The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Experimental Design in Systems Biology, Based on Parameter Sensitivity Analysis Using a Monte Carlo Method: A Case Study for the TNFα-Mediated NF-κ B Signal Transduction Pathway
Abstract
Mathematical modeling and dynamic simulation of signal transduction pathways is a central theme in systems biology and is increasingly attracting attention in the postgenomic era. The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in this area. This study’s aim is to introduce a new strategy for experimental design based on parameter sensitivity analysis. The approach identifies key parameters/variables in a signal transduction pathway model and can thereby provide experimental biologists with guidance on which proteins to consider for measurement. The article focuses on applying this approach to the TNFα-mediated NF-κB pathway, which plays an important role in immunity and inflammation and in the control of cell proliferation, differentiation, and apoptosis. A mathematical model of this pathway is proposed, and the sensitivity analysis of model parameters is illustrated for this model by employing the Monte Carlo method over a broad range of parameter values.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“On the other hand, collectively fitting parameters [11,12] by optimizing the agreement between the model and available data often yields large parameter uncertainties [13–15].”
From Universally Sloppy Parameter Sensitivities in Systems Biology Models (2007) · cites this paper as [15]
Citations
Cites
Simulation Modeling and Analysis.
Schervish, M. J., Law, A. M., & Kelton, W. D. (1983) · Journal of the American Statistical Association · doi:10.2307/2288169 · Cited by 8,884
Söderström, T., & Stoica, P. (1988) · Cited by 5,162
Monte Carlo Methods in Statistical Physics
Newman, M. E. J., & Barkema, G. T. (1999) · doi:10.1093/oso/9780198517962.001.0001 · Cited by 3,191
NF-κB at the crossroads of life and death
Karin, M., & Lin, A. (2002) · Nature Immunology · doi:10.1038/ni0302-221 · Cited by 2,709
The IκB-NF-κB Signaling Module: Temporal Control and Selective Gene Activation
Hoffmann, A., Levchenko, A., Scott, M. L., & Baltimore, D. (2002) · Science · doi:10.1126/science.1071914 · Cited by 1,890
Signal transduction by tumor necrosis factor and its relatives.
Baud, V., & Karin, M. (2001) · Trends in Cell Biology · doi:10.1016/s0962-8924(01)02064-5 · Cited by 1,741
Emergent Properties of Networks of Biological Signaling Pathways
Bhalla, U. S., & Iyengar, R. (1999) · Science · doi:10.1126/science.283.5400.381 · Cited by 1,686
Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D., & Müller, G. I. (2002) · Nature Biotechnology · doi:10.1038/nbt0402-370 · Cited by 943
Complexity in Biological Signaling Systems
Weng, G., Bhalla, U. S., & Iyengar, R. (1999) · Science · doi:10.1126/science.284.5411.92 · Cited by 616
Swameye, I., Müller, T. G., Timmer, J., Sandra, O., & Klingmüller, U. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0237333100 · Cited by 316
Research works printed but not linked: 4
- [4] Voit, E. O. 2000. Computational analysis of biochemical systems. Cambridge, UK: Cambridge University Press .
- [11] Schoeberl, B., E. D. Gilles, and P. Scheurich. 2001. A mathematical vision of TNF receptor interaction . In Proceedings of 2nd International Conference on Systems Biology (ICSB2001), Pasadena, pp. 158-167 .
- [30] Kuo, B. C. 1995. Automatic control system. Englewood Cliffs, NJ: Prentice Hall .
- [31] Khalil, K. K. 2002. Nonlinear systems. Englewood Cliffs, NJ: Prentice Hall .
