Literature DB >> 35188665

Mathematical Modelling in Plant Synthetic Biology.

Anna Deneer1, Christian Fleck2,3.   

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.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

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

Mesh:

Year:  2022        PMID: 35188665     DOI: 10.1007/978-1-0716-1791-5_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  57 in total

1.  Noise in eukaryotic gene expression.

Authors:  William J Blake; Mads KAErn; Charles R Cantor; J J Collins
Journal:  Nature       Date:  2003-04-10       Impact factor: 49.962

Review 2.  Network motifs: theory and experimental approaches.

Authors:  Uri Alon
Journal:  Nat Rev Genet       Date:  2007-06       Impact factor: 53.242

Review 3.  Metabolic flux analysis in plants: coping with complexity.

Authors:  Doug K Allen; Igor G L Libourel; Yair Shachar-Hill
Journal:  Plant Cell Environ       Date:  2009-04-22       Impact factor: 7.228

Review 4.  Systems biology: parameter estimation for biochemical models.

Authors:  Maksat Ashyraliyev; Yves Fomekong-Nanfack; Jaap A Kaandorp; Joke G Blom
Journal:  FEBS J       Date:  2009-02       Impact factor: 5.542

5.  Computational procedures for optimal experimental design in biological systems.

Authors:  E Balsa-Canto; A A Alonso; J R Banga
Journal:  IET Syst Biol       Date:  2008-07       Impact factor: 1.615

Review 6.  Parameter estimation and optimal experimental design.

Authors:  Julio R Banga; Eva Balsa-Canto
Journal:  Essays Biochem       Date:  2008       Impact factor: 8.000

7.  A universal biomolecular integral feedback controller for robust perfect adaptation.

Authors:  Stephanie K Aoki; Gabriele Lillacci; Ankit Gupta; Armin Baumschlager; David Schweingruber; Mustafa Khammash
Journal:  Nature       Date:  2019-06-19       Impact factor: 49.962

Review 8.  Synthetic Biology: Engineering Living Systems from Biophysical Principles.

Authors:  Bryan A Bartley; Kyung Kim; J Kyle Medley; Herbert M Sauro
Journal:  Biophys J       Date:  2017-03-28       Impact factor: 4.033

9.  Bayesian design of synthetic biological systems.

Authors:  Chris P Barnes; Daniel Silk; Xia Sheng; Michael P H Stumpf
Journal:  Proc Natl Acad Sci U S A       Date:  2011-08-29       Impact factor: 11.205

Review 10.  Optimization in computational systems biology.

Authors:  Julio R Banga
Journal:  BMC Syst Biol       Date:  2008-05-28
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