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Model identification of signal transduction networks from data using a state regulator problem.
Abstract
Advances in molecular biology provide an opportunity to develop detailed models of biological processes that can be used to obtain an integrated understanding of the system. However, development of useful models from the available knowledge of the system and experimental observations still remains a daunting task. In this work, a model identification strategy for complex biological networks is proposed. The approach includes a state regulator problem (SRP) that provides estimates of all the component concentrations and the reaction rates of the network using the available measurements. The full set of the estimates is utilised for model parameter identification for the network of known topology. An a priori model complexity test that indicates the feasibility of performance of the proposed algorithm is developed. Fisher information matrix (FIM) theory is used to address model identifiability issues. Two signalling pathway case studies, the caspase function in apoptosis and the MAP kinase cascade system, are considered. The MAP kinase cascade, with measurements restricted to protein complex concentrations, fails the a priori test and the SRP estimates are poor as expected. The apoptosis network structure used in this work has moderate complexity and is suitable for application of the proposed tools. Using a measurement set of seven protein concentrations, accurate estimates for all unknowns are obtained. Furthermore, the effects of measurement sampling frequency and quality of information in the measurement set on the performance of the identified model are described.
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The difficulty of extracting precise parameter values from collective fits in systems biology modeling is well-known.
“The difficulty of extracting precise parameter values from collective fits in systems biology modeling is well-known [26].”
From Universally Sloppy Parameter Sensitivities in Systems Biology Models (2007) · cites this paper as [26]
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“Recently, several methods have been developed for estimating parameters in linear fractional models [6,7,11,12].”
BackgroundMethodSelecting parameters to estimate to obtain the best model predictions
McAuley, K. B., Wu, S.-H., & Harris, T. J. (2010) · International Conference on Modelling, Identification and Control · Cited by 4
The Use of Interval Methods in Signal Processing and Control for Systems Biology
Edmonson, W., Ocloo, S., Williams, C., & Alexander, W. E. (2007) · IEEE Symposium on Foundations of Computational Intelligence (FOCI) · doi:10.1109/foci.2007.372159 · Cited by 4
“Network topologies have been derived from varying amounts of information, which include sequence analysis [8], in-depth study of molecular interactions, and gene expression analysis [ 9 ] [10].”
BackgroundMethodInfluentialDynamic Modeling and Parameter Identification for Biological Networks
Bianconi, F., Lillacci, G., & Valigi, P. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-491-2.ch021 · Cited by 4
Complexity Analysis and Parameter Estimation of Dynamic Metabolic Systems
Tian, L., Zhong-ke, S., & Wu, F. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/698341 · Cited by 3
“Recently, several methods have been proposed for estimating parameters in rational functions [5, 6, 13, 14].”
MethodFuture Outlook for Systems Biology
Young, D. L., & Michelson, S. (2011) · Wiley eBooks · doi:10.1002/9781118016435.ch14 · Cited by 3
Discrete-Time Model Representations for Biochemical Pathways
He, F., Yeung, L. F., & Brown, M. (2008) · Lecture notes in electrical engineering · doi:10.1007/978-0-387-74935-8_19 · Cited by 2
“The linear discrete-time representation discussed above had been mentioned in some pathway modeling literature [11, 12 ].”
MethodLuan, D., Zai, M., & Varner, J. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030142.eor · Cited by 1
Self-citationDynamic Modeling of Apoptosis and its Interaction with Cell Growth in Mammalian Cell Culture
Meshram, M., Naderi, S., Wei, Y., McConkey, B. J., Ingalls, B., Budman, H. M., & Scharer, J. M. (2011) · IFAC Proceedings Volumes · doi:10.3182/20110828-6-it-1002.02462 · Cited by 1
de Lomana, A. L. G. (2010) · Repositori digital de la UPF (Universitat Pompeu Fabra) · Cited by 1
Williams, C., Alexander, W. E., & Edmonson, W. (2006) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2006.355083 · Cited by 1
“This has led to the development of analytical approaches that are capable of estimating the unmeasured dynamics of these systems based on the measurements that can be obtained from current experimental techniques [4] [5] [6] [7] [8].”
BackgroundMethodResultInfluentialExperiment design for optimal excitation of gene regulatory networks
Nordling, T. E. M., & Jacobsen, E. W. (2006) · International Conference on Systems · Cited by 1
Automated cell cultivation on a well-plate
Kuncová-Kallio, J. (2007) · Tampere University Institutional Repository (Tampere University)
A Power-Law Based Model for Caspase Activated Apoptosis and Its Parameter Estimation
Tian, L. (2011) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2011.5780460
“After model complexity analysis, Gadkar et al [3, 4] employed the maximum a posteriori method to estimate the parameter in 11fractional reaction rates of this model.”
MethodResultInfluentialSelection of Simplified Models and Parameter Estimation Using Limited Data
Wu, S.-H. (2009) · QSpace (Queen's University Library)
Lazzara, M. J. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7855-3_2
Parameter Estimation of Complex Systems from Sparse and Noisy Data
Chu, Y. (2010) · OakTrust (Texas A&M University Libraries)
Modeling and Analysis of Signal Transduction Networks
Hahn, J. (2016) · MDPI eBooks · doi:10.3390/books978-3-03842-142-9
Costa, R. S., Machado, C. D., Rocha, I., & Ferreira, E. C. (2010) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Metadata
- Authors
- Gadkar, K. G., Doyle III, F. J., & Varner, J.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- Systems Biology
- Published
- 2005-03-01
- Volume
- 2
- Issue
- 1
- Pages
- 17-30
- ISSN
- 1741-2471 (ISSN-L)
- 1741-248X
- OpenAlex
- W2108182880
- Semantic Scholar
- bf7fb13093f6297f48413dfd006cf51b786c2708
- Cited by
- 75
- References
- 49
- Citations by year
- 2020: 1 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 2 · 2013: 5 · 2012: 3
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Systems Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Army Research Office
- Air Force Research Laboratory
- Institute for Collaborative Biotechnologies
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- system identification, signaling networks, parameter estimation, identifiability, Fisher information matrix, caspases, apoptosis, MAP kinase cascade
- MeSH terms
- Algorithms, Cell Physiological Phenomena, Computer Simulation, Feedback, Models, Biological, Signal Transduction, Databases, Factual, Information Storage and Retrieval
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