ライフサイエンスコーパス: coarse-graining

1 markedly reduced for this ensemble type with coarse graining.

2 rmal connections to microscopic physics, and coarse graining.

3 of network visualization, data ordering and coarse-graining.

4 ple and applicable to models at any level of coarse-graining.

5 is particularly well-suited for Hamiltonian coarse-graining.

6 mation at small scales, an approach known as coarse-graining.

7 , which we also use as a means of principled coarse-graining.

8 we implement a continuum model obtained from coarse graining a collection of self-propelled rods, wit

9 that of a macromolecule for the criteria of coarse-graining a cytoplasmic model.

10 o identify suitable groups of components for coarse-graining a network and achieve a low computationa

11 for network visualization, data ordering and coarse-graining accomplished this goal.

12 processes-plays a key role, especially when coarse-graining across scales to capture the system's ef

13 Here we use an iterative data coarse-graining algorithm termed 'diffusion condensation

14 e any additional adjustable parameters after coarse graining and is computationally very fast.

15 l strategies with variable discretization or coarse graining and unbinding dynamics, and although gen

16 n protocol which decreases the resolution by coarse-graining and averaging over short similarity dist

17 The storage errors due to coarse-graining and diffusion trade off so that informat

18 based multiscale simulations where a dynamic coarse-graining and force-blending method is required.

19 cussed, with a key focus on structure factor coarse-graining and hydration contribution.

20 We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for c

21 greater challenge in complex systems, where coarse-graining and statistical mechanics descriptions b

22 the theoretical underpinnings and history of coarse-graining and summarize the state of the field, or

23 allows us to relate model parameters between coarse-grainings and which provides a more precise meani

24 g ODE, PDE, and SDE discovery, as well as in coarse-graining applications, such as homogenization and

25 bonding contact metric which is an intuitive coarse graining approach.

26 the development of the model, two levels of coarse-graining are explored and the importance of retai

27 Using coarse-graining as an analysis method reveals that cofil

28 onally-reduced macroscopic variable (e.g., a coarse-graining) as emergent to the extent that it behav

29 Furthermore, we discuss how organizational coarse-graining can be applied to spatial dynamics by sh

30 ions of molecular states to provide powerful coarse-graining capabilities, for example to merge Boole

31 In this work we extend this coarse-graining capability to the setting of Hamiltonian

32 matic methodology, called essential dynamics coarse-graining (ED-CG), has been developed for defining

33 ematic methodology called essential dynamics coarse-graining (ED-CG).

34 olids and nonperturbative approach (by super-coarse-graining elasticity into internal bending modes)

35 Retraining for different coarse-graining factors shows the parameterization perfo

36 um models are not known a priori or analytic coarse graining fails, as often is the case for nondilut

37 A sequence of manual and automatic coarse-grainings finally leads to the coarsest determini

38 Systematic coarse-graining from an all-atom description of the disa

39 Because of its large size, coarse graining helps to simplify and to aid in the unde

40 In this study, we used a combination of coarse graining, hierarchical natural move Monte Carlo a

41 Although coarse-graining is employed in MD and other approaches,

42 ndition under which non-reciprocity survives coarse-graining, leading to a wealth of dynamical patter

43 ve explicitly, non-reciprocity may fade upon coarse-graining, leading to large-scale equilibrium desc

44 We use a coarse-graining method in the graph-theoretic linear fra

45 Here, we use a coarse-graining method to analyze scales much larger tha

46 tonomous predictor of chaotic dynamics, as a coarse-graining method, and as a data-adaptive de-noisin

47 Extension Algorithm via Covariance Hessian) coarse-graining method, in which the force constants of

48 Here, a coarse-graining method, REACH, is introduced, in which t

49 To deal with such multiscale systems, coarse-graining methods are needed that allow the dynami

50 amework for complex systems where analytical coarse-graining methods are not applicable, and can, in

51 Efficient coarse-graining methods are required to reduce the intra

52 monstrate the orchestration of various novel coarse-graining methods by applying them to the mitotic

53 So far, exact and heuristic coarse-graining methods have been mostly restricted to t

54 ar systems can greatly benefit from a set of coarse-graining methods that, ideally, can be automatica

55 oying a combination of experimental data and coarse-graining methods, are used to explore the structu

56 challenge, multiscale approaches, including coarse-graining methods, become necessary.

57 We present a strategy for coarse-graining multidimensional data while maintaining

58 ian-Langevin dynamics principles to derive a coarse-graining multiscale myofilament model that can de

59 ial tradeoffs associated with the process of coarse graining NMMII ensembles and highlight the robust

60 Coarse graining of protein interactions provides a means

61 lly combining the Morone-Makse algorithm and coarse graining of the network in which we regard a comm

62 This relationship was robust to scaling and coarse graining of the sensor array.

63 A technique, using coarse graining of the Vlasov equation, is proposed, sho

64 Coarse graining of these ensemble types from two sets of

65 Coarse-graining of atomistic force fields allows us to i

66 ion without explicit enumeration of rates or coarse-graining of configuration space, and so the proce

67 his functional coordinate system, permitting coarse-graining of microbiomes in terms of ecological ni

68 As such, we propose a coarse-graining of neuronal networks to ensemble-nodes,

69 Coarse-graining of protein interactions provides a means

70 n is progressively decreased by hierarchical coarse-graining of the anatomical regions.

71 It has been recently shown that the coarse-graining of the structures of polypeptide chains

72 uch as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approxima

73 neities and discontinuities, are ignored by 'coarse graining' or 'smoothing'.

74 ssues is how to identify the right scale for coarse-graining, or equivalently, the right number of de

75 r the first time, a long-standing problem in coarse-graining polymer systems, namely, how to accurate

76 rge problems, we also develop an approximate coarse-graining procedure that avoids the need for negat

77 l populations in the mouse under an activity coarse-graining procedure, and they were explained as a

78 to improve model elaboration, refinement and coarse graining procedures to better understand the rele

79 n modeling large regions of the cortex, many coarse-graining procedures have been invoked to obtain e

80 Built on a coarse-graining process called diffusion condensation, M

81 lations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative disti

82 function of shear strain, and use that in a coarse-graining rate equation formulation for constructi

83 acids and lipid molecules); 3), shape-based coarse-graining (resolving overall protein and membrane

84 l-atom molecular dynamics; 2), residue-based coarse-graining (resolving single amino acids and lipid

85 sent substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG

86 oretical modeling, molecular simulation, and coarse-graining strategies for the transport of gases an

87 These "coarse-graining" studies have addressed observed as well

88 applying our molecular renormalization group coarse-graining technique to double-stranded DNA, we sol

89 To analyze it we construct an exact coarse graining that reduces the model to a Markov proce

90 nd we explicitly construct such a picture by coarse graining the microscopic dynamics of our simulati

91 Our approach solves this bottleneck by coarse-graining the infinite search space of atomic coor

92 By directly coarse-graining the kinetic energy and independently usi

93 By coarse-graining the spoken word testimony into synonym s

94 tarting model and approximations inherent in coarse graining, these results are consistent with exper

95 mechanics, allowed better contractility with coarse graining, though connectivity was still markedly

96 in difficulties by applying a gradient-based coarse graining to RNA-ligand systems and solving the pr

97 nd for correlated neurons, required temporal coarse-graining to compensate for spatial subsampling of

98 twork model, which invoke similar degrees of coarse-graining to the dynamics but use different potent