The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems

2017 ◽  
Vol 13 (3) ◽  
pp. 1424-1438 ◽  
Author(s):  
Patrice Koehl ◽  
Frédéric Poitevin ◽  
Rafael Navaza ◽  
Marc Delarue
Keyword(s):  
Renormalization Group ◽  
Coarse Grained ◽  
Molecular Systems

scholarly journals Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

2020 ◽  
Author(s):  
Charly Empereur-mot ◽  
Luca Pesce ◽  
Davide Bochicchio ◽  
Claudio Perego ◽  
Giovanni M. Pavan
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Structural Complexity ◽  
Coarse Grained ◽  
Complex Molecules ◽  
Swarm Optimization ◽  
Molecular Systems ◽  
Self Tuning ◽  
User Friendly ◽  
Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default<br>settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.<br>Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force<br>fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.

An efficient coarse-grained approach for the electron transport through large molecular systems under dephasing environment

2016 ◽  
Vol 89 (4) ◽  
Author(s):  
Daijiro Nozaki ◽  
Raul Bustos-Marún ◽  
Carlos J. Cattena ◽  
Gianaurelio Cuniberti ◽  
Horacio M. Pastawski
Keyword(s):  
Electron Transport ◽  
Coarse Grained ◽  
Molecular Systems

Compatible observable decompositions for coarse-grained representations of real molecular systems

2019 ◽  
Vol 151 (13) ◽  
pp. 134115 ◽  
Author(s):  
Thomas Dannenhoffer-Lafage ◽  
Jacob W. Wagner ◽  
Aleksander E. P. Durumeric ◽  
Gregory A. Voth
Keyword(s):  
Coarse Grained ◽  
Molecular Systems

Parametrizing coarse grained models for molecular systems at equilibrium

2016 ◽  
Vol 225 (8-9) ◽  
pp. 1347-1372 ◽  
Author(s):  
E. Kalligiannaki ◽  
A. Chazirakis ◽  
A. Tsourtis ◽  
M.A. Katsoulakis ◽  
P. Plecháč ◽  
...  
Keyword(s):  
Coarse Grained ◽  
Molecular Systems

scholarly journals Quantitative understanding of molecular competition as a hidden layer of gene regulatory network

2018 ◽  
Author(s):  
Ye Yuan ◽  
Lei Wei ◽  
Tao Hu ◽  
Shuailin Li ◽  
Tianrun Cheng ◽  
...  
Keyword(s):  
Biological Networks ◽  
Regulatory Mechanism ◽  
Response Speed ◽  
Coarse Grained ◽  
Motif Model ◽  
Molecular Systems ◽  
Trade Offs ◽  
Quantitative Understanding ◽  
Hidden Layer ◽  
Cellular Components

AbstractMolecular competition is ubiquitous, essential and multifunctional throughout diverse biological processes. Competition brings about trade-offs of shared limited resources among the cellular components, and it thus introduce a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. By abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically compare experimental evidences in these processes and analyzed general properties shared behind them. We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. This work uncovered the complexity and generality of molecular competition effect, which might act as a hidden regulatory mechanism with multiple functions throughout biological networks in both natural and synthetic systems.

Introduction To Renormalization Group Modeling Of Turbulence

1996 ◽  
Author(s):  
Steven A. Orszag ◽  
I. Staroselsky,
Keyword(s):  
Renormalization Group ◽  
Turbulent Flows ◽  
Equations Of Motion ◽  
Turbulence Models ◽  
Coarse Grained ◽  
Small Scale ◽  
Flow Problems ◽  
Group Modeling ◽  
Systematic Derivation ◽  
Incompressible Turbulence

