Mathematical Modelling in Plant Synthetic Biology

Anna Deneer, Christian Fleck*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.

Original languageEnglish
Pages (from-to)209-251
Number of pages43
JournalMethods in molecular biology (Clifton, N.J.)
Volume2379
DOIs
Publication statusPublished - 1 Jan 2022

Keywords

  • Descriptive and predictive modelling
  • Mathematical modelling
  • Model-based design
  • Parameter analysis
  • Spatio-temporal scales
  • Stochastic modelling
  • Theoretical–experimental plant synthetic biology approach
  • Uncertainty quantification

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