Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model

Casey, F.P.; Baird, D.; Feng, Q.; Gutenkunst, R.N.; Waterfall, J.J.; Myers, C.R.; Brown, K.S.; Cerione, R.A.; Sethna, J.P.

doi:10.1049/iet-syb:20060065

Cited by 82Optimal Experimental Design MethodsGene Regulatory Network AnalysisControl Systems and Identification

The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.

Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model

Casey, F.P., Baird, D., Feng, Q., Gutenkunst, R.N., Waterfall, J.J., Myers, C.R., Brown, K.S., Cerione, R.A., Sethna, J.P.·2007·IET Systems Biology

DOI 10.1049/iet-syb:20060065

Open this paper in CoChat — free account

Abstract

We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (beta -Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.

Integrity signals

Registry

Crossref · 10.1049/iet-syb:20060065

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Optimal experimental design can have an impact on the parameter estimation problem for a single experiment.
“It is well established in the systems biology literature that optimal experimental design can have an impact on the parameter estimation problem for a single experiment.23 ,25,27-29”
From Sloppy models, parameter uncertainty, and the role of experimental design (2010) · cites this paper as [23]
Existing design criteria pertain to effectively constrain specific parameters or target the variance of predictions using model linearization.
“Where existing design criteria pertain to effectively constrain specific parameters or target the variance of predictions using model linearization (Casey et al., 2007; Faller et al., 2003; Rodriguez-Fernandez et al., 2006), this method is not limited to any specific error model or assumption regarding the parameter distribution.”
From A bayesian approach to targeted experiment design (2012) · cites this paper as Casey et al., 2007

