Validation methods for population models of gene expression dynamics

The advent of experimental techniques for the time-course monitoring of gene expression at the single-cell level has paved the way to the model-based study of gene expression variability within- an across-cells. A number of approaches to the inference of models accounting for variability of gene exp...

Full description

Autores:
González Vargas, Andrés Mauricio
Cinquemani, Eugenio
Ferrari Trecate, Giancarlo
Tipo de recurso:
Article of journal
Fecha de publicación:
2016
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/11041
Acceso en línea:
http://hdl.handle.net/10614/11041
https://doi.org/10.1016/j.ifacol.2016.12.112
Palabra clave:
Statistical methods
System biology
Stochastic modelling
Mixed-effects modelling
Gene expression
Expresión del gen
Modelos biológicos
Biological models
Rights
openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
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oai_identifier_str oai:red.uao.edu.co:10614/11041
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repository_id_str
dc.title.eng.fl_str_mv Validation methods for population models of gene expression dynamics
title Validation methods for population models of gene expression dynamics
spellingShingle Validation methods for population models of gene expression dynamics
Statistical methods
System biology
Stochastic modelling
Mixed-effects modelling
Gene expression
Expresión del gen
Modelos biológicos
Biological models
title_short Validation methods for population models of gene expression dynamics
title_full Validation methods for population models of gene expression dynamics
title_fullStr Validation methods for population models of gene expression dynamics
title_full_unstemmed Validation methods for population models of gene expression dynamics
title_sort Validation methods for population models of gene expression dynamics
dc.creator.fl_str_mv González Vargas, Andrés Mauricio
Cinquemani, Eugenio
Ferrari Trecate, Giancarlo
dc.contributor.author.none.fl_str_mv González Vargas, Andrés Mauricio
dc.contributor.author.spa.fl_str_mv Cinquemani, Eugenio
Ferrari Trecate, Giancarlo
dc.subject.eng.fl_str_mv Statistical methods
System biology
Stochastic modelling
Mixed-effects modelling
Gene expression
topic Statistical methods
System biology
Stochastic modelling
Mixed-effects modelling
Gene expression
Expresión del gen
Modelos biológicos
Biological models
dc.subject.armarc.spa.fl_str_mv Expresión del gen
Modelos biológicos
dc.subject.armarc.eng.fl_str_mv Biological models
description The advent of experimental techniques for the time-course monitoring of gene expression at the single-cell level has paved the way to the model-based study of gene expression variability within- an across-cells. A number of approaches to the inference of models accounting for variability of gene expression over isogenic cell populations have been developed and applied to real-world scenarios. The development of a systematic approach for the validation of population models is however lagging behind, and accuracy of the models obtained is often assessed on a semi-empirical basis. In this paper we study the problem of validating models of gene network dynamics for cell populations, providing statistical tools for qualitative and quantitative model validation and comparison, and guidelines for their application and interpretation based on a real biological case study
publishDate 2016
dc.date.issued.spa.fl_str_mv 2016
dc.date.accessioned.spa.fl_str_mv 2019-09-04T15:46:47Z
dc.date.available.spa.fl_str_mv 2019-09-04T15:46:47Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 2405-8963
dc.identifier.uri.spa.fl_str_mv http://hdl.handle.net/10614/11041
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.ifacol.2016.12.112
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Repositorio Educativo Digital UAO
identifier_str_mv 2405-8963
Universidad Autónoma de Occidente
Repositorio Educativo Digital UAO
url http://hdl.handle.net/10614/11041
https://doi.org/10.1016/j.ifacol.2016.12.112
