TY - CHAP
T1 - Mathematical Modelling in Plant Synthetic Biology
AU - Deneer, Anna
AU - Fleck, Christian
PY - 2022/2/22
Y1 - 2022/2/22
N2 - 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.
AB - 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.
KW - Descriptive and predictive modelling
KW - Mathematical modelling
KW - Model-based design
KW - Parameter analysis
KW - Spatio-temporal scales
KW - Stochastic modelling
KW - Theoretical–experimental plant synthetic biology approach
KW - Uncertainty quantification
U2 - 10.1007/978-1-0716-1791-5_13
DO - 10.1007/978-1-0716-1791-5_13
M3 - Chapter
C2 - 35188665
AN - SCOPUS:85125005098
SN - 9781071617908
SN - 9781071617939
T3 - Methods in molecular biology
SP - 209
EP - 251
BT - Plant Synthetic Biology
A2 - Zurbriggen, M.D.
PB - Springer
CY - New York
ER -