Comprehensive kinetic models on metabolic systems facilitate understanding the dynamics, functional behavior, and
mechanisms of living organisms at the system level. It is critical in revealing interplays of molecules and between
molecules and conditional factors. In the present work, we attempted towards developing better modeling and analysis method
for investigating complex metabolic systems, using organisms
examples. Firstly, we extended the traditional biochemical system theory (BST) by integrating genetic regulations, forming
a new strategy and formulism of kinetic modeling (Li
BMC Syst Biol
, 2011). It is exemplified that our new modeling
method generates results that are more consistent with experimental ones and superior to those produced by previous methods.
Secondly, we developed a new method for characterizing system-level properties of components in a metabolic system. This
new method not only differs from traditional topology-based ones but also improves the conventional metabolic control
analysis (MCA). Our method turns the framework of individual sensitivity analysis in MCA into systemic criticality analysis,
which is exemplified to be advantageous over previous methods by its results. Moreover, the theoretical basis of the method
is not confined to metabolism, but can be applied to other molecular interaction systems including transcriptional networks
and signaling networks. Prospective researches based on these methodological advancements are underway to study systems
biology properties of molecules, as well as complex biological systems themselves.
Ru-Dong Li received both his B.S degree in bioinformatics and B.S. degree in mathematics & applied mathematics from Shanghai Jiaotong
University in 2008. After that, he did doctoral studies on computational systems biology in the Key Laboratory of Systems Biology, Chinese Academy
of Sciences. His recent research is focused on mathematical modeling of molecular interaction systems, and application of mathematical methods
and theories in computational studies of complex biological systems.
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