer stochastic gene expression models from single-cell data dedicate little attention to the reconstruction of mother–daughter inheritance dynamics. Results Starting from a transcription and translation model of gene expression, we propose a stochastic model for the evolution of gene expression dynamics in a population of dividing cells. Based on this model, we develop a method for the direct quantification of inheritance and variability of kinetic gene expression parameters from single-cell gene expression and lineage data. We demonstrate that our approach provides unbiased estimates of mother–daughter inheritance parameters, whereas indirect approaches using lineage information only in the post-processing of individual-cell parameters underestimate inheritance. Finally, we show on yeast osmotic shock response data that daughter cell parameters are largely determined by the mother, thus confirming the relevance of our method for the correct assessment of the onset of gene expression variability and the study of the transmission of regulatory factors. Availability and implementation Software code is available at https://github.com/almarguet/IdentificationWithARME. Lineage tree data is available upon request. Supplementary information Supplementary material is available at Bioinformatics online.
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Affiliation(s)
| | - Marc Lavielle
- Inria Saclay & Ecole Polytechnique, Palaiseau, France
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25
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Abstract
Ever since Clausius in 1865 and Boltzmann in 1877, the concepts of entropy and of its maximization have been the foundations for predicting how material equilibria derive from microscopic properties. But, despite much work, there has been no equally satisfactory general variational principle for nonequilibrium situations. However, in 1980, a new avenue was opened by E.T. Jaynes and by Shore and Johnson. We review here maximum caliber, which is a maximum-entropy-like principle that can infer distributions of flows over pathways, given dynamical constraints. This approach is providing new insights, particularly into few-particle complex systems, such as gene circuits, protein conformational reaction coordinates, network traffic, bird flocking, cell motility, and neuronal firing.
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Affiliation(s)
- Kingshuk Ghosh
- Department of Physics and Astronomy, University of Denver, Denver, Colorado 80209, USA
| | - Purushottam D. Dixit
- Department of Systems Biology, Columbia University, New York, NY 10032, USA,Department of Physics, University of Florida, Gainesville, Florida 32611, USA
| | - Luca Agozzino
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
| | - Ken A. Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
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26
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Dixit PD, Lyashenko E, Niepel M, Vitkup D. Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks. Cell Syst 2020; 10:204-212.e8. [PMID: 31864963 PMCID: PMC7047530 DOI: 10.1016/j.cels.2019.11.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 07/30/2019] [Accepted: 11/25/2019] [Indexed: 12/13/2022]
Abstract
Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements.
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Affiliation(s)
- Purushottam D Dixit
- Department of Systems Biology, Columbia University, New York, NY, USA; Department of Physics, University of Florida, Gainesville, FL, USA.
| | - Eugenia Lyashenko
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Mario Niepel
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Dennis Vitkup
- Department of Systems Biology, Columbia University, New York, NY, USA; Department of Biomedical Informatics, Columbia University, New York, NY, USA; Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA.
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27
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Lee D, Jayaraman A, Kwon JS. Identification of cell‐to‐cell heterogeneity through systems engineering approaches. AIChE J 2020. [DOI: 10.1002/aic.16925] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Dongheon Lee
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
| | - Joseph S.‐I. Kwon
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
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28
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Estimating numbers of intracellular molecules through analysing fluctuations in photobleaching. Sci Rep 2019; 9:15238. [PMID: 31645577 PMCID: PMC6811640 DOI: 10.1038/s41598-019-50921-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 09/18/2019] [Indexed: 01/18/2023] Open
Abstract
The impact of fluorescence microscopy has been limited by the difficulties of expressing measurements of fluorescent proteins in numbers of molecules. Absolute numbers enable the integration of results from different laboratories, empower mathematical modelling, and are the bedrock for a quantitative, predictive biology. Here we propose an estimator to infer numbers of molecules from fluctuations in the photobleaching of proteins tagged with Green Fluorescent Protein. Performing experiments in budding yeast, we show that our estimates of numbers agree, within an order of magnitude, with published biochemical measurements, for all six proteins tested. The experiments we require are straightforward and use only a wide-field fluorescence microscope. As such, our approach has the potential to become standard for those practising quantitative fluorescence microscopy.
