Computational and Mathematical Biophysics最新文献_第7页

Mathematical modeling, analysis, and simulation of the COVID-19 pandemic with explicit and implicit behavioral changes 具有显性和隐性行为变化的新冠肺炎大流行的数学建模、分析和模拟

Computational and Mathematical Biophysics Pub Date : 2020-01-01 DOI: 10.1515/cmb-2020-0113

Comfort Ohajunwa, Kirthi Kumar, P. Seshaiyer

{"title":"Mathematical modeling, analysis, and simulation of the COVID-19 pandemic with explicit and implicit behavioral changes","authors":"Comfort Ohajunwa, Kirthi Kumar, P. Seshaiyer","doi":"10.1515/cmb-2020-0113","DOIUrl":"https://doi.org/10.1515/cmb-2020-0113","url":null,"abstract":"Abstract As COVID-19 cases continue to rise globally, many researchers have developed mathematical models to help capture the dynamics of the spread of COVID-19. Specifically, the compartmental SEIR model and its variations have been widely employed. These models differ in the type of compartments included, nature of the transmission rates, seasonality, and several other factors. Yet, while the spread of COVID-19 is largely attributed to a wide range of social behaviors in the population, several of these SEIR models do not account for such behaviors. In this project, we consider novel SEIR-based models that incorporate various behaviors. We created a baseline model and explored incorporating both explicit and implicit behavioral changes. Furthermore, using the Next Generation Matrix method, we derive a basic reproduction number, which indicates the estimated number of secondary cases by a single infected individual. Numerical simulations for the various models we made were performed and user-friendly graphical user interfaces were created. In the future, we plan to expand our project to account for the use of face masks, age-based behaviors and transmission rates, and mixing patterns.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"216 - 232"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42360705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Pharmacokinetics and Pharmacodynamics Models of Tumor Growth and Anticancer Effects in Discrete Time 肿瘤生长和抗癌作用的离散时间药代动力学和药效学模型

Computational and Mathematical Biophysics Pub Date : 2020-01-01 DOI: 10.1515/cmb-2020-0105

F. Atici, Ngoc Nguyen, Kamala Dadashova, S. E. Pedersen, G. Koch

引用次数: 14

A reaction-diffusion system to better comprehend the unlockdown: Application of SEIR-type model with diffusion to the spatial spread of COVID-19 in France 更好理解解锁的反应扩散系统：带扩散的SEIR型模型在法国新冠肺炎空间传播中的应用

Computational and Mathematical Biophysics Pub Date : 2020-01-01 DOI: 10.1515/cmb-2020-0104

Y. Mammeri

引用次数: 31

On Linear Growth in COVID-19 Cases 新冠肺炎病例线性增长研究

Computational and Mathematical Biophysics Pub Date : 2020-01-01 DOI: 10.1101/2020.06.19.20135640

M. Grinfeld, P. Mulheran

引用次数: 2

The Weighted Gaussian Curvature Derivative of a Space-Filling Diagram 空间填充图的加权高斯曲率导数

Computational and Mathematical Biophysics Pub Date : 2019-08-19 DOI: 10.1515/cmb-2020-0101

A. Akopyan, H. Edelsbrunner

引用次数: 4

The Weighted Mean Curvature Derivative of a Space-Filling Diagram 空间填充图的加权平均曲率导数

Computational and Mathematical Biophysics Pub Date : 2019-08-19 DOI: 10.1515/cmb-2020-0100

A. Akopyan, H. Edelsbrunner

引用次数: 4

A simple algorithm to compute link polynomials defined by using skein relations 利用skein关系计算链路多项式的一种简单算法

Computational and Mathematical Biophysics Pub Date : 2019-08-10 DOI: 10.1515/cmb-2020-0102

Xuezhi Zhao

引用次数: 0

Improving RNA secondary structure prediction via state inference with deep recurrent neural networks 基于深度递归神经网络状态推理的RNA二级结构预测改进

Computational and Mathematical Biophysics Pub Date : 2019-06-26 DOI: 10.1515/cmb-2020-0002

