Optimality principles have been used to explain many biological processes and systems. However, the functions being optimized are in general unknown a priori. In [1] we have presented an inverse optimal control (IOC) framework for modeling dynamics in systems biology. The objective is to identify the underlying optimality principle from observed time-series data and simultaneously estimate unmeasured time-dependent inputs and time-invariant model parameters. As a special case, we also consider the problem of optimal simultaneous estimation of inputs and parameters from noisy data. Here we provide the scripts necessary to reproduce the case studies considered in [1]. In order to run these scripts, users will need: - a Matlab R2015 (or later) installation, under Windows or Linux operating systems - the AMIGO2 toolbox with the IOC add-on, available at: https://sites.google.com/site/amigo2toolbox/home/amigo2_ioc Users need to make sure that the above AMIGO2 toolbox is fully functional before attempting to run the inverse optimal control case studies. Please refer to the AMIGO2 documentation. All the input files for the 4 case studies and their particular subcases are given with the same names as reported in the main paper and the supplementary information of [1]. Detailed instructions to run each case study are provided in readme.txt files. References [1] Tsiantis, N., E. Balsa-Canto and J. R. Banga (2018) Optimality and identification of dynamic models in systems biology: an inverse optimal control framework. Bioinformatics 34(14):2433-2440. https://doi.org/10.1093/bioinformatics/bty139

{"references": ["[1] Nikolaos Tsiantis, Eva Balsa-Canto, Julio R Banga; Optimality and identification of dynamic models in systems biology: an inverse optimal control framework, Bioinformatics, Volume 34, Issue 14, 15 July 2018, Pages 2433\u20132440, https://doi.org/10.1093/bioinformatics/bty139"]}

Contact: nikolaostsiantis@gmail.com, ebalsa@iim.csic.es, julio@iim.csic.es