Cited by
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
Sensitivity analysis approaches applied to systems biology models
Zi, Z. (2011) · IET Systems Biology · doi:10.1049/iet-syb.2011.0015 · Cited by 400
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
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems
Rodriguez-Fernandez, M., Egea, J. A., & Banga, J. R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-483 · Cited by 278
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
van Riel, N. A. W. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbl040 · Cited by 259
Iterative approach to model identification of biological networks
Gadkar, K. G., Gunawan, R., & Doyle, F. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-155 · Cited by 154
Yue, H., Brown, M., Knowles, J. D., Wang, H., Broomhead, D. S., & Kell, D. B. (2006) · Molecular BioSystems · doi:10.1039/b609442b · Cited by 146
Ihekwaba, A., Benson, N., Kell, D. B., Broomhead, D. S., & Grimley, R. L. (2004) · Systems Biology · doi:10.1049/sb:20045009 · Cited by 143
Global Sensitivity Analysis Challenges in Biological Systems Modeling
Kiparissides, A., Kucherenko, S. S., Mantalaris, A., & Pistikopoulos, E. N. (2009) · Industrial & Engineering Chemistry Research · doi:10.1021/ie900139x · Cited by 147
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
Loizou, G. D., Spendiff, M., Barton, H. A., Bessems, J. G. M., Bois, F. Y., d’Yvoire, M. B., Buist, H. E., M.A., H. J. C. I., Meek, B., Gundert‐Remy, U., et al. (2008) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2008.01.011 · Cited by 130
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
Comparative study of parameter sensitivity analyses of the TCR-activated Erk-MAPK signalling pathway
Zheng, Y., & Rundell, A. (2006) · IEE Proceedings - Systems Biology · doi:10.1049/ip-syb:20050088 · Cited by 121
In silico identification of the key components and steps in IFN‐γ induced JAK‐STAT signaling pathway
Zi, Z., Cho, K., Sung, M., Xia, X., Zheng, J., & Sun, Z. (2005) · FEBS Letters · doi:10.1016/j.febslet.2005.01.009 · Cited by 115
Self-citationThe 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
Sensitivity analysis: from model parameters to system behaviour
Ingalls, B. (2008) · Essays in Biochemistry · doi:10.1042/bse0450177 · Cited by 105
Luan, D., Zai, M., & Varner, J. D. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030142 · Cited by 105
Yan, H., Zhang, B., Li, S., & Zhao, Q. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-50 · Cited by 73
Model identification of signal transduction networks from data using a state regulator problem.
Gadkar, K. G., Doyle III, F. J., & Varner, J. (2005) · Systems Biology · doi:10.1049/sb:20045029 · Cited by 75
SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool
Zi, Z., Zheng, Y., Rundell, A. E., & Klipp, E. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-342 · 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
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
Sensitivity analysis and robust experimental design of a signal transduction pathway system
Yue, H., Brown, M., He, F., Jia, J., & Kell, D. B. (2008) · International Journal of Chemical Kinetics · doi:10.1002/kin.20369 · Cited by 71
Survival and apoptotic pathways initiated by TNF‐α: Modeling and predictions
Rangamani, P., & Sirovich, L. (2006) · Biotechnology and Bioengineering · doi:10.1002/bit.21307 · Cited by 72
Parameter identifiability of power-law biochemical system models
Srinath, S., & Gunawan, R. (2010) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2010.02.019 · Cited by 68
Park, S., Lee, T., Kang, H. Y., Park, K., Cho, K., & Jung, G. (2006) · FEBS Letters · doi:10.1016/j.febslet.2006.01.004 · Cited by 57
Self-citationHow Do Control-Based Approaches Enter into Biology?
