scholarly journals Topological constraints and their influence on the properties of synthetic macromolecular systems. 1. Cyclic macromolecules

1987 ◽  
Vol 20 (3) ◽  
pp. 480-485 ◽  
Author(s):  
G. Ten Brinke ◽  
G. Hadziioannou
Keyword(s):  
Topological Constraints ◽  
Macromolecular Systems

Relationships between hierarchical structure and properties in macromolecular systems

1987 ◽  
Vol 45 ◽  
pp. 440-443
Author(s):  
E. Baer
Keyword(s):  
Uniaxial Tension ◽  
Hierarchical Structure ◽  
Inorganic Material ◽  
Hierarchical Structures ◽  
Bone Collagen ◽  
Structure And Properties ◽  
Connective Tissues ◽  
Scale Level ◽  
Fibrous Protein ◽  
Macromolecular Systems

The most advanced macromolecular materials are found in plants and animals, and certainly the connective tissues in mammals are amongst the most advanced macromolecular composites known to mankind. The efficient use of collagen, a fibrous protein, in the design of both soft and hard connective tissues is worthy of comment. Very crudely, in bone collagen serves as a highly efficient binder for the inorganic hydroxyappatite which stiffens the structure. The interactions between the organic fiber of collagen and the inorganic material seem to occur at the nano (scale) level of organization. Epitatic crystallization of the inorganic phase on the fibers has been reported to give a highly anisotropic, stress responsive, structure. Soft connective tissues also have sophisticated oriented hierarchical structures. The collagen fibers are “glued” together by a highly hydrated gel-like proteoglycan matrix. One of the simplest structures of this type is tendon which functions primarily in uniaxial tension as a reinforced elastomeric cable between muscle and bone.

Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors

2019 ◽  
Vol 25 (7) ◽  
pp. 750-773 ◽  
Author(s):  
Pabitra Narayan Samanta ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Drug Targets ◽  
Biological Activities ◽  
Scoring Function ◽  
Md Simulations ◽  
Energy Calculation ◽  
Binding Modes ◽  
Macromolecular Systems ◽  
In Silico Modeling ◽  
Mathematical Algorithms ◽  
The Impact

The rapid advancement of computer architectures and development of mathematical algorithms offer a unique opportunity to leverage the simulation of macromolecular systems at physiologically relevant timescales. Herein, we discuss the impact of diverse structure-based and ligand-based molecular modeling techniques in designing potent and selective antagonists against each adenosine receptor (AR) subtype that constitutes multitude of drug targets. The efficiency and robustness of high-throughput empirical scoring function-based approaches for hit discovery and lead optimization in the AR family are assessed with the help of illustrative examples that have led to nanomolar to sub-micromolar inhibition activities. Recent progress in computer-aided drug discovery through homology modeling, quantitative structure-activity relation, pharmacophore models, and molecular docking coupled with more accurate free energy calculation methods are reported and critically analyzed within the framework of structure-based virtual screening of AR antagonists. Later, the potency and applicability of integrated molecular dynamics (MD) methods are addressed in the context of diligent inspection of intricated AR-antagonist binding processes. MD simulations are exposed to be competent for studying the role of the membrane as well as the receptor flexibility toward the precise evaluation of the biological activities of antagonistbound AR complexes such as ligand binding modes, inhibition affinity, and associated thermodynamic and kinetic parameters.

scholarly journals ModularBoost: an efficient network inference algorithm based on module decomposition

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinyu Li ◽  
Wei Zhang ◽  
Jianming Zhang ◽  
Guang Li
Keyword(s):  
Network Inference ◽  
Detection Methods ◽  
Inference Problem ◽  
Topological Constraints ◽  
Inference Algorithms ◽  
Module Detection ◽  
Series Expression ◽  
Gene Modules ◽  
Inference Methods ◽  
Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

scholarly journals Topological constraints in the reconnection of vortex braids

2021 ◽  
Vol 33 (5) ◽  
pp. 056101
Author(s):  
S. Candelaresi ◽  
G. Hornig ◽  
B. Podger ◽  
D. I. Pontin
Keyword(s):  
Topological Constraints
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