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Imre Derényi
Researcher at Eötvös Loránd University
Publications - 94
Citations - 10742
Imre Derényi is an academic researcher from Eötvös Loránd University. The author has contributed to research in topics: Brownian motion & Kinesin. The author has an hindex of 35, co-authored 93 publications receiving 10059 citations. Previous affiliations of Imre Derényi include Curie Institute & University of Chicago.
Papers
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Ac separation of particles by biased brownian motion in a two-dimensional sieve
Imre Derényi,R. Dean Astumian +1 more
TL;DR: In this article, a nonlinear coupling between an applied force and the induced transverse drift in a two-dimensional system with broken reflection symmetry transverse to the force is proposed.
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A chemically reversible Brownian motor: application to kinesin and Ncd.
R. Dean Astumian,Imre Derényi +1 more
TL;DR: This work presents another possibility, based on a Brownian ratchet, in which the direction of motion of the motor is controlled by the chemical mechanism of ATP hydrolysis and is an inherent property of a single head, and provides a way of understanding recent experiments on the ATP dependence of the variance of the distance moved in a given time.
Journal ArticleDOI
The kinesin walk: a dynamic model with elastically coupled heads
Imre Derényi,Tamás Vicsek +1 more
TL;DR: In this paper, a simple and robust model for the kinesin stepping process with elastically coupled Brownian heads is proposed, which is consistent with the measured pathway of the Kinesin ATPase.
Proceedings Article
The Kinesin Walk: A Dynamic Model with Elastically Coupled Heads.
Imre Derényi,Tamás Vicsek +1 more
TL;DR: This paper proposes a simple and robust model for the kinesin stepping process with elastically coupled Brownian heads that results in a very good fit to the experimental data and practically has no free parameters.
Journal ArticleDOI
Networks in life: Scaling properties and eigenvalue spectra
Illés J. Farkas,Imre Derényi,Imre Derényi,Hawoong Jeong,Zoltán Néda,Zoltán Néda,Zoltán N. Oltvai,Erzsébet Ravasz,András Schubert,Albert-László Barabási,Tamás Vicsek +10 more
TL;DR: This work analyzes growing networks ranging from collaboration graphs of scientists to the network of similarities defined among the various transcriptional profiles of living cells and demonstrates the use of determining the eigenvalue spectra of sparse random graph models for the categorization of small measured networks.