Matrix Computations
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Epidermal Growth Factor Receptor in Non–Small-Cell Lung Carcinomas: Correlation Between Gene Copy Number and Protein Expression and Impact on Prognosis
Hirsch, F. R., Varella‐Garcia, M., Bunn, P. A., Maria, M. V. D., Veve, R., Bremnes, R. M., Barón, A. E., Zeng, C., & Franklin, W. A. (2003) · Journal of Clinical Oncology · doi:10.1200/jco.2003.11.069 · Cited by 1,476
Optimum Experimental Designs
Wheeler, R. E., Atkinson, A. C., & Donev, A. N. (1993) · Technometrics · doi:10.2307/1270278 · Cited by 1,455
Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261
Self-citation
Ubiquitin Ligase Activity and Tyrosine Phosphorylation Underlie Suppression of Growth Factor Signaling by c-Cbl/Sli-1
Levkowitz, G., Waterman, H., Ettenberg, S. A., Katz, M., Tsygankov, A. Y., Alroy, I., Lavi, S., Iwaï, K., Reiss, Y., Ciechanover, A. J., et al. (1999) · Molecular Cell · doi:10.1016/s1097-2765(00)80231-2 · Cited by 1,003
Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors
Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D., & Müller, G. I. (2002) · Nature Biotechnology · doi:10.1038/nbt0402-370 · Cited by 943
Clathrin-independent endocytosis of ubiquitinated cargos
Sigismund, S., Woelk, T., Puri, C., Maspero, E., Tacchetti, C., Transidico, P., Di Fiore, P. P., & Polo, S. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409817102 · Cited by 739
Quantification of Short Term Signaling by the Epidermal Growth Factor Receptor
Kholodenko, B. Ν., Demin, O. V., Moehren, G., & Hoek, J. B. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.42.30169 · Cited by 618
Physicochemical modelling of cell signalling pathways
Aldridge, B. B., Burke, J. M., Lauffenburger, D. A., & Sorger, P. K. (2006) · Nature Cell Biology · doi:10.1038/ncb1497 · Cited by 592
Optimal Design
Silvey, S. D. (1980) · Springer eBooks · doi:10.1007/978-94-009-5912-5 · Cited by 576
Ligand-Induced Transformation by a Noninternalizing Epidermal Growth Factor Receptor
Wells, A. H., Welsh, J. B., Lazar, C. S., Wiley, H. S., Gill, G. N., & Rosenfeld, M. G. (1990) · Science · doi:10.1126/science.2305263 · Cited by 413
Statistical mechanical approaches to models with many poorly known parameters.
Brown, K. S., & Sethna, J. P. (2003) · Physical Review E · doi:10.1103/physreve.68.021904 · Cited by 398
Self-citation
Regulation of Epidermal Growth Factor Receptor Signaling by Endocytosis and Intracellular Trafficking
Burke, P., Schooler, K., & Wiley, H. S. (2001) · Molecular Biology of the Cell · doi:10.1091/mbc.12.6.1897 · Cited by 387
Ligand binding and nuclear receptor evolution
Escrivá, H., Delaunay, F., & Laudet, V. (2000) · BioEssays · doi:10.1002/1521-1878(200008)22:8 · Cited by 307
A hybrid approach for efficient and robust parameter estimation in biochemical pathways
Rodriguez-Fernandez, M., Mendes, P., & Banga, J. R. (2006) · Biosystems · doi:10.1016/j.biosystems.2005.06.016 · Cited by 304
Endocytosis of Receptor Tyrosine Kinases Is Driven by Monoubiquitylation, Not Polyubiquitylation
Mosesson, Y., Shtiegman, K., Katz, M., Zwang, Y., Vereb, G., Szollosi, J., & Yarden, Y. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.c300096200 · Cited by 286
Sprouty2 attenuates epidermal growth factor receptor ubiquitylation and endocytosis, and consequently enhances Ras/ERK signalling
Wong, E. S. M., Fong, C. W., Lim, J., Yusoff, P., Low, B. C., Langdon, W. Y., & Guy, G. R. (2002) · The EMBO Journal · doi:10.1093/emboj/cdf493 · Cited by 236
Relationship of Epidermal Growth Factor Receptor Expression to ErbB-2 Signaling Activity and Prognosis in Breast Cancer Patients
DiGiovanna, M. P., Stern, D. F., Edgerton, S. M., Whalen, S. G., Moore, D. H., & Thor, A. D. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.09.055 · Cited by 233
Activated Cdc42 Sequesters c-Cbl and Prevents EGF Receptor Degradation
Wu, W. J., Tu, S. S., & Cerione, R. A. (2003) · Cell · doi:10.1016/s0092-8674(03)00688-3 · Cited by 203
Self-citation
Direct interaction of Cbl with pTyr 1045 of the EGF receptor (EGFR) is required to sort the EGFR to lysosomes for degradation
Grøvdal, L. M., Stang, E., Sorkin, A. D., & Madshus, I. H. (2004) · Experimental Cell Research · doi:10.1016/j.yexcr.2004.07.003 · Cited by 182
Cbl-mediated Ubiquitinylation Is Required for Lysosomal Sorting of Epidermal Growth Factor Receptor but Is Dispensable for Endocytosis
