Approaches to embryonic neurodevelopment: from neural cell to neural tube formation through mathematical models

Ali H Rafati; Sâmia Joca; Regina T Vontell; Gregers Wegener; Maryam Ardalan

doi:10.1093/bib/bbae265

Approaches to embryonic neurodevelopment: from neural cell to neural tube formation through mathematical models

Ali H Rafati, Sâmia Joca, Regina T Vontell, Gregers Wegener, Maryam Ardalan

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Abstract

The development of the human central nervous system initiates in the early embryonic period until long after delivery. It has been shown that several neurological and neuropsychiatric diseases originate from prenatal incidents. Mathematical models offer a direct way to understand neurodevelopmental processes better. Mathematical modelling of neurodevelopment during the embryonic period is challenging in terms of how to 'Approach', how to initiate modelling and how to propose the appropriate equations that fit the underlying dynamics of neurodevelopment during the embryonic period while including the variety of elements that are built-in naturally during the process of neurodevelopment. It is imperative to answer where and how to start modelling; in other words, what is the appropriate 'Approach'? Therefore, one objective of this study was to tackle the mathematical issue broadly from different aspects and approaches. The approaches were divided into three embryonic categories: cell division, neural tube growth and neural plate growth. We concluded that the neural plate growth approach provides a suitable platform for simulation of brain formation/neurodevelopment compared to cell division and neural tube growth. We devised a novel equation and designed algorithms that include geometrical and topological algorithms that could fit most of the necessary elements of the neurodevelopmental process during the embryonic period. Hence, the proposed equations and defined mathematical structure would be a platform to generate an artificial neural network that autonomously grows and develops.

Original language	English
Article number	bbae265
Journal	Briefings in bioinformatics
Volume	25
Issue	4
ISSN	1467-5463
DOIs	https://doi.org/10.1093/bib/bbae265
Publication status	Published - 1 Jul 2024

Keywords

Algorithms
Animals
Cell Division
Embryonic Development
Humans
Models, Neurological
Neural Networks, Computer
Neural Plate/cytology
Neural Tube/embryology
Neurogenesis
Neurons/cytology
natural transformation
topological spaces
neurodevelopment
mathematical equations
embryonic period

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10.1093/bib/bbae265Licence: CC BY

Cite this

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title = "Approaches to embryonic neurodevelopment: from neural cell to neural tube formation through mathematical models",

abstract = "The development of the human central nervous system initiates in the early embryonic period until long after delivery. It has been shown that several neurological and neuropsychiatric diseases originate from prenatal incidents. Mathematical models offer a direct way to understand neurodevelopmental processes better. Mathematical modelling of neurodevelopment during the embryonic period is challenging in terms of how to 'Approach', how to initiate modelling and how to propose the appropriate equations that fit the underlying dynamics of neurodevelopment during the embryonic period while including the variety of elements that are built-in naturally during the process of neurodevelopment. It is imperative to answer where and how to start modelling; in other words, what is the appropriate 'Approach'? Therefore, one objective of this study was to tackle the mathematical issue broadly from different aspects and approaches. The approaches were divided into three embryonic categories: cell division, neural tube growth and neural plate growth. We concluded that the neural plate growth approach provides a suitable platform for simulation of brain formation/neurodevelopment compared to cell division and neural tube growth. We devised a novel equation and designed algorithms that include geometrical and topological algorithms that could fit most of the necessary elements of the neurodevelopmental process during the embryonic period. Hence, the proposed equations and defined mathematical structure would be a platform to generate an artificial neural network that autonomously grows and develops.",

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author = "Rafati, {Ali H} and S{\^a}mia Joca and Vontell, {Regina T} and Gregers Wegener and Maryam Ardalan",

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Approaches to embryonic neurodevelopment: from neural cell to neural tube formation through mathematical models. / Rafati, Ali H ; Joca, Sâmia; Vontell, Regina T et al.
In: Briefings in bioinformatics, Vol. 25, No. 4, bbae265, 01.07.2024.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - Approaches to embryonic neurodevelopment

T2 - from neural cell to neural tube formation through mathematical models

AU - Rafati, Ali H

AU - Joca, Sâmia

AU - Vontell, Regina T

AU - Wegener, Gregers

AU - Ardalan, Maryam

N1 - © The Author(s) 2024. Published by Oxford University Press.

PY - 2024/7/1

Y1 - 2024/7/1

N2 - The development of the human central nervous system initiates in the early embryonic period until long after delivery. It has been shown that several neurological and neuropsychiatric diseases originate from prenatal incidents. Mathematical models offer a direct way to understand neurodevelopmental processes better. Mathematical modelling of neurodevelopment during the embryonic period is challenging in terms of how to 'Approach', how to initiate modelling and how to propose the appropriate equations that fit the underlying dynamics of neurodevelopment during the embryonic period while including the variety of elements that are built-in naturally during the process of neurodevelopment. It is imperative to answer where and how to start modelling; in other words, what is the appropriate 'Approach'? Therefore, one objective of this study was to tackle the mathematical issue broadly from different aspects and approaches. The approaches were divided into three embryonic categories: cell division, neural tube growth and neural plate growth. We concluded that the neural plate growth approach provides a suitable platform for simulation of brain formation/neurodevelopment compared to cell division and neural tube growth. We devised a novel equation and designed algorithms that include geometrical and topological algorithms that could fit most of the necessary elements of the neurodevelopmental process during the embryonic period. Hence, the proposed equations and defined mathematical structure would be a platform to generate an artificial neural network that autonomously grows and develops.

AB - The development of the human central nervous system initiates in the early embryonic period until long after delivery. It has been shown that several neurological and neuropsychiatric diseases originate from prenatal incidents. Mathematical models offer a direct way to understand neurodevelopmental processes better. Mathematical modelling of neurodevelopment during the embryonic period is challenging in terms of how to 'Approach', how to initiate modelling and how to propose the appropriate equations that fit the underlying dynamics of neurodevelopment during the embryonic period while including the variety of elements that are built-in naturally during the process of neurodevelopment. It is imperative to answer where and how to start modelling; in other words, what is the appropriate 'Approach'? Therefore, one objective of this study was to tackle the mathematical issue broadly from different aspects and approaches. The approaches were divided into three embryonic categories: cell division, neural tube growth and neural plate growth. We concluded that the neural plate growth approach provides a suitable platform for simulation of brain formation/neurodevelopment compared to cell division and neural tube growth. We devised a novel equation and designed algorithms that include geometrical and topological algorithms that could fit most of the necessary elements of the neurodevelopmental process during the embryonic period. Hence, the proposed equations and defined mathematical structure would be a platform to generate an artificial neural network that autonomously grows and develops.

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KW - Animals

KW - Cell Division

KW - Embryonic Development

KW - Humans

KW - Models, Neurological

KW - Neural Networks, Computer

KW - Neural Plate/cytology

KW - Neural Tube/embryology

KW - Neurogenesis

KW - Neurons/cytology

KW - natural transformation

KW - topological spaces

KW - neurodevelopment

KW - mathematical equations

KW - embryonic period

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DO - 10.1093/bib/bbae265

M3 - Journal article

C2 - 38851297

SN - 1467-5463

VL - 25

JO - Briefings in bioinformatics

JF - Briefings in bioinformatics

IS - 4

M1 - bbae265

ER -

Approaches to embryonic neurodevelopment: from neural cell to neural tube formation through mathematical models

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Keywords

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