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Collignon J, Naeimi W, Serio TR, Sindi S. [PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies. Bull Math Biol 2024; 87:12. [PMID: 39641894 PMCID: PMC11624247 DOI: 10.1007/s11538-024-01379-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 10/25/2024] [Indexed: 12/07/2024]
Abstract
The [ P S I + ] prion phenotype in yeast manifests as a white, pink, or red color pigment. Experimental manipulations destabilize prion phenotypes, and allow colonies to exhibit [ p s i - ] (red) sectored phenotypes within otherwise completely white colonies. Further investigation of the size and frequency of sectors that emerge as a result of experimental manipulation is capable of providing critical information on mechanisms of prion curing, but we lack a way to reliably extract this information. Images of experimental colonies exhibiting sectored phenotypes offer an abundance of data to help uncover molecular mechanisms of sectoring, yet the structure of sectored colonies is ignored in traditional biological pipelines. In this study, we present [PSI]-CIC, the first computational pipeline designed to identify and characterize features of sectored yeast colonies. To overcome the barrier of a lack of manually annotated data of colonies, we develop a neural network architecture that we train on synthetic images of colonies and apply to real images of [ P S I + ] , [ p s i - ] , and sectored colonies. In hand-annotated experimental images, our pipeline correctly predicts the state of approximately 95% of colonies detected and frequency of sectors in approximately 89.5% of colonies detected. The scope of our pipeline could be extended to categorizing colonies grown under different experimental conditions, allowing for more meaningful and detailed comparisons between experiments. Our approach streamlines the analysis of sectored yeast colonies providing a rich set of quantitative metrics and provides insight into mechanisms driving the curing of prion phenotypes.
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Affiliation(s)
- Jordan Collignon
- Department of Applied Mathematics, University of California, Merced, 5200 N Lake Drive, Merced, CA, 95343, USA
| | - Wesley Naeimi
- Department of Biochemistry and Molecular Biology, University of Massachusetts, Amherst, 240 Thatcher Rd, Amherst, MA, 01003, USA
| | - Tricia R Serio
- Department of Chemistry and Biochemistry, University of Washington, 109 Bagley Hall, Seattle, WA, 98195, USA
| | - Suzanne Sindi
- Department of Applied Mathematics, University of California, Merced, 5200 N Lake Drive, Merced, CA, 95343, USA.
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2
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Jager K, Orozco-Hidalgo MT, Springstein BL, Joly-Smith E, Papazotos F, McDonough E, Fleming E, McCallum G, Yuan AH, Hilfinger A, Hochschild A, Potvin-Trottier L. Measuring prion propagation in single bacteria elucidates a mechanism of loss. Proc Natl Acad Sci U S A 2023; 120:e2221539120. [PMID: 37738299 PMCID: PMC10523482 DOI: 10.1073/pnas.2221539120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/26/2023] [Indexed: 09/24/2023] Open
Abstract
Prions are self-propagating protein aggregates formed by specific proteins that can adopt alternative folds. Prions were discovered as the cause of the fatal transmissible spongiform encephalopathies in mammals, but prions can also constitute nontoxic protein-based elements of inheritance in fungi and other species. Prion propagation has recently been shown to occur in bacteria for more than a hundred cell divisions, yet a fraction of cells in these lineages lost the prion through an unknown mechanism. Here, we investigate prion propagation in single bacterial cells as they divide using microfluidics and fluorescence microscopy. We show that the propagation occurs in two distinct modes. In a fraction of the population, cells had multiple small visible aggregates and lost the prion through random partitioning of aggregates to one of the two daughter cells at division. In the other subpopulation, cells had a stable large aggregate localized to the pole; upon division the mother cell retained this polar aggregate and a daughter cell was generated that contained small aggregates. Extending our findings to prion domains from two orthologous proteins, we observe similar propagation and loss properties. Our findings also provide support for the suggestion that bacterial prions can form more than one self-propagating state. We implement a stochastic version of the molecular model of prion propagation from yeast and mammals that recapitulates all the observed single-cell properties. This model highlights challenges for prion propagation that are unique to prokaryotes and illustrates the conservation of fundamental characteristics of prion propagation.