Duan, L., Miura, Y., Dimri, M., Majumder, B., Dodge, I. L., Reddi, A. L., Ghosh, A., Fernandes, N., Zhou, P., Mullane-Robinson, K., et al. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m304474200 · Cited by 182
Optimal Design: An Introduction to the Theory for Parameter Estimation.
Atwood, C. L., & Silvey, S. D. (1981) · Journal of the American Statistical Association · doi:10.2307/2287609 · Cited by 182
Shp2, an SH2-containing Protein-tyrosine Phosphatase, Positively Regulates Receptor Tyrosine Kinase Signaling by Dephosphorylating and Inactivating the Inhibitor Sprouty
Hanafusa, H., Torii, S., Yasunaga, T., Matsumoto, K., & Nishida, E. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m312498200 · Cited by 176
An Integrated Model of Epidermal Growth Factor Receptor Trafficking and Signal Transduction
Resat, H., Ewald, J. A., Dixon, D. A., & Wiley, H. S. (2003) · Biophysical Journal · doi:10.1016/s0006-3495(03)74516-0 · Cited by 170
A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity
Blinov, M. L., Faeder, J. R., Goldstein, B., & Hlavacek, W. S. (2005) · Biosystems · doi:10.1016/j.biosystems.2005.06.014 · Cited by 165
Optimum Experimental Designs
Wheeler, R. E. (1993) · Technometrics · doi:10.1080/00401706.1993.10485359 · Cited by 164
Sloppy-model universality class and the Vandermonde matrix.
Waterfall, J. J., Casey, F. P., Gutenkunst, R. N., Brown, K. S., Myers, C. R., Brouwer, P. W., Elser, V., & Sethna, J. P. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.150601 · Cited by 164
Self-citation
Cbl-b-dependent Coordinated Degradation of the Epidermal Growth Factor Receptor Signaling Complex
Ettenberg, S. A., Magnifico, A., Cuello, M. A., Nau, M. M., Rubinstein, Y., Yarden, Y., Weissman, A. M., & Lipkowitz, S. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.m102641200 · Cited by 158
Epidermal Growth Factor Receptor Internalization through Clathrin‐Coated Pits Requires Cbl RING Finger and Proline‐Rich Domains But Not Receptor Polyubiquitylation
Jiang, X., & Sorkin, A. D. (2003) · Traffic · doi:10.1034/j.1600-0854.2003.t01-1-00109.x · Cited by 143
Growth Factor Receptor Binding Protein 2-mediated Recruitment of the RING Domain of Cbl to the Epidermal Growth Factor Receptor Is Essential and Sufficient to Support Receptor Endocytosis
Huang, F., & Sorkin, A. D. (2005) · Molecular Biology of the Cell · doi:10.1091/mbc.e04-09-0832 · Cited by 140
CIN85 Participates in Cbl-b-mediated Down-regulation of Receptor Tyrosine Kinases
Szymkiewicz, I., Kowanetz, K., Soubeyran, P., Dinarina, A., Lipkowitz, S., & Đikić, I. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m205535200 · Cited by 137
Simulation methods for optimal experimental design in systems biology
Faller, D., Klingmüller, U., & Timmer, J. (2003) · SIMULATION · doi:10.1177/0037549703040937 · Cited by 137
Autocrine loops with positive feedback enable context-dependent cell signaling
Shvartsman, S. Y., Hagan, M. P., Yacoub, A., Dent, P., Wiley, H. S., & Lauffenburger, D. A. (2002) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00260.2001 · Cited by 136
Cool-1 functions as an essential regulatory node for EGFreceptor- and Src-mediated cell growth
Feng, Q., Baird, D., Peng, X., Wang, J., Ly, T. K., Guan, J., & Cerione, R. A. (2006) · Nature Cell Biology · doi:10.1038/ncb1453 · Cited by 126
Self-citation
Spatial Range of Autocrine Signaling: Modeling and Computational Analysis
Shvartsman, S. Y., Wiley, H. S., Deen, W. M., & Lauffenburger, D. A. (2001) · Biophysical Journal · doi:10.1016/s0006-3495(01)75837-7 · Cited by 125
Optimal sampling time selection for parameter estimation in dynamic pathway modeling
Kutalik, Z., Cho, K.-H., & Wolkenhauer, O. (2004) · Biosystems · doi:10.1016/j.biosystems.2004.03.007 · Cited by 79
Computational modelling of ErbB family phosphorylation dynamics in response to transforming growth factor alpha and heregulin indicates spatial compartmentation of phosphatase activity
Hendriks, B. S., Cook, J., Burke, J. M., Beusmans, J. M., Lauffenburger, D. A., & de Graaf, D. (2006) · Systems Biology · doi:10.1049/ip-syb:20050057 · Cited by 39
Sloppy systems biology: tight predictions with loose parameters
Sethna, J. P., Gutenkunst, R. N., Waterfall, J. J., Casey, F., Brown, K., & Myers, C. R. (2007) · Bulletin of the American Physical Society · Cited by 3
Self-citation

