Optimality and identification of dynamic models in systems biology: an inverse optimal control framework

Tsiantis, Nikolaos; Balsa‐Canto, Eva; Banga, Julio R.

doi:10.1093/bioinformatics/bty139

Cited by 25 publications

(14 citation statements)

References 65 publications

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“…In order to take into account the estimation of not only the unknown model parameters but also any unknown inputs, the above PE formulation can be generalized into an optimal tracking problem . This type of problem, as a special case of nonlinear optimal control problem (described in [ 35 ] as IOCP-1), defines the simultaneous estimation problem of both unknown time-dependent inputs w ( t ) and unknown time-invariant parameters θ .…”

Section: Methodsmentioning

confidence: 99%

“…To avoid any confusion, we remark that in the optimal tracking problem stated above we do not seek the inference of the underlying optimality principles, as considered in the more general inverse optimal control formulation (see [ 35 ] and references therein). In other words, the problem considered here is restricted to estimating the unknown inputs and parameters of the model that best explain (fit) the available data.…”

Section: Methodsmentioning

confidence: 99%

“…the FISPO analysis) has been solved? In a previous work [ 35 ], we addressed the estimation problem with an optimal tracking approach, with which we inferred both the time-dependent inputs and the time-invariant parameters simultaneously from noisy dynamic data. We provided an implementation of this approach along with examples, as an add-on for the AMIGO2 toolbox [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…An alternative open source software for input reconstruction is Data2Dynamics/d2d [ 37 ]. We remark that in [ 35 ] we did not consider the reconstructibility problem, i.e. we did not assess the FISPO of the models used as case studies, since we did not have the tools for such analysis.…”

Section: Introductionmentioning

confidence: 99%

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Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models

Villaverde¹,

Tsiantis

Banga³

2019

J. R. Soc. Interface.

Self Cite

137

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models

Villaverde¹,

Tsiantis

Banga³

2019

J. R. Soc. Interface.

Self Cite

137

View full text Add to dashboard Cite

show abstract

“…In most cases, data collected in experiments are not actively designed for model parameter estimation and not necessarily cover all the features of the cell system (Franceschini & Macchietto, ; Van Daele et al, ). Even with proper source data to be fitted, the correctness of a constructed model and its identifiability remain a big issue in the iterative process (Anane et al, ; Balsa‐Canto, Henriques, Gábor, & Banga, ; Krausch et al, ; Muñoz‐Tamayo et al, ; Stapor et al, ; Tsiantis, Balsa‐Canto, & Banga, ; Villaverde et al, ). Besides these, the kinetic model also suffered from a lack of standardization regardless of the fact that SBML (Hucka et al, ) has already been brought out more than 15 years and it was supported by a lot of construction toolboxes (Choi et al, ; Funahashi et al, ; Hoops et al, ; Schmidt & Jirstrand, ; Villaverde et al, ).…”

Section: Future Prospectmentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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pyFOOMB: Python framework for object oriented modeling of bioprocesses

Hemmerich

Tenhaef

Wiechert

et al. 2021

Engineering in Life Sciences

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Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black‐box bioprocess modeling with non‐linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi‐purpose programing language, ODEs can be formulated in an object‐oriented manner, which facilitates their modular design, reusability, and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi‐stage processes, discontinuities in the right‐hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programing language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.

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Optimality and identification of dynamic models in systems biology: an inverse optimal control framework

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 25 publications

References 65 publications

Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models

Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

pyFOOMB: Python framework for object oriented modeling of bioprocesses

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