Parameter estimation in biochemical pathways: a comparison of global optimization methods
Open accessAbstract
Here we address the problem of parameter estimation (inverse problem)of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP)problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based)local optimization methods fail to arrive at satisfactory solutions. To surmount this limitation, the use of several state-of-the-art deterministic and stochastic global optimization methods is explored. A case study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark. Only a certain type of stochastic algorithm, evolution strategies (ES), is able to solve this problem successfully. Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Stochastic optimization methods usually take longer to converge but run a smaller risk of converging to a local sub-optimal minimum than deterministic methods.
“Whereas the stochastic methods usually take longer to converge, they run a smaller risk of converging to a local sub-optimal minimum [53,54].”
From Parameter uncertainty in biochemical models described by ordinary differential equations (2013) · cites this paper as [53]
Approximate methods such as stochastic algorithms and metaheuristics are a more attractive alternative for problems of realistic size.
“Therefore, based on the current status of the NP issue [111], approximate methods (such as stochastic algorithms and metaheuristics) are a more attractive alternative for problems of realistic size [112–114].”
From Reverse engineering and identification in systems biology: strategies, perspectives and challenges (2014) · cites this paper as [112]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134 · see citing works
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569 · see citing works
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391 · see citing works
Genetic Algorithms + Data Structures = Evolution Programs
Michalewicz, Z. (1992) · Artificial intelligence · doi:10.1007/978-3-662-02830-8 · Cited by 11,581 · see citing works
On the Origin of Species by Means of Natural Selection
Livezey, R. L., & Darwin, C. R. (1953) · The American Midland Naturalist · doi:10.2307/2485224 · Cited by 5,430 · see citing works
Genetic Algorithms + Data Structures = Evolution Programs
Michalewicz, Z. (1996) · Springer eBooks · doi:10.1007/978-3-662-03315-9 · Cited by 5,108 · see citing works
Simulated annealing: theory and applications.
(1988) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(88)90023-7 · Cited by 2,966 · see citing works
Artificial Intelligence through Simulated Evolution
Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966) · International Linear Collider · Cited by 2,814 · see citing works
Evolution strategies – A comprehensive introduction
Beyer, H.-G., & Schwefel, H. (2002) · Natural Computing · doi:10.1023/a:1015059928466 · Cited by 2,810 · see citing works
Simulated Annealing: Theory and Applications
van Laarhoven, P. J. M., & Aarts, E. (1987) · Springer eBooks · doi:10.1007/978-94-015-7744-1 · Cited by 2,389 · see citing works
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Fogel, D. B. (1995) · IEEE Press eBooks · Cited by 2,283 · see citing works
Schwefel, H. (1995) · Cited by 2,197 · see citing works
Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., & Price, K. V. (1999) · Cited by 2,136 · see citing works
Lipschitzian optimization without the Lipschitz constant
Jones, D. R., Perttunen, C. D., & Stuckman, B. E. (1993) · Journal of Optimization Theory and Applications · doi:10.1007/bf00941892 · Cited by 2,005 · see citing works
Stochastic ranking for constrained evolutionary optimization
Rúnarsson, T. P., & Yao, X. (2000) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.873238 · Cited by 1,816 · see citing works
Global Optimization: Deterministic Approaches
Horst, R., & Hoang, T. (1992) · Cited by 1,641 · see citing works
Optimization Toolbox User's Guide
Coleman, T. F., Branch, M. A., & Grace, A. (1998) · Cited by 843 · see citing works
Mendes, P., & Kell, D. B. (1998) · Bioinformatics · doi:10.1093/bioinformatics/14.10.869 · Cited by 683 · see citing works
Self-citationMendes, P. (1993) · Computer applications in the biosciences · doi:10.1093/bioinformatics/9.5.563 · Cited by 523 · see citing works
Self-citationGlobal Optimization by Multilevel Coordinate Search
Huyer, W., & Neumaier, A. (1999) · Journal of Global Optimization · doi:10.1023/a:1008382309369 · Cited by 513 · see citing works
Pintér, J. D. (1996) · Nonconvex optimization and its applications · doi:10.1007/978-1-4757-2502-5 · Cited by 486 · see citing works
Stochastic global optimization methods part I: Clustering methods
Rinnooy Kan, A. H. G., & Timmer, G. T. (1987) · Mathematical Programming · doi:10.1007/bf02592070 · Cited by 463 · see citing works
Horst, R., & Tụy, H. (1990) · Springer eBooks · doi:10.1007/978-3-662-02598-7 · Cited by 392 · see citing works
A Discussion of Random Methods for Seeking Maxima
Brooks, S. H. (1958) · Operations Research · doi:10.1287/opre.6.2.244 · Cited by 322 · see citing works
