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Pavlou A, Cinquemani E, Geiselmann J, de Jong H. Maturation models of fluorescent proteins are necessary for unbiased estimates of promoter activity. Biophys J 2022; 121:4179-4188. [PMID: 36146937 PMCID: PMC9675035 DOI: 10.1016/j.bpj.2022.09.021] [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: 11/23/2021] [Revised: 06/13/2022] [Accepted: 09/19/2022] [Indexed: 11/27/2022] Open
Abstract
Fluorescent proteins (FPs) are a powerful tool to quantitatively monitor gene expression. The dynamics of a promoter and its regulation can be inferred from fluorescence data. The interpretation of fluorescent data, however, is strongly dependent on the maturation of FPs since different proteins mature in distinct ways. We propose a novel approach for analyzing fluorescent reporter data by incorporating maturation dynamics in the reconstruction of promoter activities. Our approach consists of developing and calibrating mechanistic maturation models for distinct FPs. These models are then used alongside a Bayesian approach to estimate promoter activities from fluorescence data. We demonstrate by means of targeted experiments in Escherichia coli that our approach provides robust estimates and that accounting for maturation is, in many cases, essential for the interpretation of gene expression data.
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Affiliation(s)
- Antrea Pavlou
- University Grenoble Alpes, Inria, Grenoble, France; University Grenoble Alpes, CNRS, LIPhy, Grenoble, France
| | | | - Johannes Geiselmann
- University Grenoble Alpes, Inria, Grenoble, France; University Grenoble Alpes, CNRS, LIPhy, Grenoble, France.
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2
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A Mathematical Modeling and Statistical Analysis of Phycobiliprotein Fluorescence Decay under Exposure to Excitation Light. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Photosynthetic phycobiliprotein complexes from Spirulina maxima were purified and fractioned by gel chromatography. A mathematical model was developed for the fractionated phycobiliprotein complexes to successfully represent the fluorescence decay rate under exposure to excitation light. Each fractionated complex had a different ratio of phycobiliproteins, such as allophycocyanin, phycocyanin, or phycoerythrin, but their fluorescence decay trends were determined to statistically have a high similarity. The mathematical model was derived based on mass balance in the sense that the fluorescence of phycobiliprotein complex was linearly dependent on its mass. The model considered both exponentially decreasing (early light-exposure period) and linearly decreasing (later period), and successfully fit the whole period of fluorescence decay trend.
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3
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Guerra P, Vuillemenot LA, Rae B, Ladyhina V, Milias-Argeitis A. Systematic In Vivo Characterization of Fluorescent Protein Maturation in Budding Yeast. ACS Synth Biol 2022; 11:1129-1141. [PMID: 35180343 PMCID: PMC8938947 DOI: 10.1021/acssynbio.1c00387] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Fluorescent protein
(FP) maturation can limit the accuracy with
which dynamic intracellular processes are captured and reduce the in vivo brightness of a given FP in fast-dividing cells.
The knowledge of maturation timescales can therefore help users determine
the appropriate FP for each application. However, in vivo maturation rates can greatly deviate from in vitro estimates that are mostly available. In this work, we present the
first systematic study of in vivo maturation for
12 FPs in budding yeast. To overcome the technical limitations of
translation inhibitors commonly used to study FP maturation, we implemented
a new approach based on the optogenetic stimulations of FP expression
in cells grown under constant nutrient conditions. Combining the rapid
and orthogonal induction of FP transcription with a mathematical model
of expression and maturation allowed us to accurately estimate maturation
rates from microscopy data in a minimally invasive manner. Besides
providing a useful resource for the budding yeast community, we present
a new joint experimental and computational approach for characterizing
FP maturation, which is applicable to a wide range of organisms.
