Data2Dynamics

Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems. Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting. We present a modeling environment for MATLAB that pioneers these challenges. The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications. AVAILABILITY AND IMPLEMENTATION: The Data2Dynamics modeling environment is MATLAB based, open source and freely available at http://www.data2dynamics.org.


References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
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  1. Jakob Vanhoefer, Marta R. A. Matos, Dilan Pathirana, Yannik Schälte, Jan Hasenauer: yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML (2021) not zbMATH
  2. Leonard Schmiester, Yannik Schälte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Fröhlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Müller, Dilan Pathirana, Elba Raimúndez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Städter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, Daniel Weindl: PEtab - interoperable specification of parameter estimation problems in systems biology (2020) arXiv
  3. Paul F. Lang, Sungho Shin, Victor M. Zavala: SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization (2020) arXiv
  4. Qian, George; Mahdi, Adam: Sensitivity analysis methods in the biomedical sciences (2020)
  5. Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan: Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach (2020)
  6. Schweinoch, Darius; Bachmann, Pia; Clausznitzer, Diana; Binder, Marco; Kaderali, Lars: Mechanistic modeling explains the dsRNA length-dependent activation of the RIG-I mediated immune response (2020)
  7. Clément, Frédérique; Robin, Frédérique; Yvinec, Romain: Analysis and calibration of a linear model for structured cell populations with unidirectional motion: application to the morphogenesis of ovarian follicles (2019)
  8. Pfister, Niklas; Bauer, Stefan; Peters, Jonas: Learning stable and predictive structures in kinetic systems (2019)
  9. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  10. Abdulla, Ugur G.; Poteau, Roby: Identification of parameters in systems biology (2018)
  11. Rami Yaari; Itai Dattner: simode: R Package for statistical inference of ordinary differential equations using separable integral-matching (2018) arXiv