‘Closing the loop’ in biological systems modeling — From the in silico to the in vitro

Kiparissides, Alexandros; Koutinas, Michalis; Kontoravdi, Cleo; Mantalaris, Athanasios; Pistikopoulos, Efstratios N.

doi:10.1016/j.automatica.2011.01.013

Cited by 90 publications

(78 citation statements)

References 45 publications

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“…The model at this point included 283 variables and 400 parameters (measured and derived, see §6 in the electronic supplementary material). Global sensitivity analysis (GSA) identifies the parameters that have an impact on model output, by observing the change in the outputs when parameter values are varied [33]. It assesses which experimental values critically need to be determined with experimental accuracy, and which others can be estimated or kept at their nominal values for model validity.…”

Section: Experimental Validation Of the Population Balance Modelmentioning

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: Experimental Validation Of the Population Balance Modelmentioning

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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“…Las practicas actuales de construcción de modelos en biología aun ignoran en gran medida pasos cruciales como el análisis de identifiabilidad, el diseño optimo de experimentos, y la cuantificación de la incertidumbre, aspecto esteúltimo de vital importancia en aplicaciones biológicas (Villaverde and Banga, 2014;Kiparissides et al, 2011).…”

Section: Conclusiónunclassified

Modelado de sistemas bioquímicos: De la Ley de Acción de Masas a la Aproximación Lineal del Ruido

Picó

Vignoni²,

Picó-Marco

et al. 2015

Revista Iberoamericana de Automática e Informática Industrial R

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ResumenDurante la ultima década hemos vivido una creciente aplicación de técnicas propias de las ingenierías a la biología.Áreas como la Biología de Sistemas o, más recientemente, la Biología Sintética, reciben una atención cada vez mayor por parte de los ingenieros. En particular, el modelado en estosámbitos permite la generación de nuevas hipótesis contrastables experimentalmente, y de nuevas formas de intervención biológica, así como explicaciones más o menos mecanicistas de los resultados experimentales. Una aproximación basada en modelo requiere considerar la dinámica de las reacciones bioquímicas y su regulación. En la primera parte de este tutorial se introducen el modelado determinista y reducción de modelos de la clase de reacciones bioquímicas propias de la biología molecular celular.El ruido juega un papel crucial en la dinámica de los circuitos biológicos. En elárea de control automático hay una larga tradición de modelado mediante ecuaciones diferenciales estocásticas lineales, bajo la hipótesis simplificadora de asumir que el ruido tiene una magnitud independiente de la del estado. Esta hipótesis no es válida en los circuitos biológicos. En la segunda parte del tutorial se describen los métodos de modelado estocástico más usados en biología molecular, con especial atención a denominada aproximación lineal del ruido. Copyright c 2015 CEA. Publicado por Elsevier España, S.L. Todos los derechos reservados.Palabras Clave: Sistemas estocásticos, Ecuaciones diferenciales, Modelado de sistemas continuos, Reducción de modelos, Simulación de sistemas, Ruido, Sistemas biológicos, biotecnológicos y bioprocesos. IntroducciónDurante la ultima década hemos vivido una creciente aplicación de técnicas propias de las ingenierías a la biología.Áreas como la Biología de Sistemas o, más recientemente, la Biología Sintética, reciben una atención cada vez mayor por parte de los ingenieros. El diseño y producción clásicos en biotecnología, mediante el uso de organismos genéticamente modificados, ha seguido tradicionalmente una estrategia esencialmente de tipo prueba-y-error.En losúltimos años, esta aproximación estaba prácticamen-te agotada, llevando a un cuello de botella en la generación de nuevos productos biotecnológicos. Es en este punto cuando se ve clara la necesidad de aplicar modelado computacional, de optimizar computacionalmente en lugar de adivinar por pruebay-error cada nueva ruta metabólica (Kwok, 2010). Este proceso * Autor en correspondencia.Correos electrónicos: jpico@ai2.upv.es (Jesús Picó), vignoni@mpi-cbg.de (Alejandro Vignoni), enpimar@isa.upv.es (Enric Picó-Marco), yaboa@upv.es (Yadira Boada) URL: http://sb2cl.ai2.upv.es (Jesús Picó) de incorporación de herramientas de la ingeniería a la biología se ha intensificado tremendamente con el advenimiento de la Biología Sintética. Esta se define como la ingeniería de la biología: el (re)diseño y construcción deliberados de nuevos componentes, dispositivos y sistemas biológicos para realizar nuevas funciones con un propósito utilitario (De Lorenzo, 2014). C...

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“…As outlined in the mathematical modelling development framework [45][46][47], following the development of the model type and structure, the influence of parameter uncertainty on model output was evaluated. GSA was applied in order to systematically evaluate output variability due to the nonlinear nature of the model.…”

Section: Model Analysis and Parameter Estimationmentioning

confidence: 99%

“…Each of these markers can be quantitatively experimentally validated. The model was developed using the mathematical modelling framework previously described [45][46][47]. Specifically, the parameters' effect on the model output was evaluated by GSA and the identified significant parameters were re-estimated using a set of batch control experiments.…”

Section: Introductionmentioning

confidence: 99%

A Cyclin Distributed Cell Cycle Model in GS-NS0

Munzer

Kostoglou

Georgiadis

et al. 2014

Computer Aided Chemical Engineering

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Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins-key molecules that regulate cell cycle transitionhave been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cyclespecific production rates. The solution method delivered fast computational time that renders the model's use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.PLOS Computational Biology |

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‘Closing the loop’ in biological systems modeling — From the in silico to the in vitro

Cited by 90 publications

References 45 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

Modelado de sistemas bioquímicos: De la Ley de Acción de Masas a la Aproximación Lineal del Ruido

A Cyclin Distributed Cell Cycle Model in GS-NS0

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