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Affiliation(s)
- Paolo Guerra
- Molecular Systems Biology, Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, 9747 AG Groningen, Netherlands
| | - Luc-Alban Vuillemenot
- Molecular Systems Biology, Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, 9747 AG Groningen, Netherlands
| | - Brady Rae
- Molecular Systems Biology, Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, 9747 AG Groningen, Netherlands
| | - Valeriia Ladyhina
- Molecular Systems Biology, Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, 9747 AG Groningen, Netherlands
| | - Andreas Milias-Argeitis
- Molecular Systems Biology, Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, 9747 AG Groningen, Netherlands
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4
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Genetic circuits combined with machine learning provides fast responding living sensors. Biosens Bioelectron 2021; 178:113028. [DOI: 10.1016/j.bios.2021.113028] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 01/05/2021] [Accepted: 01/19/2021] [Indexed: 12/24/2022]
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5
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Eason MG, Pandelieva AT, Mayer MM, Khan ST, Garcia HG, Chica RA. Genetically Encoded Fluorescent Biosensor for Rapid Detection of Protein Expression. ACS Synth Biol 2020; 9:2955-2963. [PMID: 33044070 DOI: 10.1021/acssynbio.0c00407] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fluorescent proteins are widely used as fusion tags to detect protein expression in vivo. To become fluorescent, these proteins must undergo chromophore maturation, a slow process with a half-time of 5 to >30 min that causes delays in real-time detection of protein expression. Here, we engineer a genetically encoded fluorescent biosensor to enable detection of protein expression within seconds in live bacteria. This sensor for transiently expressed proteins (STEP) is based on a fully matured but dim green fluorescent protein in which pre-existing fluorescence increases 11-fold in vivo following the specific and rapid binding of a protein tag (Kd 120 nM, kon 1.7 × 105 M-1 s-1). In live E. coli cells, our STEP biosensor enables detection of protein expression twice as fast as the use of standard fluorescent protein fusions. Our biosensor opens the door to the real-time study of short timescale processes in live cells with high spatiotemporal resolution.
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Affiliation(s)
- Matthew G. Eason
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Antonia T. Pandelieva
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Marc M. Mayer
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Safwat T. Khan
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Hernan G. Garcia
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, California 94720, United States
- Department of Physics, University of California, Berkeley, Berkeley, California 94720, United States
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California 94720, United States
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, Berkeley, California 94720, United States
| | - Roberto A. Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
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6
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Prangemeier T, Lehr FX, Schoeman RM, Koeppl H. Microfluidic platforms for the dynamic characterisation of synthetic circuitry. Curr Opin Biotechnol 2020; 63:167-176. [PMID: 32172160 DOI: 10.1016/j.copbio.2020.02.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/31/2020] [Accepted: 02/03/2020] [Indexed: 01/28/2023]
Abstract
Generating novel functionality from well characterised synthetic parts and modules lies at the heart of synthetic biology. Ideally, circuitry is rationally designed in silico with quantitatively predictive models to predetermined design specifications. Synthetic circuits are intrinsically stochastic, often dynamically modulated and set in a dynamic fluctuating environment within a living cell. To build more complex circuits and to gain insight into context effects, intrinsic noise and transient performance, characterisation techniques that resolve both heterogeneity and dynamics are required. Here we review recent advances in both in vitro and in vivo microfluidic technologies that are suitable for the characterisation of synthetic circuitry, modules and parts.
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Affiliation(s)
- Tim Prangemeier
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany
| | - François-Xavier Lehr
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany
| | - Rogier M Schoeman
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany
| | - Heinz Koeppl
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany.
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7
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Litsios A, Huberts DHEW, Terpstra HM, Guerra P, Schmidt A, Buczak K, Papagiannakis A, Rovetta M, Hekelaar J, Hubmann G, Exterkate M, Milias-Argeitis A, Heinemann M. Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast. Nat Cell Biol 2019; 21:1382-1392. [PMID: 31685990 DOI: 10.1038/s41556-019-0413-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 09/25/2019] [Indexed: 12/28/2022]
Abstract
In the unicellular eukaryote Saccharomyces cerevisiae, Cln3-cyclin-dependent kinase activity enables Start, the irreversible commitment to the cell division cycle. However, the concentration of Cln3 has been paradoxically considered to remain constant during G1, due to the presumed scaling of its production rate with cell size dynamics. Measuring metabolic and biosynthetic activity during cell cycle progression in single cells, we found that cells exhibit pulses in their protein production rate. Rather than scaling with cell size dynamics, these pulses follow the intrinsic metabolic dynamics, peaking around Start. Using a viral-based bicistronic construct and targeted proteomics to measure Cln3 at the single-cell and population levels, we show that the differential scaling between protein production and cell size leads to a temporal increase in Cln3 concentration, and passage through Start. This differential scaling causes Start in both daughter and mother cells across growth conditions. Thus, uncoupling between two fundamental physiological parameters drives cell cycle commitment.
