Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Giulini, Marco"'
Autor:
Giulini, Marco, Fiorentini, Raffaele, Tubiana, Luca, Potestio, Raffaello, Menichetti, Roberto
Bottom-up coarse-grained (CG) models proved to be essential to complement and sometimes even replace all-atom representations of soft matter systems and biological macromolecules. The development of low-resolution models takes the moves from the redu
Externí odkaz:
http://arxiv.org/abs/2403.08097
Autor:
Giulini, Marco
Low-resolution, coarse-grained models are powerful computational tools to investigate the behavior of biological systems over time and length scales that are not accessible to all-atom Molecular Dynamics simulations. While several algorithms exist th
Externí odkaz:
https://hdl.handle.net/11572/330532
Complex systems are characterised by a tight, nontrivial interplay of their constituents, which gives rise to a multi-scale spectrum of emergent properties. In this scenario, it is practically and conceptually difficult to identify those degrees of f
Externí odkaz:
http://arxiv.org/abs/2203.00100
Simplified representations of macromolecules help in rationalising and understanding the outcome of atomistic simulations, and serve to the construction of effective, coarse-grained models. The number and distribution of coarse-grained sites bears a
Externí odkaz:
http://arxiv.org/abs/2106.08223
Autor:
Errica, Federico, Giulini, Marco, Bacciu, Davide, Menichetti, Roberto, Micheli, Alessio, Potestio, Raffaello
The limits of molecular dynamics (MD) simulations of macromolecules are steadily pushed forward by the relentless developments of computer architectures and algorithms. This explosion in the number and extent (in size and time) of MD trajectories ind
Externí odkaz:
http://arxiv.org/abs/2007.08658
In the theoretical modelling of a physical system a crucial step consists in the identification of those degrees of freedom that enable a synthetic, yet informative representation of it. While in some cases this selection can be carried out on the ba
Externí odkaz:
http://arxiv.org/abs/2004.03988
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Giulini, Marco, Potestio, Raffaello
Deep Learning (DL) algorithms hold great promise for applications in the field of computational biophysics. In fact, the vast amount of available molecular structures, as well as their notable complexity, constitutes an ideal context in which DL-base
Externí odkaz:
http://arxiv.org/abs/1901.00915
Autor:
Giulini, Marco1 (AUTHOR), Honorato, Rodrigo V.1 (AUTHOR), Rivera, Jesús L.1 (AUTHOR), Bonvin, Alexandre M. J. J.1 (AUTHOR) a.m.j.j.bonvin@uu.nl
Publikováno v:
Communications Biology. 1/6/2024, Vol. 7 Issue 1, p1-9. 9p.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.