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Affiliation(s)
- Krista Jager
- Department of Biology, Concordia University, Montréal, QCH4B 1R6, Canada
| | | | | | - Euan Joly-Smith
- Department of Physics, University of Toronto, Toronto, ONM5S 1A7, Canada
| | - Fotini Papazotos
- Department of Biology, Concordia University, Montréal, QCH4B 1R6, Canada
| | | | - Eleanor Fleming
- Department of Microbiology, Harvard Medical School, Boston, MA02115
| | - Giselle McCallum
- Department of Biology, Concordia University, Montréal, QCH4B 1R6, Canada
| | - Andy H. Yuan
- Department of Cell Biology, Harvard Medical School, Boston, MA02115
| | - Andreas Hilfinger
- Department of Physics, University of Toronto, Toronto, ONM5S 1A7, Canada
- Department of Mathematics, University of Toronto, Toronto, ONM5S 2E4, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ONM5S 3G5, Canada
| | - Ann Hochschild
- Department of Microbiology, Harvard Medical School, Boston, MA02115
| | - Laurent Potvin-Trottier
- Department of Biology, Concordia University, Montréal, QCH4B 1R6, Canada
- Department of Physics, Concordia University, Montréal, QCH4B 1R6, Canada
- Center for Applied Synthetic Biology, Concordia University, Montréal, QCH4B 1R6, Canada
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3
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Acevedo S, Stewart AJ. Eco-evolutionary trade-offs in the dynamics of prion strain competition. Proc Biol Sci 2023; 290:20230905. [PMID: 37403499 PMCID: PMC10320356 DOI: 10.1098/rspb.2023.0905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/07/2023] [Indexed: 07/06/2023] Open
Abstract
Prion and prion-like molecules are a type of self-replicating aggregate protein that have been implicated in a variety of neurodegenerative diseases. Over recent decades, the molecular dynamics of prions have been characterized both empirically and through mathematical models, providing insights into the epidemiology of prion diseases and the impact of prions on the evolution of cellular processes. At the same time, a variety of evidence indicates that prions are themselves capable of a form of evolution, in which changes to their structure that impact their rate of growth or fragmentation are replicated, making such changes subject to natural selection. Here we study the role of such selection in shaping the characteristics of prions under the nucleated polymerization model (NPM). We show that fragmentation rates evolve to an evolutionary stable value which balances rapid reproduction of PrPSc aggregates with the need to produce stable polymers. We further show that this evolved fragmentation rate differs in general from the rate that optimizes transmission between cells. We find that under the NPM, prions that are both evolutionary stable and optimized for transmission have a characteristic length of three times the critical length below which they become unstable. Finally, we study the dynamics of inter-cellular competition between strains, and show that the eco-evolutionary trade-off between intra- and inter-cellular competition favours coexistence.
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Affiliation(s)
- Saul Acevedo
- Department of Biology, University of Houston, Houston, TX, USA
| | - Alexander J. Stewart
- School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9SS, UK
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4
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Jager K, Orozco-Hidalgo MT, Springstein BL, Joly-Smith E, Papazotos F, McDonough E, Fleming E, McCallum G, Hilfinger A, Hochschild A, Potvin-Trottier L. Measuring prion propagation in single bacteria elucidates mechanism of loss. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.11.523042. [PMID: 36712035 PMCID: PMC9882039 DOI: 10.1101/2023.01.11.523042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Prions are self-propagating protein aggregates formed by specific proteins that can adopt alternative folds. Prions were discovered as the cause of the fatal transmissible spongiform encephalopathies in mammals, but prions can also constitute non-toxic protein-based elements of inheritance in fungi and other species. Prion propagation has recently been shown to occur in bacteria for more than a hundred cell divisions, yet a fraction of cells in these lineages lost the prion through an unknown mechanism. Here, we investigate prion propagation in single bacterial cells as they divide using microfluidics and fluorescence microscopy. We show that the propagation occurs in two distinct modes with distinct stability and inheritance characteristics. We find that the prion is lost through random partitioning of aggregates to one of the two daughter cells at division. Extending our findings to prion domains from two orthologous proteins, we observe similar propagation and loss properties. Our findings also provide support for the suggestion that bacterial prions can form more than one self-propagating state. We implement a stochastic version of the molecular model of prion propagation from yeast and mammals that recapitulates all the observed single-cell properties. This model highlights challenges for prion propagation that are unique to prokaryotes and illustrates the conservation of fundamental characteristics of prion propagation across domains of life.