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29
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Baum K, Schuchhardt J, Wolf J, Busse D. Of Gene Expression and Cell Division Time: A Mathematical Framework for Advanced Differential Gene Expression and Data Analysis. Cell Syst 2019; 9:569-579.e7. [PMID: 31521604 DOI: 10.1016/j.cels.2019.07.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 04/15/2019] [Accepted: 07/23/2019] [Indexed: 12/31/2022]
Abstract
Estimating fold changes of average mRNA and protein molecule counts per cell is the most common way to perform differential expression analysis. However, these gene expression data may be affected by cell division, an often-neglected phenomenon. Here, we develop a quantitative framework that links population-based mRNA and protein measurements to rates of gene expression in single cells undergoing cell division. The equations we derive are easy-to-use and widely robust against biological variability. They integrate multiple "omics" data into a coherent, quantitative description of single-cell gene expression and improve analysis when comparing systems or states with different cell division times. We explore these ideas in the context of resting versus activated B cells. Analyzing differences in protein synthesis rates enables to account for differences in cell division times. We demonstrate that this improves the resolution and hit rate of differential gene expression analysis when compared to analyzing population protein abundances alone.
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Affiliation(s)
- Katharina Baum
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Str. 10, 13125 Berlin, Germany; Luxembourg Institute of Health, 1A-B rue Thomas Edison, L-1445 Strassen, Luxembourg.
| | | | - Jana Wolf
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Str. 10, 13125 Berlin, Germany.
| | - Dorothea Busse
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Str. 10, 13125 Berlin, Germany; Integrative Research Institute for the Life Sciences, Humboldt University Berlin, Philippstr. 13, 10115 Berlin, Germany.
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30
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Yu C, Liu Q, Chen C, Yu J, Wang J. Landscape perspectives of tumor, EMT, and development. Phys Biol 2019; 16:051003. [PMID: 31067516 DOI: 10.1088/1478-3975/ab2029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A tumor is rarely fatal until becoming metastatic. Recent discoveries suggest that epithelial mesenchymal transition(EMT) is an important process which contributes to not only cancer metastasis but also increased stemness. Cancer cells with stem cell characteristics are called cancer stem cells (CSCs). We review recent efforts to quantify and delineate the relationship among EMT, CSC and tumor development. When the gene regulatory network is tightly regulated through the effectively fast regulatory binding, Cancer, Premalignant, Normal, CSC, stem cell (SC), Lesion and Hyperplasia states emerged. The corresponding landscape topography for all of these states can be quantified to a global way for uncovering the relationship among the tumor, metastasis, and development. On the other hand, phenotypic and functional heterogeneity is regarded as one of the greatest challenge in cancer treatment. Cancer and CSCs are heterogeneous and give rise to tumorigenic and non-tumorigenic cells during self-renewal, differentiation and epigenetic diversification. Further, if the gene regulatory network is weakly regulated through the effective slow regulatory binding (by DNA methylation or histone modification etc), multiple meta-stable states can emerge. This model can provide an epigenetic and physical rather than genetic and fixed origin of heterogeneity. Elucidating the origin of and dynamic nature of tumor cells will likely help better understand the cellular basis of therapeutic response, resistance, and relapse.
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Affiliation(s)
- Chong Yu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, People's Republic of China. University of Science and Technology of China, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
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31
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Marinkovic ZS, Vulin C, Acman M, Song X, Di Meglio JM, Lindner AB, Hersen P. A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies. eLife 2019; 8:e47951. [PMID: 31259688 PMCID: PMC6624017 DOI: 10.7554/elife.47951] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 06/30/2019] [Indexed: 01/15/2023] Open
Abstract
Microbial colonies are fascinating structures in which growth and internal organization reflect complex morphogenetic processes. Here, we generated a microfluidics device with arrays of long monolayer yeast colonies to further global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined boundary conditions. We observed the emergence of stable glucose gradients using fluorescently labeled hexose transporters and quantified the spatial correlations with intra-colony growth rates and expression of other genes regulated by glucose availability. These landscapes depended on the external glucose concentration as well as secondary gradients, for example amino acid availability. This work demonstrates the regulatory genetic networks governing cellular physiological adaptation are the key to internal structuration of cellular assemblies. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development and morphogenesis in more complex systems.
