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Karlsson, M.; Janzén, D.L.I.; Durrieu, L.; Colman-Lerner, A.; Kjellsson, M.C.; Cedersund, G. "Nonlinear mixed-effects modelling for single cell estimation: When, why, and how to use it" (2015) BMC Systems Biology. 9(1)
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Abstract:

Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improved bioanalytical techniques which facilitates determination of small changes with high uncertainty. Like much high-quality data, single-cell data is best analysed using a systems biology approach. The most common systems biology approach to single-cell data is the standard two-stage (STS) approach. In STS, data from each cell is analysed in a separate sub-problem, meaning that only data from the same cell is used to calculate the parameter values within that cell. Because only parts of the data are considered, problems with parameter unidentifiability are exaggerated in STS. In contrast, a related approach to data analysis has been developed for the studies of patient-to-patient variations. This approach, called nonlinear mixed-effects modelling (NLME), makes use of all data, when estimating the patient-specific parameters. NLME would therefore be advantageous compared to STS also for the study of cell-to-cell variation. However, no such systematic evaluation of the two approaches exists. Results: Herein, such a systematic comparison between STS and NLME has been performed. Different examples, both linear and nonlinear, and both simulated and real experimental data, have been examined. With informative data, there is no significant difference in the results for either parameter or noise estimation. However, when data becomes uninformative, NLME is significantly superior to STS. These results hold independently of whether the loss of information is due to a low signal-to-noise ratio, too few data points, or a bad input signal. The improvement is shown to come from both the consideration of a joint likelihood (JLH) function, describing all parameters and data, and from an a priori postulated form of the population parameters. Finally, we provide a small tutorial that shows how to use NLME for single-cell analysis, using the free and user-friendly software Monolix. Conclusions: When considering uninformative single-cell data, NLME yields more accurate parameter and noise estimates, compared to more traditional approaches, such as STS and JLH. © 2015 Karlsson et al.

Registro:

Documento: Artículo
Título:Nonlinear mixed-effects modelling for single cell estimation: When, why, and how to use it
Autor:Karlsson, M.; Janzén, D.L.I.; Durrieu, L.; Colman-Lerner, A.; Kjellsson, M.C.; Cedersund, G.
Filiación:Department of Biomedical Engineering, Linköping University, Linköping, SE-58185, Sweden
Department of Clinical and Experimental Medicine, Linköping University, Uppsala, SE-58185, Sweden
Instituto de Fisiología, Biología Molecular y Neurociencias, Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Pharmacometrics Group, Pharmaceutical Biosciences, Uppsala University, Uppsala, SE-75124, Sweden
Biomedical and Biological Systems Laboratory, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
Modeling and Simulation, AstraZeneca, Mölndal, Sweden
Department of Systems and Data Analysis, Fraunhofer-Chalmers Centre, Chalmers Science Park, Gothenburg, SE-412 88, Sweden
IKE, Linköping University, Linköping, 58185, Sweden
Palabras clave:FRAP; NLME; Nonlinear mixed-effects modelling; Singe cell analysis; Single cell modelling; biological model; fluorescence recovery after photobleaching; kinetics; nonlinear system; single cell analysis; statistical model; Fluorescence Recovery After Photobleaching; Kinetics; Linear Models; Models, Biological; Nonlinear Dynamics; Single-Cell Analysis
Año:2015
Volumen:9
Número:1
DOI: http://dx.doi.org/10.1186/s12918-015-0203-x
Título revista:BMC Systems Biology
Título revista abreviado:BMC Syst. Biol.
ISSN:17520509
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_17520509_v9_n1_p_Karlsson

Referencias:

  • Colman-Lerner, A., Gordon, A., Serra, E., Chin, T., Resnekov, O., Endy, D., Regulated cell-to-cell variation in a cell-fate decision system (2005) Nature, 437 (7059), pp. 699-706
  • Choi, H.S., Han, S., Yokota, H., Cho, K.H., Coupled positive feedbacks provoke slow induction plus fast switching in apoptosis (2008) FEBS Lett, 581 (14), pp. 2684-2690
  • Neumüller, R.A., Knoblich, J.A., Dividing cellular asymmetry: assymetric cell division and its implications for stem cells and cancer (2009) Genes Dev, 23 (23), pp. 2675-2699
  • Su, M., Jiang, H., Zhang, P., Liu, Y., Wang, E., Hsu, A., Knee-loading modality drives molecular transport in mouse femur (2006) Ann Biomed Eng, 34 (10), pp. 1600-1606
  • Enderling, H., Anderson, A.R., Chaplain, M.A., Rowe, G.W., Visualisation of the numerical solution of partial differential equation systems in three space dimensions and its importance for mathematical models in biology (2006) Math Biosci Eng, 3 (4), pp. 571-582
  • Ingolia, N.T., Weissman, J.S., Systems biology: Reverse engineering the cell (2008) Nature, 454 (7208), pp. 1059-1062
  • Cedersund, G., Roll, J., Systems biology: model based evaluation and comparison of potential explanations for given biological data (2009) FEBS J, 276 (4), pp. 903-922
  • Brännmark, C., Nyman, E., Fagerholm, S., Bergenholm, L., Ekstrand, E.M., Cedersund, G., Insulin Signaling in Type 2 Diabetes: experimental and modeling analyses reveal mechanisms of insulin resistance in human adipocytes (2013) J Biol Chem, 288 (14), pp. 9867-9880
  • Nyman, E., Lindgren, I., Lövfors, W., Lundengård, K., Cervin, I., Mathematical modeling improves EC50 estimations from classical dose-response curves (2015) FEBS J, 282 (5), pp. 951-962
  • Jonsson, E.N., Wade, J.R., Karlsson, M.O., Nonlinearity detection: advantages of nonlinear mixed-effects modeling (2000) AAPS Pharm Sci, 2 (3), pp. 114-123
  • Bonate, P.L., Recommended Reading in Population Pharmacokinetic Pharmacodynamics (2005) AAPS Pharm Sci, 2 (2), pp. E363-E373
  • Caruana, R., Multitask learning (1997) Mach Learn, 28 (1), pp. 41-75
  • Pan, S.J., Yang, Q., A survey on transfer learning (2010) IEEE Trans Knowledge Data Eng, 22 (10), pp. 1345-1359
  • Evgeniou, T., Micchelli, C.A., Pontil, M., Learning multiple tasks with kernel methods (2005) J Mach Learn Res, 6, pp. 615-637
  • Grauman, K., Darrell, T., The pyramid match kernel: Efficient learning with sets of features (2007) J Mach Learn Res, 8, pp. 725-760
  • Zhang, T., Ghanem, B., Liu, S., Ahuja, N., Robust visual tracking via structured multi-task sparse learning (2013) Int J Comput Vis, 101 (2), pp. 367-383
  • Romain, B., Letort, V., Lucidarme, O., Rouet, L., Dalché-Buc, F., A multi-task learning approach for compartmental model parameter estimation in DCE-CT sequences (2013) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 271-278. , Berlin Heidelberg, Germany: Springer
  • Zechner, C., Pelet, S., Peter, M., Koeppl, H., Recursive Bayesian Estimation of Stochastic Rate Constants from Heterogeneous Cell Populations (2011) 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 5837-5843. , Piscataway, NJ, USA: IEEE
  • Koeppl, H., Zechner, C., Ganguly, A., Pelet, S., Peter, M., Accounting for extrinsic variability in the estimation of stochastic rate constants (2012) Int J Robust Nonlin, 22 (10), pp. 1103-1119
  • Zechner, C., Ruess, J., Krenn, P., Pelet, S., Peter, M., Lygeros, J., Moment-based inference predicts bimodality in transient gene expression (2012) Proc Natl Acad Sci USA, 109 (21), pp. 8340-8345
  • Zechner, C., Unger, M., Pelet, S., Peter, M., Koeppl, H., Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings (2014) Nat Methods, 11 (2), pp. 197-202
  • Hübner, K., Sahle, S., Kummer, U., Applications and trends in systems biology in biochemistry (2011) FEBS J, 278 (16), pp. 2767-2857
  • Gonzalez, A.M., Uhlendorf, J., Schaul, J., Cinquemani, E., Batt, G., Ferrari-Trecate, G., Identification of biological models from single-cell data: a comparison between mixed-effects and moment-based inference (2013) European Control Conference, ECC, pp. 3652-3657. , Piscataway, NJ, USA: IEEE
  • Fletcher, R., Fortran Subroutines for Minimization by Quasi-Newton Methods (1972), Report R-7125, A.E.R.E.England: Harwell; Delyon, B., Lavielle, M., Moulines, E., Convergence of a stochastic approximation version of the EM algorithm (1999) Ann Stat, 27 (1), pp. 94-128
  • Bauer, R.J., Guzy, S., Ng, C., A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples (2007) AAPS J, 9 (1), pp. E60-E83
  • Koeppll, H., Zechner, C., Ganguly, A., Pelet, S., Peter, M., NONMEM User's Guides (1989-2009) (2009) Icon Development Solutions, , https://nonmem.iconplc.com/nonmem7/Release_Notes_Plus/nm720.pdf, Accessed: 4th june 2015
  • Lindbom, L., Ribbing, J., Jonsson, E.N., Perl-speaks-NONMEM (PsN)-a Perl module for NONMEM related programming (2004) Comput Methods Programs Biomed, 75 (2), pp. 85-94
  • Lindbom, L., Ribbing, J., Jonsson, E.N., PsN-Toolkit-A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM (2005) Comput Methods Programs Biomed, 79 (3), pp. 241-257
  • Lixoft: Monolix® http://www.lixoft.eu/products/monolix/product-monolix-overview/, Accessed: 4th June 2015; Lixoft: Monolix® Users Guide http://www.lixoft.eu/wp-content/resources/docs/UsersGuide.pdf, Accessed: 4th June 2015; Durrieu, L., Johansson, R., Bush, A., Janzén, D., Gollvik, M., Cedersund, G., Quantification of nuclear transport in single cells bioRxiv The Preprint Server for Biology
  • Gennemark, P., Nordlander, B., Hohmann, S., Wedelin, D., A simple mathematical model of adaptation to high osmolarity in yeast (2006) In Silico Biology, 6 (3), pp. 193-214
  • Jonsson, E.N., Karlsson, M.O., Automated covariate model building within NONMEM (1998) Pharm Res, 15 (9), pp. 1463-1468

