Hasil untuk "Biology"

J. Shepherd, R. Huganir

T. Finkel, M. Serrano, M. Blasco

P. Medawar

J. Massoulie, L. Pezzementi, S. Bon et al.

P. Mignatti, D. Rifkin

J. Pober, R. Cotran

H. Sverdrup, Martin W. Johnson, R. H. Fleming

L. Allen

B. Perthame

H. Thieme

The formulation, analysis, and re-evaluation of mathematical models in population biology has become a valuable source of insight to mathematicians and biologists alike. This book presents an overview and selected sample of these results and ideas, organized by biological theme rather than mathematical concept, with an emphasis on helping the reader develop appropriate modeling skills through use of well-chosen and varied examples. Part I starts with unstructured single species population models, particularly in the framework of continuous time models, then adding the most rudimentary stage structure with variable stage duration. The theme of stage structure in an age-dependent context is developed in Part II, covering demographic concepts, such as life expectation and variance of life length, and their dynamic consequences. In Part III, the author considers the dynamic interplay of host and parasite populations, i.e., the epidemics and endemics of infectious diseases. The theme of stage structure continues here in the analysis of different stages of infection and of age-structure that is instrumental in optimizing vaccination strategies. Each section concludes with exercises, some with solutions, and suggestions for further study. The level of mathematics is relatively modest; a "toolbox" provides a summary of required results in differential equations, integration, and integral equations. In addition, a selection of Maple worksheets is provided. The book provides an authoritative tour through a dazzling ensemble of topics and is both an ideal introduction to the subject and reference for researchers.

T. Ideker, N. Krogan

Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re‐wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis.

Xingming Zhao, Weidong Tian, R. Jiang et al.

The complex biological systems consist of distinct molecules that exert their functions by interacting with each other, which makes it a big challenge to understand how the cellular machinery works. Recently, the accumulation of a large amount of multiscale omics data, such as next-generation sequencing data and protein interaction data, provides opportunity to investigate the functions of molecules from a systematic perspective. On the other hand, the analysis of these huge datasets demands efficient and robust computational methods. In this special issue, we reported the recent progress made in developing new computational methodologies to analyze the genomics data, construct gene networks, and identify disease genes. Understanding the Functions of Molecules in the Postgenomic Age. In recent years, the advance of next-generation sequencing (NGS) technology makes it more easier for researchers to access and analyze genetics data and has influential effects on the biomedical research community. However, compared with sequencing, computational analysis of the flooding sequencing data with appropriate tools is becoming a more important task when interpreting the data. In their review paper, M. P. Dolled-Filhart et al. described the pipeline for bioinformatics analysis of the NGS data, starting from alignment to variant calling as well as filtering and annotation. In each step, they discussed the tools or software that should be used as well as their advantages and caveats. This survey of the bioinformatics analysis of NGS data can help researchers to choose appropriate tools when dealing with the sequencing data. Along with the sequencing technology, lines of evidence show that a lot of noncoding RNAs (ncRNAs) play important roles in various biological processes. Unlike the protein-coding genes that are well studied, the functions of most ncRNAs are not clear. Therefore, it is highly desirable to develop computational methods to predict the functions of the ncRNAs. H. Ma et al. conducted a survey about the computational approaches developed to predict and annotate the long noncoding RNAs (lncRNAs), which can help researchers to learn the progress in this filed and future directions in which bioinformaticists should work while annotating lncRNAs. While annotating the functions of molecules, standard and controlled vocabularies are required. Hence, the ontologies that are represented as abstract description systems of knowledge are becoming more and more popular recently. At the same time, it is becoming a difficult task to calculate the semantic similarity between ontology terms quantitatively. M. Gan et al. introduced popular methods in quantitating the semantic similarity between ontology terms and their software implementations. Furthermore, they classified these methods into distinct categories and discussed their advantages and shortcomings, which can help researchers to select appropriate tools and methods when working on ontologies. Gene expression profiles can describe the molecular mechanisms that underlie certain phenotypes. However, while analyzing the gene expression data, it is inappropriate to treat genes independently considering genes interact with each other within the cell. O. Frings et al. proposed a network-based approach to analyze the gene expression data and applied it to investigate the development of sex-specific chicken gonad and brain tissues. By combining the chicken network and the gene expression data, they identified some sex-biased characteristics, for example, same sex-biased genes tend to be tightly connected in the network, and provided new insights into the molecular underpinnings of sex-biased genes. Construction and Analysis of Gene Networks. Construction of gene regulatory networks (GRNs) is a crucial step in systems biology, where gene expression data is widely explored to infer the GRNs. However, the high dimensionality and notorious noise of the gene expression data makes it a nontrivial task to infer the GRNs. N. You et al. presented a new Laplace error penalty (LEP) model to calculate the partial correlation coefficients between genes and construct the GRNs. Compared with the popular least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) approaches, the LEP method reached the highest precision. Except for gene expression data, integration of different data sources may improve the accuracy of inferred GRNs. H. Chen et al. surveyed the strategies to integrate distinct data sources and their effectiveness and recommended how to choose an appropriate strategy while integrating distinct data sources. N. Nakajima et al. proposed a novel network completion approach, DPLSQ, to infer gene networks. Benchmarking on artificial datasets, their proposed DPLSQ outperforms popular ARACNE and GeneNet with the highest accuracy. By investigating a 2-gene network, A. V. Spirov et al. found that gene cooption can affect the robustness of GRNs, and the findings provide new insights into the evolvability and robustness of GRNs. Network modules are found to be functional blocks of gene networks, the identification of which is becoming a hot research topic. By taking the hierarchical modular structure into account, S. Zhang presented a new stochastic block model to detect the hierarchical modules. Applied to the real yeast gene coexpression network, the proposed method can efficiently detect the hierarchical modular structures that are consistent with biological functions. Recently, it is found that a particular type of ncRNAs, microRNAs, plays important roles in gene regulation by working together with transcription factors. W. Mu et al. proposed a new local genetic algorithm to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes, and these modules provide useful insights into the regulatory mechanisms underlying gene expression. Computational Approaches to Hunting Disease-Associated Genes. The identification of genetic variants that are responsible for human diseases is critical for understanding the development of diseases and designing new effective drugs. Thanks to the genome-wide association studies (GWASs), some genetic variants that drive diseases have been identified, among which single nucleotide polymorphisms (SNPs) and nonsynonymous single nucleotide polymorphisms (nsSNPs) are receiving more and more attention. In this issue, J. Wu and R. Jiang reviewed the databases that collect nsSNPs and summarized popular computational methods that identify deleterious nsSNPs. In addition, they introduced machine learning models that are useful in predicting deleterious nsSNPs. Beyond SNP-based association analysis, gene-based association analysis is receiving increasing attention. X. Guo et al. comprehensively compared these two approaches on the data from the study of addiction and found that these two approaches complement with each other and can get better results when used together. The differentially expressed genes identified from microarray data are generally regarded as candidate disease genes. However, the number of differentially expressed genes may reach hundreds or even thousands, thereby making it difficult to identify the potential disease genes. In this issue, L. Li et al. proposed a new hybrid approach to predict disease genes based on estimation of distribution algorithm and support vector machine. Benchmarking on B-cell lymphoma and colon cancer datasets, their method outperforms two other popular approaches and identify some new candidate genes for future validation.

