The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Computational procedures for optimal experimental design in biological systems
Abstract
Mathematical models of complex biological systems, such as metabolic or cell-signalling pathways, usually consist of sets of nonlinear ordinary differential equations which depend on several non-measurable parameters that can be hopefully estimated by fitting the model to experimental data. However, the success of this fitting is largely conditioned by the quantity and quality of data. Optimal experimental design (OED) aims to design the scheme of actuations and measurements which will result in data sets with the maximum amount and/or quality of information for the subsequent model calibration. New methods and computational procedures for OED in the context of biological systems are presented. The OED problem is formulated as a general dynamic optimisation problem where the time-dependent stimuli profiles, the location of sampling times, the duration of the experiments and the initial conditions are regarded as design variables. Its solution is approached using the control vector parameterisation method. Since the resultant nonlinear optimisation problem is in most of the cases non-convex, the use of a robust global nonlinear programming solver is proposed. For the sake of comparing among different experimental schemes, a Monte-Carlo-based identifiability analysis is then suggested. The applicability and advantages of the proposed techniques are illustrated by considering an example related to a cell-signalling pathway.
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?
The practical identifiability of a model can be improved by means of an adequate experimental design.
“It is known that the practical identifiability of a model can be improved by means of an adequate experimental design [2, 4, 41, 24], and a similar procedure can be used to reduce sloppiness.”
From On the relationship between sloppiness and identifiability (2016) · cites this paper as [4]
The topic of optimal design of dynamic experiments in biological systems is receiving increased attention.
“The topic of optimal design of dynamic experiments in biological systems is receiving increased attention [144–152].”
From Reverse engineering and identification in systems biology: strategies, perspectives and challenges (2014) · cites this paper as [145]
Citations
Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
System Identification: Theory for the User
Lennart Ljung, A. (1987) · Cited by 17,690
Experimental Design and Data Analysis for Biologists
Quinn, G. P., & Keough, M. J. (2002) · doi:10.1017/cbo9780511806384 · Cited by 10,674
A Multiple Shooting Algorithm for Direct Solution of Optimal Control Problems *
Bock, H. G., & Plitt, K. J. (1984) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)61205-9 · Cited by 1,317
Identification of parametric models : from experimental data
Walter, É., & Pronzato, L. (1997) · Cited by 1,197
Parameter estimation in biochemical pathways: a comparison of global optimization methods
Moles, C. G., Mendes, P., & Banga, J. R. (2003) · Genome Research · doi:10.1101/gr.1262503 · Cited by 858
Self-citationAdvances in simultaneous strategies for dynamic process optimization
Biegler, L. T., Cervantes, A. M., & Wächter, A. (2002) · Chemical Engineering Science · doi:10.1016/s0009-2509(01)00376-1 · Cited by 428
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
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
Self-citationDynamic optimization of bioprocesses: Efficient and robust numerical strategies
Banga, J. R., Balsa‐Canto, E., Moles, C. G., & Alonso, A. A. (2005) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2005.02.013 · Cited by 217
Self-citationDesigning robust optimal dynamic experiments
Asprey, S. P., & Macchietto, S. (2002) · Journal of Process Control · doi:10.1016/s0959-1524(01)00020-8 · Cited by 199
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
Vassiliadis, V. S. (1993) · Spiral (Imperial College London) · Cited by 144
Simulation methods for optimal experimental design in systems biology
Faller, D., Klingmüller, U., & Timmer, J. (2003) · SIMULATION · doi:10.1177/0037549703040937 · Cited by 137
Kremling, A., Fischer, S., Gadkar, K., Doyle, F. J., Sauter, T., Bullinger, E., Allgöwer, F., & Gilles, E. D. (2004) · Genome Research · doi:10.1101/gr.1226004 · Cited by 127
Modeling and Simulation of Intracellular Dynamics: Choosing an Appropriate Framework
Olaf Wolkenhauer, A., Ullah, M., Kölch, W., & Cho, K.-H. (2004) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2004.833694 · Cited by 125
Peifer, M., & Timmer, J. (2007) · IET Systems Biology · doi:10.1049/iet-syb:20060067 · Cited by 123
A biological approach to computational models of proteomic networks
Janes, K. A., & Lauffenburger, D. A. (2006) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2005.12.016 · Cited by 118
Global Optimization of Chemical Processes using Stochastic Algorithms
Banga, J. R., & Seider, W. D. (1996) · Nonconvex optimization and its applications · doi:10.1007/978-1-4613-3437-8_33 · Cited by 103
Self-citationOptimal 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
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 · Cited by 77
Systems biology—an engineering perspective
Kremling, A., & Saez-Rodriguez, J. (2007) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2007.02.009 · Cited by 76
Sensitivity analysis of systems of differential and algebraic equations
Leis, J. R., & Kramer, M. A. (1985) · Computers & Chemical Engineering · doi:10.1016/0098-1354(85)87008-3 · Cited by 73
Banga, J. R., Versyck, K. J. E., & van Impe, J. F. M. (2002) · Industrial & Engineering Chemistry Research · doi:10.1021/ie010183d · Cited by 69
Self-citationOptimal Identification of Biochemical Reaction Networks
Feng, X., & RABITZ, H. A. (2004) · Biophysical Journal · doi:10.1016/s0006-3495(04)74201-0 · Cited by 58
Globally optimised parameters for a model of mitotic control in frog egg extracts.
