PESTO - Parameter EStimation TOolbox: PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers state-of-the art algorithms for optimization and uncertainty analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, which provides an objective function in MATLAB. PESTO has been used in various computational biology research projects. PESTO features include: Multistart optimization; Sampling routines; Profile-likelihood analysis; Visualization routines and more

References in zbMATH (referenced in 9 articles )

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  1. Abdulla, Ugur G.; Poteau, Roby: Identification of parameters for large-scale kinetic models (2021)
  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. Loos, Carolin; Hasenauer, Jan: Robust calibration of hierarchical population models for heterogeneous cell populations (2020)
  4. Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan: Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach (2020)
  5. Boiger, R.; Fiedler, A.; Hasenauer, J.; Kaltenbacher, B.: Continuous analogue to iterative optimization for PDE-constrained inverse problems (2019)
  6. Pedretscher, B.; Kaltenbacher, B.; Pfeiler, O.: Parameter identification and uncertainty quantification in stochastic state space models and its application to texture analysis (2019)
  7. Saccomani, Maria Pia; Thomaseth, Karl: Calculating all multiple parameter solutions of ODE models to avoid biological misinterpretations (2019)
  8. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  9. Abdulla, Ugur G.; Poteau, Roby: Identification of parameters in systems biology (2018)