Flagged first, then by citations

1 of the 39 references couldn’t be matched to a known paper.

Research works printed but not linked: 27

Carpenter, 2000, 22, 697
Hirsch, 2003, 20, 3798
DiGiovanna, 2005, 23, 1152
Wu, 2003, 114, 715
Wong, 2002, 21, 4796
Levkowitz, 1999, 4, 1029
Ettenberg, 2001, 276, 27677
Grovdal, 2004, 300, 388
Huang, 2005, 16, 1268
Jiang, 2003, 4, 529
Mosesson, 2003, 278, 21323
Duan, 2003, 278, 28950
Sigismund, 2005, 102, 2760
Szymkiewicz, 2002, 277, 39666
Schoeberl, 2002, 20, 370
Kholodenko, 1999, 274, 30169
Blinov, 2006, 83, 136
Atkinson, A.C., and Donev, A.N.: ‘Optimum experimental design’, 1st(Oxford University Press, New York 1992)
Faller, 2003, 79, 717
Rodriguez-Fernandez, 2006, 83, 248
Kutalik, 2004, 75, 43
Brown, 2003, 68, 021904
Hanafusa, 2004, 279, 22992
Burke, 2001, 12, 1897
Shvartsman, 2002, 282, C545
Hendriks, 2006, 1, 22
Aldridge, 2006, 8, 1195