Citas:

---------- APA ----------
Karlsson, M., Janzén, D.L.I., Durrieu, L., Colman-Lerner, A., Kjellsson, M.C. & Cedersund, G. (2015) . Nonlinear mixed-effects modelling for single cell estimation: When, why, and how to use it. BMC Systems Biology, 9(1).
http://dx.doi.org/10.1186/s12918-015-0203-x
---------- CHICAGO ----------
Karlsson, M., Janzén, D.L.I., Durrieu, L., Colman-Lerner, A., Kjellsson, M.C., Cedersund, G. "Nonlinear mixed-effects modelling for single cell estimation: When, why, and how to use it" . BMC Systems Biology 9, no. 1 (2015).
http://dx.doi.org/10.1186/s12918-015-0203-x
---------- MLA ----------
Karlsson, M., Janzén, D.L.I., Durrieu, L., Colman-Lerner, A., Kjellsson, M.C., Cedersund, G. "Nonlinear mixed-effects modelling for single cell estimation: When, why, and how to use it" . BMC Systems Biology, vol. 9, no. 1, 2015.
http://dx.doi.org/10.1186/s12918-015-0203-x
---------- VANCOUVER ----------
Karlsson, M., Janzén, D.L.I., Durrieu, L., Colman-Lerner, A., Kjellsson, M.C., Cedersund, G. Nonlinear mixed-effects modelling for single cell estimation: When, why, and how to use it. BMC Syst. Biol. 2015;9(1).
http://dx.doi.org/10.1186/s12918-015-0203-x