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Affiliation(s)
- Athanasios Litsios
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Daphne H E W Huberts
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Hanna M Terpstra
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Paolo Guerra
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Katarzyna Buczak
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Alexandros Papagiannakis
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Mattia Rovetta
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Johan Hekelaar
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Georg Hubmann
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
- Department of Biology, Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU Leuven, Heverlee, Belgium
- Center for Microbiology, VIB, Heverlee, Belgium
| | - Marten Exterkate
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Andreas Milias-Argeitis
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands.
| | - Matthias Heinemann
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands.
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8
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WellInverter: a web application for the analysis of fluorescent reporter gene data. BMC Bioinformatics 2019; 20:309. [PMID: 31185910 PMCID: PMC6558888 DOI: 10.1186/s12859-019-2920-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 05/29/2019] [Indexed: 12/20/2022] Open
Abstract
Background Fluorescent reporter genes have become widely used for monitoring gene expression in living cells. When a microbial strain carrying a reporter gene is grown in a microplate reader, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The extraction of useful information from the resulting large amounts of data is not easy to achieve, because the fluorescence and absorbance measurements are only indirectly related to promoter activities and protein concentrations, requiring mathematical models of the expression of reporter genes for their interpretation. Although the principles of the analysis of reporter gene data are well-established today, there is a lack of general-purpose bioinformatics tools based on generic measurement models and sound inference procedures. This has motivated the development of WellInverter, a web application based on well-known methods for regularized linear inversion. Results We present a new version of WellInverter that considerably improves the performance and usability of the original application. In particular, we have put in place a parallel computing architecture with a load balancer to distribute analysis queries over several back-end servers, we have completely redesigned the graphical user interface to better support the different analysis steps, and we have developed a plug-in system for the parsing of data files produced by microplate readers from different manufacturers. We illustrate the functioning of WellInverter by analyzing data of the expression of a fluorescent reporter gene controlled by a phage promoter in growing Escherichia coli populations. We show that the expression pattern in different growth media, supporting different growth rates, corresponds to the pattern expected for a constitutive gene. Conclusions The new version of WellInverter is a robust, easy-to-use and scalable web application, which has been deployed on two publicly accessible web servers and which can also be installed locally. A demo version of the application with two sample datasets is available on-line. Electronic supplementary material The online version of this article (10.1186/s12859-019-2920-4) contains supplementary material, which is available to authorized users.
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9
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He L, Binari R, Huang J, Falo-Sanjuan J, Perrimon N. In vivo study of gene expression with an enhanced dual-color fluorescent transcriptional timer. eLife 2019; 8:46181. [PMID: 31140975 PMCID: PMC6660218 DOI: 10.7554/elife.46181] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 05/28/2019] [Indexed: 12/28/2022] Open
Abstract
Fluorescent transcriptional reporters are widely used as signaling reporters and biomarkers to monitor pathway activities and determine cell type identities. However, a large amount of dynamic information is lost due to the long half-life of the fluorescent proteins. To better detect dynamics, fluorescent transcriptional reporters can be destabilized to shorten their half-lives. However, applications of this approach in vivo are limited due to significant reduction of signal intensities. To overcome this limitation, we enhanced translation of a destabilized fluorescent protein and demonstrate the advantages of this approach by characterizing spatio-temporal changes of transcriptional activities in Drosophila. In addition, by combining a fast-folding destabilized fluorescent protein and a slow-folding long-lived fluorescent protein, we generated a dual-color transcriptional timer that provides spatio-temporal information about signaling pathway activities. Finally, we demonstrate the use of this transcriptional timer to identify new genes with dynamic expression patterns.