Sorted by citations

Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261
Self-citation
Model-based design of experiments for parameter precision: State of the art
Franceschini, G., & Macchietto, S. (2007) · Chemical Engineering Science · doi:10.1016/j.ces.2007.11.034 · Cited by 841
Perspective: Sloppiness and emergent theories in physics, biology, and beyond
Transtrum, M. K., Machta, B. B., Brown, K., Daniels, B. C., Myers, C. R., & Sethna, J. P. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4923066 · Cited by 343
Self-citation
Parameter Space Compression Underlies Emergent Theories and Predictive Models
Machta, B. B., Chachra, R., Transtrum, M. K., & Sethna, J. P. (2013) · Science · doi:10.1126/science.1238723 · Cited by 288
Self-citation
Optimization in computational systems biology
Banga, J. R. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-47 · Cited by 278
Systems biology: experimental design
Kreutz, C., & Timmer, J. (2009) · The FEBS Journal · doi:10.1111/j.1742-4658.2008.06843.x · Cited by 257
Geometry of nonlinear least squares with applications to sloppy models and optimization
Transtrum, M. K., Machta, B. B., & Sethna, J. P. (2011) · Physical Review E · doi:10.1103/physreve.83.036701 · Cited by 226
Self-citation
Maximizing the information content of experiments in systems biology
Liepe, J., Filippi, S., Komorowski, M., & Stumpf, M. P. H. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002888 · Cited by 170
A protocol for dynamic model calibration
Villaverde, A. F., Pathirana, D., Fröhlich, F., Hasenauer, J., & Banga, J. R. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab387 · Cited by 147
Sloppy models, parameter uncertainty, and the role of experimental design
Apgar, J. F., Witmer, D. K., White, F. M., & Tidor, B. (2010) · Molecular BioSystems · doi:10.1039/b918098b · Cited by 146
Parameter estimation and optimal experimental design
Banga, J. R., & Balsa-Canto, E. (2008) · Essays in Biochemistry · doi:10.1042/bse0450195 · Cited by 141
Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model
Bandara, S., Schlöder, J. P., Eils, R., Bock, H. G., & Meyer, T. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000558 · Cited by 135
Computational procedures for optimal experimental design in biological systems
Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2008) · IET Systems Biology · doi:10.1049/iet-syb:20070069 · Cited by 124
Parameter uncertainty in biochemical models described by ordinary differential equations
Vanlier, J., Tiemann, C. A., Hilbers, P. A. J., & van Riel, N. A. W. (2013) · Mathematical Biosciences · doi:10.1016/j.mbs.2013.03.006 · Cited by 116
The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems
White, A., Tolman, M., Thames, H. D., Withers, H. R., Mason, K. A., & Transtrum, M. K. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005227 · Cited by 110
A bayesian approach to targeted experiment design
Vanlier, J., Tiemann, C. A., Hilbers, P. A. J., & van Riel, N. A. W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts092 · Cited by 98
Bridging Mechanistic and Phenomenological Models of Complex Biological Systems
Transtrum, M. K., & Qiu, P. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004915 · Cited by 81
Stimulus Design for Model Selection and Validation in Cell Signaling
Apgar, J. F., Toettcher, J. E., Endy, D., White, F. M., & Tidor, B. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0040030 · Cited by 79
Optimal experimental design with the sigma point method
Schenkendorf, R., Kremling, A., & Mangold, M. (2009) · IET Systems Biology · doi:10.1049/iet-syb:20080094 · Cited by 79
Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F., Brown, K., Myers, C. R., & Sethna, J. P. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189.eor · Cited by 77
Self-citation
Digital Twins for Tissue Culture Techniques—Concepts, Expectations, and State of the Art
Möller, J., & Pörtner, R. (2021) · Processes · doi:10.3390/pr9030447 · Cited by 63
Information geometry for multiparameter models: new perspectives on the origin of simplicity
Quinn, K. N., Abbott, M. C., Transtrum, M. K., Machta, B. B., & Sethna, J. P. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/aca6f8 · Cited by 62
Self-citation
Discriminating between rival biochemical network models: three approaches to optimal experiment design
Mélykúti, B., August, E., Papachristodoulou, A., & El-Samad, H. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-38 · Cited by 58
Analyzing the Functional Properties of the Creatine Kinase System with Multiscale ‘Sloppy’ Modeling
Hettling, H., & van Beek, H. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002130 · Cited by 55
Model‐based design of experiments for cellular processes
Chakrabarty, A., Buzzard, G. T., & Rundell, A. E. (2013) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1204 · Cited by 54
Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data
Monsalve-Bravo, G. M., Lawson, B., Drovandi, C., Burrage, K., Brown, K., Baker, C. M., Vollert, S. A., Mengersen, K. L., McDonald‐Madden, E., & Adams, M. P. (2022) · Science Advances · doi:10.1126/sciadv.abm5952 · Cited by 50
Self-citation
An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters
Abdullah, A., Deris, S., Anwar, S., & Arjunan, S. N. V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056310 · Cited by 49
The ABC of reverse engineering biological signalling systems
Secrier, M., Toni, T., & Stumpf, M. P. H. (2009) · Molecular BioSystems · doi:10.1039/b908951a · Cited by 47
Optimal experiment design for model selection in biochemical networks