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Affiliation(s)
- Zoran S Marinkovic
- Laboratoire Matière et Systèmes ComplexesUMR 7057 CNRS and Université de ParisParisFrance
- U1001 INSERMParisFrance
- CRIUniversité de ParisParisFrance
| | - Clément Vulin
- Laboratoire Matière et Systèmes ComplexesUMR 7057 CNRS and Université de ParisParisFrance
- Institute of Biogeochemistry and Pollutant DynamicsETH ZürichZürichSwitzerland
- Department of Environmental MicrobiologyEawagDübendorfSwitzerland
| | - Mislav Acman
- Laboratoire Matière et Systèmes ComplexesUMR 7057 CNRS and Université de ParisParisFrance
- CRIUniversité de ParisParisFrance
| | | | - Jean-Marc Di Meglio
- Laboratoire Matière et Systèmes ComplexesUMR 7057 CNRS and Université de ParisParisFrance
| | | | - Pascal Hersen
- Laboratoire Matière et Systèmes ComplexesUMR 7057 CNRS and Université de ParisParisFrance
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32
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Hyperosmotic Stress Response Memory is Modulated by Gene Positioning in Yeast. Cells 2019; 8:cells8060582. [PMID: 31200564 PMCID: PMC6627694 DOI: 10.3390/cells8060582] [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: 05/15/2019] [Revised: 06/11/2019] [Accepted: 06/12/2019] [Indexed: 12/19/2022] Open
Abstract
Cellular memory is a critical ability that allows microorganisms to adapt to potentially detrimental environmental fluctuations. In the unicellular eukaryote Saccharomyces cerevisiae, cellular memory can take the form of faster or slower responses within the cell population to repeated stresses. Using microfluidics and fluorescence time-lapse microscopy, we studied how yeast responds to short, pulsed hyperosmotic stresses at the single-cell level by analyzing the dynamic behavior of the stress-responsive STL1 promoter (pSTL1) fused to a fluorescent reporter. We established that pSTL1 exhibits variable successive activation patterns following two repeated short stresses. Despite this variability, most cells exhibited a memory of the first stress as decreased pSTL1 activity in response to the second stress. Notably, we showed that genomic location is important for the memory effect, since displacement of the promoter to a pericentromeric chromatin domain decreased the transcriptional strength of pSTL1 and led to a loss of memory. This study provides a quantitative description of a cellular memory that includes single-cell variability and highlights the contribution of chromatin structure to stress memory.
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33
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Zhang W, Li W, Zhang J, Wang N. Optimal parameter identification of synthetic gene networks using harmony search algorithm. PLoS One 2019; 14:e0213977. [PMID: 30925150 PMCID: PMC6440652 DOI: 10.1371/journal.pone.0213977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 02/09/2019] [Indexed: 12/03/2022] Open
Abstract
Computational modeling of engineered gene circuits is an important while challenged task in systems biology. In order to describe and predict the response behaviors of genetic circuits using reliable model parameters, this paper applies an optimal experimental design(OED) method to obtain input signals. In order to obtain informative observations, this study focuses on maximizing Fisher information matrix(FIM)-based optimal criteria and to provide optimal inputs. Furthermore, this paper designs a two-stage optimization with the modified E-optimal criteria and applies harmony search(HS)-based OED algorithm to minimize estimation errors. The proposed optimal identification methodology involves estimation errors and the sample size to pursue a trade-off between estimation accuracy and measurement cost in modeling gene networks. The designed cost function takes two major factors into account, in which experimental costs are proportional to the number of time points. Experiments select two types of synthetic genetic networks to validate the effectiveness of the proposed HS-OED approach. Identification outcomes and analysis indicate the proposed HS-OED method outperforms two candidate OED approaches, with reduced computational effort.