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Affiliation(s)
- Li He
- Department of Genetics, Harvard Medical School, Boston, United States
| | - Richard Binari
- Department of Genetics, Harvard Medical School, Boston, United States.,Howard Hughes Medical Institute, Boston, United States
| | - Jiuhong Huang
- International Academy of Targeted Therapeutics and Innovation, Chongqing University of Arts and Sciences, Chongqing, China
| | | | - Norbert Perrimon
- Department of Genetics, Harvard Medical School, Boston, United States.,Howard Hughes Medical Institute, Boston, United States
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10
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Mean-Independent Noise Control of Cell Fates via Intermediate States. iScience 2018; 3:11-20. [PMID: 30428314 PMCID: PMC6137274 DOI: 10.1016/j.isci.2018.04.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 02/21/2018] [Accepted: 03/09/2018] [Indexed: 11/24/2022] Open
Abstract
Stochasticity affects accurate signal detection and robust generation of correct cell fates. Although many known regulatory mechanisms may reduce fluctuations in signals, most simultaneously influence their mean dynamics, leading to unfaithful cell fates. Through analysis and computation, we demonstrate that a reversible signaling mechanism acting through intermediate states can reduce noise while maintaining the mean. This mean-independent noise control (MINC) mechanism is investigated in the context of an intracellular binding protein that regulates retinoic acid (RA) signaling during zebrafish hindbrain development. By comparing our models with experimental data, we find that the MINC mechanism allows for sharp boundaries of gene expression without sacrificing boundary accuracy. In addition, this MINC mechanism can modulate noise to levels that we show are beneficial to spatial patterning through noise-induced cell fate switching. These results reveal a design principle that may be important for noise regulation in many systems that control cell fate determination. Mean-independent noise control allows noise attenuation without affecting the mean Intermediate states enable such control through proportional coupling This controls spatial gene expression noise without shifting boundary locations Specific noise levels are required for successful downstream boundary sharpening
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11
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Markakis K, De Las Heras A, Elfick A. Analytical approach for the calculation of promoter activities based on fluorescent protein expression data. ENGINEERING BIOLOGY 2017. [DOI: 10.1049/enb.2017.0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Konstantinos Markakis
- School of Engineering, Institute of Bioengineering The University of Edinburgh Edinburgh EH9 3DW UK
- SynthSys – Synthetic and Systems Biology Research Centre Edinburgh EH9 3BF UK
| | - Aitor De Las Heras
- School of Engineering, Institute of Bioengineering The University of Edinburgh Edinburgh EH9 3DW UK
- SynthSys – Synthetic and Systems Biology Research Centre Edinburgh EH9 3BF UK
| | - Alistair Elfick
- School of Engineering, Institute of Bioengineering The University of Edinburgh Edinburgh EH9 3DW UK
- SynthSys – Synthetic and Systems Biology Research Centre Edinburgh EH9 3BF UK
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12
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Wang LZ, Wu F, Flores K, Lai YC, Wang X. Build to understand: synthetic approaches to biology. Integr Biol (Camb) 2016; 8:394-408. [PMID: 26686885 PMCID: PMC4837018 DOI: 10.1039/c5ib00252d] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this review we discuss how synthetic biology facilitates the task of investigating genetic circuits that are observed in naturally occurring biological systems. Specifically, we give examples where experimentation with synthetic gene circuits has been used to understand four fundamental mechanisms intrinsic to development and disease: multistability, stochastic gene expression, oscillations, and cell-cell communication. Within each area, we also discuss how mathematical modeling has been employed as an essential tool to guide the design of novel gene circuits and as a theoretical basis for exploring circuit topologies exhibiting robust behaviors in the presence of noise.
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Affiliation(s)
- Le-Zhi Wang
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA
| | - Fuqing Wu
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85287, USA.
| | - Kevin Flores
- Department of Mathematics, Center for Quantitative Sciences in Biomedicine, Center for Research in Scientific Computation, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA
- Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen AB24 3UE, UK
- Department of Physics, Arizona State University, Tempe, Arizona 85287, USA
| | - Xiao Wang
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85287, USA.
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13
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Zulkower V, Page M, Ropers D, Geiselmann J, de Jong H. Robust reconstruction of gene expression profiles from reporter gene data using linear inversion. Bioinformatics 2015; 31:i71-9. [PMID: 26072511 PMCID: PMC4765859 DOI: 10.1093/bioinformatics/btv246] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest. RESULTS We propose a general approach based on regularized linear inversion to solve a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles. We evaluate the validity of the approach using in silico simulation studies, and observe that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data. We apply the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with Escherichia coli. The methods are capable of reliably reconstructing time-course profiles of growth rate, promoter activity and protein concentration from weak and noisy signals at low population volumes. Moreover, they capture critical features of those profiles, notably rapid changes in gene expression during growth transitions. AVAILABILITY AND IMPLEMENTATION The methods described in this article are made available as a Python package (LGPL license) and also accessible through a web interface. For more information, see https://team.inria.fr/ibis/wellinverter.