Vanlier, J., Tiemann, C. A., Hilbers, P. A. J., & van Riel, N. A. W. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-20 · Cited by 45
Convergence in parameters and predictions using computational experimental design
Hagen, D. R., White, J. K., & Tidor, B. (2013) · Interface Focus · doi:10.1098/rsfs.2013.0008 · Cited by 41
“Existing methods also vary in terms of their goal, such as the various optimality functions of the covariance matrix [44] and minimizing prediction uncertainty [45].”
Method
Sloppiness and the Geometry of Parameter Space
Mannakee, B. K., Ragsdale, A. P., Transtrum, M. K., & Gutenkunst, R. N. (2015) · Studies in mechanobiology, tissue engineering and biomaterials · doi:10.1007/978-3-319-21296-8_11 · Cited by 37
Self-citation
Optimal experiment selection for parameter estimation in biological differential equation models
Transtrum, M. K., & Qiu, P. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-181 · Cited by 34
Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering
Menolascina, F., Bellomo, D., Maiwald, T., Bevilacqua, V., Ciminelli, C., Paradiso, A., & Tommasi, S. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s12-s4 · Cited by 34
Ensembles of uncertain mathematical models can identify network response to therapeutic interventions
Luan, D., Szlam, F., Tanaka, K. A., Barie, P. S., & Varner, J. D. (2010) · Molecular BioSystems · doi:10.1039/b920693k · Cited by 30
A global parallel model based design of experiments method to minimize model output uncertainty
Bazil, J. N., Buzzard, G. T., & Rundell, A. E. (2012) · Bulletin of Mathematical Biology · doi:10.1007/s11538-011-9686-9 · Cited by 28
Design of Optimal Sequential Experiments to Improve Model Predictions from a Polyethylene Molecular Weight Distribution Model
Thompson, D. E., McAuley, K. B., & McLellan, P. J. (2009) · Macromolecular Reaction Engineering · doi:10.1002/mren.200900033 · Cited by 28
Identification of a rice drying model with an improved sequential optimal design of experiments
Goujot, D., Meyer, X.-M., & Courtois, F. (2011) · Journal of Process Control · doi:10.1016/j.jprocont.2011.10.003 · Cited by 26
A Bayesian active learning strategy for sequential experimental design in systems biology
Pauwels, E., Lajaunie, C., & Vert, J. (2014) · BMC Systems Biology · doi:10.1186/s12918-014-0102-6 · Cited by 26
Biological Network Inference: A Review of Methods and Assessment of Tools and Techniques
Omony, J. (2014) · Annual Research & Review in Biology · doi:10.9734/arrb/2014/5718 · Cited by 26
Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model
Sinkoe, A. L., & Hahn, J. (2017) · Processes · doi:10.3390/pr5030049 · Cited by 25
Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs)
Song, S. O., Chakrabarti, A., & Varner, J. D. (2010) · Biotechnology Journal · doi:10.1002/biot.201000059 · Cited by 23
Comment on “Sloppy models, parameter uncertainty, and the role of experimental design”
Chachra, R., Transtrum, M. K., & Sethna, J. P. (2011) · Molecular BioSystems · doi:10.1039/c1mb05046j · Cited by 21
Self-citation
Sloppiness, Modeling, and Evolution in Biochemical Networks
Gutenkunst, R. N. (2007) · eCommons (Cornell University) · Cited by 20
Self-citation
A mathematical model and quantitative comparison of the small RNA circuit in theVibrio harveyiandVibrio choleraequorum sensing systems
Hunter, G. A. M., Vasquez, F. G., & Keener, J. P. (2013) · Physical Biology · doi:10.1088/1478-3975/10/4/046007 · Cited by 19
Prediction uncertainty and optimal experimental design for learning dynamical systems
Letham, B., Letham, P. A., Rudin, C. D., & Browne, E. P. (2016) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4953795 · Cited by 19
Trajectory-oriented Bayesian experiment design versus fisher a-optimal design: an in depth comparison study
Weber, P., Kramer, A., Dingler, C., & Radde, N. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts377 · Cited by 14
“The project script including example data can be downloaded from http://www.ist.unistuttgart.de/%7eweber/BayesFisher2012.”
Method
Experimental design and model reduction in systems biology
Jeong, J. E., Zhuang, Q. K.-W., Transtrum, M. K., Zhou, E., & Qiu, P. (2018) · Quantitative Biology · doi:10.1007/s40484-018-0150-9 · Cited by 14
Structural susceptibility and separation of time scales in the van der Pol oscillator
Chachra, R., Transtrum, M. K., & Sethna, J. P. (2012) · Physical Review E · doi:10.1103/physreve.86.026712 · Cited by 12
Self-citation
Predictive power of non-identifiable models
Grabowski, F., Nałęcz‐Jawecki, P., & Tomasz Lipniacki, B. (2023) · Scientific Reports · doi:10.1038/s41598-023-37939-8 · Cited by 10
Unwinding the model manifold: Choosing similarity measures to remove local minima in sloppy dynamical systems
Francis, B. L., & Transtrum, M. K. (2019) · Physical review. E · doi:10.1103/physreve.100.012206 · Cited by 10
Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments
Yang, J., Daily, N., Pullinger, T. K., Wakatsuki, T., & Sobie, E. A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011806 · Cited by 9
Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models
Jashnsaz, H., Fox, Z., Hughes, J. J., Li, G., Munsky, B. E., & Neuert, G. (2020) · iScience · doi:10.1016/j.isci.2020.101565 · Cited by 9
Studying Cellular Signal Transduction with OMIC Technologies
Landry, B. D., Clarke, D. C., & Lee, M. J. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.021 · Cited by 8
Understanding the complexity of computational models through optimization and sloppy parameter analyses: The case of the Connectionist Dual-Process Model