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Affiliation(s)
- Wei Zhang
- Institute of Cyber-Systems and Control, Department of Control and Engineering, Zhejiang University, Hangzhou, China
| | - Wenchao Li
- Institute of Cyber-Systems and Control, Department of Control and Engineering, Zhejiang University, Hangzhou, China
| | - Jianming Zhang
- Institute of Cyber-Systems and Control, Department of Control and Engineering, Zhejiang University, Hangzhou, China
| | - Ning Wang
- Institute of Cyber-Systems and Control, Department of Control and Engineering, Zhejiang University, Hangzhou, China
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34
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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: 1.5] [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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35
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Fröhlich F, Reiser A, Fink L, Woschée D, Ligon T, Theis FJ, Rädler JO, Hasenauer J. Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection. NPJ Syst Biol Appl 2018; 5:1. [PMID: 30564456 PMCID: PMC6288153 DOI: 10.1038/s41540-018-0079-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 11/09/2018] [Indexed: 11/10/2022] Open
Abstract
Single-cell time-lapse studies have advanced the quantitative understanding of cellular pathways and their inherent cell-to-cell variability. However, parameters retrieved from individual experiments are model dependent and their estimation is limited, if based on solely one kind of experiment. Hence, methods to integrate data collected under different conditions are expected to improve model validation and information content. Here we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.
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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
| | - Anita Reiser
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, 80539 Germany
| | - Laura Fink
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, 80539 Germany
| | - Daniel Woschée
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, 80539 Germany
| | - Thomas Ligon
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, 80539 Germany
| | - Fabian Joachim Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, 85764 Germany
- Center for Mathematics, Technische Universität München, Garching, 85748 Germany
| | - Joachim Oskar Rädler
- Faculty of Physics and Center for NanoScience, 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
- Faculty of Mathematics and Natural Sciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
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36
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Role of noise and parametric variation in the dynamics of gene regulatory circuits. NPJ Syst Biol Appl 2018; 4:40. [PMID: 30416751 PMCID: PMC6218471 DOI: 10.1038/s41540-018-0076-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 10/14/2018] [Accepted: 10/16/2018] [Indexed: 12/21/2022] Open
Abstract
Stochasticity in gene expression impacts the dynamics and functions of gene regulatory circuits. Intrinsic noises, including those that are caused by low copy number of molecules and transcriptional bursting, are usually studied by stochastic simulations. However, the role of extrinsic factors, such as cell-to-cell variability and heterogeneity in the microenvironment, is still elusive. To evaluate the effects of both the intrinsic and extrinsic noises, we develop a method, named sRACIPE, by integrating stochastic analysis with random circuit perturbation (RACIPE) method. RACIPE uniquely generates and analyzes an ensemble of models with random kinetic parameters. Previously, we have shown that the gene expression from random models form robust and functionally related clusters. In sRACIPE we further develop two stochastic simulation schemes, aiming to reduce the computational cost without sacrificing the convergence of statistics. One scheme uses constant noise to capture the basins of attraction, and the other one uses simulated annealing to detect the stability of states. By testing the methods on several synthetic gene regulatory circuits and an epithelial-mesenchymal transition network in squamous cell carcinoma, we demonstrate that sRACIPE can interpret the experimental observations from single-cell gene expression data. We observe that parametric variation (the spread of parameters around a median value) increases the spread of the gene expression clusters, whereas high noise merges the states. Our approach quantifies the robustness of a gene circuit in the presence of noise and sheds light on a new mechanism of noise-induced hybrid states. We have implemented sRACIPE as an R package.
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37
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Duveau F, Hodgins-Davis A, Metzger BP, Yang B, Tryban S, Walker EA, Lybrook T, Wittkopp PJ. Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. eLife 2018; 7:37272. [PMID: 30124429 PMCID: PMC6133559 DOI: 10.7554/elife.37272] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 08/17/2018] [Indexed: 01/22/2023] Open
Abstract
Gene expression noise is an evolvable property of biological systems that describes differences in expression among genetically identical cells in the same environment. Prior work has shown that expression noise is heritable and can be shaped by selection, but the impact of variation in expression noise on organismal fitness has proven difficult to measure. Here, we quantify the fitness effects of altering expression noise for the TDH3 gene in Saccharomyces cerevisiae. We show that increases in expression noise can be deleterious or beneficial depending on the difference between the average expression level of a genotype and the expression level maximizing fitness. We also show that a simple model relating single-cell expression levels to population growth produces patterns consistent with our empirical data. We use this model to explore a broad range of average expression levels and expression noise, providing additional insight into the fitness effects of variation in expression noise.
