Quantitative Biology最新文献

{"title":"Simulating the whole brain as an alternative way to achieve AGI","authors":"Jianfeng Feng","doi":"10.1002/qub2.6","DOIUrl":"https://doi.org/10.1002/qub2.6","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"76 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From qualitative to quantitative: the state of the art and challenges for plant synthetic biology","authors":"Chenfei Tian, Jianhua Li, Yong Wang","doi":"10.15302/j-qb-022-0326","DOIUrl":"https://doi.org/10.15302/j-qb-022-0326","url":null,"abstract":"The flourishing plant science promotes the exploding number of data and the expansion of toolkits. Plant synthetic biology is still in its early stages and requires more quantitative and predictable study. Despite the challenges, some pioneering examples have been successfully demonstrated in model plants. Backgrounds As an increasing number of synthetic switches and circuits have been created for plant systems and of synthetic products produced in plant chassis, plant synthetic biology is taking a strong foothold in agriculture and medicine. The ever‐exploding data has also promoted the expansion of toolkits in this field. Genetic parts libraries and quantitative characterization approaches have been developed. However, plant synthetic biology is still in its infancy. The considerations for selecting biological parts to design and construct genetic circuits with predictable functions remain desired. Results In this article, we review the current biotechnological progresses in field of plant synthetic biology. Assembly standardization and quantitative approaches of genetic parts and genetic circuits are discussed. We also highlight the main challenges in the iterative cycles of design‐build‐test‐learn for introducing novel traits into plants. Conclusion Plant synthetic biology promises to provide important solutions to many issues in agricultural production, human health care, and environmental sustainability. However, tremendous challenges exist in this field. For example, the quantitative characterization of genetic parts is limited; the orthogonality and the transfer functions of circuits are unpredictable; and also, the mathematical modeling‐assisted circuits design still needs to improve predictability and reliability. These challenges are expected to be resolved in the near future as interests in this field are intensifying.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction","authors":"Qijin Yin, Rui Fan, Xusheng Cao, Qiao Liu, Rui Jiang, Wanwen Zeng","doi":"10.15302/j-qb-022-0320","DOIUrl":"https://doi.org/10.15302/j-qb-022-0320","url":null,"abstract":"Computational methods for DDIs and DTIs prediction are essential for accelerating the drug discovery process. We proposed a novel deep learning method DeepDrug, to tackle these two problems within a unified framework. DeepDrug is capable of extracting comprehensive features of both drug and target protein, thus demonstrating a superior prediction performance in a series of experiments. The downstream applications show that DeepDrug is useful in facilitating drug repositioning and discovering the potential drug against specific disease. Background Computational approaches for accurate prediction of drug interactions, such as drug‐drug interactions (DDIs) and drug‐target interactions (DTIs), are highly demanded for biochemical researchers. Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively, their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure. Methods In this paper, we develop DeepDrug, a deep learning framework for overcoming the above limitation by using residual graph convolutional networks (Res‐GCNs) and convolutional networks (CNNs) to learn the comprehensive structure‐ and sequence‐based representations of drugs and proteins. Results DeepDrug outperforms state‐of‐the‐art methods in a series of systematic experiments, including binary‐class DDIs, multi‐class/multi‐label DDIs, binary‐class DTIs classification and DTIs regression tasks. Furthermore, we visualize the structural features learned by DeepDrug Res‐GCN module, which displays compatible and accordant patterns in chemical properties and drug categories, providing additional evidence to support the strong predictive power of DeepDrug. Ultimately, we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS‐CoV‐2, where 7 out of 10 top‐ranked drugs are reported to be repurposed to potentially treat coronavirus disease 2019 (COVID‐19). Conclusions To sum up, we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cell‐based allometry: an approach for evaluation of complexity in morphogenesis","authors":"Ali Tarihi, Mojtaba Tarihi, T. Tiraihi","doi":"10.15302/j-qb-022-0319","DOIUrl":"https://doi.org/10.15302/j-qb-022-0319","url":null,"abstract":"Morphogenesis is a complex process in a developing animal at the organ, cellular and molecular levels. In this investigation, allometry at the cellular level was evaluated.Geometric information, including the time‐lapse Cartesian coordinates of each cell’s center, was used for calculating the allometric coefficients. A zero‐centroaxial skew‐symmetrical matrix ( CSSM), was generated and used for constructing another square matrix (basic square matrix: BSM), then the determinant of BSM was calculated ( d). The logarithms of absolute d (Lad) of cell group at different stages of development were plotted for all of the cells in a range of development stages; the slope of the regression line was estimated then used as the allometric coefficient. Moreover, the lineage growth rate (LGR) was also calculated by plotting the Lad against the logarithm of the time. The complexity index at each stage was calculated. The method was tested on a developing Caenorhabditis elegans embryo.We explored two out of the four first generated blastomeres in C. elegans embryo. The ABp and EMS lineages show that the allometric coefficient of ABp was higher than that of EMS, which was consistent with the complexity index as well as LGR.The conclusion of this study is that the complexity of the differentiating cells in a developing embryo can be evaluated by allometric scaling based on the data derived from the Cartesian coordinates of the cells at different stages of development.