Perry, C., Evertz, R., Zorzi, M., & Ziegler, J. C. (2023) · Journal of Memory and Language · doi:10.1016/j.jml.2023.104468 · Cited by 7
Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions
Flassig, R., Migal, I., van der Zalm, E., Rihko‐Struckmann, L., & Sundmacher, K. (2015) · BMC Bioinformatics · doi:10.1186/s12859-014-0436-5 · Cited by 6
Accurate Broadband Gradient Estimates Enable Local Sensitivity Analysis of Ocean Acoustic Models
Mortenson, M. C., Neilsen, T. B., Transtrum, M. K., & Knobles, D. P. (2022) · Journal of Theoretical and Computational Acoustics · doi:10.1142/s2591728522500153 · Cited by 6
Reply to Comment on “Sloppy models, parameter uncertainty, and the role of experimental design”
Hagen, D., Apgar, J. F., Witmer, D. K., White, F. M., & Tidor, B. (2011) · Molecular BioSystems · doi:10.1039/c1mb05200d · Cited by 6
An information-matching approach to optimal experimental design and active learning
Kurniawan, Y., Neilsen, T. B., Francis, B. L., Stanković, A. M., Wen, M., Nikiforov, I., Tadmor, E. B., Bulatov, V. V., Lordi, V., & Transtrum, M. K. (2026) · Applied Physics Letters · doi:10.1063/5.0296026 · Cited by 5
In Vivo, In Vitro, In Silico: Computational Tools for Product and Process Design in Tissue Engineering
Geris, L. (2012) · Studies in mechanobiology, tissue engineering and biomaterials · doi:10.1007/8415_2012_144 · Cited by 5
Model-based design of experiments for efficient and accurate isotherm model identification in High Performance Liquid Chromatography
Katsoulas, K., Galvanin, F., Mazzei, L., Besenhard, M. O., & Sørensen, E. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109021 · Cited by 4
Designing Optimal Experiments to Discriminate Interaction Graph Models
Thiele, S., Heise, S., Hessenkemper, W., Bongartz, H., Fensky, M., Schaper, F., & Klamt, S. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2812184 · Cited by 4
Quantifying the relative importance of experimental data points in parameter estimation
Jeong, J. E., & Qiu, P. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0622-6 · Cited by 4
Optimal Experimental Design and Model Selection by a Sigma Point Approach
Schenkendorf, R., Kremling, A., & Mangold, M. (2009) · Max Planck Institute for Plasma Physics · Cited by 4
Zen and the art of parameter estimation in systems biology
Myers, C. R. (2018) · CRC Press eBooks · doi:10.1201/9781315119847-8 · Cited by 4
Self-citation
Constraining the geometry and volume of the Barents Sea Ice Sheet
Kachuck, S. B., & Cathles, L. M., III (2018) · Journal of Quaternary Science · doi:10.1002/jqs.3031 · Cited by 3
Theoretical advances in the modelling and interrogation of biochemical reaction systems: alternative formulations of the chemical Langevin equation and optimal experiment design for model discrimination
Mélykúti, B. (2010) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-2zbye6bye · Cited by 2
Information Geometry in Underwater Acoustics: Tutorial, Case Study, and Outlook
Spendlove, J. C., Mortenson, M. C., Neilsen, T. B., & Transtrum, M. K. (2024) · Journal of Theoretical and Computational Acoustics · doi:10.1142/s2591728524500117 · Cited by 1
Computational Modeling And Simulation Of Thrombus Formation
Luan, D. (2009) · eCommons (Cornell University) · Cited by 1
Computational approaches to the modelling of topological and dynamical aspects of biochemical networks
de Lomana, A. L. G. (2010) · Repositori digital de la UPF (Universitat Pompeu Fabra) · Cited by 1
A CRISP approach to QSP: XAI enabling fit-for-purpose modelsPreprint
DeTal, N., Anderson, C. N. K., & Transtrum, M. K. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2505.02750 · Cited by 1
Unraveling the intricacies of spatial organization of the ErbB receptors and downstream signaling pathways
Costa, M. N. (2009) · UNM’s Digital Repository (University of New Mexico)
Viable Models of Energy Metabolism
Hettling, J. (2014) · Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)
Parameter and topology uncertainty for optimal experimental design
Hagen, D. (2014) · DSpace@MIT (Massachusetts Institute of Technology)
Cellular level models as tools for cytokine design
Radhakrishnan, M. L., & Tidor, B. (2010) · Biotechnology Progress · doi:10.1002/btpr.387
Direct Response Analysis in cellular signalling networks
Shayeghi, N., Ng, T., & Coolen, A. C. C. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.04.001
Model Identification and Parameter Estimation
Świerniak, A., Kimmel, M., Śmieja, J., Puszyński, K., & Psiuk-Maksymowicz, K. (2016) · Springer eBooks · doi:10.1007/978-3-319-28095-0_6
Timing observations of diffusions
Javeed, A., & Hooker, G. (2019) · Statistics and Computing · doi:10.1007/s11222-019-09883-z
The relative importance of data points in systems biology and parameter estimation
Jeong, J. E., & Qiu, P. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217677
A qualitative analysis of ubiquitous regulatory motifs in Saccharomyces cerevisiae genetic networks
Camacho, E. T., Rǎdulescu, A., Wirkus, S. A., & Marshall, P. A. (2018) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2018.09.001
Biological Networks
Gunawan, R., & Bagheri, N. (2018) · MDPI eBooks · doi:10.3390/books978-3-03897-434-5
MicroRNA-embedding Regulation Networks, Logical Modeling
Guebel, D. V., Schmitz, U., & Vera, J. (2013) · Springer eBooks · doi:10.1007/978-1-4419-9863-7_1469
A generalized Bayesian framework for maximizing information gain and model selection
Jagadeesan, P., Raman, K., & Tangirala, A. K. (2026) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000082