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Affiliation(s)
- Valentin Zulkower
- INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin d'Hères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin d'Hères, France
| | - Michel Page
- INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin d'Hères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin d'Hères, France INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin d'Hères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin d'Hères, France
| | - Delphine Ropers
- INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin d'Hères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin d'Hères, France
| | - Johannes Geiselmann
- INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin d'Hères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin d'Hères, France INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin d'Hères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin d'Hères, France
| | - Hidde de Jong
- INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin d'Hères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin d'Hères, France
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14
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Sherman MS, Lorenz K, Lanier MH, Cohen BA. Cell-to-cell variability in the propensity to transcribe explains correlated fluctuations in gene expression. Cell Syst 2015; 1:315-325. [PMID: 26623441 DOI: 10.1016/j.cels.2015.10.011] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Random fluctuations in gene expression lead to wide cell-to-cell differences in RNA and protein counts. Most efforts to understand stochastic gene expression focus on local (intrinisic) fluctuations, which have an exact theoretical representation. However, no framework exists to model global (extrinsic) mechanisms of stochasticity. We address this problem by dissecting the sources of stochasticity that influence the expression of a yeast heat shock gene, SSA1. Our observations suggest that extrinsic stochasticity does not influence every step of gene expression, but rather arises specifically from cell-to-cell differences in the propensity to transcribe RNA. This led us to propose a framework for stochastic gene expression where transcription rates vary globally in combination with local, gene-specific fluctuations in all steps of gene expression. The proposed model better explains total expression stochasticity than the prevailing ON-OFF model and offers transcription as the specific mechanism underlying correlated fluctuations in gene expression.
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Affiliation(s)
- Marc S Sherman
- Computational and Molecular Biophysics, Washington University in St. Louis, St. Louis, MO, United States. ; Center for Genome Sciences, Department of Genetics, Washington University in St. Louis, St. Louis, MO, United States
| | - Kim Lorenz
- Center for Genome Sciences, Department of Genetics, Washington University in St. Louis, St. Louis, MO, United States
| | - M Hunter Lanier
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri
| | - Barak A Cohen
- Center for Genome Sciences, Department of Genetics, Washington University in St. Louis, St. Louis, MO, United States
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15
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Inference of quantitative models of bacterial promoters from time-series reporter gene data. PLoS Comput Biol 2015; 11:e1004028. [PMID: 25590141 PMCID: PMC4295839 DOI: 10.1371/journal.pcbi.1004028] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 11/05/2014] [Indexed: 12/31/2022] Open
Abstract
The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters. A wide variety of methods for the reverse engineering of regulatory networks and the identification of quantitative regulation functions are available. We investigate some common assumptions that are made in the application of these methods to time-series transcriptomics data, in the context of a central module in the motility network of E. coli. We show that these assumptions, which hypothesize that mRNA concentrations are good proxies for protein concentrations and that the gene expression machinery is equally active across different physiological conditions, are often not valid and may lead to biased inference results. We also show how models of gene expression can be used in combination with suitable experimental controls to correct for this bias and improve the inference process. The contribution of our work is thus not the addition of another method to the rich store of available reverse engineering algorithms, but lies in the critical examination of the information provided by the experimental data and new ways to exploit this information in the algorithms. The proposed approach is relevant for a wide range of applications using time-series transcriptomics data. For the motility system under study, it has underlined the importance of global physiological effects, the active degradation of the transcription factor FliA as well as the secretion of the anti-sigma factor FlgM for the network dynamics.