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 · see citing works
Genetic Algorithms and evolution strategies: Similarities and differences
Hoffmeister, F., & Bäck, T. (2006) · Springer eBooks · doi:10.1007/bfb0029787 · Cited by 226 · see citing works
Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems
Costa, L. A., & Oliveira, P. (2001) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(00)00653-0 · Cited by 214 · see citing works
Global Optimization in Engineering Design
Grossmann, I. E. (1996) · Nonconvex optimization and its applications · doi:10.1007/978-1-4757-5331-8 · Cited by 199 · see citing works
Global optimization for the parameter estimation of differential-algebraic systems
Esposito, W. R., & Floudas, C. A. (2000) · Industrial & Engineering Chemistry Research · doi:10.1021/ie990486w · Cited by 188 · see citing works
Direct Global Optimization Algorithm
Jones, D. R. (2001) · Encyclopedia of Optimization · doi:10.1007/0-306-48332-7_93 · Cited by 172 · see citing works
Global optimization for parameter estimation of differential-algebraic systems
Čižniar, M., Podmajerský, M., Hirmajer, T., Fikar, M., & Latifi, M. A. (2009) · Chemical Papers · doi:10.2478/s11696-009-0017-7 · Cited by 163 · see citing works
The TOMLAB Optimization Environment
Holmström, K., & Edvall, M. M. (2004) · Applied optimization · doi:10.1007/978-1-4613-0215-5_19 · Cited by 144 · see citing works
Pure adaptive search in global optimization
Zabinsky, Z. B., & Smith, R. L. (1992) · Mathematical Programming · doi:10.1007/bf01585710 · Cited by 131 · see citing works
A Rigorous Global Optimization Algorithm for Problems with Ordinary Differential Equations
Papamichail, I., & Adjiman, C. S. (2002) · Journal of Global Optimization · doi:10.1023/a:1016259507911 · Cited by 130 · see citing works
Stochastic Global Optimization: Problem Classes and Solution Techniques
Törn, A., Ali, M. M., & Viitanen, S. (1999) · Journal of Global Optimization · doi:10.1023/a:1008395408187 · Cited by 122 · see citing works
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 · see citing works
Self-citationMathematical Modeling of the Influence of RKIP on the ERK Signaling Pathway
Kwang-Hyun, C., Shin, S., Kim, H., Olaf Wolkenhauer, A., McFerran, B. W., & Kölch, W. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36481-1_11 · Cited by 101 · see citing works
Application of Stochastic Global Optimization Algorithms to Practical Problems
Ali, M. M., Storey, C., & Törn, A. (1997) · Journal of Optimization Theory and Applications · doi:10.1023/a:1022617804737 · Cited by 94 · see citing works
Stochastic Dynamic Optimization of Batch and Semicontinuous Bioprocesses
Banga, J. R., Alonso, A. A., & Singh, R. P. (1997) · Biotechnology Progress · doi:10.1021/bp970015+ · Cited by 91 · see citing works
Self-citationA comparison of methods for self-adaptation in evolutionary algorithms
Saravanan, N., Fogel, D. B., & Nelson, K. M. (1995) · Biosystems · doi:10.1016/0303-2647(95)01534-r · Cited by 90 · see citing works
Boender, C. G. E., & Romeijn, H. E. (1995) · Nonconvex optimization and its applications · doi:10.1007/978-1-4615-2025-2_15 · Cited by 68 · see citing works
Case studies in evolutionary experimentation and computation
Rechenberg, I. (2000) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/s0045-7825(99)00381-3 · Cited by 66 · see citing works
Evolution strategies: An alternative evolutionary algorithm
Bäck, T. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61108-8_27 · Cited by 54 · see citing works
Integrated Process Design and Control Via Global Optimization
Moles, C. G., Gutiérrez, G., Alonso, A. A., & Banga, J. R. (2003) · Process Safety and Environmental Protection · doi:10.1205/026387603765444465 · Cited by 44 · see citing works
Self-citationGoulcher, R. W., & Long, J. J. C. (1978) · Computers & Chemical Engineering · doi:10.1016/0098-1354(78)80004-0 · Cited by 42 · see citing works
INTEGRATED CONTROLLED RANDOM SEARCH: APPLICATION TO A WASTEWATER TREATMENT PLANT MODEL
Banga, J. R., & Long, J. J. C. (1987) · Elsevier eBooks · doi:10.1016/b978-0-85295-205-4.50019-0 · Cited by 27 · see citing works
Self-citationConvex underestimators for variational and optimal control problems
Singer, A. B., Bok, J.-K., & Bartona, P. I. (2001) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/s1570-7946(01)80122-x · Cited by 15 · see citing works
DYNAMIC OPTIMIZATION OF BIOPROCESSES: DETERMINISTIC AND STOCHASTIC STRATEGIES
Balsa‐Canto, E., Alonso, A. A., & Banga, J. R. (2007) · Cited by 14 · see citing works
Self-citationModeling Large Biological Systems From Functional Genomic Data
Mendes, P. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/3087.003.0013 · Cited by 13 · see citing works
Self-citationMoles, C. G., Gutierrez, G., Alonso, A. A., et al. (2001) · European Control Conference (ECC) · doi:10.23919/ecc.2001.7076389 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- Banga, J.R. and Casares, J.J. 1987. ICRS: Application to a wastewater treatment plant model. In IChemE Symposium Series No. 100, pp. 183-192. Pergamon Press, Oxford, UK.