Authors: Casey, F. P., Baird, D., Feng, Q., Gutenkunst, R. N., Waterfall, J. J., Myers, C. R., Brown, K. S., Cerione, R. A., & Sethna, J. P.
Affiliations: Cornell University (US)
Howard Hughes Medical Institute (US)
Harvard University (US)
ORCID: Casey, F.P.
Feng, Q.
Gutenkunst, R.N.
Waterfall, J.J.
Myers, C.R.
Brown, K.S.
Cerione, R.A.
Sethna, J.P.
Venue: IET Systems Biology
Publisher: Institution of Engineering and Technology (IET)
Published: 2007-05-15
Volume: 1
Issue: 3
Pages: 190-202
ISSN: 1751-8849 (ISSN-L)
1751-8857
DOI: 10.1049/iet-syb:20060065
OpenAlex: W2086215140
Cited by: 77
References: 39
Citations by year: 2026: 2 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 7 · 2017: 2 · 2016: 4 · 2015: 4 · 2014: 5 · 2013: 8 · 2012: 6
FWCI: 9.54
Type: journal article
Language: en
Open access: closed
Locations: IET Systems Biology (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords: optimal experimental design, epidermal growth factor receptor, receptor signaling, receptor down-regulation, CBL, betaPIX, Cdc42, Rho GTPases, E3 ubiquitin ligase, guanine nucleotide exchange factor, receptor trafficking, parameter uncertainty, predictive modeling
MeSH terms: Computer Simulation, Models, Biological, Research, Signal Transduction, Down-Regulation, Cell Cycle Proteins, Guanine Nucleotide Exchange Factors, Rho Guanine Nucleotide Exchange Factors, ErbB Receptors

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks