Mathematical modeling of biochemical systems with PottersWheel.

The program PottersWheel has been developed to provide an intuitive and yet powerful framework for data-based modeling of dynamical systems like biochemical reaction networks. Its key functionality is multi-experiment fitting, where several experimental data sets from different laboratory conditions are fitted simultaneously in order to improve the estimation of unknown model parameters, to check the validity of a given model, and to discriminate competing model hypotheses. New experiments can be designed interactively. Models are either created text-based or using a visual model designer. Dynamically generated and compiled C files provide fast simulation and fitting procedures. Each function can either be accessed using a graphical user interface or via command line, allowing for batch processing within custom Matlab scripts. PottersWheel is designed as a Matlab toolbox, comprises 250,000 lines of Matlab and C code, and is freely available for academic usage at www.potterswheel.de .

[1]  D. Lauffenburger,et al.  Physicochemical modelling of cell signalling pathways , 2006, Nature Cell Biology.

[2]  Jens Timmer,et al.  Dynamical modeling and multi-experiment fitting with PottersWheel , 2008, Bioinform..

[3]  Julio R. Banga,et al.  Scatter search for chemical and bio-process optimization , 2007, J. Glob. Optim..

[4]  H. Akaike.  A new look at the statistical model identification , 1974 .

[5]  D. Lauffenburger,et al.  Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction , 2009, Molecular systems biology.

[6]  Ursula Klingmüller,et al.  Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood , 2009, Bioinform..

[7]  Daniel T Gillespie,et al.  Stochastic simulation of chemical kinetics. , 2007, Annual review of physical chemistry.

[8]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .

[9]  Kevin Burrage,et al.  Stochastic approaches for modelling in vivo reactions , 2004, Comput. Biol. Chem..

[10]  William S. Hlavacek,et al.  BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains , 2004, Bioinform..

[11]  G. Seber,et al.  Nonlinear Regression: Seber/Nonlinear Regression , 2005 .

[12]  J. Timmer,et al.  Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Jens Timmer,et al.  Data-based identifiability analysis of non-linear dynamical models , 2007, Bioinform..

[14]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[15]  Carol S. Woodward,et al.  Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers , 2020, ACM Trans. Math. Softw..

[16]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[17]  Jens Timmer,et al.  An error model for protein quantification , 2007, Bioinform..

[18]  E. Hairer,et al.  Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems , 2010 .

[19]  B. Kholodenko,et al.  Domain-oriented reduction of rule-based network models. , 2008, IET systems biology.

[20]  John F. Kennedy,et al.  Fundamentals of enzyme kinetics (revised edition) , 1997 .