{"title":"大规模的数据驱动模型和基于物理学的模型为染色体的结构、动力学和功能之间的关系提供了洞察力。","authors":"Cibo Feng, Jin Wang, Xiakun Chu","doi":"10.1093/jmcb/mjad042","DOIUrl":null,"url":null,"abstract":"<p><p>The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression. When the cell changes its identity in the cell-fate decision-making process, extensive rearrangements of chromosome structures occur accompanied by large-scale adaptations of gene expression, underscoring the importance of chromosome dynamics in shaping genome function. Over the last two decades, rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes. In parallel, these enormous data offer valuable opportunities for developing quantitative computational models. Here, we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes. Different from the underlying modeling strategies, these approaches can be classified into data-driven ('top-down') and physics-based ('bottom-up') categories. We discuss their contributions to offering valuable insights into the relationships among the structures, dynamics, and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.</p>","PeriodicalId":16433,"journal":{"name":"Journal of Molecular Cell Biology","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10782906/pdf/","citationCount":"0","resultStr":"{\"title\":\"Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of chromosomes.\",\"authors\":\"Cibo Feng, Jin Wang, Xiakun Chu\",\"doi\":\"10.1093/jmcb/mjad042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression. When the cell changes its identity in the cell-fate decision-making process, extensive rearrangements of chromosome structures occur accompanied by large-scale adaptations of gene expression, underscoring the importance of chromosome dynamics in shaping genome function. Over the last two decades, rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes. In parallel, these enormous data offer valuable opportunities for developing quantitative computational models. Here, we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes. Different from the underlying modeling strategies, these approaches can be classified into data-driven ('top-down') and physics-based ('bottom-up') categories. We discuss their contributions to offering valuable insights into the relationships among the structures, dynamics, and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.</p>\",\"PeriodicalId\":16433,\"journal\":{\"name\":\"Journal of Molecular Cell Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2023-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10782906/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Molecular Cell Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/jmcb/mjad042\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Cell Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/jmcb/mjad042","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of chromosomes.
The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression. When the cell changes its identity in the cell-fate decision-making process, extensive rearrangements of chromosome structures occur accompanied by large-scale adaptations of gene expression, underscoring the importance of chromosome dynamics in shaping genome function. Over the last two decades, rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes. In parallel, these enormous data offer valuable opportunities for developing quantitative computational models. Here, we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes. Different from the underlying modeling strategies, these approaches can be classified into data-driven ('top-down') and physics-based ('bottom-up') categories. We discuss their contributions to offering valuable insights into the relationships among the structures, dynamics, and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.
期刊介绍:
The Journal of Molecular Cell Biology ( JMCB ) is a full open access, peer-reviewed online journal interested in inter-disciplinary studies at the cross-sections between molecular and cell biology as well as other disciplines of life sciences. The broad scope of JMCB reflects the merging of these life science disciplines such as stem cell research, signaling, genetics, epigenetics, genomics, development, immunology, cancer biology, molecular pathogenesis, neuroscience, and systems biology. The journal will publish primary research papers with findings of unusual significance and broad scientific interest. Review articles, letters and commentary on timely issues are also welcome.
JMCB features an outstanding Editorial Board, which will serve as scientific advisors to the journal and provide strategic guidance for the development of the journal. By selecting only the best papers for publication, JMCB will provide a first rate publishing forum for scientists all over the world.