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de Jong H, Geiselmann J. Fluorescent Reporter Genes and the Analysis of Bacterial Regulatory Networks. HYBRID SYSTEMS BIOLOGY 2015. [DOI: 10.1007/978-3-319-27656-4_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Mazo-Vargas A, Park H, Aydin M, Buchler NE. Measuring fast gene dynamics in single cells with time-lapse luminescence microscopy. Mol Biol Cell 2014; 25:3699-708. [PMID: 25232010 PMCID: PMC4230627 DOI: 10.1091/mbc.e14-07-1187] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Beetle luciferases and time-lapse luminescence microscopy were optimized to measure the dynamics of cell cycle genes in yeast with subminute time resolution. This method is faster and the cells are smaller than in previous work. It is shown that luciferase reporters are better than fluorescent proteins at tracking gene expression. Time-lapse fluorescence microscopy is an important tool for measuring in vivo gene dynamics in single cells. However, fluorescent proteins are limited by slow chromophore maturation times and the cellular autofluorescence or phototoxicity that arises from light excitation. An alternative is luciferase, an enzyme that emits photons and is active upon folding. The photon flux per luciferase is significantly lower than that for fluorescent proteins. Thus time-lapse luminescence microscopy has been successfully used to track gene dynamics only in larger organisms and for slower processes, for which more total photons can be collected in one exposure. Here we tested green, yellow, and red beetle luciferases and optimized substrate conditions for in vivo luminescence. By combining time-lapse luminescence microscopy with a microfluidic device, we tracked the dynamics of cell cycle genes in single yeast with subminute exposure times over many generations. Our method was faster and in cells with much smaller volumes than previous work. Fluorescence of an optimized reporter (Venus) lagged luminescence by 15–20 min, which is consistent with its known rate of chromophore maturation in yeast. Our work demonstrates that luciferases are better than fluorescent proteins at faithfully tracking the underlying gene expression.
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Affiliation(s)
- Anyimilehidi Mazo-Vargas
- Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710 Duke Center for Systems Biology, Duke University, Durham, NC 27710 Department of Biology, Duke University, Durham, NC 27710
| | - Heungwon Park
- Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710 Duke Center for Systems Biology, Duke University, Durham, NC 27710 Department of Biology, Duke University, Durham, NC 27710 Department of Physics, Duke University, Durham, NC 27710
| | - Mert Aydin
- Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710 Duke Center for Systems Biology, Duke University, Durham, NC 27710 Department of Biology, Duke University, Durham, NC 27710
| | - Nicolas E Buchler
- Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710 Duke Center for Systems Biology, Duke University, Durham, NC 27710 Department of Biology, Duke University, Durham, NC 27710 Department of Physics, Duke University, Durham, NC 27710
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Dai W, Hahn J, Kang J. Reconstruction of transcription factor profiles from fluorescent protein reporter systems via dynamic optimization and Tikhonov regularization. AIChE J 2014. [DOI: 10.1002/aic.14559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Wei Dai
- Dept. of Chemical & Biological Engineering; Rensselaer Polytechnic Institute; Troy NY 12180
- Dept. of Biomedical Engineering; Rensselaer Polytechnic Institute; Troy NY 12180
| | - Juergen Hahn
- Dept. of Chemical & Biological Engineering; Rensselaer Polytechnic Institute; Troy NY 12180
- Dept. of Biomedical Engineering; Rensselaer Polytechnic Institute; Troy NY 12180
| | - Jia Kang
- Dept. of Chemical Engineering; Texas A&M University; College Station TX 77843
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Jahn M, Mölle A, Rödel G, Ostermann K. Temporal and spatial properties of a yeast multi-cellular amplification system based on signal molecule diffusion. SENSORS (BASEL, SWITZERLAND) 2013; 13:14511-22. [PMID: 24233076 PMCID: PMC3871124 DOI: 10.3390/s131114511] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Revised: 10/11/2013] [Accepted: 10/18/2013] [Indexed: 11/29/2022]
Abstract
We report on the spatial and temporal signaling properties of a yeast pheromone-based cell communication and amplifier system. It utilizes the Saccharomyces cerevisiae mating response pathway and relies on diffusion of the pheromone α-factor as key signaling molecule between two cell types. One cell type represents the α-factor secreting sensor part and the other the reporter part emitting fluorescence upon activation. Although multi-cellular signaling systems promise higher specificity and modularity, the complex interaction of the cells makes prediction of sensor performance difficult. To test the maximum distance and response time between sensor and reporter cells, the two cell types were spatially separated in defined compartments of agarose hydrogel (5 x 5 mm) and reconnected by diffusion of the yeast pheromone. Different ratios of sensor to reporter cells were tested to evaluate the minimum amount of sensor cells required for signal transduction. Even the smallest ratio, one α-factor-secreting cell to twenty reporter cells, generated a distinct fluorescence signal. When using a 1:1 ratio, the secreted pheromone induced fluorescence in a distance of up to four millimeters after six hours. We conclude from both our experimental results and a mathematical diffusion model that in our approach: (1) the maximum dimension of separated compartments should not exceed five millimeters in gradient direction; and (2) the time-limiting step is not diffusion of the signaling molecule but production of the reporter protein.