Zwolak, J. W., Tyson, J. J., & Watson, L. T. (2005) · IEE Proceedings - Systems Biology · doi:10.1049/ip-syb:20045032 · Cited by 49
Optimal experimental design in an EGFR signaling and down-regulation modelPreprint
Casey, F., Baird, D., Feng, Q., et al. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0610024 · Cited by 9
Flagged first, then by citations
Research works printed but not linked: 10
- Janes, 2006, 10, 73
- Kremling, 2007, 129, 329
- Moles, 2003, 13, 2467
- Rodriguez-Fernandez, 2006, 83, 24
- Kutalik, 2004, 75, 43
- Kremling, 2004, 14, 1773
- Gadkar, 2005, 6, 155
- Banga, 2005, 117, 407
- Bock, 1984, Proc. 9th IFAC World Congress, 242
- Storn, 1997, 11, 341
Cited by
Sorted by citations
Structural identifiability of systems biology models: a critical comparison of methods
Chis, O.-T., Banga, J. R., & Balsa-Canto, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027755 · Cited by 448
Self-citationSystems biology informed deep learning for inferring parameters and hidden dynamics
Yazdani, A. Z. K., Lu, L., Raissi, M., & Karniadakis, G. E. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007575 · Cited by 350
Reverse engineering and identification in systems biology: strategies, perspectives and challenges
Villaverde, A. F., & Banga, J. R. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0505 · Cited by 229
Self-citationAn iterative identification procedure for dynamic modeling of biochemical networks
Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-11 · Cited by 202
Self-citationParameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
Sun, J., Garibaldi, J. M., & Hodgman, C. T. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.63 · Cited by 156
Addressing parameter identifiability by model-based experimentation
Raue, A., Kreutz, C., Maiwald, T., Klingmüller, U., & Timmer, J. (2011) · IET Systems Biology · doi:10.1049/iet-syb.2010.0061 · Cited by 145
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
Self-citationOptimal 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
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
On the relationship between sloppiness and identifiability
Chis, O.-T., Villaverde, A. F., Banga, J. R., & Balsa-Canto, E. (2016) · Mathematical Biosciences · doi:10.1016/j.mbs.2016.10.009 · Cited by 84
Self-citationEfficient reverse-engineering of a developmental gene regulatory network
Crombach, A., Wotton, K. R., Cicin-Sain, D., Ashyraliyev, M., & Jaeger, J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002589 · Cited by 83
“OED uses algorithms for global optimisation and calculation of confidence intervals similar to those used here to predict which measurements in a data set are most relevant to accurately estimate parameter values (see, for example, [80,81]).”