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Affiliation(s)
- Fabien Duveau
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Laboratoire Matière et Systèmes Complexes, CNRS UMR 7057, Université Paris Diderot, Paris, France
| | - Andrea Hodgins-Davis
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Brian Ph Metzger
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Department of Ecology and Evolution, University of Chicago, Chicago, United States
| | - Bing Yang
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Stephen Tryban
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Elizabeth A Walker
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Tricia Lybrook
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Patricia J Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, United States
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38
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Huang B, Jia D, Feng J, Levine H, Onuchic JN, Lu M. RACIPE: a computational tool for modeling gene regulatory circuits using randomization. BMC SYSTEMS BIOLOGY 2018; 12:74. [PMID: 29914482 PMCID: PMC6006707 DOI: 10.1186/s12918-018-0594-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 05/31/2018] [Indexed: 01/14/2023]
Abstract
Background One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks. Results We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. Taking the network topology as the only input, RACIPE generates an ensemble of circuit models with distinct randomized parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss the implementation of the software and the statistical analysis methods of RACIPE-generated data to identify robust gene expression patterns and the functions of genes and regulatory links. Finally, we apply the tool on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis. Conclusions We expect our new computational tool to contribute to a more comprehensive and unbiased understanding of mechanisms underlying gene regulatory networks. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub (https://github.com/simonhb1990/RACIPE-1.0). Electronic supplementary material The online version of this article (10.1186/s12918-018-0594-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bin Huang
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX, USA
| | - Jingchen Feng
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA. .,Department of Bioengineering, Rice University, Houston, TX, USA. .,Department of Biosciences, Rice University, Houston, TX, USA. .,Department of Physics and Astronomy, Rice University, Houston, TX, USA.
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA. .,Department of Biosciences, Rice University, Houston, TX, USA. .,Department of Physics and Astronomy, Rice University, Houston, TX, USA. .,Department of Chemistry, Rice University, Houston, TX, USA.
| | - Mingyang Lu
- The Jackson Laboratory, Bar Harbor, ME, USA.
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39
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Salignon J, Richard M, Fulcrand E, Duplus-Bottin H, Yvert G. Genomics of cellular proliferation in periodic environmental fluctuations. Mol Syst Biol 2018; 14:e7823. [PMID: 29507053 PMCID: PMC5836541 DOI: 10.15252/msb.20177823] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 02/01/2018] [Accepted: 02/06/2018] [Indexed: 11/17/2022] Open
Abstract
Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out-of-equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high-affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.
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Affiliation(s)
- Jérôme Salignon
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France
| | - Magali Richard
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France
| | - Etienne Fulcrand
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France
| | - Hélène Duplus-Bottin
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France
| | - Gaël Yvert
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France
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40
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Ho PW, Klein M, Futschik M, Nevoigt E. Glycerol positive promoters for tailored metabolic engineering of the yeast Saccharomyces cerevisiae. FEMS Yeast Res 2018; 18:4898018. [DOI: 10.1093/femsyr/foy019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 02/21/2018] [Indexed: 01/27/2023] Open
Affiliation(s)
- Ping-Wei Ho
- Department of Life Sciences and Chemistry, Jacobs University Bremen gGmbH, Campus Ring 1, 28759 Bremen, Germany
| | - Mathias Klein
- Department of Life Sciences and Chemistry, Jacobs University Bremen gGmbH, Campus Ring 1, 28759 Bremen, Germany
| | - Matthias Futschik
- School of Biomedical and Healthcare Sciences, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, Devon, PL4 8AA, UK
- Centre of Marine Sciences (CCMAR), Universidade do Algarve, Faro, 8005-139, Portugal
| | - Elke Nevoigt
- Department of Life Sciences and Chemistry, Jacobs University Bremen gGmbH, Campus Ring 1, 28759 Bremen, Germany
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41
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Richard M, Chuffart F, Duplus-Bottin H, Pouyet F, Spichty M, Fulcrand E, Entrevan M, Barthelaix A, Springer M, Jost D, Yvert G. Assigning function to natural allelic variation via dynamic modeling of gene network induction. Mol Syst Biol 2018; 14:e7803. [PMID: 29335276 PMCID: PMC5787706 DOI: 10.15252/msb.20177803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be “personalized” according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non‐synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.
