ArticleBMC systems biology2013
Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality.NPJ systems biology and applications · 2024Article
- A mathematical model for strigolactone biosynthesis in plants.Frontiers in plant science · 2022Article
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
- The best models of metabolism.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- Performance of objective functions and optimisation procedures for parameter estimation in system biology models.NPJ systems biology and applications · 2017Article
- A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology.PloS one · 2017Article
- Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach.Bioinformatics (Oxford, England) · 2015Article
- A technique for determining the signs of sensitivities of steady states in chemical reaction networks.IET systems biology · 2014Article
- Computational strategies for a system-level understanding of metabolism.Metabolites · 2014Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality.
resultsHere, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions.
conclusionThe capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters.
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