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Complex Nonlinear Networks

Achievement/Results

Garrett Jenkinson studies questions that involve the time-dependent aspects (dynamics) of large networks composed of many parts communicating with each other in complex nonlinear ways.

This research can help reveal how the difference between cells are regulated, and how might diseases like cancer might arise from mis-regulation. It can also be applied to issues of disease propogation through a population- for example, how does an infection like SARS spread through a large population of people, and what public policies might prevent pandemics?

To address these questions, Garrett is developing a general and mathematically rigorous foundation for the modeling and analysis of complex nonlinear networks. Because realistic computing constraints must be considered, he uses theoretically justified averages and approximations when necessary. Physical and thermodynamic considerations prevent oversimplification of the problem – a commonly made mistake when using unjustified assumptions (i.e. linearity) which leads to erroneous conclusions. This approach will provide answers to many fundamental questions associated with the study of complex nonlinear networks and produce new insights across many scientific disciplines.

Address Goals

Garrett’s research in applying insights from dynamical systems analysis to cells is at the forefront of the field of systems biology. Insights gained from these systems can be used to design cellular experiments. As a theorist and modeller, Garrett’s participation in journal tutorial club with his cohort has enriched the scope of their disucssions.