ArticleJournal of the Royal Society, Interface2019
Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells.PLoS computational biology · 2024Article
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- Structural Identifiability and Observability of Microbial Community Models.Bioengineering (Basel, Switzerland) · 2023Article
- Data-Driven Control of Neuronal Networks with Population-Level Measurement.Research square · 2023Article
- Benchmarking tools for a priori identifiability analysis.Bioinformatics (Oxford, England) · 2023Article
- Improving dynamic predictions with ensembles of observable models.Bioinformatics (Oxford, England) · 2023Article
- STRIKE-GOLDD 4.0: user-friendly, efficient analysis of structural identifiability and observability.Bioinformatics (Oxford, England) · 2023Article
- Optimal phase-selective entrainment of heterogeneous oscillator ensembles.SIAM journal on applied dynamical systems · 2023Article
- Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks.ACS synthetic biology · 2022Article
- Ratiometric control of cell phenotypes in monostrain microbial consortia.Journal of the Royal Society, Interface · 2022Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In this paper, we address the system identification problem in the context of biological modelling. We present and demonstrate a methodology for (i) assessing the possibility of inferring the unknown quantities in a dynamic model and (ii) effectively estimating them from output data. We introduce the term Full Input-State-Parameter Observability (FISPO) analysis to refer to the simultaneous assessment of state, input and parameter observability (note that parameter observability is also known as identifiability). This type of analysis has often remained elusive in the presence of unmeasured inputs. The method proposed in this paper can be applied to a general class of nonlinear ordinary differential equations models. We apply this approach to three models from the recent literature. First, we determine whether it is theoretically possible to infer the states, parameters and inputs, taking only the model equations into account. When this analysis detects deficiencies, we reformulate the model to make it fully observable. Then we move to numerical scenarios and apply an optimization-based technique to estimate the states, parameters and inputs. The results demonstrate the feasibility of an integrated strategy for (i) analysing the theoretical possibility of determining the states, parameters and inputs to a system and (ii) solving the practical problem of actually estimating their values.
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