Devin Willmott, D. Murrugarra, Q. Ye

{"title":"Improving RNA secondary structure prediction via state inference with deep recurrent neural networks","authors":"Devin Willmott, D. Murrugarra, Q. Ye","doi":"10.1515/cmb-2020-0002","DOIUrl":"https://doi.org/10.1515/cmb-2020-0002","url":null,"abstract":"Abstract The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA sequences can be used to generate auxiliary information for data-directed RNA secondary structure prediction. Typical tools for state inference, such as hidden Markov models, exhibit poor performance in RNA state inference, owing in part to their inability to recognize nonlocal dependencies. Bidirectional long short-term memory (LSTM) neural networks have emerged as a powerful tool that can model global nonlinear sequence dependencies and have achieved state-of-the-art performances on many different classification problems. This paper presents a practical approach to RNA secondary structure inference centered around a deep learning method for state inference. State predictions from a deep bidirectional LSTM are used to generate synthetic SHAPE data that can be incorporated into RNA secondary structure prediction via the Nearest Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary structures for a diverse test set of 16S ribosomal RNA that are, on average, 25 percentage points more accurate than undirected MFE structures. Accuracy is highly dependent on the success of our state inference method, and investigating the global features of our state predictions reveals that accuracy of both our state inference and structure inference methods are highly dependent on the similarity of pairing patterns of the sequence to the training dataset. Availability of a large training dataset is critical to the success of this approach. Code available at https://github.com/dwillmott/rna-state-inf.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"36 - 50"},"PeriodicalIF":0.0,"publicationDate":"2019-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46581978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Thermal Response in Cellulose Iβ Based on Molecular Dynamics 基于分子动力学的纤维素Iβ的热响应

Computational and Mathematical Biophysics Pub Date : 2019-01-01 DOI: 10.1515/cmb-2019-0007

Xuewei Jiang, Yu Chen, Yue Yuan, Lu Zheng

{"title":"Thermal Response in Cellulose Iβ Based on Molecular Dynamics","authors":"Xuewei Jiang, Yu Chen, Yue Yuan, Lu Zheng","doi":"10.1515/cmb-2019-0007","DOIUrl":"https://doi.org/10.1515/cmb-2019-0007","url":null,"abstract":"Abstract The structural details of cellulose I β were discussed according to molecular dynamics simulations with the GLYCAM-06 force field. The simulation outcomes were in agreement with previous experimental data, including structural parameters and hydrogen bond pattern at 298 K. We found a new conformation of cellulose Iβ existed at the intermediate temperature that is between the low and high temperatures. Partial chain rotations along the backbone direction were found and conformations of hydroxymethyl groups that alternated from tg to either gt or gg were observed when the temperature increased from 298 K to 400 K. In addition, the gg conformation is preferred than gt. For the structure adopted at high temperature of 500 K, major chains were twisted and two chains detached from each plain. In contrast to the observation under intermediate temperature, the population of hydroxymethyl groups in gt exceeded that in gg conformation at high temperature. In addition, three patterns of hydrogen bonding were identified at low, intermediate and high temperatures in the simulations. The provided structural information indicated the transitions occurred around 350 K and 450 K, considered as the transitional temperatures of cellulose Iβ in this work.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"7 1","pages":"85 - 97"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2019-0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47009650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Using computational approaches to study dengue virus capsid assembly 利用计算方法研究登革热病毒衣壳组装

Computational and Mathematical Biophysics Pub Date : 2019-01-01 DOI: 10.1515/cmb-2019-0005

G. S. Salas, A. E. L. Hernandez, Jiadi He, C. Karki, Yixin Xie, Shengjie Sun, Yuejiao Xian, Lin Li

{"title":"Using computational approaches to study dengue virus capsid assembly","authors":"G. S. Salas, A. E. L. Hernandez, Jiadi He, C. Karki, Yixin Xie, Shengjie Sun, Yuejiao Xian, Lin Li","doi":"10.1515/cmb-2019-0005","DOIUrl":"https://doi.org/10.1515/cmb-2019-0005","url":null,"abstract":"Abstract Dengue viral capsid plays a significant role in viral life cycle of dengue, especially in vial genome protection and virus-cell fusion. Revealing mechanisms of the viral capsid protein assembly may lead to the discovery of anti-viral drugs that inhibit the assembly of the viral capsid. The E and M-proteins are arranged into heterotetramers, which consists of two copies of E and M-protein. The heterotetramers are assembled into a highly ordered capsid. While many investigations of the interactions between E and M-proteins have been performed, there are very few studies on the interactions between the heterotetramers and their roles in capsid assembly. Utilizing a series of computational approaches, this study focuses on the assembly mechanism of the heterotetramers. Our electrostatic analyses lead to the identification of four binding modes between each two dengue heterotetramers that repeat periodically throughout the virus capsid. Among these four binding modes, heterotetramers in binding modes I, II and IV are attractive. But in the binding mode III the heterotetramers repel each other, making mode III a suitable target for drug design. Furthermore, MD simulations were performed following by salt bridges analysis. This study demonstrates that using computational approaches is a promising direction to study the dengue virus.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"7 1","pages":"64 - 72"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2019-0005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42471344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10