Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems

Raue, Andreas; Steiert, Bernhard; Schelker, Max; Kreutz, Clemens; Maiwald, Thomas; Hass, Helge; Vanlier, J Joep; Tönsing, Christian; Adlung, Lorenz; Engesser, Raphael; Mader, Wolfgang; Heinemann, Tim; Hasenauer, Jan; Schilling, Marcel; Höfer, Thomas; Klipp, Edda; Theis, Fabian J.; Klingmüller, Ursula; Schöberl, B.; Timmer, Jens

doi:10.1093/bioinformatics/btv405

Cited by 229 publications

(250 citation statements)

References 12 publications

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“…33; Table S3), with the exception that a different measuring chamber, allowing measurement of current and fluorescence from approximately the same oocyte surface, was used for the kinetics experiments. Kinetic models were fitted directly to the measured currents using the Data2Dynamics software (34). Data are presented as mean ± SEM.…”

Section: Methodsmentioning

confidence: 99%

Conformational dynamics and role of the acidic pocket in ASIC pH-dependent gating

Vullo

Bonifacio

Roy

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Acid-sensing ion channels (ASICs) are proton-activated Na + channels expressed in the nervous system, where they are involved in learning, fear behaviors, neurodegeneration, and pain sensation. In this work, we study the role in pH sensing of two regions of the ectodomain enriched in acidic residues: the acidic pocket, which faces the outside of the protein and is the binding site of several animal toxins, and the palm, a central channel domain. Using voltage clamp fluorometry, we find that the acidic pocket undergoes conformational changes during both activation and desensitization. Concurrently, we find that, although proton sensing in the acidic pocket is not required for channel function, it does contribute to both activation and desensitization. Furthermore, protonationmimicking mutations of acidic residues in the palm induce a dramatic acceleration of desensitization followed by the appearance of a sustained current. In summary, this work describes the roles of potential pH sensors in two extracellular domains, and it proposes a model of acidification-induced conformational changes occurring in the acidic pocket of ASIC1a.

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Section: Methodsmentioning

confidence: 99%

Conformational dynamics and role of the acidic pocket in ASIC pH-dependent gating

Vullo

Bonifacio

Roy

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…are applicable (Raue et al , 2013). We therefore augment an existing and well-tested implementation for parameter estimation for such systems (Raue et al , 2015) to perform selection of cell type-specific parameters based on L 1 regularization. For this purpose, trust-region optimization (Coleman and Li, 1996) was combined with a suitable strategy as presented in Schmidt et al (2009) to enable efficient optimization in the presence of L 1 penalties in nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

L 1 regularization facilitates detection of cell type-specific parameters in dynamical systems

Steiert¹,

Timmer

Kreutz

2016

Bioinformatics

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Motivation: A major goal of drug development is to selectively target certain cell types. Cellular decisions influenced by drugs are often dependent on the dynamic processing of information. Selective responses can be achieved by differences between the involved cell types at levels of receptor, signaling, gene regulation or further downstream. Therefore, a systematic approach to detect and quantify cell type-specific parameters in dynamical systems becomes necessary.Results: Here, we demonstrate that a combination of nonlinear modeling with L1 regularization is capable of detecting cell type-specific parameters. To adapt the least-squares numerical optimization routine to L1 regularization, sub-gradient strategies as well as truncation of proposed optimization steps were implemented. Likelihood-ratio tests were used to determine the optimal regularization strength resulting in a sparse solution in terms of a minimal number of cell type-specific parameters that is in agreement with the data. By applying our implementation to a realistic dynamical benchmark model of the DREAM6 challenge we were able to recover parameter differences with an accuracy of 78%. Within the subset of detected differences, 91% were in agreement with their true value. Furthermore, we found that the results could be improved using the profile likelihood. In conclusion, the approach constitutes a general method to infer an overarching model with a minimum number of individual parameters for the particular models.Availability and Implementation: A MATLAB implementation is provided within the freely available, open-source modeling environment Data2Dynamics. Source code for all examples is provided online at http://www.data2dynamics.org/.Contact: bernhard.steiert@fdm.uni-freiburg.de

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“…Using a recently proposed mathematical modeling framework (38), likelihood-based CIs are considered instead of CIs based on Fisher information which cannot be applied for nonlinear CMRO 2 quantification models with sparsely sampled 17 O signaltime curves (39). The likelihood-based CI for a parameter is determined by scanning the particular parameter along its axis while reoptimizing all other parameters in the model (see below for a detailed description), thereby revealing nonlinear relations between parameters.…”

Section: Introductionmentioning

confidence: 99%

Quantification of oxygen metabolic rates in Human brain with dynamic ¹⁷O MRI: Profile likelihood analysis

Kurzhunov

Borowiak

Hass

et al. 2016

Magnetic Resonance in Med

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Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 229 publications

References 12 publications

Conformational dynamics and role of the acidic pocket in ASIC pH-dependent gating

Conformational dynamics and role of the acidic pocket in ASIC pH-dependent gating

L 1 regularization facilitates detection of cell type-specific parameters in dynamical systems

Quantification of oxygen metabolic rates in Human brain with dynamic ¹⁷O MRI: Profile likelihood analysis

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Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 229 publications

References 12 publications

Conformational dynamics and role of the acidic pocket in ASIC pH-dependent gating

Conformational dynamics and role of the acidic pocket in ASIC pH-dependent gating

L 1 regularization facilitates detection of cell type-specific parameters in dynamical systems

Quantification of oxygen metabolic rates in Human brain with dynamic 17O MRI: Profile likelihood analysis

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Product

Resources

About

Quantification of oxygen metabolic rates in Human brain with dynamic ¹⁷O MRI: Profile likelihood analysis