- Banga, J.R., Alonso, A.A., Moles, C.G., and Balsa-Canto, E. 2002. Efficient and robust numerical strategies for the optimal control of non-linear bio-processes. In Proceedings of the Mediterranean Conference on Control and Automation (MED2002), (eds. J.S. Sentievio and M. Attrans), CD-ROM. IEEE CSS, Lisbon, Portugal.
- Hansen, N. and Ostermeier, A. 1997. Convergence properties of evolution strategies with the derandomized covariance matrix adaptation: the (µ/µI, )-CMA-ES. In Proceedings of the 5th European Congress on Intelligent Techniques and Soft Computing, EUFIT '97 pp. 650-654. Verlag Maing, Auchen, Germany.
- Hindmarsh, A.C. 1983. ODEPACK, a systematized collection of ODE solvers. In Scientific computing (ed. R.S. Stepleman), pp. 55-64. North-Holland, Amsterdam.
Cited by
Sorted by citations
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M. P. H. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0172 · Cited by 1,498 · see citing works
Gene regulatory network inference: Data integration in dynamic models—A review
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755 · see citing works
Parameter estimation for differential equations: a generalized smoothing approach
Ramsay, J. O., Hooker, G., Campbell, D., & Cao, J. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00610.x · Cited by 613 · see citing works
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 · see citing works
Kell, D. B. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-2 · Cited by 554 · see citing works
Metabolomics and systems biology: making sense of the soup
KELL, D. (2004) · Current Opinion in Microbiology · doi:10.1016/j.mib.2004.04.012 · Cited by 529 · see citing works
Kell, D. B. (2010) · Archives of Toxicology · doi:10.1007/s00204-010-0577-x · Cited by 361 · see citing works
Systems biology: parameter estimation for biochemical models
Ashyraliyev, M., Fomekong‐Nanfack, Y., Kaandorp, J. A., & Blom, J. G. (2009) · The FEBS Journal · doi:10.1111/j.1742-4658.2008.06844.x · Cited by 323 · see citing works
Parameter estimation and model selection in computational biology
Lillacci, G., & Khammash, M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000696 · Cited by 325 · see citing works
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 · see citing works
Self-citationThe Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging
Ritter, P., Schirner, M., McIntosh, A. R., & Jirsa, V. K. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0120 · Cited by 268 · see citing works
Optimization in computational systems biology
Banga, J. R. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-47 · Cited by 278 · see citing works
Self-citationNovel 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 · see citing works
Self-citationLinking data to models: data regression
Jaqaman, K., & Danuser, G. (2006) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2030 · Cited by 243 · see citing works
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 · see citing works
Self-citationBayesian methods in bioinformatics and computational systems biology
Wilkinson, D. J. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbm007 · Cited by 230 · see citing works
An 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 · see citing works
Self-citationExploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems
Joshi, M., Seidel-Morgenstern, A., & Kremling, A. (2006) · Metabolic Engineering · doi:10.1016/j.ymben.2006.04.003 · Cited by 190 · see citing works
Scatter search for chemical and bio-process optimization
Egea, J. A., Rodríguez-Fernández, M., Banga, J. R., & Martí, R. (2007) · Journal of Global Optimization · doi:10.1007/s10898-006-9075-3 · Cited by 171 · see citing works
Self-citationNetPyNE, a tool for data-driven multiscale modeling of brain circuits
Dura-Bernal, S., Suter, B. A., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez, F., Kedziora, D. J., Chadderdon, G. L., Kerr, C. C., Neymotin, S. A., et al. (2019) · eLife · doi:10.7554/elife.44494 · Cited by 169 · see citing works
Saa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166 · see citing works
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 · see citing works
Parameter estimation and optimal experimental design
Banga, J. R., & Balsa-Canto, E. (2008) · Essays in Biochemistry · doi:10.1042/bse0450195 · Cited by 141 · see citing works
Self-citationABC-SysBio approximate Bayesian computation in Python with GPU support
Liepe, J., Barnes, C., Cule, E., Erguler, K., Kirk, P., Toni, T., & Stumpf, M. P. H. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq278 · Cited by 136 · see citing works
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 · see citing works
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 · see citing works
Self-citationInverse problems in systems biology
Engl, H. W., Flamm, C., Kügler, P., Lu, J., Müller, S., & Schuster, P. (2009) · Inverse Problems · doi:10.1088/0266-5611/25/12/123014 · Cited by 122 · see citing works
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 · see citing works
Hybrid optimization method with general switching strategy for parameter estimation.