The renormalization group (RNG) and related e-expansion methods are a powerful technique that allow the systematic derivation of coarse-grained equations of motion for turbulent flows and, in particular, the derivation of sophisticated turbulence models based on the fundamental underlying physics. The RNG method provides a convenient calculus for the analysis of complex physical effects in complex flows. The details of the RNG method applied to fluid mechanics differ in some crucial respects from how renormalization group techniques are applied to field theories in other branches of physics. At the present time, the RNG methods for fluid dynamics are by no means rigorously justified, so their utility must be based on the quality and quantity of results to which they lead. In this paper we discuss the basis for the RNG method and then illustrate its application to a variety of turbulent flow problems, emphasizing those points where further analysis is needed. The application of a field-theoretic method like the RNG technique to turbulence is based on the fundamental assumption of universality of small scales in turbulent flows. Such universal behavior was first suggested over 50 years ago in the seminal work of A. N. Kolmogorov who argued that the small-scale spectrum of incompressible turbulence is universal and characterized by two numbers, the rate of energy dissipation ε per unit mass and the kinematic viscosity v.

Efficient parallelization of perturbative Monte Carlo QM/MM simulations in heterogeneous platforms

2016 ◽  
Vol 31 (6) ◽  
pp. 499-516 ◽  
Author(s):  
Sebastião Miranda ◽  
Jonas Feldt ◽  
Frederico Pratas ◽  
Ricardo A Mata ◽  
Nuno Roma ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Heterogeneous Systems ◽  
Coarse Grained ◽  
Molecular Systems ◽  
Fine Grained ◽  
Central Processing ◽  
Speed Up ◽  
Graphical Processing ◽  
Computational Bottleneck ◽  
The Cost

A novel perturbative Monte Carlo mixed quantum mechanics (QM)/molecular mechanics (MM) approach has been recently developed to simulate molecular systems in complex environments. However, the required accuracy to efficiently simulate such complex molecular systems is usually granted at the cost of long executing times. To alleviate this problem, a new parallelization strategy of multi-level Monte Carlo molecular simulations is herein proposed for heterogeneous systems. It simultaneously exploits fine-grained (at the data level), coarse-grained (at the Markov chain level) and task-grained (pure QM, pure MM and QM/MM procedures) parallelism to ensure an efficient execution in heterogeneous systems composed of central processing units and multiple and possibly different graphical processing units. This is achieved by making use of the OpenCL library, together with appropriate dynamic load balancing schemes. From the conducted evaluation with real benchmarking data, a speed-up of 56x in the computational bottleneck part was observed, which results in a global speed-up of 38x for the whole simulation, reducing the time of a typical simulation from 80 hours to only 2 hours.

scholarly journals Swarm-CG: Automatic Parametrization of Bonded Termsin Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

2020 ◽  
Author(s):  
Charly Empereur-mot ◽  
Luca Pesce ◽  
Davide Bochicchio ◽  
Claudio Perego ◽  
Giovanni M. Pavan
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Structural Complexity ◽  
Coarse Grained ◽  
Complex Molecules ◽  
Swarm Optimization ◽  
Molecular Systems ◽  
Self Tuning ◽  
User Friendly ◽  
Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in<br>coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-<br>CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms<br>within 4h-24h on standard desktop machines, using an AA trajectory as reference and default<br>settings of the software. The software benefits from a user-friendly interface and two different usage<br>modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the<br>development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.<br>Excellent performances are demonstrated using a benchmark of 9 molecules of diverse<br>nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force<br>fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for<br>the optimization of models built based also on other CG schemes. Swarm-CG is available with all its<br>dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data<br>are available at: www.github.com/GMPavanLab/SwarmCG.

scholarly journals 107 Study of Coarse-Grained Method of Inelastic Cohesive Law Using Renormalization Group

2014 ◽  
Vol 2014.89 (0) ◽  
pp. _1-7_
Author(s):  
Takuya AKIYAMA ◽  
Yusuke DOI ◽  
Akihiro NAKATANI ◽  
Takaki OGAWA
Keyword(s):  
Renormalization Group ◽  
Coarse Grained ◽  
Cohesive Law

scholarly journals The Automated Optimisation of a Coarse-Grained Force Field Using Free Energy Data

2021 ◽  
Author(s):  
Javier Caceres-Delpiano ◽  
Lee-Ping Wang ◽  
Jonathan Wynne Essex
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Coarse Grained ◽  
Energy Data ◽  
Molecular Systems ◽  
Atomistic Models ◽  
Large Systems ◽  
Long Timescales

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number...

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