Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved

Soft Matter ◽  
2020 ◽  
Vol 16 (36) ◽  
pp. 8330-8344
Author(s):  
Shu Wang ◽  
Zhan Ma ◽  
Wenxiao Pan
Keyword(s):  
Structural Properties ◽  
Dynamic Properties ◽  
Data Driven ◽  
Coarse Grained ◽  
System P

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system.

Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism

Soft Matter ◽  
2019 ◽  
Vol 15 (38) ◽  
pp. 7567-7582 ◽  
Author(s):  
Shu Wang ◽  
Zhen Li ◽  
Wenxiao Pan
Keyword(s):  
Polymer Solutions ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Implicit Solvent ◽  
Bottom Up ◽  
System P

We present a bottom-up coarse-graining (CG) method to establish implicit-solvent CG modeling for polymers in solution, which conserves the dynamic properties of the reference microscopic system.

Data-driven Coarse-grained Modeling of Non-equilibrium Systems

Soft Matter ◽  
2021 ◽  
Author(s):  
Shu Wang ◽  
Zhan Ma ◽  
Wenxiao Pan
Keyword(s):  
Hamiltonian System ◽  
Computational Cost ◽  
Data Driven ◽  
Coarse Grained ◽  
High Dimensional ◽  
Bottom Up ◽  
Reduced Dimensions ◽  
System P ◽  
Non Equilibrium

Modeling a high-dimensional Hamiltonian system in reduced dimensions with respect to coarse-grained (CG) variables can greatly reduce computational cost and enable efficient bottom-up prediction of main features of the system...

A new spectral coarse-graining algorithm based on K-means clustering in complex networks

2019 ◽  
Vol 33 (01) ◽  
pp. 1850421 ◽  
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Original Network ◽  
Topological Information ◽  
Real World Applications ◽  
Large Scale Networks

Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm.

scholarly journals Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Adrian C. Scott ◽  
Michelle Hays ◽  
Nicholas Flann ◽  
Olli Yli-Harja ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Course ◽  
Model Development ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Data Driven ◽  
Coarse Grained ◽  
Intercellular Signaling ◽  
Growth Data ◽  
Metabolic Coupling

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

scholarly journals Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jukka Intosalmi ◽  
Adrian C. Scott ◽  
Michelle Hays ◽  
Nicholas Flann ◽  
Olli Yli-Harja ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Course ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Data Driven ◽  
Coarse Grained ◽  
Colony Development ◽  
Intercellular Signaling ◽  
Metabolic Coupling ◽  
The Impact

Abstract Background Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell–cell and metabolic coupling lead to functionally optimized structures is still limited. Results Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. Conclusions We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

scholarly journals Low-amplitude dynamic tests of fine gravel in a resonant column

2019 ◽  
Vol 97 ◽  
pp. 04024
Author(s):  
Zaven Ter-Martirosyan ◽  
Evgeny Sobolev ◽  
George Anzhelo
Keyword(s):  
Mechanical Properties ◽  
Dynamic Properties ◽  
Sample Volume ◽  
Coarse Grained ◽  
Software Systems ◽  
Resonant Column ◽  
Dynamic Effects ◽  
Laboratory Equipment ◽  
Aggregate Fractions ◽  
Sample Assembly

Construction of industrial and civil buildings, taking into account the dynamic effects on the foundations, requires special experiments on the mechanical properties of soils. This article presents the results of studying the dynamic properties of coarse gravelly soils using the resonant column method. These studies are relevant, since the determination of the dynamic properties of coarse-grained soils under laboratory conditions is associated with a restriction on the size of the fractions in the sample volume. This circumstance leads to the fact that at the moment most of the laboratory tests of the dynamic properties of coarse-grained soils are performed on smaller aggregate fractions, which, in general, significantly reduces the resulting mechanical properties of soils. It does not reflect the real operation of the foundation of buildings during dynamic effects. This paper presents a description of the available laboratory equipment, the sequence of preparation of samples of coarse grained crushed stone soil and sample assembly in the working chamber of the installation. The article contains the main graphs characterizing the change in shear modulus and damping coefficient depending on shear deformations. It is noted that the results obtained are particularly relevant for modeling the dynamic effects of natural and man-made character on the foundations of industrial and civil buildings, the bases of which are composed of coarse-grained soils. Dynamic parameters considered in this paper, can and must be used in numerical calculations by finite element method with the use of modern groundwater models in geotechnical software systems.

scholarly journals Energy renormalization for coarse-graining polymers having different segmental structures

2019 ◽  
Vol 5 (4) ◽  
pp. eaav4683 ◽  
Author(s):  
Wenjie Xia ◽  
Nitin K. Hansoge ◽  
Wen-Sheng Xu ◽  
Frederick R. Phelan ◽  
Sinan Keten ◽  
...  
Keyword(s):  
Dynamic Properties ◽  
Interaction Strength ◽  
Configurational Entropy ◽  
Coarse Graining ◽  
Soft Materials ◽  
Polymer Materials ◽  
Coarse Grained ◽  
Art Form ◽  
Polymer Models ◽  
Energy Renormalization

Multiscale coarse-grained (CG) modeling of soft materials, such as polymers, is currently an art form because CG models normally have significantly altered dynamics and thermodynamic properties compared to their atomistic counterparts. We address this problem by exploiting concepts derived from the generalized entropy theory (GET), emphasizing the central role of configurational entropy sc in the dynamics of complex fluids. Our energy renormalization (ER) method involves varying the cohesive interaction strength in the CG models in such a way that dynamic properties related to sc are preserved. We test this ER method by applying it to coarse-graining polymer melts (i.e., polybutadiene, polystyrene, and polycarbonate), representing polymer materials having a relatively low, intermediate, and high degree of glass “fragility”. We find that the ER method allows the dynamics of the atomistic polymer models to be faithfully described to a good approximation by CG models over a wide temperature range.

Extraction algorithm for optimal coarse-grained networks on complex networks

2019 ◽  
Vol 30 (11) ◽  
pp. 1950081
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Network Science ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Urgent Problem ◽  
Original Network ◽  
Topological Information ◽  
Extraction Algorithm

Coarse-graining of complex networks is a hot topic in network science. Coarse-grained networks are required to preserve the topological information or dynamic properties of the original network. Some effective coarse-graining methods have been proposed, while an urgent problem is how to obtain coarse-grained network with optimal scale. In this paper, we propose an extraction algorithm (EA) for optimal coarse-grained networks. Numerical simulation for EA on four kinds of networks and performing Kuramoto model on optimal coarse-grained networks, we find our algorithm can effectively obtain the optimal coarse-grained network.

How Does the Ribosome Fold the Proteome?

2020 ◽  
Vol 89 (1) ◽  
pp. 389-415 ◽  
Author(s):  
Anaïs M.E. Cassaignau ◽  
Lisa D. Cabrita ◽  
John Christodoulou
Keyword(s):  
Structural Properties ◽  
Protein Misfolding ◽  
Dynamic Properties ◽  
Protein Translation ◽  
Biologically Active ◽  
Active Structures ◽  
Protein Misfolding And Aggregation ◽  
Polypeptide Chains ◽  
Intrinsic Dynamic ◽  
Insight Into

Folding of polypeptides begins during their synthesis on ribosomes. This process has evolved as a means for the cell to maintain proteostasis, by mitigating the risk of protein misfolding and aggregation. The capacity to now depict this cellular feat at increasingly higher resolution is providing insight into the mechanistic determinants that promote successful folding. Emerging from these studies is the intimate interplay between protein translation and folding, and within this the ribosome particle is the key player. Its unique structural properties provide a specialized scaffold against which nascent polypeptides can begin to form structure in a highly coordinated, co-translational manner. Here, we examine how, as a macromolecular machine, the ribosome modulates the intrinsic dynamic properties of emerging nascent polypeptide chains and guides them toward their biologically active structures.

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