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**Harvard**

Jullesson, D., Johansson, R., Rajan, M., Strålfors, P. och Cedersund, G. (2015) *Dominant negative inhibition data should be analyzed using mathematical modeling - Re-interpreting data from insulin signaling*.

** BibTeX **

@article{

Jullesson2015,

author={Jullesson, David and Johansson, Rikard and Rajan, Meenu R. and Strålfors, Peter and Cedersund, Gunnar},

title={Dominant negative inhibition data should be analyzed using mathematical modeling - Re-interpreting data from insulin signaling},

journal={The FEBS Journal},

issn={1742-464X},

volume={282},

issue={4},

pages={788},

abstract={As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data. Dominant negative inhibition data is commonly used to experimentally unravel signaling systems. We show that the traditional interpretation of such data is incomplete, and propose an improved model-based approach. The improvements are demonstrated on real data from insulin signaling. These results demonstrate the need for mathematical modeling as a tool for data analysis, even for small systems and simple data.},

year={2015},

keywords={dominant negative, insulin signaling, S6K, uncertainty analysis},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 230986

A1 Jullesson, David

A1 Johansson, Rikard

A1 Rajan, Meenu R.

A1 Strålfors, Peter

A1 Cedersund, Gunnar

T1 Dominant negative inhibition data should be analyzed using mathematical modeling - Re-interpreting data from insulin signaling

YR 2015

JF The FEBS Journal

SN 1742-464X

VO 282

IS 4

AB As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data. Dominant negative inhibition data is commonly used to experimentally unravel signaling systems. We show that the traditional interpretation of such data is incomplete, and propose an improved model-based approach. The improvements are demonstrated on real data from insulin signaling. These results demonstrate the need for mathematical modeling as a tool for data analysis, even for small systems and simple data.

LA eng

DO 10.1111/febs.13182

LK http://dx.doi.org/10.1111/febs.13182

OL 30