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Affiliation(s)
- Krista Jager
- Department of Biology, Concordia University, Montréal, Québec, Canada
| | | | | | - Euan Joly-Smith
- Department of Physics, University of Toronto, Toronto, Ontario, Canada
| | - Fotini Papazotos
- Department of Biology, Concordia University, Montréal, Québec, Canada
| | - EmilyKate McDonough
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Eleanor Fleming
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Giselle McCallum
- Department of Biology, Concordia University, Montréal, Québec, Canada
| | - Andreas Hilfinger
- Department of Physics, University of Toronto, Toronto, Ontario, Canada
- Department of Mathematics, University of Toronto, Toronto, Ontario, Canada
- Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Ann Hochschild
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Laurent Potvin-Trottier
- Department of Biology, Concordia University, Montréal, Québec, Canada
- Department of Physics, Concordia University, Montréal, Québec, Canada
- Center for Applied Synthetic Biology, Concordia University, Montréal, Québec, Canada
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5
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Santiago F, Sindi S. A structured model and likelihood approach to estimate yeast prion propagon replication rates and their asymmetric transmission. PLoS Comput Biol 2022; 18:e1010107. [PMID: 35776712 PMCID: PMC9249220 DOI: 10.1371/journal.pcbi.1010107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/14/2022] [Indexed: 11/18/2022] Open
Abstract
Prion proteins cause a variety of fatal neurodegenerative diseases in mammals but are generally harmless to Baker’s yeast (Saccharomyces cerevisiae). This makes yeast an ideal model organism for investigating the protein dynamics associated with these diseases. The rate of disease onset is related to both the replication and transmission kinetics of propagons, the transmissible agents of prion diseases. Determining the kinetic parameters of propagon replication in yeast is complicated because the number of propagons in an individual cell depends on the intracellular replication dynamics and the asymmetric division of yeast cells within a growing yeast cell colony. We present a structured population model describing the distribution and replication of prion propagons in an actively dividing population of yeast cells. We then develop a likelihood approach for estimating the propagon replication rate and their transmission bias during cell division. We first demonstrate our ability to correctly recover known kinetic parameters from simulated data, then we apply our likelihood approach to estimate the kinetic parameters for six yeast prion variants using propagon recovery data. We find that, under our modeling framework, all variants are best described by a model with an asymmetric transmission bias. This demonstrates the strength of our framework over previous formulations assuming equal partitioning of intracellular constituents during cell division. In this work we investigate the transmissible [PSI+] phenotype in yeast. The agents responsible for this phenotype are propagons, misfolded protein aggregates of a naturally occurring protein. These propagons increase in number within a cell and are distributed between cells during division. We use mathematical modeling to infer the replication rate of propagons within cells and if propagons are transmitted equally or unequally during cell division. Prior models in this area assumed only symmetric transmission when fitting replication rates. We couple this model with a novel likelihood framework allowing us to exclude influential outliers from our datasets when inferring parameters. We find that for all six protein variants we study, propagons are transmitted asymmetrically with different biases. Our results can be reproduced with the code and data available at https://github.com/FS-CodeBase/propagon_replication_and_transmission/.
