Awesome

Data2Dynamics Software

Contact: Andreas Raue - andreas.raue@uni-a.de (for support issues, please use the issues and forum, thanks!)

Cite:

• Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems. Raue A., et al. Bioinformatics, 31(21), 3558-3560, 2015.

• Lessons Learned from Quantitative Dynamical Modeling in Systems Biology. Raue A., et al. PLOS ONE, 8(9), e74335, 2013.

Video: See how easy model calibration can be using D2D Software in this YouTube Video. The video is recorded in real time on a dual core laptop with the Bachmann et al. 2011 example using the first data set.

Features

Major purpose: Establishing ODE models based on experimental data. The software is designed for biochemical reaction networks, but is not limited to this.

Key feature: Reliable and efficient parameter estimation techniques and statistical assessment of parameter-, measurement- and prediction uncertainties.

Some special features:

• The framework can deal with xperimental error bars but also allows fitting of error parameters (error models).

• Model inputs can be implemented as parameterized functions or cubic splines and can be estimated together with the model dynamics (read more).

• For model calibration, i.e. parameter estimation, both stochastic and deterministic numerical optimization algorithms can be used.

• For uncertainty analysis of model parameter and predictions, the profile likelihood approach and Markov chain Monte Carlo sampling approaches are available (read more).

• Efficient numerical calculation of the dynamics and derivatives in a parallelized manner (read more).

• L1 regularization of parameter fold-changes can be used (read more).

• L2 regularization and incorporation of prior knowledge

• Identification of informative experimental designs

• The software is open source and free for non-commercial usage and supports parallelization

A more comprehensive description of features is available features page in the Wiki.

Awards

The software was awarded twice as best performer in the Dialogue for Reverse Engineering Assessments and Methods (DREAM). 2011 in the Estimation of Model Parameters Challenge (DREAM6) and 2012 in the Network Topology and Parameter Inference Challenge (DREAM7). Read more about this in: Steiert B., et al. Experimental Design for Parameter Estimation of Gene Regulatory Networks. PLoS ONE 7(7), e40052, 2012

Further topics

Please read our Wiki if you are interested in details.

Installation

Check out our Wiki for installation instructions.

Copyright notice

Copyright © 2016 D2D development team. All rights reserved. Copyright text and third party software license information.