LeDuc, P. R., MESSNER, W. C., & Wikswo, J. P. (2011) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071910-124651 · Cited by 53
Rodriguez-Fernández, M., Banga, J. R., & Doyle, F. J. (2012) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.2797 · Cited by 51
Mathematical Modeling: Bridging the Gap between Concept and Realization in Synthetic Biology
Zheng, Y., & Sriram, G. (2010) · BioMed Research International · doi:10.1155/2010/541609 · Cited by 53
van Riel, N. A. W., & Sontag, E. D. (2006) · Systems Biology · doi:10.1049/ip-syb:20050076 · Cited by 47
Systematic calibration of a cell signaling network model
Kim, K. A. E., Spencer, S. L., Albeck, J. G., Burke, J. M., Sorger, P. K., Gaudet, S., & Kim, D. H. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-202 · Cited by 44
Yoon, J., & Deisboeck, T. S. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004560 · Cited by 40
Modelling cellular signalling systems
Rangamani, P., & Iyengar, R. (2008) · Essays in Biochemistry · doi:10.1042/bse0450083 · Cited by 36
Liu, B., Liu, Q., Yang, L., Palaniappan, S. K., Bahar, İ., Thiagarajan, P. S., & Ding, J. L. (2016) · Science Signaling · doi:10.1126/scisignal.aac9340 · Cited by 38
Koh, G., & Lee, D. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.04.017 · Cited by 51
Ochoa, S., Yoo, A., Repke, J., Wozny, G., & Yang, D. R. (2007) · Biotechnology Progress · doi:10.1021/bp0702119 · Cited by 36
Shahmohammadi, A., & McAuley, K. B. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b03047 · Cited by 35
Statistical Model Checking Based Calibration and Analysis of Bio-pathway Models
Palaniappan, S. K., Gyori, B. M., Liu, B., Hsu, D., & Thiagarajan, P. S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40708-6_10 · Cited by 34
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
Parameter Trajectory Analysis to Identify Treatment Effects of Pharmacological Interventions
Tiemann, C. A., Vanlier, J., Oosterveer, M. H., Groen, A. K., Hilbers, P. A. J., & van Riel, N. A. W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003166 · Cited by 32
Using prior parameter knowledge in model‐based design of experiments for pharmaceutical production
Shahmohammadi, A., & McAuley, K. B. (2020) · AIChE Journal · doi:10.1002/aic.17021 · Cited by 31
Renardy, M., Yi, T., Xiu, D., & Chou, C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006181 · Cited by 31
Oguz, C., Laomettachit, T., Chen, K. C., Watson, L. T., Baumann, W. T., & Tyson, J. J. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-53 · Cited by 31
Skin Disease Modeling from a Mathematical Perspective
Tanaka, R. J., & Ono, M. (2013) · Journal of Investigative Dermatology · doi:10.1038/jid.2013.69 · Cited by 29
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
Mathematical model of PAR1-mediated activation of human platelets
Lenoci, L., Duvernay, M. T., Satchell, S., DiBenedetto, E., & Hamm, H. E. (2011) · Molecular BioSystems · doi:10.1039/c0mb00250j · Cited by 28
Maheshwari, V., Rangaiah, G. P., & Lakshminarayanan, S. (2013) · Industrial & Engineering Chemistry Research · doi:10.1021/ie400133m · Cited by 27
Luan, D., Szlam, F., Tanaka, K. A., Barie, P. S., & Varner, J. D. (2010) · Molecular BioSystems · doi:10.1039/b920693k · Cited by 30
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
He, F., Brown, M., & Yue, H. (2010) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.1558 · Cited by 24
Przedborski, M., Smalley, M., Thiyagarajan, S., Goldman, A. J., & Kohandel, M. (2021) · Communications Biology · doi:10.1038/s42003-021-02393-7 · Cited by 25
A hybrid systems framework for cellular processes
Cho, K., Johansson, K. H., & Olaf Wolkenhauer, A. (2005) · Biosystems · doi:10.1016/j.biosystems.2004.12.002 · Cited by 24
Self-citationMODELING AND ANALYSIS OF BIOPATHWAYS DYNAMICS
Liu, B., & Thiagarajan, P. S. (2012) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720012310014 · Cited by 23
Probabilistic approximations of ODEs based bio-pathway dynamics
