• 主题
高级搜索  >
历史搜索 清空历史
您现在的位置：首页> 中文期刊>>定量生物学:英文版

定量生物学:英文版

定量生物学:英文版

本刊涉及领域包括电气工程、应用电子技术、通信工程、自动控制技术、信息检测与处理技术、微电子技术、电磁场理论与微波技术、光电技术及其与其他学科的交叉研究领域等。
  • 发文量：49
  • 被引量：5
  • H指数：0
  • 开始收录时间：
  • CNKI综合因子：
  • 维普期刊影响因子：
  • 万方期刊影响因子：
  • 创刊时间：
  • 国内刊号/CN：10-1082/Q
    国际刊号/ISSN：2095-4689
  • 发行周期：季刊
    邮发代号：80-971
  • 主管单位：Ministry of Education of the People's Republic of China
    主办单位：Higher Education Press,China;Tsinghua University,China
定量生物学:英文版-联系信息
  • 主编：Chao Tang;Michael Q. Zhang
  • 电话：010-58556485
  • 邮箱：
  • 地址：北京市朝阳区惠新东街4号富盛大厦15层
  • 邮编：100029
定量生物学:英文版-获奖情况
  • 暂无获奖信息
  • 收录汇总
  • 刊内文献检索
  • 投稿信息

刊期浏览

定量生物学:英文版 >2022年第4期
搜索

学科

年度

全选（0）
排序：
共4384条结果
  • 摘要：Background:Identifying patient-specific flow of signal transduction perturbed by multiple single-nucleotide alterations is critical for improving patient outcomes in cancer cases.However,accurate estimation of mutational effects at the pathway level for such patients remains an open problem.While probabilistic pathway topology methods are gaining interest among the scientific community,the overwhelming majority do not account for network perturbation effects from multiple single-nucleotide alterations.Methods:Here we present an improvement of the mutational forks formalism to infer the patient-specific flow of signal transduction based on multiple single-nucleotide alterations,including non-synonymous and synonymous mutations.The lung adenocarcinoma and skin cutaneous melanoma datasets from TCGA Pan-Cancer Atlas have been employed to show the utility of the proposed method.Results:We have comprehensively characterized six mutational forks.The number of mutated nodes ranged from one to four depending on the topological characteristics of a fork.Transitional confidences(TCs)have been computed for every possible combination of single-nucleotide alterations in the fork.The performed analysis demonstrated the capacity of the mutational forks formalism to follow a biologically explainable logic in the identification of high-likelihood signaling routes in lung adenocarcinoma and skin cutaneous melanoma patients.The findings have been largely supported by the evidence from the biomedical literature.Conclusion:We conclude that the formalism has a great chance to enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease.
  • 摘要：Background:With improvements in next-generation DNA sequencing technology,lower cost is needed to collect genetic data.More machine learning techniques can be used to help with cancer analysis and diagnosis.Methods:We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer types.Our ensemble system consists of five weak classifiers(logistic regression,SVM,random forest,XGBoost and neural networks).We first used cross-validation to get the predicted results for the five classifiers.The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions.The final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted probability.Results:Using the somatic mutation count of each gene as the input feature,the overall accuracy of the performance-weighted-voting model reached 71.46％,which was significantly higher than the five weak classifiers and two other ensemble models:the hard-voting model and the soft-voting model.In addition,by analyzing the predictive pattern of the performance-weighted-voting model,we found that in most cancer types,higher tumor mutational burden can improve overall accuracy.Conclusion:This study has important clinical significance for identifying the origin of cancer,especially for those where the primary cannot be determined.In addition,our model presents a good strategy for using ensemble systems for cancer type classification.
  • 摘要：Background:Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic cells.Chromatin immunoprecipitation coupled with high-throughput sequencing(ChIP-seq)technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein factors.Some histone modifications,such as H3K27me3 and H3K9me3,usually mark broad domains in the genome ranging from kilobases(kb)to megabases(Mb)long,resulting in diffuse patterns in the ChIP-seq data that are challenging for signal separation.While most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features,a principled method to identify scale-free board domains has been lacking.Methods:Here we present RECOGNICER(Recursive coarse-graining identification for ChIP-seq enriched regions),a computational method for identifying ChIP-seq enriched domains on a large range of scales.The algorithm is based on a coarse-graining approach,which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length scales.Results:We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data,and validate the results based on H3K27me3's association with repressive gene expression.We show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated pieces.Conclusion:RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.
  • 摘要：Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain enigmatic.PARIS(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in vivo.However,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base resolution.Methods:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS data.IRIS generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS data.Results:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural data.HIS is implemented in Python and freely available at capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure ensembles.We expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.
  • 摘要：Background:Microfluidic systems have advantages such as a high throughput,small reaction volume,and precise control of the cellular position and environment.These advantages have allowed microfluidics to be widely used in several fields of synthetic biology in recent years.Results:In this article,we reviewed the microfluidic-based methods for synthetic biology from two aspects:the construction of synthetic gene circuits and the analysis of synthetic gene systems.We used some examples to illuminate the progresses and challenges in the steps of synthetic gene circuits construction and approaches of gene expression analysis with microfluidic systems.Conclusion:Comparing to traditional methods,microfluidic tools promise great advantages in the synthetic genetic circuit building and analysis process.Moreover,new microfluidic systems together with the mathematical modeling of synthetic circuits or consortiums are desirable to perform complex genetic circuit construction and understand the natural gene regulation in cells and population interactions better.
  • 摘要：Background:The COVID-19 pandemic has become a formidable threat to global health and economy.The coronavirus SARS-CoV-2 that causes COVID-19 is known to spread by human-to-human transmission,and in about 40％cases,the exposed individuals are asymptomatic which makes it difficult to contain the virus.Methods:This paper presents a modified SEIR epidemiological model and uses concepts of optimal control theory for analysis of the effects of intervention methods of the COVID19.Fundamentally the pandemic intervention problem can be viewed as a mathematical optimization problem as there are contradictory outcomes in terms of reduced infection and fatalities but with serious economic downturns.Results:Concepts of optimal control theory have been used to determine the optimal control(intervention)levels of ⅰ)social contact mitigation and suppression,and ⅱ)pharmaceutical intervention modalities,with minimum impacts on the economy.Numerical results show that with optimal intervention policies,there is a significant reduction in the number of infections and fatalities.The computed optimum intervention policy also provides a timeline of systematic enforcement and relaxation of stay-at-home regulations,and an estimate of the peak time and number of hospitalized critical care patients.Conclusion:The proposed method could be used by local and state governments in planning effective strategies in combating the pandemic.The optimum intervention policy provides the necessary lead time to establish necessary field hospitals before getting overwhelmed by new patient arrivals.Our results also allow the local and state governments to relax social contact suppression guidelines in an orderly manner without triggering a second wave.
  • 摘要：On November 28th 2020,SyngenTech,a China-based biotechnology company,announced that the company's leading oncolytic virotherapeutic product,SynOV1.1,was approved for clinical trial by the U.S.Food and Drug Administration.After synthetic bacterial or cell therapy[1,2],this is the first introduction of a viral therapy engineered using synthetic gene circuits into the clinic.Together with the clinical development of variable living therapeutics,we anticipate that synthetic gene circuits will receive increasing attention as a revolutionary technology to improve disease-treatment efficacy.
  • 摘要：The Human Genome Project(HGP)is a historical and landmark scientific project.In spite of initial controversy it has become a bedrock foundation for much progress in biological science and human health.After the Human Genome Project was completed in the early 2000s,next generation sequencing technologies were developed and that has revolutionized genomics.Here is a brief account of the May 1985 meeting at University of California Santa Cruz.
  • 摘要：In February 2001,the IHGSC(International Human Genome Sequencing Consortium)[1]and Celera Geno-mics[2]each reported draft sequences providing a first overall view of the human genome.On the occasion of the 20th anniversary celebration,I would like to provide some of my personal account on how the HGP(Human Genome Project)has impacted my academic research interests and to offer some candid advice for the new comers.
  • 摘要：COVID-19 is now rapidly spreading worldwide.While the majority of COVID-19 patients show only mild or moderate symptoms,some could deteriorate quickly and may succumb to a sudden death.It is therefore important to identify who will be more likely to develop severe outcomes and be treated with particular or preventive care.Here in this literature survey,we collected epidemiologic and clinical data from 36 articles on 51,270 patients with different severity of COVID-19,aiming to characterize the population that are prone to severe condition and bad outcomes.These data reveal that old males and those with high BMI or underlying diseases,especially cardiovascular disease,hypertension and diabetes,are overrepresented among severe cases.High leukocyte and lymphopenia are common features in severe and critical patients.Upon deterioration of the disease,both CD4+and CD8+T cells are decreased,while almost all serum cytokines,especially pro-inflammatory cytokines,increased.
  • 摘要：Background:The lipostatic set-point theory,ascribing fat mass homeostasis to leptin mediated central feedback regulation targeting the body's fat storage,has caused a variety of conundrums.We recently proposed a leanocentric locking-point theory and the corresponding mathematical model,which not only resolve these conundrums but also provide valuable insights into weight control and health assessment.This paper aims to further test the leanocentric theory.Methods:Partial lipectomy is a touchstone to test both the leanocentric and lipostatic theories.Here we perform in silico lipectomy by using a mathematical model embodying the leanocentric theory to simulate the long-term body fat change after removing some fat cells in the body.Results:The mathematical modeling uncovers a phenomenon called post-surgical fat loss,which was well-documented in real partial lipectomy surgeries;thus,the phenomenon can serve as an empirical support to the leanocentric theory.On the other hand,the leanocentric theory,but not the lipostatic theory,can well explain the post-surgical fat loss.Conclusions:The leanocentric locking-point theory is a promising theory and deserves further testing.Partial lipectomy surgeries are beneficial to obese patients for quite a long period.
  • 摘要：Since the first success of genome wide association study(GWAS)was reported in 2005 by Hoh and colleagues on the identification of a major gene for age related macular degeneration,many thousands of published studies have reported reproducible association signals between hundreds of thousands of genetic variants and thousands of traits and diseases in the past 16 years.There is a growing number of biobanks with up to one million participants developed across the world to facilitate genetic studies of complex traits through GWAS.Furthermore,many consortium projects have been initiated to collect diverse types of data that have proved very valuable for integrated analysis of GWAS data.These rich data present both great opportunities and challenges to scientists on data management,computation,analysis,integration,and interpretation.This special issue of Quantitative Biology collects nine papers that cover major progresses,challenges,and future directions for GWAS analysis.
  • 摘要：Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide association studies(TWAS)have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant-trait associations.Specifically,TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype.Various methods have been developed for performing TWAS and/or similar integrative analysis.Each such method has a different modeling assumption and many were initially developed to answer different biological questions.Consequently,it is not straightforward to understand their modeling property from a theoretical perspective.Results:We present a technical review on thirteen TWAS methods.Importantly,we show that these methods can all be viewed as two-sample Mendelian randomization(MR)analysis,which has been widely applied in GWASs for examining the causal effects of exposure on outcome.Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls.We systematically introduce the MR analysis framework,explain how features of the GWAS and expression data influence the adaptation of MR for TWAS,and re-interpret the modeling assumptions made in different TWAS methods from an MR angle.We finally describe future directions for TWAS methodology development.Conclusions:We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.
  • 摘要：Background:Polygenic risk score(PRS)derived from summary statistics of genome-wide association studies(GWAS)is a useful tool to infer an individual's genetic risk for health outcomes and has gained increasing popularity in human genetics research.PRS in its simplest form enjoys both computational efficiency and easy accessibility,yet the predictive performance of PRS remains moderate for diseases and traits.Results:We provide an overview of recent advances in statistical methods to improve PRS's performance by incorporating information from linkage disequilibrium,functional annotation,and pleiotropy.We also introduce model validation methods that fine-tune PRS using GWAS summary statistics.Conclusion:In this review,we showcase methodological advances and current limitations of PRS,and discuss several emerging issues in risk prediction research.
  • 摘要：Background:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limited statistical power and difficulties in biological interpretation.With the recent progress in expression quantitative trait loci(eQTL)studies,transcriptome-wide association studies(TWAS)provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies.Results:In this review,we will introduce the general framework of TWAS,the relevant resources,and the computational tools.Extensions of the original TWAS methods will also be discussed.Furthermore,we will briefly introduce methods that are closely related to TWAS,including MR-based methods and colocalization approaches.Connection and difference between these approaches will be discussed.Conclusion:Finally,we will summarize strengths,limitations,and potential directions for TWAS.
  • 摘要：Background:The Genotype-Tissue Expression(GTEx)Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased individuals,providing an opportunity to study the regulatory roles of genetic variants in transcriptome activities from both cross-tissue and tissue-specific perspectives.Moreover,transcriptome activities(e.g.,transcript abundance and alternative splicing)can be treated as mediators between genotype and phenotype to achieve phenotypic alteration.Knowing the genotype associated transcriptome status,researchers can better understand the biological and molecular mechanisms of genetic risk variants in complex traits.Results:In this article,we first explore the genetic architecture of gene expression traits,and then review recent methods on quantitative trait locus(QTL)and co-expression network analysis.To further exemplify the usage of associations between genotype and transcriptome status,we briefly review methods that either directly or indirectly integrate expression/splicing QTL information in genome-wide association studies(GWASs).Conclusions:The GTEx Project provides the largest and useful resource to investigate the associations between genotype and transcriptome status.The integration of results from the GTEx Project and existing GWASs further advances our understanding of roles of gene expression changes in bridging both the genetic variants and complex traits.
  • 摘要：Background:Genome-wide association studies(GWAS)have been widely adopted in studies of human complex traits and diseases.Results:This review surveys areas of active research:quantifying and partitioning trait heritability,fine mapping functional variants and integrative analysis,genetic risk prediction of phenotypes,and the analysis of sequencing studies that have identified millions of rare variants.Current challenges and opportunities are highlighted.Conclusion:GWAS have fundamentally transformed the field of human complex trait genetics.Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.
  • 摘要：Background:The direct-to-consumer genetic testing(DTC-GT)industry has exploded in recent years,initiated by market pioneers from the United States and quickly followed by companies from Europe and Asia.In addition to their primary objective of providing ancestry and health information to customers,DTC-GT services have emerged as a valuable data resource for large-scale population and genetics studies.Methods:We assessed DTC-GT market leaders in the U.S.and China,user participation in research,and academic reports based on this information.We also investigated DTC-GT end-user value by tracing key updates of companies provided via health risk reports and evaluating their predictive power.We then assessed the replicability of several genome-wide association studies(GWAS)based on a Chinese DTC-GT biobank.Results:As recent entrants to the market,Chinese DTC-GT service providers have published less academic research than their Western counterparts;however,a larger proportion of Chinese users consent to participate in research projects.Dramatic increases in user volume and resultant report updates led to reclassification of some users'polygenic risk levels,but within a reasonable scale and with increased predictive power.Replicability among GWAS using the Chinese DTC-GT biobank varied by studied trait,population background,and sample size.Conclusions:We speculate that the rapid growth in DTC-GT services,particularly in non-Caucasian populations,will yield an important and much-needed resource for biobanking,large-scale genetic studies,clinical trials,and post-clinical applications.
  • 摘要：Background:Whole-exome sequencing(WES)studies have identified multiple genes enriched for de novo mutations(DNMs)in congenital heart disease(CHD)probands.However,risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene.Methods:In this manuscript,we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted variants.Through integrative modeling of multiple types of genetic variants,gene-level annotations,and reference data from large population cohorts,our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only.Results:Applied to WES data of 2,645 CHD proband-parent trios,our method identified 15 significant genes,half of which are novel,leading to new insights into the genetic bases of CHD.Conclusion:These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.
  • 摘要：Background:Genome wide association studies(GWAS)have identified many genetic variants associated with increased risk of Alzheimer's disease(AD).These susceptibility loci may effect AD indirectly through a combination of physiological brain changes.Many of these neuropathologic features are detectable via magnetic resonance imaging(MRI).Methods:In this study,we examine the effects of such brain imaging derived phenotypes(IDPs)with genetic etiology on AD,using and comparing the following methods:two-sample Mendelian randomization(2SMR),generalized summary statistics based Mendelian randomization(GSMR),transcriptome wide association studies(TWAS)and the adaptive sum of powered score(aSPU)test.These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics.Results:Using publicly available GWAS datasets from the International Genomics of Alzheimer's Project(IGAP)and UK Biobank's(UKBB)brain imaging initiatives,we identify 35 IDPs possibly associated with AD,many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease.Conclusions:Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods,i.e.,aSPU,GSMR and TWAS,over MR methods based on independent SNPs(as instrumental variables).Availability:Example code is available at .
  • 摘要：Background:Mendelian randomization(MR)analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies.Many statistical approaches have been developed and each of these methods require specific assumptions.Results:In this article,we review the pros and cons of these methods.We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.Conclusion:The current available MR approaches allow us to study causality among risk factors and outcomes.However,novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.
