A framework for the design, modeling and optimization of biomedical systems

Velliou, Eirini; Fuentes-Garí, María; Misener, Ruth; Pefani, Eleni; Rende, Maria; Panoskaltsis, Nicki; Mantalaris, Athanasios; Pistikopoulos, Efstratios N.

doi:10.1016/b978-0-444-63433-7.50023-7

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…The development of detailed models of the cell cycle that are experimentally validated is critical in the implementation of more advanced PK/PD models [50,51]. Additionally, linking a small subset of measurable variables to unique characteristics of the individual is necessary for the development of personalized treatment.…”

Section: Discussionmentioning

confidence: 99%

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

Fuentes-Garí

Misener

García-Münzer

et al. 2015

J. R. Soc. Interface.

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Acute myeloid leukaemia is characterized by marked inter-and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics.

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Section: Discussionmentioning

confidence: 99%

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

Fuentes-Garí

Misener

García-Münzer

et al. 2015

J. R. Soc. Interface.

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“…Use of mp-MPC in biomedical applications: While mp-MPC has found applications within biomedical sciences (Dua et al, 2006), the general complexity of biological and biomedical systems makes a model-based optimisation approach rather challenging. Applications of PAROC to such systems include type-1 diabetes (Zavitsanou et al, 2014), leukemia (Velliou et al, 2014) and anesthesia (Chang et al, 2014).…”

Section: Authorsmentioning

confidence: 99%

PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems

Pistikopoulos

Diangelakis

Oberdieck

et al. 2015

Chemical Engineering Science

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134

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“…[22] Furthermore the framework is versatile and process-independent with wide applicability; [23] implemented to systems including: (i) a combined heat and power (CHP) co-generation system for residential use, [24,25] (ii) distillation column, [19] (iii) a periodic chromatographic separation system of monoclonal antibodies, [26] (iv) pressure swing absorption, [27] (v) PEM fuel cell energy systems, [28,29] (vi) hydrogen storage tank, [30] (vii) batch polymerization system, [31] (viii) wind turbines, [32] and (ix) drug delivery systems including anaesthesia, [33] type-1 diabetes, [34,35] and leukemia. [36,37] PAROC addresses different classes of control problems such as: (i) nominal mpMPC, (ii) hybrid mpMPC, (iii) robust mpMPC, (iv) simultaneous mpMPC and moving horizon estimation, (v) integration of scheduling and control, and (vi) development of mp-MPC for periodic systems. The main features of PAROC are briefly discussed next.…”

Section: The Paroc Frameworkmentioning

confidence: 99%

A multiparametric model‐based optimization and control approach to anaesthesia

Nașcu

Pistikopoulos

2016

Can J Chem Eng

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In this work we present PAROC, an integrated framework software platform for the development of explicit model predictive controllers, and we apply it to the intravenous anaesthesia process. First, we overview the main features of PAROC, including high-fidelity modelling, state-of-the-art multiparametric optimization techniques, explicit/multiparametric model predictive control strategies, and a closed loop validation step. We then apply PAROC to the anaesthesia process, in particular for the development of explicit/multiparametric model predictive controllers for the induction and maintenance phases of intravenous anaesthesia validated on a set of 12 patients.

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A framework for the design, modeling and optimization of biomedical systems

Cited by 13 publications

References 18 publications

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems

A multiparametric model‐based optimization and control approach to anaesthesia

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