Liu, B., Hsu, D., & Thiagarajan, P. S. (2011) · Theoretical Computer Science · doi:10.1016/j.tcs.2011.01.021 · Cited by 22
Shahmohammadi, A., & McAuley, K. B. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.06.029 · Cited by 22
Necessary condition for applying experimental design criteria to global sensitivity analysis results
Chu, Y., & Hahn, J. (2012) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2012.09.007 · Cited by 21
Calibration of dynamic models of biological systems with KInfer
Lecca, P., Palmisano, A., Ihekwaba, A., & Priami, C. (2009) · European Biophysics Journal · doi:10.1007/s00249-009-0520-3 · Cited by 19
Nayak, S., Salim, S., Luan, D., Zai, M., & Varner, J. D. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002016 · Cited by 19
Optimal Control Strategy for Abnormal Innate Immune Response
Tan, J., & Zou, X. (2015) · Computational and Mathematical Methods in Medicine · doi:10.1155/2015/386235 · Cited by 18
Banaras, S., Paracha, R. Z., Nisar, M., Arif, A., Ahmad, J., Saeed, M. T., Mustansar, Z., Shuja, M. N., & Paracha, R. N. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.872421 · Cited by 17
Modeling the Intra- and Extracellular Cytokine Signaling Pathway under Heat Stroke in the Liver
Rodriguez-Fernández, M., Grosman, B., Yuraszeck, T. M., Helwig, B. G., Leon, L. R., & Doyle, F. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073393 · Cited by 16
Zhang, H.-X., & Goutsias, J. I. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-246 · Cited by 17
Mechanistic modeling of lipid nanoparticle formation for the delivery of nucleic acid therapeutics
Inguva, P., Mukherjee, S., Walker, P. J., Tenberg, V., Devos, C., Shin, S., Wu, Y., Santra, S., Wang, J., Singh, S., et al. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108643 · Cited by 19
Self-citationSensitivity analysis of dynamic biological systems with time-delays
Wu, W. H., Wang, F. S., & Chang, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s7-s12 · Cited by 18
Maiwald, T., Kreutz, C., Pfeifer, A. C., Böhl, S., Klingmüller, U., & Timmer, J. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1407.007 · Cited by 16
Hsiao, Y., & Lee, W. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s7-s8 · Cited by 15
Shin, S., Choo, S., Woo, S., & Cho, K. (2008) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2007_093 · Cited by 15
Self-citationOhshima, D., Inoue, J., & Ichikawa, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046911 · Cited by 14
Shin, S., Kim, M.-W., Cho, K., & Nguyen, L. K. (2019) · Scientific Reports · doi:10.1038/s41598-019-46592-z · Cited by 14
Self-citationProbabilistic Approximations of Signaling Pathway Dynamics
Liu, B., Thiagarajan, P. S., & Hsu, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03845-7_17 · Cited by 14
A global sensitivity analysis approach for morphogenesis models
Boas, S., Navarro Jimenez, M., Merks, R. M. H., & Blom, J. (2015) · Data Archiving and Networked Services (DANS) · Cited by 14
An Early Stage Researcher's Primer on Systems Medicine Terminology
Zanin, M., Aitya, N. A. A., Basílio, J., Baumbach, J., Benis, A., Behera, C. K., Bucholc, M., Castiglione, F., Chouvarda, I., Comte, B., et al. (2021) · Network and Systems Medicine · doi:10.1089/nsm.2020.0003 · Cited by 13
Kim, T., Shin, S., Choo, S., & Cho, K. (2008) · Biotechnology Journal · doi:10.1002/biot.200700247 · Cited by 14
Self-citationSystem Analysis of a Programmed Cell Death Model
Bullinger, E. (2006) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2005.1583455 · Cited by 13
Parameter estimation and dynamic control analysis of central carbon metabolism in Escherichia coli
Won, W., Park, C., Park, C., Lee, S. Y., Lee, K. S., & Lee, J. (2011) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-010-0238-1 · Cited by 12
Serrano‐Bermúdez, L. M., Barrios, A. F. G., & Castaño, D. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209447 · Cited by 14
Systems biology: will it work?