  • 摘要：The 20th anniversary of the completion of the Human Genome Project offers an opportunity to reflect on early efforts to make biological sense out of genomic data.My interest in genomic analysis began at Washington University in 1988 while I was a postdoc in Doug Berg's laboratory,which was in the same building as the laboratory of Maynard Olson,one of the founders of the Human Genome Project.At the time,my focus was on the molecular biology of E.coli-an area of research that had appealed to me due to its being information rich.However,other,emerging areas of research began to offer great opportunities going forward.At an inhouse seminar,Maynard Olson made a visionary statement foreseeing the day when each patient's genome would be sequenced as part of a routine medical exam.After having spent years as a graduate student sequencing a mere 5,000 base pairs using the Maxam and Gilbert method,Maynard's statement initially struck me as incredible,but later as eye opening.The whole genome analysis initiative also raised the question of how to best obtain biological information from the vast amount of sequence data becoming available.This directed me toward computer science and statistcs,which were soon to become very critical components of biomedical research.
  • 摘要：Background:The concept of biomolecular condensate was put forward recently to emphasize the ability of certain cellular compartments to concentrate molecules and comprise proteins and nucleic acids with specific biological functions,from ribosome genesis to RNA splicing.Due to their unique role in biological processes,it is crucial to investigate their compositions,which is a primary determinant of condensate properties.Results:Since a wide range of macromolecules comprise biomolecular condensates,it is necessary for researchers to investigate them using high-throughput methodologies while low-throughput experiments are not efficient enough.These high-throughput methods usually purify interacting protein libraries from condensates before being scanned in mass spectrometry.It is possible to extract organelles as a whole for specific condensates for further analysis,however,most condensates do not have a distinguishable marker or are sensitive to shear force to be extracted as a whole.Affinity tagging allows a comprehensive view of interacting proteins of target molecule yet only proteins with strong bonds may be pulled down.Proximity labeling serves as a complementary method to label more dynamic proteins with weaker interactions,increasing sensitivity while decreasing specificity.Image-based fluorescent screening takes another path by scanning images automatically to illustrate the condensing state of biomolecules within membraneless organelles,which is a unique feature unlike the previous mass spectrometry-based methods.Conclusion:This review presents a rough glimpse into high-throughput methodologies for biomolecular condensate investigation to encourage usage of bioinformatic tools by researchers in relevant fields.
  • 摘要：Background:The core concept of cell engineering is the use of synthetic biological methods to engineer and design genetically modified cells with specific functions,which has revolutionized the biotechnology industry and cell therapy.Implanted cells play an important role in the cell therapy,but the currently used implanted cells are unable to fully meet the needs of researchers and clinicians.Therefore,the construction of engineered implanted cells has become a new research area,with many groups exploring the working principles of implanted cells,allowing them to better exert their repair function.Results:Based on the existing cell engineering platforms,this paper summarizes the main types of chassis cells used in implanted cell engineering,progress in the development of gene editing tools and delivery systems,as well as strategies for the construction of engineered implanted cells.Conclusions:The rational use of synthetic biology methods to program and control the function of implanted cells with high spatiotemporal accuracy provides new ideas for the development of cell therapy,and opens up new possibilities for exploring the mechanism of implanted cell action to allow them to better exert their role in promoting the progress of repair.
  • 摘要：This article records the author's experience in participating in the early human genome and bioinformatics research in China,especially the non-coding sequence of the genome.It also introduced the beginning of human genome research in china,including the experts and teams involved in the International Human Genome Project.All the progress of bioinformatics originates from the inheritance of theoretical biology and the layout of philosophers in china.
  • 摘要：Background:The coronavirus pandemic (COVID-19) is causing a havoc globally,exacerbated by the newly dis-covered SARS-CoV-2 virus.Due to its high population density,India is one of the most badly effected countries from the first wave of COVID-19.Therefore,it is extremely necessary to accurately predict the state-wise and overall dynamics of COVID-19 to get the effective and efficient organization of resources across India.Methods:In this study,the dynamics of COVID-19 in India and several of its selected states with different demo-graphic structures were analyzed using the SEIRD epidemiological model.The basic reproductive ratio Ro was sys-temically estimated to predict the dynamics of the temporal progression of COVID-19 in India and eight of its states,Andhra Pradesh,Chhattisgarh,Delhi,Gujarat,Madhya Pradesh,Maharashtra,Tamil Nadu,and Uttar Pradesh.Results:For India,the SEIRD model calculations show that the peak of infection is expected to appear around the middle of October,2020.Furthermore,we compared the model scenario to a Gaussian fit of the daily infected cases and obtained similar results.The early imposition of a nation-wide lockdown has reduced the number of infected cases but delayed the appearance of the infection peak significantly.Conclusion:After comparing our calculations using India's data to the real life dynamics observed in Italy and Russia,we can conclude that the SEIRD model can predict the dynamics of COVID-19 with sufficient accuracy.
  • 摘要：Background:Inhibitors of B-cell CLL/lymphoma 2 (Bcl-2) family proteins have shown hope as antitumor drugs.While the notion that it is efficient to coordinate,balance,and neutralize both arms of the anti-apoptotic Bcl-2 family has been validated in many cancer cells,the weights of the two arms contributing to apoptosis inhibition have not been explored.This study analyzed the best combination ratio for different Bcl-2 selective inhibitors.Methods:We used a previously established mathematical model to study the weights of Bcl-2 (representing both Bcl-2 and Bcl-xL in this study) and myeloid cell leukemia-1 (Mcl-1).Correlation and single-parameter sensitivity analysis were used to find the major molecular determinants for Bcl-2 and Mcl-1 dependency,as well as their weights.Biological experiments were used to verify the mathematical model.Results:Bcl-2 protein level and Mcl-1 protein level,production,and degradation rates were the major molecular determinants for Bcl-2 and Mcl-1 dependency.The model gained agreement with the experimental assays for ABT-737/A-1210477 and ABT-737/compound 5 combination effect in MCF-7 and MDA-MB-231.Two sets of equations composed of Bcl-2 and Mcl-1 levels were obtained to predict the best combination ratio for Bcl-2 inhibitors with Mcl-1 inhibitors that stabilize and downregulate Mcl-1,respectively.Conclusions:The two sets of equations can be used as tools to bypass time-consuming and laborious experimental screening to predict the best drug combination ratio for treatment.

