scholarly journals Simulation of defects, flexibility and rupture in biopolymer networks

RSC Advances ◽  
2022 ◽  
Vol 12 (4) ◽  
pp. 2171-2180
Author(s):  
Matthew H. J. Bailey ◽  
Mark Wilson
Keyword(s):  
Simple Graph ◽  
Graph Representation ◽  
Coarse Grained ◽  
Polymer Model

We use a coarse grained polymer model and a simple graph representation to introduce defects into a biopolymer network, then cause them to rupture.

scholarly journals Self Assembly of Model Polymers into Biological Random Networks

2020 ◽  
Author(s):  
Matthew Bailey ◽  
Mark Wilson
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Self Assembly ◽  
Short Range ◽  
External Pressure ◽  
Coarse Grained ◽  
Random Networks ◽  
Network Structures ◽  
Polymer Model ◽  
Microscope Images

<div>The properties of biological networks, such as those found in the ocular lens capsule, are difficult to study without simplified models.</div><div>Model polymers are developed, inspired by "worm-like'' curve models, that are shown to spontaneously self assemble</div><div>to form networks similar to those observed experimentally in biological systems.</div><div>These highly simplified coarse-grained models allow the self assembly process to be studied on near-realistic time-scales.</div><div>Metrics are developed (using a polygon-based framework)</div><div>which are useful for describing simulated networks and can also be applied to images of real networks.</div><div>These metrics are used to show the range of control that the computational polymer model has over the networks, including the polygon structure and short range order.</div><div>The structure of the simulated networks are compared to previous simulation work and microscope images of real networks. </div><div>The network structure is shown to be a function of the interaction strengths, cooling rates and external pressure. </div><div>In addition, "pre-tangled'' network structures are introduced and shown to significantly influence the subsequent network structure.</div><div>The network structures obtained fit into a region of the network landscape effectively inaccessible to random</div><div>(entropically-driven) networks but which are occupied by experimentally-derived configurations.</div>

Clique-Based Neural Associative Memories with Local Coding and Precoding

2016 ◽  
Vol 28 (8) ◽  
pp. 1553-1573 ◽  
Author(s):  
Asieh Abolpour Mofrad ◽  
Matthew G. Parker ◽  
Zahra Ferdosi ◽  
Mohammad H. Tadayon
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Coding Theory ◽  
Simple Graph ◽  
Graph Representation ◽  
Associative Memories ◽  
Additive Codes ◽  
Partial Error ◽  
Pattern Retrieval ◽  
Local Coding

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

scholarly journals Self Assembly of Model Polymers into Biological Random Networks

2020 ◽  
Author(s):  
Matthew Bailey ◽  
Mark Wilson
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Self Assembly ◽  
Short Range ◽  
External Pressure ◽  
Coarse Grained ◽  
Random Networks ◽  
Network Structures ◽  
Polymer Model ◽  
Microscope Images

<div>The properties of biological networks, such as those found in the ocular lens capsule, are difficult to study without simplified models.</div><div>Model polymers are developed, inspired by "worm-like'' curve models, that are shown to spontaneously self assemble</div><div>to form networks similar to those observed experimentally in biological systems.</div><div>These highly simplified coarse-grained models allow the self assembly process to be studied on near-realistic time-scales.</div><div>Metrics are developed (using a polygon-based framework)</div><div>which are useful for describing simulated networks and can also be applied to images of real networks.</div><div>These metrics are used to show the range of control that the computational polymer model has over the networks, including the polygon structure and short range order.</div><div>The structure of the simulated networks are compared to previous simulation work and microscope images of real networks. </div><div>The network structure is shown to be a function of the interaction strengths, cooling rates and external pressure. </div><div>In addition, "pre-tangled'' network structures are introduced and shown to significantly influence the subsequent network structure.</div><div>The network structures obtained fit into a region of the network landscape effectively inaccessible to random</div><div>(entropically-driven) networks but which are occupied by experimentally-derived configurations.</div>

scholarly journals Autocorrelation study of the Θ transition for a coarse-grained polymer model

