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Patterning of the neural tube: A 3D computational modelling approach

Ernst, Ariane LU (2019) FYTM03 20191
Department of Astronomy and Theoretical Physics - Undergoing reorganization
Computational Biology and Biological Physics - Undergoing reorganization
Abstract
Neurodegenerative diseases such as Parkinson’s can be treated with stem-cell derived specialized neurons. In order to achieve precise directed neural differentiation in vitro we need to understand the gene regulatory mechanisms behind in vivo neural tube patterning. We implement a 3D computational model of brain patterning to simulate this process. The mathematical model is set up by unifying two existing gene regulatory network models for patterning of the dorsoventral and rostrocaudal axes on the basis of WNT-signaling. A custom Python-based simulation tool was implemented in order to accurately simulate reaction-diffusion equations and signaling secretion sources. We discuss the validity of the implemented model and explore its properties... (More)
Neurodegenerative diseases such as Parkinson’s can be treated with stem-cell derived specialized neurons. In order to achieve precise directed neural differentiation in vitro we need to understand the gene regulatory mechanisms behind in vivo neural tube patterning. We implement a 3D computational model of brain patterning to simulate this process. The mathematical model is set up by unifying two existing gene regulatory network models for patterning of the dorsoventral and rostrocaudal axes on the basis of WNT-signaling. A custom Python-based simulation tool was implemented in order to accurately simulate reaction-diffusion equations and signaling secretion sources. We discuss the validity of the implemented model and explore its properties in two different geometries. We find a possible correlation between dorsoventral patterning and vesicle formation as well as a segment in which fore-, mid- and hindbrain expression are stacked dorsoventrally. Furthermore, we identify a need for an adaption of one of the networks to signal exposure time. While it is hard to make any specific predictions for in vitro differentiation at this point, we have created a working fundament for future insights using our simulation tool and a more extensive mathematical model. (Less)
Popular Abstract (German)
In dieser Arbeit simulieren wir einen wichtigen Prozess der frühen neuralen Embryonalentwicklung in drei Dimensionen mithilfe von genregulierenden Netzwerken (GRN).
Ein GRN beschreibt die Wechselwirkung zwischen chemikalischen Schaltern, welche die Zelle in ihrem Zustand halten oder sie zu einer Spezialisierung, z.B. zum Neuron, veranlassen, unter Nutzung von Differentialgleichungen. Unsere Hoffnung ist, dass mithilfe des von uns entwickelten Modells sowie des Simulations-Tools in Zukunft Vorhersagen darüber getroffen werden können, unter welchen Bedingungen Zellen sich im Labor zur neuralen Spezialisierung veranlassen könnten. Solch spezialisierte Zellen können in verschiedenen medizinischen Gebieten Anwendung finden, z.B. um... (More)
In dieser Arbeit simulieren wir einen wichtigen Prozess der frühen neuralen Embryonalentwicklung in drei Dimensionen mithilfe von genregulierenden Netzwerken (GRN).
Ein GRN beschreibt die Wechselwirkung zwischen chemikalischen Schaltern, welche die Zelle in ihrem Zustand halten oder sie zu einer Spezialisierung, z.B. zum Neuron, veranlassen, unter Nutzung von Differentialgleichungen. Unsere Hoffnung ist, dass mithilfe des von uns entwickelten Modells sowie des Simulations-Tools in Zukunft Vorhersagen darüber getroffen werden können, unter welchen Bedingungen Zellen sich im Labor zur neuralen Spezialisierung veranlassen könnten. Solch spezialisierte Zellen können in verschiedenen medizinischen Gebieten Anwendung finden, z.B. um neurodegenerative Krankheiten wie Parkinson zu behandeln. Numerisches und mathematisches Modellieren und Simulieren hat einen deutlichen wirtschaftlichen Vorteil im Vergleich zur Laborarbeit und erlaubt uns außerdem, die biologischen Prozessen zugrunde liegende Logik zu verstehen. Letzteres wäre ohne die Hilfe eines Computers nicht möglich, da solche Vorgänge oft Millionen von Molekülen und noch mehr komplizierte Interaktionen involvieren. In diesem Projekt implementieren wir ein 3D Simulations-Programm, welches die an der frühen Formation des Neuralrohrs beteiligten GRN modelliert. (Less)
Popular Abstract
In this work we simulate an important process of early embryonic neural development in three dimensions using gene regulatory networks (GRN). A GRN describes the interaction of chemical switches that keep a cell in its state or make it specialize to a specific type, e.g. neurons, using differential equations. Our aim is to make predictions on how we can direct neural cell specialization in the lab based on this model and the simulation tool we developed. These specialized cells can be used in various medical applications, for example to treat neurodegenerative diseases like Parkinson’s. Computational and mathematical modelling and simulation comes with the advantage of reduced cost in comparison to experiments and it also allows us to... (More)
In this work we simulate an important process of early embryonic neural development in three dimensions using gene regulatory networks (GRN). A GRN describes the interaction of chemical switches that keep a cell in its state or make it specialize to a specific type, e.g. neurons, using differential equations. Our aim is to make predictions on how we can direct neural cell specialization in the lab based on this model and the simulation tool we developed. These specialized cells can be used in various medical applications, for example to treat neurodegenerative diseases like Parkinson’s. Computational and mathematical modelling and simulation comes with the advantage of reduced cost in comparison to experiments and it also allows us to understand the logic behind biological activities. The latter would not be possible without computational aid, since such processes often involve millions of molecules and even more complicated interactions. In this project we implement a 3D simulation program that models the GRN involved in early neural tube formation. (Less)
Please use this url to cite or link to this publication:
author
Ernst, Ariane LU
supervisor
organization
course
FYTM03 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
gene regulatory networks, neural tube patterning, patterning of the neural tube, dopaminergic neurons, parkinson's disease, computational modelling, stem cell, GRN
language
English
id
8980148
date added to LUP
2019-06-12 08:40:01
date last changed
2019-06-12 08:40:01
@misc{8980148,
  abstract     = {{Neurodegenerative diseases such as Parkinson’s can be treated with stem-cell derived specialized neurons. In order to achieve precise directed neural differentiation in vitro we need to understand the gene regulatory mechanisms behind in vivo neural tube patterning. We implement a 3D computational model of brain patterning to simulate this process. The mathematical model is set up by unifying two existing gene regulatory network models for patterning of the dorsoventral and rostrocaudal axes on the basis of WNT-signaling. A custom Python-based simulation tool was implemented in order to accurately simulate reaction-diffusion equations and signaling secretion sources. We discuss the validity of the implemented model and explore its properties in two different geometries. We find a possible correlation between dorsoventral patterning and vesicle formation as well as a segment in which fore-, mid- and hindbrain expression are stacked dorsoventrally. Furthermore, we identify a need for an adaption of one of the networks to signal exposure time. While it is hard to make any specific predictions for in vitro differentiation at this point, we have created a working fundament for future insights using our simulation tool and a more extensive mathematical model.}},
  author       = {{Ernst, Ariane}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Patterning of the neural tube: A 3D computational modelling approach}},
  year         = {{2019}},
}