Williamson, M. P. (2005) · Biochemical Society Transactions · doi:10.1042/bst0330503 · Cited by 12
An island particle algorithm for rare event analysis
Vergé, C., Morio, J., & Moral, P. D. (2015) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2015.11.017 · Cited by 11
On robustness of phase resetting to cell division under entrainment
Ahmed, H., Ushirobira, R., & Efimov, D. V. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.09.033 · Cited by 12
Smalley, M., Przedborski, M., Thiyagarajan, S., Pellowe, M., Verma, A. K., Brijwani, N., Datta, D., Jain, M., Shanthappa, B., Kapoor, V., et al. (2020) · iScience · doi:10.1016/j.isci.2020.101229 · Cited by 11
Systemic Analysis of Tumor Cell-Induced Endothelial Calcium Signaling and Junction Disassembly
Peng, H., & Dong, C. (2009) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-009-0067-5 · Cited by 11
A Life Cycle for Modeling Biology at Different Scales
Harline, K., Martínez‐Gómez, J., Specht, C. D., & Roeder, A. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.710590 · Cited by 10
A global sensitivity analysis approach for morphogenesis models
Boas, S. E. M., Jimenez, M. I. N., Merks, R. M. H., & Blom, J. G. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0222-7 · Cited by 13
Ihekwaba, A., Mura, I., & Barker, G. C. (2014) · BMC Systems Biology · doi:10.1186/s12918-014-0119-x · Cited by 10
Abbiati, R. A., Pourdehnad, M., Carrancio, S., Pierce, D. W., Kasibhatla, S., McConnell, M. T., Trotter, M. W. B., Loos, R., Santini, C. C., & Ratushny, A. V. (2021) · The AAPS Journal · doi:10.1208/s12248-021-00623-8 · Cited by 8
van Halder, Y., Sanderse, B., & Koren, B. (2019) · SIAM Journal on Scientific Computing · doi:10.1137/18m1219643 · Cited by 9
Circuit development using biological components: Principles, models and experimental feasibility
Krishnan, R., & Purdy, C. C. (2007) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-007-9094-0 · Cited by 9
Model-based selection of the robust JAK-STAT activation mechanism
Rybiński, M., & Gambin, A. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.04.031 · Cited by 8
Gutiérrez, J., Laurent, G. S., & Inchima, S. U. (2010) · Theoretical Biology and Medical Modelling · doi:10.1186/1742-4682-7-7 · Cited by 7
Jia, J., Yue, H., Liu, T., & Wang, H. (2007) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2007.114 · Cited by 7
Global sensitivity analysis of ordinary differential equations
Weiße, A. Y. (2009) · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · doi:10.17169/refubium-13784 · Cited by 7
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
RKappa: Statistical Sampling Suite for Kappa Models
Sorokin, A., Sorokina, O., & Armstrong, J. D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27656-4_8 · Cited by 6
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
Multi-element probabilistic collocation for sensitivity analysis in cellular signalling networks
Foo, J., Sindi, S., & Karniadakis, G. E. (2009) · IET Systems Biology · doi:10.1049/iet-syb.2008.0126 · Cited by 6
Hsiao, Y., & Lee, W. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s15-s8 · Cited by 6
Regulation of signal transduction by spatial parameters: a case in NF– κ B oscillation
Ichikawa, K., Ohshima, D., & Sagara, H. (2014) · IET Systems Biology · doi:10.1049/iet-syb.2013.0020 · Cited by 6
Metadata
- Authors
- Cho, K.-H., Shin, S.-Y., Kolch, W., & Wolkenhauer, O.
- Affiliations
- University of Ulsan (KR)
- Beatson Institute for Cancer Research, Cancer Research UK, Beatson Laboratories, Switchback Road, Glasgow, G61 1BD, United Kingdom, Institute of Biomedical and Life Sciences, University of Glasgow University Avenue, Glasgow, G12 8QQ, United Kingdom
- Department of Computer Science, University of Rostock, Albert Einstein Str. 21 18059 Rostock, Germany,
- Corresponding author
- Cho, Kwang-Hyun
- Venue
- SIMULATION
- Publisher
- SAGE Publications
- Published
- 2003-12
- Volume
- 79
- Issue
- 12
- Pages
- 726-739
- ISSN
- 0037-5497 (ISSN-L)
- 1741-3133
- OpenAlex
- W2166565152
- Cited by
- 172
- References
- 31
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 5 · 2020: 2 · 2019: 6 · 2018: 4 · 2016: 5 · 2015: 12 · 2014: 10 · 2013: 18 · 2012: 11
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- SIMULATION (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › NF-κB Signaling Pathways
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Keywords
- experimental design, parameter sensitivity analysis, Monte Carlo methods, signaling pathways, TNF-α, NF-κB signaling, parameter estimation, model reduction, inflammation, immunity
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