Advancements in ‘omics technologies now allow acquisition of enormous amounts of
quantitative information about biomolecules. This has led to the emergence of new
scientific sub‐disciplines e.g. computational, systems and ‘quantitative’ biology. These
disciplines examine complex biological behaviour through computational and
mathematical approaches and have resulted in substantial insights and advances in
molecular biology and physiology. Capitalizing on the accumulated knowledge and data, it
is possible to construct dynamic models of complex biological systems, thereby initiating
the so‐called ‘experiment‐model cycle’, which has been shown to be a powerful catalyst for
scientific progress. Particularly, the advances in functional genomics (e.g. mass
spectrometry‐based proteomics and next‐generation sequencing) have provided the
wealth of data fuelling these efforts.
The agriculturally and ecologically important glucosinolate (GLS) compounds of
cruciferous plants – including the model plant Arabidopsis thaliana – have been studied
extensively with regards to their biosynthesis and degradation. However, efforts to
construct a dynamic model unifying the regulatory aspects have not been made. This has
partly been due to the lack of reliable methods to quantify the associated proteins, but also
because unknown regulatory components have yet to be identified.
In this thesis, I implement a method that generates reliable and high‐quality quantitative
information about enzymes in GLS biosynthesis. This enables comparison of transcript and
protein levels across mutants and upon induction. I find that unchallenged plants show
good correspondence between protein and transcript, but that treatment with methyljasmonate
results in significant differences (chapter 1). Functional genomics are used to
study the protein interactions of the GLS enzymes, which we find to be placed in large
networks connecting GLS biosynthesis to broader physiological functions (chapter 2).
Examination of a co‐expressed gene that influences accumulation of specific GLS upon
treatment with methyl‐jasmonate is reported (chapter 3). The construction a dynamic
quantitative model of GLS hydrolysis is described. Simulations reveal potential effects on
auxin signalling that could reflect defensive strategies (chapter 4).
The results presented grant insights into, not only the dynamics of GLS biosynthesis and
hydrolysis, but also the relationship between GLS biosynthesis and the broader plant
Quantitative aspects and dynamic modelling of glucosinolate metabolism physiology. Collectively, these findings present a theoretical and experimental foundation
from which future studies into GLS dynamics can be done. Further refinement and
construction of new dynamic models will fuel the experimental exploration and give new
understanding of GLS homeostasis.