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv IFAC-PapersOnLine, volumen 49, issue 26, páginas 114-119, 2016
dc.relation.cites.spa.fl_str_mv González-Vargas, A. M., Cinquemani, E., & Ferrari-Trecate, G. (2016). Validation methods for population models of gene expression dynamics. IFAC-PapersOnLine, 49(26), 114-119. http://hdl.handle.net/10614/11041
dc.relation.references.none.fl_str_mv Bauer, R. J., Guzy, S., andng, C. (2007). Asurvey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples. AAPS Journal, (1)
Cantone et al., 2009 I. Cantone, L. Marucci, F. Iorio, M.A. Ricci, V. Bel-castro, M. Bansal, S. Santini, M. di Bernardo, D. di Bernardo, M.P. Cosma A. yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches Cell, 137 (1) (2009), pp. 172-181
Comets et al., 2010 E. Comets, K. Brendel, F. Mentre Model evaluation in nonlinear mixed effect models, with applications to pharmacokinetics Journal de la Societe Francaise de Statistiques, 151 (2010), pp. 106-128
Delyon et al., 1999 B. Delyon, M. Lavielle, E. Moulines Convergence of a Stochastic Approximation Version of the EM Algorithm The Annals of Statistics, 27 (1) (1999), pp. 94-128
El Samad et al., 2005 H. El Samad, M. Khammash, L. Petzold, D. Gillespie Stochastic modelling of gene regulatory networks International Journal of Robust and Nonlinear Control, 15 (15) (2005), pp. 691-711
Elowitz et al., 2002 M.B. Elowitz, A.J. Levine, E.D. Siggia, P.S. Swain Stochastic gene expression in a single cell Science, 297 (5584) (2002), pp. 1183-1186
Gillespie, 1992 D.T. Gillespie A rigorous derivation of the chemical master equation Physica A, 188 (1) (1992), pp. 404-425
Gonzalez et al., 2013 A.M. Gonzalez, J. Uhlendorf, J. Schaul, E. Cinquemani, G. Batt, G. Ferrari-Trecate Identification of biological models from single-cell data: a comparison between mixed-effects and moment-based inference Proceedings of the 12th ECC, 3652-3657 (2013)
Gonzalez-Vargas, et al., 2016 Gonzalez-Vargas, A.M., Cinquemani, E., and Ferrari-Trecate, G. (2016). Validation methods for population models of gene expression dynamics. Research Report RR-8938, INRIA Grenoble - Rhône-Alpes. URL https://hal.inria.fr/hal-01349030
Gutenkunst et al., 2007 R. N. Gutenkunst, J.J. Waterfall, F.P. Casey, K.S. Brown, C.R. Myers, J.P. Sethna Universally sloppy parameter sensitivities in systems biology models PLoS Comput Biol, 3 (10) (2007), pp. 1-8
Lavielle, 2015 M. Lavielle Mixed-Effects models for the population approach, CRC press (2015)
Lixoft, 2014 Lixoft Monolix User Manual Version 4-3.2, Lixoft (2014)
Llamosi et al., 2016 A. Llamosi, A.M. Gonzalez-Vargas, C Versari, E. Cinquemani, G. Ferrari-Trecate, P. Hersen, G. Batt What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast PLoS Comput Biol, 12 (2) (2016), pp. 1-18
Miller, 1956 L.H. Miller Table of Percentage Points of Kol-mogorov Statistics Journal of the American Statistical Association, 51 (273) (1956)
Munsky et al., 2009 B. Munsky, B. Trinh, M. Khammash Listening to the noise: random fluctuations reveal gene network parameters Molecular Systems Biology, 5 (2009)
Neuert et al., 2013 G. Neuert, B. Munsky, R. Tan, L. Teytelman, M. Khammash, A. van Oudenaarden Systematic identification of signal-activated stochastic gene regulation Science, 339 (6119) (2013), pp. 584-587
Pinheiro and Bates, 2000 J.C. Pinheiro, D.M. Bates Mixed-Effects Models in S and S-PLUS, Springer Verlag, New York (2000)
Rice, 2006 J. Rice Mathematical statistics and data analysis, Nelson Education (2006)
Schnoerr et al., 2015 D. Schnoerr, G. Sanguinetti, R. Grima Comparison of different moment-closure approximations for stochastic chemical kinetics The Journal of Chemical Physics, 143 (18) (2015), p. 185101
Smirnov, 1939 N.V. Smirnov On the Estimation of the Discrepancy Between Empirical Curves of Distribution for Two Independent Samples Bul. Math, de l’Univ. de Moscou, 2 (1939), pp. 3-14
Uhlendorf et al., 2012 J. Uhlendorf, A. Miermont, T. Delaveau, G. Charvin, F. Fages, S. Bottani, G. Batt, P. Hersen Long-term model predictive control of gene expression at the population and single-cell levels PNAS, 109 (35) (2012), pp. 14271-14276