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Affiliation(s)
- Magali Richard
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France .,Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC-IMAG, Grenoble, France
| | - Florent Chuffart
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Hélène Duplus-Bottin
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Fanny Pouyet
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Martin Spichty
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Etienne Fulcrand
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Marianne Entrevan
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Audrey Barthelaix
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Michael Springer
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Daniel Jost
- Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC-IMAG, Grenoble, France
| | - Gaël Yvert
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
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42
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Patange S, Girvan M, Larson DR. Single-cell systems biology: probing the basic unit of information flow. ACTA ACUST UNITED AC 2017; 8:7-15. [PMID: 29552672 DOI: 10.1016/j.coisb.2017.11.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Gene expression varies across cells in a population or a tissue. This heterogeneity has come into sharp focus in recent years through developments in new imaging and sequencing technologies. However, our ability to measure variation has outpaced our ability to interpret it. Much of the variability may arise from random effects occurring in the processes of gene expression (transcription, RNA processing and decay, translation). The molecular basis of these effects is largely unknown. Likewise, a functional role of this variability in growth, differentiation and disease has only been elucidated in a few cases. In this review, we highlight recent experimental and theoretical advances for measuring and analyzing stochastic variation.
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Affiliation(s)
- Simona Patange
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute. Bethesda, MD 20892
- Institute for Physical Science and Technology, University of Maryland, College Park, MD
| | - Michelle Girvan
- Institute for Physical Science and Technology, University of Maryland, College Park, MD
- Department of Physics, University of Maryland. College Park, MD
| | - Daniel R Larson
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute. Bethesda, MD 20892
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43
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Delvigne F, Baert J, Sassi H, Fickers P, Grünberger A, Dusny C. Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses. Biotechnol J 2017; 12. [PMID: 28544731 DOI: 10.1002/biot.201600549] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/10/2017] [Accepted: 04/12/2017] [Indexed: 01/19/2023]
Abstract
Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.
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Affiliation(s)
- Frank Delvigne
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Jonathan Baert
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Hosni Sassi
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Patrick Fickers
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Alexander Grünberger
- Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, Jülich, Germany.,Multiscale Bioengineering, Bielefeld University, Bielefeld, Germany
| | - Christian Dusny
- Department Solar Materials, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
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Kuzmanovska I, Milias-Argeitis A, Mikelson J, Zechner C, Khammash M. Parameter inference for stochastic single-cell dynamics from lineage tree data. BMC SYSTEMS BIOLOGY 2017; 11:52. [PMID: 28446158 PMCID: PMC5406901 DOI: 10.1186/s12918-017-0425-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Accepted: 04/12/2017] [Indexed: 11/21/2022]
Abstract
Background With the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. Most of currently available methods treat single-cell trajectories independently, ignoring the mother-daughter relationships and the information provided by the population structure. However, this information is essential if a process of interest happens at cell division, or if it evolves slowly compared to the duration of the cell cycle. Results In this work, we propose a Bayesian framework for parameter inference on single-cell time-lapse data from lineage trees. Our method relies on a combination of Sequential Monte Carlo for approximating the parameter likelihood function and Markov Chain Monte Carlo for parameter exploration. We demonstrate our inference framework on two simple examples in which the lineage tree information is crucial: one in which the cell phenotype can only switch at cell division and another where the cell state fluctuates slowly over timescales that extend well beyond the cell-cycle duration. Conclusion There exist several examples of biological processes, such as stem cell fate decisions or epigenetically controlled phase variation in bacteria, where the cell ancestry is expected to contain important information about the underlying system dynamics. Parameter inference methods that discard this information are expected to perform poorly for such type of processes. Our method provides a simple and computationally efficient way to take into account single-cell lineage tree data for the purpose of parameter inference and serves as a starting point for the development of more sophisticated and powerful approaches in the future. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0425-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Irena Kuzmanovska
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland
| | - Andreas Milias-Argeitis
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland.,Groningen Biomolecular Sciences and Biotechnology, University of Groningen, Nijenborgh 4, Groningen, 9747, AG, Netherlands
| | - Jan Mikelson
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland
| | - Christoph Zechner
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland.,Max Planck Institute of Molecular Cell Biology and Genetics and Center for Systems Biology, Pfotenhauerstrasse 108, Dresden, 01307, Germany
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland.
