Identifiability of parametric models
Identifiability of parametric models
On global identifiability for arbitrary model parametrizations
Automatica (Journal of IFAC)
On the identifiability and distinguishability of nonlinear parametric models
M2SABI Proceedings of the 1st IMACS-IFAC symposium on Mathematical modelling and simulation in agriculture and bio-industries
Sensitivity analysis of model output: variance-based methods make the difference
Proceedings of the 29th conference on Winter simulation
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
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This work presents a holistic 'closed loop' approach for the development of models of biological systems. The ever-increasing availability of experimental information necessitates the advancement of a systematic methodology to organise and utilise these data. Herein, we present a biological model building framework that maps the treatment of the information from the initial conception of the model, through its experimental validation and finally to its application in model-based optimisation studies. We highlight and discuss current issues associated with the development of mathematical models of biological systems and share our perspective towards a holistic 'closed loop' approach that will facilitate the control of the in vitro through the in silico.