On Different Uses of Abstraction in Models of Developmental Systems
type: CreativeWork
description: Abstraction is centrally important to the success of models, mathematical and otherwise, in biology. The move beyond traditional reductionist approaches towards a more holistic, system level, understanding of development, has produced a concurrent growth in the types of abstractions used in quantitative modelling. This is accompanied with new challenges and opportunities in evaluating and integrating the evidence provided by different modelling approaches and in constructing coherent and general models of complex biological processes. In this thesis, I address some of these challenges by critically assessing current models and proposing a new conceptual framework to address system level understanding of developmental systems. The integration of results from different modelling approaches is demonstrated with a novel approach to parameter inference for differential equation modelling of gene regulatory networks in a developmental patterning system. After a brief introduction motivating the work and highlighting some of the central themes. This is followed by the five principal results chapters which are each summarized below. Chapter 1 presents a critique of some recent work modelling the dynamics of stem cell differentiation. Some problems with the parameterization of one of the models are demonstrated and provide context for a more general discussion of the challenges in accounting for model complexity during model selection. The chapter concludes with a more general discussion of model selection and the appropriate integration of the resulting statistical evidence into a broader corpus of knowledge about the system. Chapter 2 considers the use of hierarchical systems of levels of abstraction in the analysis of complex developmental systems, with a particular focus on the tripartite system of levels proposed by the neuroscientist David Marr. Marr’s levels have been highly influential in computational neuroscience since their inception in the 1970’s and this chapter…
abstract: Abstraction is centrally important to the success of models, mathematical and otherwise, in biology. The move beyond traditional reductionist approaches towards a more holistic, system level, understanding of development, has produced a concurrent growth in the types of abstractions used in quantitative modelling. This is accompanied with new challenges and opportunities in evaluating and integrating the evidence provided by different modelling approaches and in constructing coherent and general models of complex biological processes. In this thesis, I address some of these challenges by critically assessing current models and proposing a new conceptual framework to address system level understanding of developmental systems. The integration of results from different modelling approaches is demonstrated with a novel approach to parameter inference for differential equation modelling of gene regulatory networks in a developmental patterning system. After a brief introduction motivating the work and highlighting some of the central themes. This is followed by the five principal results chapters which are each summarized below. Chapter 1 presents a critique of some recent work modelling the dynamics of stem cell differentiation. Some problems with the parameterization of one of the models are demonstrated and provide context for a more general discussion of the challenges in accounting for model complexity during model selection. The chapter concludes with a more general discussion of model selection and the appropriate integration of the resulting statistical evidence into a broader corpus of knowledge about the system. Chapter 2 considers the use of hierarchical systems of levels of abstraction in the analysis of complex developmental systems, with a particular focus on the tripartite system of levels proposed by the neuroscientist David Marr. Marr’s levels have been highly influential in computational neuroscience since their inception in the 1970’s and this chapter…
author: Harper-Donnelly, Giles
language: eng
keywords: Developmental Biology, Levels of Analysis, Model Selection, Bayesian Modelling, Developmental Axis Patterning
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/ee202e64-4067-4e2d-8a06-bdb89c0d6794/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2022-10-04
date modified: 2026-07-22T22:24:04Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.97027