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Affiliation(s)
- Michael Jahn
- Institute of Genetics, Technische Universität Dresden, Helmholtzstr. 10, 01062 Dresden, Germany; E-Mails: (M.J.); (A.M.); (G.R.)
- Helmholtz Centre for Environmental Research UFZ, Department for Environmental Microbiology, Permoserstr. 15, 04318 Leipzig, Germany
| | - Annett Mölle
- Institute of Genetics, Technische Universität Dresden, Helmholtzstr. 10, 01062 Dresden, Germany; E-Mails: (M.J.); (A.M.); (G.R.)
| | - Gerhard Rödel
- Institute of Genetics, Technische Universität Dresden, Helmholtzstr. 10, 01062 Dresden, Germany; E-Mails: (M.J.); (A.M.); (G.R.)
| | - Kai Ostermann
- Institute of Genetics, Technische Universität Dresden, Helmholtzstr. 10, 01062 Dresden, Germany; E-Mails: (M.J.); (A.M.); (G.R.)
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Houser JR, Ford E, Chatterjea SM, Maleri S, Elston TC, Errede B. An improved short-lived fluorescent protein transcriptional reporter for Saccharomyces cerevisiae. Yeast 2012; 29:519-30. [PMID: 23172645 DOI: 10.1002/yea.2932] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2012] [Accepted: 10/02/2012] [Indexed: 11/09/2022] Open
Abstract
Ideal reporter genes for temporal transcription programmes have short half-lives that restrict their detection to the window in which their transcripts are present and translated. In an effort to meet this criterion for reporters of transcription in individual living cells, we adapted the ubiquitin fusion strategy for programmable N-end rule degradation to generate an N-degron version of green fluorescent protein (GFP) with a half-life of ~7 min. The GFP variant we used here (designated GFP*) has excellent fluorescence brightness and maturation properties, which make the destabilized reporter well suited for tracking the induction and attenuation kinetics of gene expression in living cells. These attributes are illustrated by its ability to track galactose- and pheromone-induced transcription in S. cerevisiae. We further show that the fluorescence measurements using the short-lived N-degron GFP* reporter gene accurately predict the transient mRNA profile of the prototypical pheromone-induced FUS1 gene.
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Affiliation(s)
- John R Houser
- Department of Physics, University of North Carolina, Chapel Hill, NC 27599-7260, USA
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Bansal L, Chu Y, Laird C, Hahn J. Regularization of inverse problems to determine transcription factor profiles from fluorescent reporter systems. AIChE J 2012. [DOI: 10.1002/aic.13782] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Bakstad D, Adamson A, Spiller DG, White MRH. Quantitative measurement of single cell dynamics. Curr Opin Biotechnol 2012; 23:103-9. [PMID: 22137453 DOI: 10.1016/j.copbio.2011.11.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2011] [Revised: 11/04/2011] [Accepted: 11/07/2011] [Indexed: 01/24/2023]
Abstract
Over the past 20 years luminescent and fluorescent imaging assays have been developed to report on the dynamics of transcription and protein translocation in single cells. The combination of these measurements with mathematical analysis is having an increasingly significant impact on cell biology. There is an urgent need to translate these assays to the study of cells and tissues in vivo, which requires new tools and technologies. Emergence of these new tools and techniques will further the understanding of the role of signalling and transcriptional dynamics in the generation of cellular heterogeneity and the control of cell fate.
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Lux MW, Bramlett BW, Ball DA, Peccoud J. Genetic design automation: engineering fantasy or scientific renewal? Trends Biotechnol 2011; 30:120-6. [PMID: 22001068 DOI: 10.1016/j.tibtech.2011.09.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2011] [Revised: 09/06/2011] [Accepted: 09/06/2011] [Indexed: 01/19/2023]
Abstract
The aim of synthetic biology is to make genetic systems more amenable to engineering, which has naturally led to the development of computer-aided design (CAD) tools. Experimentalists still primarily rely on project-specific ad hoc workflows instead of domain-specific tools, which suggests that CAD tools are lagging behind the front line of the field. Here, we discuss the scientific hurdles that have limited the productivity gains anticipated from existing tools. We argue that the real value of efforts to develop CAD tools is the formalization of genetic design rules that determine the complex relationships between genotype and phenotype.