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Huang B, Lu M, Jia D, Ben-Jacob E, Levine H, Onuchic JN. Interrogating the topological robustness of gene regulatory circuits by randomization. PLoS Comput Biol 2017; 13:e1005456. [PMID: 28362798 PMCID: PMC5391964 DOI: 10.1371/journal.pcbi.1005456] [Citation(s) in RCA: 121] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 04/14/2017] [Accepted: 03/15/2017] [Indexed: 01/06/2023] Open
Abstract
One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression. Cells are able to robustly carry out their essential biological functions, possibly because of multiple layers of tight regulation via complex, yet well-designed, gene regulatory networks involving a substantial number of genes. State-of-the-art genomics technology has enabled the mapping of these large gene networks, yet it remains a tremendous challenge to elucidate their design principles and the regulatory mechanisms underlying their biological functions such as signal processing and decision-making. One of the key barriers is the absence of accurate kinetics for the regulatory interactions, especially from in vivo experiments. To this end, we have developed a new computational modeling method, Random Circuit Perturbation (RACIPE), to explore the dynamic behaviors of gene regulatory circuits without the requirement of detailed kinetic parameters. RACIPE takes a network topology as the input, and generates an unbiased ensemble of models with varying kinetic parameters. Each model is subjected to simulation, followed by statistical analysis for the ensemble. We tested RACIPE on several gene circuits, and found that the predicted gene expression patterns from all of the models converge to experimentally observed gene state clusters. We expect RACIPE to be a powerful method to identify the role of network topology in determining network operating principles.
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Affiliation(s)
- Bin Huang
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- Department of Chemistry, Rice University, Houston, TX, United States of America
| | - Mingyang Lu
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- The Jackson Laboratory, Bar Harbor, ME, United States of America
| | - Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX, United States of America
| | - Eshel Ben-Jacob
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- School of Physics and Astronomy, and The Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- Department of Bioengineering, Rice University, Houston, TX, United States of America
- Department of Biosciences, Rice University, Houston, TX, United States of America
- Department of Physics and Astronomy, Rice University, Houston, TX, United States of America
- * E-mail: (HL); (JNO)
| | - Jose N. Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- Department of Chemistry, Rice University, Houston, TX, United States of America
- Department of Biosciences, Rice University, Houston, TX, United States of America
- Department of Physics and Astronomy, Rice University, Houston, TX, United States of America
- * E-mail: (HL); (JNO)
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Ruess J, Koeppl H, Zechner C. Sensitivity estimation for stochastic models of biochemical reaction networks in the presence of extrinsic variability. J Chem Phys 2017; 146:124122. [PMID: 28388123 DOI: 10.1063/1.4978940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Determining the sensitivity of certain system states or outputs to variations in parameters facilitates our understanding of the inner working of that system and is an essential design tool for the de novo construction of robust systems. In cell biology, the output of interest is often the response of a certain reaction network to some input (e.g., stressors or nutrients) and one aims to quantify the sensitivity of this response in the presence of parameter heterogeneity. We argue that for such applications, parametric sensitivities in their standard form do not paint a complete picture of a system's robustness since one assumes that all cells in the population have the same parameters and are perturbed in the same way. Here, we consider stochasticreaction networks in which the parameters are randomly distributed over the population and propose a new sensitivity index that captures the robustness of system outputs upon changes in the characteristics of the parameter distribution, rather than the parameters themselves. Subsequently, we make use of Girsanov's likelihood ratio method to construct a Monte Carlo estimator of this sensitivity index. However, it turns out that this estimator has an exceedingly large variance. To overcome this problem, we propose a novel estimation algorithm that makes use of a marginalization of the path distribution of stochasticreaction networks and leads to Rao-Blackwellized estimators with reduced variance.