Zechner et al., 2014 C Zechner, M. Unger, S. Pelet, M. Peter, H. Koeppl Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings Nature Methods, 11 (2014), pp. 197-202
Zechner et al., 2012 C Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter, J. Lygeros, H. Koeppl Moment-based inference predicts bimodality in transient gene expression PNAS, 109 (21) (2012), pp. 8340-8345
dc.rights.spa.fl_str_mv Derechos Reservados - Universidad Autónoma de Occidente
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spelling González Vargas, Andrés Mauriciovirtual::2110-1Cinquemani, Eugenio85a5395e0886e470b12ff31cb1fe8a46-1Ferrari Trecate, Giancarlodc309302b0108ab5ed1c3ca6a8d8edbd-12019-09-04T15:46:47Z2019-09-04T15:46:47Z20162405-8963http://hdl.handle.net/10614/11041https://doi.org/10.1016/j.ifacol.2016.12.112Universidad Autónoma de OccidenteRepositorio Educativo Digital UAOThe advent of experimental techniques for the time-course monitoring of gene expression at the single-cell level has paved the way to the model-based study of gene expression variability within- an across-cells. A number of approaches to the inference of models accounting for variability of gene expression over isogenic cell populations have been developed and applied to real-world scenarios. The development of a systematic approach for the validation of population models is however lagging behind, and accuracy of the models obtained is often assessed on a semi-empirical basis. In this paper we study the problem of validating models of gene network dynamics for cell populations, providing statistical tools for qualitative and quantitative model validation and comparison, and guidelines for their application and interpretation based on a real biological case study6 páginasapplication/pdfengIFAC. International Federation of Automatic ControlIFAC-PapersOnLine, volumen 49, issue 26, páginas 114-119, 2016González-Vargas, A. M., Cinquemani, E., & Ferrari-Trecate, G. (2016). Validation methods for population models of gene expression dynamics. IFAC-PapersOnLine, 49(26), 114-119. http://hdl.handle.net/10614/11041Bauer, R. J., Guzy, S., andng, C. (2007). Asurvey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples. AAPS Journal, (1)Cantone et al., 2009 I. Cantone, L. Marucci, F. Iorio, M.A. Ricci, V. Bel-castro, M. Bansal, S. Santini, M. di Bernardo, D. di Bernardo, M.P. Cosma A. yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches Cell, 137 (1) (2009), pp. 172-181Comets et al., 2010 E. Comets, K. Brendel, F. Mentre Model evaluation in nonlinear mixed effect models, with applications to pharmacokinetics Journal de la Societe Francaise de Statistiques, 151 (2010), pp. 106-128Delyon et al., 1999 B. Delyon, M. Lavielle, E. Moulines Convergence of a Stochastic Approximation Version of the EM Algorithm The Annals of Statistics, 27 (1) (1999), pp. 94-128El Samad et al., 2005 H. El Samad, M. Khammash, L. Petzold, D. Gillespie Stochastic modelling of gene regulatory networks International Journal of Robust and Nonlinear Control, 15 (15) (2005), pp. 691-711Elowitz et al., 2002 M.B. Elowitz, A.J. Levine, E.D. Siggia, P.S. Swain Stochastic gene expression in a single cell Science, 297 (5584) (2002), pp. 1183-1186Gillespie, 1992 D.T. Gillespie A rigorous derivation of the chemical master equation Physica A, 188 (1) (1992), pp. 404-425Gonzalez et al., 2013 A.M. Gonzalez, J. Uhlendorf, J. Schaul, E. Cinquemani, G. Batt, G. Ferrari-Trecate Identification of biological models from single-cell data: a comparison between mixed-effects and moment-based inference Proceedings of the 12th ECC, 3652-3657 (2013)Gonzalez-Vargas, et al., 2016 Gonzalez-Vargas, A.M., Cinquemani, E., and Ferrari-Trecate, G. (2016). Validation methods for population models of gene expression dynamics. Research Report RR-8938, INRIA Grenoble - Rhône-Alpes. URL https://hal.inria.fr/hal-01349030Gutenkunst et al., 2007 R. N. Gutenkunst, J.J. Waterfall, F.P. Casey, K.S. Brown, C.R. Myers, J.P. Sethna Universally sloppy parameter sensitivities