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Affiliation(s)
- Matthew W Lux
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, USA
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Komorowski M, Finkenstädt B, Rand D. Using a single fluorescent reporter gene to infer half-life of extrinsic noise and other parameters of gene expression. Biophys J 2010; 98:2759-69. [PMID: 20550887 PMCID: PMC2884236 DOI: 10.1016/j.bpj.2010.03.032] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2009] [Revised: 03/02/2010] [Accepted: 03/04/2010] [Indexed: 11/22/2022] Open
Abstract
Fluorescent and luminescent proteins are often used as reporters of transcriptional activity. Given the prevalence of noise in biochemical systems, the time-series data arising from these is of significant interest in efforts to calibrate stochastic models of gene expression and obtain information about sources of nongenetic variability. We present a statistical inference framework that can be used to estimate kinetic parameters of gene expression, as well as the strength and half-life of extrinsic noise from single fluorescent-reporter-gene time-series data. The method takes into account stochastic variability in a fluorescent signal resulting from intrinsic noise of gene expression, kinetics of fluorescent protein maturation, and extrinsic noise, which is assumed to arise at transcriptional level. We use the linear noise approximation and derive an explicit formula for the likelihood of observed fluorescent data. The method is embedded in a Bayesian paradigm, so that certain parameters can be informed from other experiments allowing portability of results across different studies. Inference is performed using Markov chain Monte Carlo. Fluorescent reporters are primary tools to observe dynamics of gene expression and the correct interpretation of fluorescent data is crucial to investigating these fundamental processes of cellular life. As both magnitude and frequency of the noise may have a dramatic effect on the cell fitness, the quantification of stochastic fluctuation is essential to the understanding of how genes are regulated. Our method provides a framework that addresses this important question.
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Affiliation(s)
- Michał Komorowski
- Department of Statistics, University of Warwick, Coventry, United Kingdom.
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de Jong H, Ranquet C, Ropers D, Pinel C, Geiselmann J. Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria. BMC SYSTEMS BIOLOGY 2010; 4:55. [PMID: 20429918 PMCID: PMC2877006 DOI: 10.1186/1752-0509-4-55] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2009] [Accepted: 04/29/2010] [Indexed: 11/10/2022]
Abstract
BACKGROUND Fluorescent and luminescent reporter genes have become popular tools for the real-time monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data. RESULTS We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in Escherichia coli. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the half-lives of reporter and host gene products. The results show that large differences in protein half-lives, more than mRNA half-lives, may be critical for the interpretation of reporter gene data in the analysis of the dynamics of regulatory systems. CONCLUSIONS The paper contributes to the development of sound methods for the interpretation of reporter gene data, notably in the context of the reconstruction and validation of models of regulatory networks. The results have wide applicability for the analysis of gene expression in bacteria and may be extended to higher organisms.
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Affiliation(s)
- Hidde de Jong
- INRIA Grenoble - Rhône-Alpes, 655 Av. de l'Europe, Montbonnot, 38334 St Ismier Cedex, France
| | - Caroline Ranquet
- Institut Jean Roget, LAPM, UMR5163, Campus Santé, Université Joseph Fourier, Domaine de la Merci, 38700 La Tronche, France
- INRIA Grenoble - Rhône-Alpes, 655 Av. de l'Europe, Montbonnot, 38334 St Ismier Cedex, France
| | - Delphine Ropers
- INRIA Grenoble - Rhône-Alpes, 655 Av. de l'Europe, Montbonnot, 38334 St Ismier Cedex, France
| | - Corinne Pinel
- Institut Jean Roget, LAPM, UMR5163, Campus Santé, Université Joseph Fourier, Domaine de la Merci, 38700 La Tronche, France
- INRIA Grenoble - Rhône-Alpes, 655 Av. de l'Europe, Montbonnot, 38334 St Ismier Cedex, France
| | - Johannes Geiselmann
- Institut Jean Roget, LAPM, UMR5163, Campus Santé, Université Joseph Fourier, Domaine de la Merci, 38700 La Tronche, France
- INRIA Grenoble - Rhône-Alpes, 655 Av. de l'Europe, Montbonnot, 38334 St Ismier Cedex, France
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