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Affiliation(s)
- Fabian Santiago
- Department of Mathematics, University of Arizona, Tucson, Arizona, United States of America
| | - Suzanne Sindi
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
- * E-mail:
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6
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Candelise N, Scaricamazza S, Salvatori I, Ferri A, Valle C, Manganelli V, Garofalo T, Sorice M, Misasi R. Protein Aggregation Landscape in Neurodegenerative Diseases: Clinical Relevance and Future Applications. Int J Mol Sci 2021; 22:ijms22116016. [PMID: 34199513 PMCID: PMC8199687 DOI: 10.3390/ijms22116016] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 05/28/2021] [Accepted: 05/29/2021] [Indexed: 12/13/2022] Open
Abstract
Intrinsic disorder is a natural feature of polypeptide chains, resulting in the lack of a defined three-dimensional structure. Conformational changes in intrinsically disordered regions of a protein lead to unstable β-sheet enriched intermediates, which are stabilized by intermolecular interactions with other β-sheet enriched molecules, producing stable proteinaceous aggregates. Upon misfolding, several pathways may be undertaken depending on the composition of the amino acidic string and the surrounding environment, leading to different structures. Accumulating evidence is suggesting that the conformational state of a protein may initiate signalling pathways involved both in pathology and physiology. In this review, we will summarize the heterogeneity of structures that are produced from intrinsically disordered protein domains and highlight the routes that lead to the formation of physiological liquid droplets as well as pathogenic aggregates. The most common proteins found in aggregates in neurodegenerative diseases and their structural variability will be addressed. We will further evaluate the clinical relevance and future applications of the study of the structural heterogeneity of protein aggregates, which may aid the understanding of the phenotypic diversity observed in neurodegenerative disorders.
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Affiliation(s)
- Niccolò Candelise
- Fondazione Santa Lucia IRCCS, c/o CERC, 00143 Rome, Italy; (S.S.); (I.S.); (A.F.); (C.V.)
- Institute of Translational Pharmacology, National Research Council, 00133 Rome, Italy
- Correspondence: ; Tel.: +39-338-891-2668
| | - Silvia Scaricamazza
- Fondazione Santa Lucia IRCCS, c/o CERC, 00143 Rome, Italy; (S.S.); (I.S.); (A.F.); (C.V.)
| | - Illari Salvatori
- Fondazione Santa Lucia IRCCS, c/o CERC, 00143 Rome, Italy; (S.S.); (I.S.); (A.F.); (C.V.)
- Department of Experimental Medicine, University of Rome “La Sapienza”, 00161 Rome, Italy; (V.M.); (T.G.); (M.S.); (R.M.)
| | - Alberto Ferri
- Fondazione Santa Lucia IRCCS, c/o CERC, 00143 Rome, Italy; (S.S.); (I.S.); (A.F.); (C.V.)
- Institute of Translational Pharmacology, National Research Council, 00133 Rome, Italy
| | - Cristiana Valle
- Fondazione Santa Lucia IRCCS, c/o CERC, 00143 Rome, Italy; (S.S.); (I.S.); (A.F.); (C.V.)
- Institute of Translational Pharmacology, National Research Council, 00133 Rome, Italy
| | - Valeria Manganelli
- Department of Experimental Medicine, University of Rome “La Sapienza”, 00161 Rome, Italy; (V.M.); (T.G.); (M.S.); (R.M.)
| | - Tina Garofalo
- Department of Experimental Medicine, University of Rome “La Sapienza”, 00161 Rome, Italy; (V.M.); (T.G.); (M.S.); (R.M.)
| | - Maurizio Sorice
- Department of Experimental Medicine, University of Rome “La Sapienza”, 00161 Rome, Italy; (V.M.); (T.G.); (M.S.); (R.M.)
| | - Roberta Misasi
- Department of Experimental Medicine, University of Rome “La Sapienza”, 00161 Rome, Italy; (V.M.); (T.G.); (M.S.); (R.M.)