Balsa-Canto, E., Peifer, M., Banga, J. R., Timmer, J., & Fleck, C. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-26 · Cited by 105 · see citing works
Self-citationA Six‐Stage Workflow for Robust Application of Systems Pharmacology
Gadkar, K., Kirouac, D., Mager, D., van der Graaf, P., & Ramanujan, S. (2016) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12071 · Cited by 93 · see citing works
Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data
Liebermeister, W., & Klipp, E. (2006) · Theoretical Biology and Medical Modelling · doi:10.1186/1742-4682-3-42 · Cited by 82 · see citing works
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 · see citing works
Silk, D., Kirk, P. D. W., Barnes, C. P., Toni, T., Rose, A., Moon, S., Dallman, M. J., & Stumpf, M. P. H. (2011) · Nature Communications · doi:10.1038/ncomms1496 · Cited by 76 · see citing works
‘Glocal’ Robustness Analysis and Model Discrimination for Circadian Oscillators
Hafner, M., Koeppl, H., Hasler, M., & Wagner, A. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000534 · Cited by 63 · see citing works
Doyle, F. J., & Stelling, J. (2006) · Journal of The Royal Society Interface · doi:10.1098/rsif.2006.0143 · Cited by 60 · see citing works
When the Optimal Is Not the Best: Parameter Estimation in Complex Biological Models
Fernández Slezak, D., Suárez, C., Cecchi, G. A., Marshall, G., & Stolovitzky, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013283 · Cited by 57 · see citing works
“This is a well studied problem in systems biology, usually addressed utilizing a large variety of approaches [1–5].”
BackgroundSystems analysis of cellular networks under uncertainty
Kaltenbach, H.-M., Dimopoulos, S., & Stelling, J. (2009) · FEBS Letters · doi:10.1016/j.febslet.2009.10.074 · Cited by 40 · see citing works
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 · see citing works
Workflow for generating competing hypothesis from models with parameter uncertainty
Gomez-Cabrero, D., Compte, A., & Tegner, J. (2011) · Interface Focus · doi:10.1098/rsfs.2011.0015 · Cited by 33 · see citing works
Schaber, J., & Klipp, E. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.09.014 · Cited by 33 · see citing works
Diverse metabolic model parameters generate similar methionine cycle dynamics
Piazza, M., Feng, X.-J., Rabinowitz, J. D., & Rabitz, H. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2007.12.009 · Cited by 24 · see citing works
Penas, D. R., Henriques, D., González, P., Doallo, R., Saez-Rodriguez, J., & Banga, J. R. (2017) · PLOS ONE · doi:10.1371/journal.pone.0182186 · Cited by 16 · see citing works
Self-citationModeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient
Parmar, J. H., Davis, G., Shevchuk, H., & Mendes, P. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0431-3 · Cited by 12 · see citing works
Self-citation
- Authors
- Moles, C. G., Mendes, P., & Banga, J. R.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- Virginia Tech (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2003-01-01
- Volume
- 13
- Issue
- 11
- Pages
- 2467-2474
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC403766
- OpenAlex
- W2165603655
- Cited by
- 858
- References
- 51
- Citations by year
- 2026: 19 · 2025: 12 · 2024: 15 · 2023: 12 · 2022: 22 · 2021: 25 · 2020: 16 · 2019: 32 · 2018: 41 · 2017: 54 · 2016: 37 · 2015: 46 · 2014: 53 · 2013: 63 · 2012: 64
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- National Science Foundation (0120306, 0217653, 0109732)
- Commonwealth of Virginia
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- parameter estimation, inverse problems, metabolic pathways, nonlinear dynamic model, global optimization, evolution strategies, stochastic optimization, deterministic optimization, multimodal optimization, ill-conditioned problems
- MeSH terms
- Algorithms, Computer Simulation, Genetic Engineering, Models, Chemical, Molecular Biology, Predictive Value of Tests, Software, Stochastic Processes, HIV Protease, HIV Protease Inhibitors, Nonlinear Dynamics, Evolution, Molecular, Computational 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