Lisa Goers, P. Freemont, K. Polizzi

Heather E. McFarlane, Anett Döring, S. Persson

L. Tsimring

L. Lavis, Ronald T. Raines

Small-molecule fluorophores manifest the ability of chemistry to solve problems in biology. As we noted in a previous review (Lavis, L. D.; Raines, R. T. ACS Chem. Biol.2008, 3, 142–155), the extant collection of fluorescent probes is built on a modest set of “core” scaffolds that evolved during a century of academic and industrial research. Here, we survey traditional and modern synthetic routes to small-molecule fluorophores and highlight recent biological insights attained with customized fluorescent probes. Our intent is to inspire the design and creation of new high-precision tools that empower chemical biologists.

H. Azim, A. Partridge

Breast cancer arising at a young age is relatively uncommon, particularly in the developed world. Several studies have demonstrated that younger patients often experience a more aggressive disease course and have poorer outcome compared to older women. Expression of key biomarkers, including endocrine receptors, HER2 and proliferation markers, appears to be different in younger patients and young women are more likely to harbor a genetic predisposition. Despite these differences, little research to date has focused on the biology of these tumors to refine prognosis, and potentially direct treatment strategies, which remain similar to those offered to older patients. Accumulating evidence suggests the differences in breast stroma in younger patients and changes that occur with pregnancy and breastfeeding likely contribute to the different biology of these tumors. Reproductive behaviors appear to impact the biology of tumors developing later in life. In addition, tumors arising during or shortly following pregnancy appear to exhibit unique biological features. In this review, we discuss our emerging understanding of the biology of breast cancer arising at a young age at both the pathologic and the genomic level. We elucidate the potential role of genomic signatures, the impact of pregnancy and breastfeeding on breast cancer biology, and how even current knowledge might advance the clinical management of young breast cancer patients.

Chao Yan, Ming-tai An, Ming Tang et al.