    定量生物学:英文版的期刊信息

    • 曾用名：
    • 创刊时间：
    • 地区：
    • 语言：中文
    • 热门主题：SINGLE-CELL GENOMICS DEEP CANCER LEARNING ANALYSIS RNA-SEQ DATA SEQUENCING SARS-COV
    • 学科分类：

    定量生物学:英文版的获奖情况

    定量生物学:英文版的收录情况

    • • 化学文摘（网络版）
    • • 中国科学引文数据库（2011-2012）
    • • 中国科学引文数据库（2015-2016）
    • • 中国科学引文数据库（2017-2018）
    • • 中国科学引文数据库（2019-2020）
    • • 哥白尼索引
    • • 日本科学技术振兴机构数据库
    • • 生物医学检索系统
    • • 文摘与引文数据库

    定量生物学:英文版的投稿信息

    • 投稿方式：
    • 投稿须知：点击查看
    • 期刊官方网站：点击前往
    • 版面费：
    • 审稿费：元
    • 通讯地址：北京市朝阳区惠新东街4号富盛大厦15层
    • 邮编：100029
    • 联系电话：010-58556485
    • 邮箱：
    • 期刊栏目：
    免费订阅本刊

    客服邮箱：kefu@zhangqiaokeyan.com

    京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有 出版物经营许可证

    • 客服微信

    • 服务号