in systems biology models PLoS Comput Biol, 3 (10) (2007), pp. 1-8Lavielle, 2015 M. Lavielle Mixed-Effects models for the population approach, CRC press (2015)Lixoft, 2014 Lixoft Monolix User Manual Version 4-3.2, Lixoft (2014)Llamosi et al., 2016 A. Llamosi, A.M. Gonzalez-Vargas, C Versari, E. Cinquemani, G. Ferrari-Trecate, P. Hersen, G. Batt What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast PLoS Comput Biol, 12 (2) (2016), pp. 1-18Miller, 1956 L.H. Miller Table of Percentage Points of Kol-mogorov Statistics Journal of the American Statistical Association, 51 (273) (1956)Munsky et al., 2009 B. Munsky, B. Trinh, M. Khammash Listening to the noise: random fluctuations reveal gene network parameters Molecular Systems Biology, 5 (2009)Neuert et al., 2013 G. Neuert, B. Munsky, R. Tan, L. Teytelman, M. Khammash, A. van Oudenaarden Systematic identification of signal-activated stochastic gene regulation Science, 339 (6119) (2013), pp. 584-587Pinheiro and Bates, 2000 J.C. Pinheiro, D.M. Bates Mixed-Effects Models in S and S-PLUS, Springer Verlag, New York (2000)Rice, 2006 J. Rice Mathematical statistics and data analysis, Nelson Education (2006)Schnoerr et al., 2015 D. Schnoerr, G. Sanguinetti, R. Grima Comparison of different moment-closure approximations for stochastic chemical kinetics The Journal of Chemical Physics, 143 (18) (2015), p. 185101Smirnov, 1939 N.V. Smirnov On the Estimation of the Discrepancy Between Empirical Curves of Distribution for Two Independent Samples Bul. Math, de l’Univ. de Moscou, 2 (1939), pp. 3-14Uhlendorf et al., 2012 J. Uhlendorf, A. Miermont, T. Delaveau, G. Charvin, F. Fages, S. Bottani, G. Batt, P. Hersen Long-term model predictive control of gene expression at the population and single-cell levels PNAS, 109 (35) (2012), pp. 14271-14276Zechner et al., 2014 C Zechner, M. Unger, S. Pelet, M. Peter, H. Koeppl Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings Nature Methods, 11 (2014), pp. 197-202Zechner et al., 2012 C Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter, J. Lygeros, H. Koeppl Moment-based inference predicts bimodality in transient gene expression PNAS, 109 (21) (2012), pp. 8340-8345Derechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Statistical methodsSystem biologyStochastic modellingMixed-effects modellingGene expressionExpresión del genModelos biológicosBiological modelsValidation methods for population models of gene expression dynamicsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Publication6053e64e-a34d-4652-8fa2-6c0440556f15virtual::2110-16053e64e-a34d-4652-8fa2-6c0440556f15virtual::2110-1https://scholar.google.com.co/citations?user=oj5Tle8AAAAJ&hl=esvirtual::2110-10000-0001-6393-7130virtual::2110-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001345355virtual::2110-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://red.uao.edu.co/bitstreams/e88d6b0d-03c3-4f35-abf0-24125b6caad8/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/ae9630d3-973c-45a0-8554-02d4cb56584c/download20b5ba22b1117f71589c7318baa2c560MD53ORIGINALVALIDATION METHODOS FOR POPULATION MODELS OF GENE EXPRESSION.pdfapplication/pdf1473493https://red.uao.edu.co/bitstreams/30d1af9f-b6c5-40dc-b800-caf3e2e0058c/downloadad473f97262c39e452fb26c3a97cd95eMD54TEXTVALIDATION METHODOS FOR POPULATION MODELS OF GENE EXPRESSION.pdf.txtVALIDATION METHODOS FOR POPULATION MODELS OF GENE EXPRESSION.pdf.txtExtracted texttext/plain39141https://red.uao.edu.co/bitstreams/bd6ea108-cfd4-4f76-ae7c-227e075ceb9a/download861e5fee1289710e2b924e7bddb377ddMD55THUMBNAILVALIDATION METHODOS FOR POPULATION MODELS OF GENE EXPRESSION.pdf.jpgVALIDATION METHODOS FOR POPULATION MODELS OF GENE EXPRESSION.pdf.jpgGenerated Thumbnailimage/jpeg5136https://red.uao.edu.co/bitstreams/a6a86732-5891-4115-919f-e4283ef37eac/download22d93fb8c598974f48753772aa23bb8cMD5610614/11041oai:red.uao.edu.co:10614/110412024-05-10 03:00:25.736https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidenteopen.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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