MethodResearch agenda for mass gatherings: a call to action
Tam, J. S. L., Barbeschi, M., Shapovalova, N., Briand, S. C., Memish, Z. A., & Kiény, M.-P. (2012) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(11)70353-x · Cited by 79
AMIGO, a toolbox for advanced model identification in systems biology using global optimization
Balsa‐Canto, E., & Banga, J. R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr370 · Cited by 82
Self-citationAutomated engineering of synthetic metabolic pathways for efficient biomanufacturing
Otero‐Muras, I., & Carbonell, P. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.11.012 · Cited by 77
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
Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons
Cao, H., Gibson, T. E., Bashan, A., & Liu, Y. (2016) · BioEssays · doi:10.1002/bies.201600188 · Cited by 73
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
Lam, N. N., Docherty, P. D., & Murray, R. (2022) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2022.03.020 · Cited by 70
Daneker, M., Zhang, Z., Karniadakis, G. E., & Lu, L. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3008-2_4 · Cited by 71
Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise
Telen, D., Vallerio, M., Cabianca, L., Houska, B., van Impe, J. F. M., & Logist, F. (2015) · Journal of Process Control · doi:10.1016/j.jprocont.2015.06.011 · Cited by 60
Mélykúti, B., August, E., Papachristodoulou, A., & El-Samad, H. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-38 · Cited by 58
Fröhlich, F., Theis, F. J., & Hasenauer, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12982-2_5 · Cited by 55
Francesco Ciucci, A., Carraro, T., Chueh, W. C., & Lai, W. (2011) · Electrochimica Acta · doi:10.1016/j.electacta.2011.02.098 · Cited by 53
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
Near-optimal experimental design for model selection in systems biology
Busetto, A. G., Hauser, A., Krummenacher, G., Sunnåker, M., Dimopoulos, S., Ong, C. S., Stelling, J., & Buhmann, J. M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt436 · Cited by 50
Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster
Becker, K., Balsa‐Canto, E., Cicin-Sain, D., Hoermann, A., Janssens, H., Banga, J. R., & Jaeger, J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003281 · Cited by 45
Self-citationAn optimal experimental design approach to model discrimination in dynamic biochemical systems
Skanda, D., & Lebiedz, D. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq074 · Cited by 42
New types of experimental data shape the use of enzyme kinetics for dynamic network modeling
Tummler, K., Lubitz, T., Schelker, M., & Klipp, E. (2013) · FEBS Journal · doi:10.1111/febs.12525 · Cited by 43
Hasenauer, J., Waldherr, S., Wagner, K., & Allgöwer, F. (2010) · IET Systems Biology · doi:10.1049/iet-syb.2009.0030 · Cited by 42
Limits of epidemic prediction using SIR models
Melikechi, O., Young, A. L., Tang, T., Bowman, T., Dunson, D. B., & Johndrow, J. E. (2022) · Journal of Mathematical Biology · doi:10.1007/s00285-022-01804-5 · Cited by 35
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
Cause and cure of sloppiness in ordinary differential equation models
Tönsing, C., Timmer, J., & Kreutz, C. (2014) · Physical Review E · doi:10.1103/physreve.90.023303 · Cited by 33
Lai, X., Wolkenhauer, O., & Vera, J. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-833-7_6 · Cited by 33
Getting the most out of it: Optimal experiments for parameter estimation of microalgae growth models
Muñoz‐Tamayo, R., Martinon, P., Bougaran, G., Mairet, F., & Olivier Bernard, A. (2014) · Journal of Process Control · doi:10.1016/j.jprocont.2014.04.021 · Cited by 33
Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks
Flassig, R. J., & Sundmacher, K. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts585 · Cited by 31
Experimental Design for Biological Systems
Chung, M., & Haber, E. (2012) · SIAM Journal on Control and Optimization · doi:10.1137/100791063 · Cited by 31
Analysis of heterogeneous cell populations: A density-based modeling and identification framework