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Affiliation(s)
- Jakob Ruess
- Inria Saclay - Ile-de-France, 91120 Palaiseau, France
| | - Heinz Koeppl
- TU Darmstadt, Department of Electrical Engineering and Information Technology, 64283 Darmstadt, Germany
| | - Christoph Zechner
- Max Planck Institute of Molecular Cell Biology and Genetics and Center for Systems Biology Dresden, 01307 Dresden, Germany
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47
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Pájaro M, Alonso AA, Otero-Muras I, Vázquez C. Stochastic modeling and numerical simulation of gene regulatory networks with protein bursting. J Theor Biol 2017; 421:51-70. [PMID: 28341132 DOI: 10.1016/j.jtbi.2017.03.017] [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] [Received: 09/14/2016] [Revised: 02/28/2017] [Accepted: 03/18/2017] [Indexed: 01/01/2023]
Abstract
Gene expression is inherently stochastic. Advanced single-cell microscopy techniques together with mathematical models for single gene expression led to important insights in elucidating the sources of intrinsic noise in prokaryotic and eukaryotic cells. In addition to the finite size effects due to low copy numbers, translational bursting is a dominant source of stochasticity in cell scenarios involving few short lived mRNA transcripts with high translational efficiency (as is typically the case for prokaryotes), causing protein synthesis to occur in random bursts. In the context of gene regulation cascades, the Chemical Master Equation (CME) governing gene expression has in general no closed form solution, and the accurate stochastic simulation of the dynamics of complex gene regulatory networks is a major computational challenge. The CME associated to a single gene self regulatory motif has been previously approximated by a one dimensional time dependent partial integral differential equation (PIDE). However, to the best of our knowledge, multidimensional versions for such PIDE have not been developed yet. Here we propose a multidimensional PIDE model for regulatory networks involving multiple genes with self and cross regulations (in which genes can be regulated by different transcription factors) derived as the continuous counterpart of a CME with jump process. The model offers a reliable description of systems with translational bursting. In order to provide an efficient numerical solution, we develop a semilagrangian method to discretize the differential part of the PIDE, combined with a composed trapezoidal quadrature formula to approximate the integral term. We apply the model and numerical method to study sustained stochastic oscillations and the development of competence, a particular case of transient differentiation attained by certain bacterial cells under stress conditions. We found that the resulting probability distributions are distinguishable from those characteristic of other transient differentiation processes. In this way, they can be employed as markers or signatures that identify such phenomena from bacterial population experimental data, for instance. The computational efficiency of the semilagrangian method makes it suitable for purposes like model identification and parameter estimation from experimental data or, in combination with optimization routines, the design of gene regulatory networks under molecular noise.
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Affiliation(s)
- Manuel Pájaro
- Process Engineering Group, IIM-CSIC, Spanish Council for Scientific Research, Eduardo Cabello 6, 36208 - Vigo, Spain.
| | - Antonio A Alonso
- Process Engineering Group, IIM-CSIC, Spanish Council for Scientific Research, Eduardo Cabello 6, 36208 - Vigo, Spain.
| | - Irene Otero-Muras
- Process Engineering Group, IIM-CSIC, Spanish Council for Scientific Research, Eduardo Cabello 6, 36208 - Vigo, Spain.
| | - Carlos Vázquez
- Department of Mathematics, University of A Coruña. Campus Elviña s/n, 15071 - A Coruña, Spain.
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48
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Versari C, Stoma S, Batmanov K, Llamosi A, Mroz F, Kaczmarek A, Deyell M, Lhoussaine C, Hersen P, Batt G. Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform. J R Soc Interface 2017; 14:20160705. [PMID: 28179544 PMCID: PMC5332563 DOI: 10.1098/rsif.2016.0705] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 12/15/2016] [Indexed: 11/23/2022] Open
Abstract
With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.
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Affiliation(s)
| | - Szymon Stoma
- Scientific Center for Optical and Electron Microscopy (ScopeM), ETH Zurich, Zurich, Switzerland
| | | | - Artémis Llamosi
- Laboratoire Matières et Systèmes Complexes, UMR7057, CNRS and Université Paris Diderot, Paris, France
- Inria and Université Paris-Saclay, Palaiseau, France
| | - Filip Mroz
- Institute of Computer Science, University of Wroclaw, Wroclaw, Poland
| | - Adam Kaczmarek
- Institute of Computer Science, University of Wroclaw, Wroclaw, Poland
| | - Matt Deyell
- Laboratoire Matières et Systèmes Complexes, UMR7057, CNRS and Université Paris Diderot, Paris, France
| | | | - Pascal Hersen
- Laboratoire Matières et Systèmes Complexes, UMR7057, CNRS and Université Paris Diderot, Paris, France
| | - Gregory Batt
- Inria and Université Paris-Saclay, Palaiseau, France
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49
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González-Vargas AM, Cinquemani E, Ferrari-Trecate G. Validation methods for population models of gene expression dynamics. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.ifacol.2016.12.112] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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