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Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation
In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Expand
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A data-driven framework for identifying nonlinear dynamic models of genetic parts.
A key challenge in synthetic biology is the development of effective methodologies for characterization of component genetic parts in a form suitable for dynamic analysis and design. Expand
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The application of nonlinear system identification in the field of synthetic biology
The field of synthetic biology has progressed from early concept, to initial demonstrations of simple genetic parts, and more recently to biological systems composed of functional modules thatExpand