Hasenauer, J., Waldherr, S., Doszczak, M., Scheurich, P., Radde, N. E., & Allgöwer, F. (2011) · Journal of Process Control · doi:10.1016/j.jprocont.2011.06.020 · 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
Global Optimization in Systems Biology: Stochastic Methods and Their Applications
Balsa‐Canto, E., Banga, J. R., Egea, J. A., Villaverde, A. F., & Hijas-Liste, G. M. (2011) · Advances in experimental medicine and biology · doi:10.1007/978-1-4419-7210-1_24 · Cited by 29
Self-citationBandiera, L., Hou, Z., Kothamachu, V. B., Balsa‐Canto, E., Swain, P. S., & Menolascina, F. (2018) · Processes · doi:10.3390/pr6090148 · Cited by 26
Self-citationNew opportunities for optimal design of dynamic experiments in systems and synthetic biology
Braniff, N., & Ingalls, B. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.02.005 · Cited by 25
He, F., Brown, M., & Yue, H. (2010) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.1558 · Cited by 24
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
Investigating dynamics of inhibitory and feedback loops in ERK signalling using power-law models
Vera, J., Rath, O., Balsa‐Canto, E., Banga, J. R., Kölch, W., & Wolkenhauer, O. (2010) · Molecular BioSystems · doi:10.1039/c0mb00018c · Cited by 24
Self-citationA 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
Accelerated knowledge discovery from omics data by optimal experimental design
Wang, X., Rai, N., Pereira, B. M. P., Eetemadi, A., & Tagkopoulos, I. (2020) · Nature Communications · doi:10.1038/s41467-020-18785-y · Cited by 25
Dynamic multi-compartmental modelling of metal bioaccumulation in fish: Identifiability implications
Otero‐Muras, I., Franco‐Uría, A., Alonso, A. A., & Balsa‐Canto, E. (2009) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2009.08.009 · Cited by 23
Self-citationFranco‐Uría, A., Otero‐Muras, I., Balsa‐Canto, E., Alonso, A. A., & Roca, E. (2010) · Chemosphere · doi:10.1016/j.chemosphere.2010.02.010 · Cited by 23
Self-citationNöh, K., Niedenführ, S., Beyß, M., & Wiechert, W. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006533 · Cited by 23
Nimmegeers, P., Bhonsale, S. S., Telen, D., & van Impe, J. F. M. (2020) · Chemical Engineering Science · doi:10.1016/j.ces.2020.115651 · Cited by 22
Set membership experimental design for biological systems
Marvel, S. W., & Williams, C. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-21 · Cited by 20
Lee, D., Jayaraman, A., & Kwon, J. S. (2019) · IET Systems Biology · doi:10.1049/iet-syb.2018.5079 · Cited by 19
Nikolov, S. G., Lai, X., Liebal, U. W., Wolkenhauer, O., & Vera, J. (2010) · International Journal of Systems Science · doi:10.1080/00207720903147746 · Cited by 19
A model-based strategy to investigate the role of microRNA regulation in cancer signalling networks
Nikolov, S. G., Vera, J., Schmitz, U., & Wolkenhauer, O. (2010) · Theory in Biosciences · doi:10.1007/s12064-010-0109-5 · Cited by 18
An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection
Abdullah, A., Deris, S., Mohamad, M. S., & Anwar, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061258 · Cited by 18
Robust modeling of a microalgal heterotrophic fed-batch bioreactor
Surisetty, K., Siegler, H. D. L. H., McCaffrey, W. C., & Ben‐Zvi, A. (2010) · Chemical Engineering Science · doi:10.1016/j.ces.2010.06.008 · Cited by 18
Manheim, D. C., & Detwiler, R. L. (2019) · MethodsX · doi:10.1016/j.mex.2019.05.035 · Cited by 18
Garre, A., Peñalver-Soto, J. L., Esnoz, A., Iguaz, A., Escámez, P. S. F., & Egea, J. A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220683 · Cited by 18
Zwietering, M. H., Garre, A., & den Besten, H. M. W. (2020) · Food Research International · doi:10.1016/j.foodres.2020.109973 · Cited by 17
Quantitative Optimal Experimental Design Using Global Sensitivity Analysis via Quasi-Linearization
Chu, Y., & Hahn, J. (2010) · Industrial & Engineering Chemistry Research · doi:10.1021/ie9009827 · Cited by 16
Mohsenizadeh, D. N., Dehghannasiri, R., & Dougherty, E. R. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2602873 · Cited by 15