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"18 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139371881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of artificial neural networks to identify and analyze polymerized actin-based cytoskeletal structures in 3D confocal images","authors":"","doi":"10.15302/j-qb-022-0325","DOIUrl":"https://doi.org/10.15302/j-qb-022-0325","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepRCI: predicting RNA-chromatin interactions via deep learning with multi-omics data","authors":"Yuanpeng Xiong, Xuan He, Dan Zhao, Tao Jiang, Jianyang Zeng","doi":"10.15302/j-qb-022-0316","DOIUrl":"https://doi.org/10.15302/j-qb-022-0316","url":null,"abstract":"Background : Chromatin-associated RNA (caRNA) acts as a ubiquitous epigenetic layer in eukaryotes, and has been reported to be essential in various biological processes, including gene transcription, chromatin remodeling and cellular differentiation. Recently, numerous experimental techniques have been developed to characterize genome-wide RNA-chromatin interactions to understand their underlying biological functions. However, these experimental methods are generally expensive, time-consuming, and limited in identifying all potential sites, while most of the existing computational methods are restricted to detecting only specific types of RNAs interacting with chromatin. Methods : Here, we propose a highly interpretable computational framework, named DeepRCI, to identify the interactions between various types of RNAs and chromatin. In this framework, we introduce a novel deep learning component called variformer and integrate multi-omics data to capture intrinsic genomic features at both RNA and DNA levels. Results : Extensive experiments demonstrate that DeepRCI can detect RNA-chromatin interactions more accurately when compared to the state-of-the-art baseline prediction methods. Furthermore, the sequence features extracted by DeepRCI can be well matched to known critical gene regulatory components, indicating that our model can provide useful biological insights into understanding the underlying mechanisms of RNA-chromatin interactions. In addition, based on the prediction results, we further delineate the relationships between RNA-chromatin interactions and cellular functions, including gene expression and the modulation of cell states. Conclusions : In summary, DeepRCI can serve as a useful tool for characterizing RNA-chromatin interactions and studying the underlying gene regulatory code.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based DNA methylation detection on ionic signals from Oxford Nanopore sequencing data","authors":"Xiuquan Wang, Mian Umair Ahsan, Yunyun Zhou, Kai Wang","doi":"10.15302/j-qb-022-0323","DOIUrl":"https://doi.org/10.15302/j-qb-022-0323","url":null,"abstract":"Background : Oxford Nanopore long-read sequencing technology addresses current limitations for DNA methylation detection that are inherent in short-read bisulfite sequencing or methylation microarrays. A number of analytical tools, such as Nanopolish, Guppy/Tombo and DeepMod, have been developed to detect DNA methylation on Nanopore data. However, additional improvements can be made in computational efficiency, prediction accuracy, and contextual interpretation on complex genomics regions (such as repetitive regions, low GC density regions). Method : In the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. Transformer is an algorithm that adopts self-attention architecture in the neural networks and has been widely used in natural language processing. Results : Compared to traditional deep-learning method such as convolutional neural network (CNN) and recurrent neural network (RNN), Transformer may have specific advantages in DNA methylation detection, because the self-attention mechanism can assist the relationship detection between bases that are far from each other and pay more attention to important bases that carry characteristic methylation-specific signals within a specific sequence context. Conclusion : We demonstrated the ability of Transformers to detect methylation on ionic signal data.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LIBRA: an adaptative integrative tool for paired single-cell multi-omics data","authors":"","doi":"10.15302/j-qb-022-0318","DOIUrl":"https://doi.org/10.15302/j-qb-022-0318","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput metabarcoding of SAR11 assemblages from the southwest Atlantic shelf and arid Patagonia: richness and as-sociated rank abundance distributions","authors":"","doi":"10.15302/j-qb-023-0329","DOIUrl":"https://doi.org/10.15302/j-qb-023-0329","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building digital life systems for future biology and medicine","authors":"","doi":"10.15302/j-qb-023-0331","DOIUrl":"https://doi.org/10.15302/j-qb-023-0331","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}