Karst flora confined to isolated ‘habitat islands’ evolve specialized adaptations and unique traits, serving as ideal models for investigating adaptive evolution and species diversification mechanisms. Camellia rubituberculata, endemic to the karst habitats of Guizhou, China, can serve as a model for adaptive evolution and diversity in karst-endemic woody species. However, the lack of a chromosome-level genome for this species has limited in-depth studies on its adaptations to karst and posed a barrier to its genetic improvement. In this study, a chromosome-level genome assembly of C. rubituberculata was generated, with 15 pseudo-chromosomes and a genome size of 2.50 Gb (scaffold N50 = 168.34 Mb, 55,302 protein-coding genes). Comparative genomics revealed two whole-genome duplications (WGDs), namely, an ancient γ-event (∼120 Mya) and a subsequent genus-wide event (∼86 Mya), after which gene families linked to karst adaptation (e.g., photosynthesis) were significantly expanded. Selective sweep analysis showed that selected genes were associated with phytohormone transmission and metabolism. Genes functionally annotated as involved in stress responses—including SAUR, BSK, NCL, CDPK, and NDPK—participate in calcium homeostasis and ion transport pathways under karst-specific stresses. MYB transcription factors, which are crucial in plant responses to stresses, including drought, may be key for adaptation to the high salinity and drought stress in karst environments. The divergent selection in wild and cultivated groups highlight key adaptations in plant hormone transduction and calcium transport. By elucidating karst adaptation in C. rubituberculata, this work establishes essential genomic resources for advancing genetic evolution research and molecular breeding across Camellia species.

Dipendra Shahi, Jia Guo, Md Ali Babar et al.

Abstract Background Grain number (GN) is one of the key yield contributing factors in modern wheat (Triticum aestivum) varieties. Fruiting efficiency (FE) is a key trait for increasing GN by making more spike assimilates available to reproductive structures. Thousand grain weight (TGW) is also an important component of grain yield. To understand the genetic architecture of FE and TGW, we performed a genome-wide association study (GWAS) in a panel of 236 US soft facultative wheats that were phenotyped in three experiments at two locations in Florida and genotyped with 20,706 single nucleotide polymorphisms (SNPs) generated from genotyping-by-sequencing (GBS). Results FE showed significant positive associations with GN, grain yield (GY), and harvest index (HI). Likewise, TGW mostly had a positive correlation with GY and HI, but a negative correlation with GN. Eighteen marker-trait associations (MTAs) for FE and TGW were identified on 11 chromosomes, with nine MTAs within genes. Several MTAs associated with other traits were found within genes with different biological and metabolic functions including nuclear pore complex protein, F-box protein, oligopeptide transporter, and glycoside vacuolar protein. Two KASP markers showed significant mean differences for FE and TGW traits in a validation population. Conclusions KASP marker development and validation demonstrated the utility of these markers for improving FE and TGW in breeding programs. The results suggest that optimizing intra-spike partitioning and utilizing marker-assisted selection (MAS) can enhance GY and HI.

Chaobo Zhang, Chengshuai Xu, Zhenxia Zhu et al.

Eutrophication facilitates the proliferation of cyanobacteria, ultimately leading to the formation of harmful cyanobacterial blooms. Prodigiosin, known for its algicidal properties, presents significant potential for application in water pollution remediation. This study aims to identify and characterize a novel strain with superior prodigiosin production capabilities and to elucidate the algicidal mechanism of prodigiosin against <i>Microcystis aeruginosa</i> and <i>Anabaena</i> sp. by assessing the photosynthetic responses of algal cells in the presence of prodigiosin. The findings revealed the isolation and identification of a new strain, ZC52, classified as <i>Serratia marcescens</i>. The optimal medium composition was determined to be 20.0 mL·L⁻¹ glycerol, 15.0 g·L⁻¹ beef bone peptone, 15.0 g·L⁻¹ magnesium sulfate heptahydrate, 0.15 g·L⁻¹ corn dry powder, and 0.250% tyrosine, resulting in a 47.40% increase in prodigiosin yield, thereby achieving a production level of 7.644 g·L⁻¹. Moreover, the algicidal activity exhibited a concentration-dependent relationship, with 10.0 mg·L⁻¹ of prodigiosin leading to approximately 53.25% and 30.44% inhibition of chlorophyll a content within 24 h, demonstrating the potential of prodigiosin as an effective algicidal compound. Meanwhile, exposure to 10.0 mg·L⁻¹ of prodigiosin resulted in reductions of 46.88% and 21.02% in the Fv/Fm values of <i>M. aeruginosa</i> and <i>Anabaena</i> sp., respectively. Our results indicated that prodigiosin can inhibit the accumulation of photosynthetic pigments and significantly diminish algal photosynthetic efficiency. This study not only identifies valuable microbial resources for prodigiosin production but also provides a theoretical framework and empirical evidence to support the scientific management of cyanobacterial blooms.