Moimenta, A. R., Henriques, D., Minebois, R., Querol, A., & Balsa‐Canto, E. (2023) · Microbial Biotechnology · doi:10.1111/1751-7915.14211 · Cited by 15
Self-citationMulti-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix
Manesso, E., Sridharan, S., & Gunawan, R. (2017) · Processes · doi:10.3390/pr5040063 · Cited by 15
Modeling and analysis of the dynamic behavior of the XlnR regulon in Aspergillus niger
Omony, J., de Graaff, L. H., Straten, G. V., & van Boxtel, A. J. B. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-s1-s14 · Cited by 15
Yu, H., Yue, H., & Halling, P. J. (2018) · Chemical Engineering Science · doi:10.1016/j.ces.2018.05.047 · Cited by 13
A robust optimization approach to experimental design for model discrimination of dynamical systems
Skanda, D., & Lebiedz, D. (2012) · Mathematical Programming · doi:10.1007/s10107-012-0532-0 · Cited by 13
Use of a Generalized Fisher Equation for Global Optimization in Chemical Kinetics
Villaverde, A. F., Ross, J., Morán, F., Balsa‐Canto, E., & Banga, J. R. (2011) · The Journal of Physical Chemistry A · doi:10.1021/jp203158r · Cited by 11
Self-citationBalsa‐Canto, E., Alonso, A. A., Arias-Méndez, A., Garcia, M. R., López‐Núñez, A., Mosquera-Fernández, M., Vázquez, C., & Vilas, C. (2016) · SEMA SIMAI Springer series · doi:10.1007/978-3-319-32146-2_4 · Cited by 11
Self-citationBO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
Lu, Y., Hu, W., Paulson, J. A., & Zhang, Q. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108859 · Cited by 10
Experimental design schemes for learning Boolean network models
Atias, N., Gershenzon, M., Labazin, K., & Sharan, R. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu451 · Cited by 11
Predicting network modules of cell cycle regulators using relative protein abundance statistics
Oguz, C., Watson, L. T., Baumann, W. T., & Tyson, J. J. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0409-1 · Cited by 9
Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design
Braniff, N., Scott, M., & Ingalls, B. (2019) · Processes · doi:10.3390/pr7010052 · Cited by 9
Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts
Halter, W., Allgöwer, F., Murray, R. M., & György, A. (2018) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2018.8619039 · Cited by 9
Srinivasan, S., Cluett, W. R., & Mahadevan, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz445 · Cited by 7
Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2
Balsa‐Canto, E., Bandiera, L., & Menolascina, F. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1032-9_11 · Cited by 7
Self-citationOptimal experiment design for a bottom friction parameter estimation problem
Warder, S. C., & Piggott, M. D. (2022) · GEM - International Journal on Geomathematics · doi:10.1007/s13137-022-00196-4 · Cited by 6
Balsa‐Canto, E., & Banga, J. R. (2010) · IFAC Proceedings Volumes · doi:10.3182/20100707-3-be-2012.0053 · Cited by 6
Self-citationMethods for checking structural identifiability of nonlinear biosystems: A critical comparison.
Chis, O., Banga, J. R., & Balsa‐Canto, E. (2011) · IFAC Proceedings Volumes · doi:10.3182/20110828-6-it-1002.00800 · Cited by 5
Self-citationInformation content analysis reveals desirable aspects of in vivo experiments of a synthetic circuit
Gomez-Cabeza, D., Bandiera, L., Balsa‐Canto, E., & Menolascina, F. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791449 · Cited by 5
Self-citationAnalysis of an apoptotic core model focused on experimental design using artificial data
Schlatter, R., Conzelmann, H., Gilles, E. D., Sawodny, O., & Sauter, T. (2009) · IET Systems Biology · doi:10.1049/iet-syb.2008.0138 · Cited by 5
Kryukov, M. A., Carcano, A., Batt, G., & Ruess, J. (2019) · European Control Conference (ECC) · doi:10.23919/ecc.2019.8796209 · Cited by 4
Biological mechanisms revealed by a mathematical model for p53–Mdm2 core regulation
Yang, Y., Lee, K. S., Xiang, C., & Lin, H. (2009) · IET Systems Biology · doi:10.1049/iet-syb.2008.0152 · Cited by 4
Computational Procedures for Model Identification
Balsa‐Canto, E., & Banga, J. R. (2010) · Systems biology · doi:10.1007/978-1-4419-5797-9_5 · Cited by 4