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7
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Criddle RS, Lin HJL, James I, Park JS, Hansen LD, Price JC. Proposing a minimal set of metrics and methods to predict probabilities of amyloidosis disease and onset age in individuals. Aging (Albany NY) 2020; 12:22356-22369. [PMID: 33203794 PMCID: PMC7746394 DOI: 10.18632/aging.202208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 10/22/2020] [Indexed: 06/11/2023]
Abstract
Many amyloid-driven pathologies have both genetic and stochastic components where assessing risk of disease development requires a multifactorial assessment where many of the variables are poorly understood. Risk of transthyretin-mediated amyloidosis is enhanced by age and mutation of the transthyretin (TTR) gene, but amyloidosis is not directly initiated by mutated TTR proteins. Nearly all of the 150+ known mutations increase dissociation of the homotetrameric protein structure and increase the probability of an individual developing a TTR amyloid disease late in life. TTR amyloidosis is caused by dissociated monomers that are destabilized and refold into an amyloidogenic form. Therefore, monomer concentration, monomer proteolysis rate, and structural stability are key variables that may determine the rate of development of amyloidosis. Here we develop a unifying biophysical model that quantifies the relationships among these variables in plasma and suggest the probability of an individual developing a TTR amyloid disease can be estimated. This may allow quantification of risk for amyloidosis and provide the information necessary for development of methods for early diagnosis and prevention. Given the similar observation of genetic and sporadic amyloidoses for other diseases, this model and the measurements to assess risk may be applicable to more proteins than just TTR.
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Affiliation(s)
- Richard S. Criddle
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Hsien-Jung L. Lin
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Isabella James
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Ji Sun Park
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Lee D. Hansen
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - John C. Price
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
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8
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Lemarre P, Pujo-Menjouet L, Sindi SS. A unifying model for the propagation of prion proteins in yeast brings insight into the [PSI+] prion. PLoS Comput Biol 2020; 16:e1007647. [PMID: 32453794 PMCID: PMC7274466 DOI: 10.1371/journal.pcbi.1007647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 06/05/2020] [Accepted: 03/06/2020] [Indexed: 11/18/2022] Open
Abstract
The use of yeast systems to study the propagation of prions and amyloids has emerged as a crucial aspect of the global endeavor to understand those mechanisms. Yeast prion systems are intrinsically multi-scale: the molecular chemical processes are indeed coupled to the cellular processes of cell growth and division to influence phenotypical traits, observable at the scale of colonies. We introduce a novel modeling framework to tackle this difficulty using impulsive differential equations. We apply this approach to the [PSI+] yeast prion, which is associated with the misconformation and aggregation of Sup35. We build a model that reproduces and unifies previously conflicting experimental observations on [PSI+] and thus sheds light onto characteristics of the intracellular molecular processes driving aggregate replication. In particular our model uncovers a kinetic barrier for aggregate replication at low densities, meaning the change between prion or prion-free phenotype is a bi-stable transition. This result is based on the study of prion curing experiments, as well as the phenomenon of colony sectoring, a phenotype which is often ignored in experimental assays and has never been modeled. Furthermore, our results provide further insight into the effect of guanidine hydrochloride (GdnHCl) on Sup35 aggregates. To qualitatively reproduce the GdnHCl curing experiment, aggregate replication must not be completely inhibited, which suggests the existence of a mechanism different than Hsp104-mediated fragmentation. Those results are promising for further development of the [PSI+] model, but also for extending the use of this novel framework to other yeast prion or amyloid systems.
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Affiliation(s)
- Paul Lemarre
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, F-69622 Villeurbanne cedex, France
- INRIA Rhônes-Alpes, INRIA, Villeurbanne, France
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
| | - Laurent Pujo-Menjouet
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, F-69622 Villeurbanne cedex, France
- INRIA Rhônes-Alpes, INRIA, Villeurbanne, France
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
- * E-mail:
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