2014 ◽  
Vol 141 (7) ◽  
pp. 074101 ◽  
Author(s):  
Kai Qi ◽  
Michael Bachmann
Keyword(s):  
Coarse Grained ◽  
Polymer Model

scholarly journals Improved Selection of Rare Reactions in Template-Based Retrosynthesis Predictions

2021 ◽  
Author(s):  
Mads Koerstz ◽  
Samuel Genheden ◽  
Ola Engkvist ◽  
Jan H. Jensen ◽  
Esben Jannik Bjerrum
Keyword(s):  
Policy Networks ◽  
Simple Graph ◽  
New Drugs ◽  
Graph Representation ◽  
Computer Assisted ◽  
Training Set ◽  
Synthesis Planning ◽  
Synthetic Routes ◽  
Accuracy Increase ◽  
Selection Of

Identifying synthetic routes for molecules of interest is a crucial step when discovering new drugs or materials. To find synthetic routes, we can use computer-assisted synthesis planning using expansion policy networks trained on reaction templates extracted from patents and the literature. However, experience has shown that these networks are biased towards frequently reported reactions. This study shows that changing the molecular representation from an extended-connectivity fingerprint to a simple graph representation can increase the accuracy for templates used less than five times by 5.0- 8.5% points. We also illustrate that a simple oversampling of the training set yielded a top-1 accuracy increase in the 17-20% point range for templates used five times or less.

The statistical and dynamics properties of hard-sphere polymer chains

2015 ◽  
Vol 29 (18) ◽  
pp. 1550091 ◽  
Author(s):  
Zi-Wen Huang ◽  
Ji-Xuan Hou ◽  
Chun Xie ◽  
Hao Zhang
Keyword(s):  
Relaxation Time ◽  
Chain Length ◽  
Hard Sphere ◽  
Dynamic Properties ◽  
Polymer Chains ◽  
Coarse Grained ◽  
Power Exponent ◽  
Polymer Model ◽  
Time Saving ◽  
Scaling Relationship

We investigated a highly coarse-grained polymer model — hard-sphere (HS) model. This model is characterized by its time-saving computation and strictly guaranteed uncrossability of polymer chains. Regarding the statistical and dynamic properties of HS model, our simulating results perfectly coincide with pre-existing scaling theory. Additionally, we point out that the power exponent of scaling relationship between its relaxation time and chain length is 2.2.

scholarly journals A coarse-grained polymer model for studying the glass transition

2019 ◽  
Vol 150 (9) ◽  
pp. 091101 ◽  
Author(s):  
Hsiao-Ping Hsu ◽  
Kurt Kremer
Keyword(s):  
Glass Transition ◽  
Coarse Grained ◽  
Polymer Model

scholarly journals Self Assembly of Model Polymers into Biological Random Networks

2020 ◽  
Author(s):  
Matthew Bailey ◽  
Mark Wilson
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Self Assembly ◽  
Short Range ◽  
External Pressure ◽  
Coarse Grained ◽  
Random Networks ◽  
Network Structures ◽  
Polymer Model ◽  
Microscope Images

<div>The properties of biological networks, such as those found in the ocular lens capsule, are difficult to study without simplified models.</div><div>Model polymers are developed, inspired by "worm-like'' curve models, that are shown to spontaneously self assemble</div><div>to form networks similar to those observed experimentally in biological systems.</div><div>These highly simplified coarse-grained models allow the self assembly process to be studied on near-realistic time-scales.</div><div>Metrics are developed (using a polygon-based framework)</div><div>which are useful for describing simulated networks and can also be applied to images of real networks.</div><div>These metrics are used to show the range of control that the computational polymer model has over the networks, including the polygon structure and short range order.</div><div>The structure of the simulated networks are compared to previous simulation work and microscope images of real networks. </div><div>The network structure is shown to be a function of the interaction strengths, cooling rates and external pressure. </div><div>In addition, "pre-tangled'' network structures are introduced and shown to significantly influence the subsequent network structure.</div><div>The network structures obtained fit into a region of the network landscape effectively inaccessible to random</div><div>(entropically-driven) networks but which are occupied by experimentally-derived configurations.</div>

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