Self-citationSloppy models can be identifiablePreprint
Chis, O.-T., Banga, J. R., & Balsa‐Canto, E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.1417 · Cited by 4
Self-citationExamining the impact of forcing function inputs on structural identifiability
Conrad, J. R., Hyman, J. M., & Eisenberg, M. C. (2026) · Mathematical Biosciences · doi:10.1016/j.mbs.2026.109814 · Cited by 3
Multifactorial Shelf-life Prediction of Refrigerated Broccoli at the Household Level
Garre, A., Aguayo, E., & Castillejo, N. (2024) · Food and Bioprocess Technology · doi:10.1007/s11947-024-03523-9 · Cited by 3
A Normalisation Strategy to Optimally Design Experiments in Computational Biology
Garcia, M. R., Alonso, A. A., & Balsa‐Canto, E. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-60816-7_16 · Cited by 3
Self-citationDesigning 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
Madi, M. K., & Karameh, F. N. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aac3f7 · Cited by 3
Future Outlook for Systems Biology
Young, D. L., & Michelson, S. (2011) · Wiley eBooks · doi:10.1002/9781118016435.ch14 · Cited by 3
Wang, X., Sun, B., Liu, B., Fu, Y., & Zheng, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186853 · Cited by 2
Mathematical Modelling in Plant Synthetic Biology
Deneer, A., & Fleck, C. C. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1791-5_13 · Cited by 2
Optimally designed vs intuition-driven inputs: the study case of promoter activity modellingPreprint
Bandiera, L., Kothamachu, V. B., Balsa‐Canto, E., Swain, P. S., & Menolascina, F. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/346379 · Cited by 2
Self-citationMélykúti, B. (2010) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-2zbye6bye · Cited by 2
Experimental Design for Predictive Models in Microbiology Depending on Environmental Variables
Gaindrik, P., Pleyer, J., Heger, D., & Fleck, C. C. (2012) · Methods and protocols in food science · doi:10.1007/978-1-0716-4112-5_4 · Cited by 1
Adaptive input design for nonlinear Kalman-based estimation of neuronal activity models
Madi, M. K., & Karameh, F. N. (2015) · International Conference on Advances in Biomedical Engineering (ICABME) · doi:10.1109/icabme.2015.7323267 · Cited by 1
Selecting the best design for nonstandard toxicology experiments
Webb, J. M., Smucker, B. J., & Bailer, A. J. (2014) · Environmental Toxicology and Chemistry · doi:10.1002/etc.2671 · Cited by 1
Madi, M. K., & Karameh, F. N. (2016) · Middle East Conference on Biomedical Engineering · doi:10.1109/mecbme.2016.7745400 · Cited by 1
Generalized inverse optimal control and its application in biology
Banga, J. R., & Säger, S. (2025) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2025.101029 · Cited by 3
Self-citationInvestigating Bayesian Robust Experimental Design with Principles of Global Sensitivity Analysis
He, F., Yue, H., & Brown, M. (2010) · IFAC Proceedings Volumes · doi:10.3182/20100705-3-be-2011.00096 · Cited by 1
Metadata
- Authors
- Balsa-Canto, E., Alonso, A. A., & Banga, J. R.
- Affiliations
- Process Engineering Group, IIM-CSIC, C/Eduardo Cabello, 6, Vigo, 36208, Spain
- Consejo Superior de Investigaciones Científicas (ES)
- Venue
- IET Systems Biology
- Published
- 2008-07-14
- Volume
- 2
- Issue
- 4
- Pages
- 163-172
- ISSN
- 1751-8849 (ISSN-L)
- 1751-8857
- OpenAlex
- W2042439614
- Cited by
- 124
- References
- 27
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 4 · 2020: 6 · 2019: 7 · 2018: 10 · 2017: 5 · 2016: 7 · 2015: 3 · 2014: 10 · 2013: 8 · 2012: 10
- FWCI
- 20.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IET Systems Biology (publishedVersion)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- optimal experimental design, biological systems, dynamic optimization, parameter identifiability, cell signalling pathways, metabolic pathways, model calibration, nonlinear ordinary differential equations
- MeSH terms
- Algorithms, Computer Simulation, Models, Biological, Research Design, Signal Transduction, Proteome, Systems Biology
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
