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  • PERSPECTIVE
    Evelyn Shue, Li Liu, Bingxin Li, Zifeng Feng, Xin Li, Gangqing Hu
    Quantitative Biology, 2023, 11(2): 105-108. https://doi.org/10.15302/J-QB-023-0327

    The impressive conversational and programming abilities of ChatGPT make it an attractive tool for facilitating the education of bioinformatics data analysis for beginners. In this study, we proposed an iterative model to fine-tune instructions for guiding a chatbot in generating code for bioinformatics data analysis tasks. We demonstrated the feasibility of the model by applying it to various bioinformatics topics. Additionally, we discussed practical considerations and limitations regarding the use of the model in chatbot-aided bioinformatics education.

  • REVIEW
    Zijie Zhao, Jie Song, Tuo Wang, Qiongshi Lu
    Quantitative Biology, 2021, 9(2): 133-140. https://doi.org/10.15302/J-QB-021-0238

    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.

  • REVIEW
    Xiaofeng Zhu
    Quantitative Biology, 2021, 9(2): 122-132. https://doi.org/10.1007/s40484-020-0216-3

    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.

  • COMMENTARY
    Dong Xu
    Quantitative Biology, 2023, 11(2): 204-206. https://doi.org/10.15302/J-QB-023-0328
  • REVIEW
    Yuhan Xie, Nayang Shan, Hongyu Zhao, Lin Hou
    Quantitative Biology, 2021, 9(2): 141-150. https://doi.org/10.15302/J-QB-020-0228

    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.

  • RESEARCH ARTICLE
    Limin Jiang, Hui Yu, Yan Guo
    Quantitative Biology, 2023, 11(1): 31-43. https://doi.org/10.15302/J-QB-022-0309

    Background: Mutational signatures computed from somatic mutations, allow an in-depth understanding of tumorigenesis and may illuminate early prevention strategies. Many studies have shown the regulation effects between somatic mutation and gene expression dysregulation.

    Methods: We hypothesized that there are potential associations between mutational signature and gene expression. We capitalized upon RNA-seq data to model 49 established mutational signatures in 33 cancer types. Both accuracy and area under the curve were used as performance measures in five-fold cross-validation.

    Results: A total of 475 models using unconstrained genes, and 112 models using protein-coding genes were selected for future inference purposes. An independent gene expression dataset on lung cancer smoking status was used for validation which achieved over 80% for both accuracy and area under the curve.

    Conclusion: These results demonstrate that the associations between gene expression and somatic mutations can translate into the associations between gene expression and mutational signatures.

  • REVIEW
    Lili Zhu, Zhenyu Qian
    Quantitative Biology, 2022, 10(1): 17-34. https://doi.org/10.15302/J-QB-021-0271

    Background: Alzheimer’s disease (AD) is one of the most popular tauopathies. Neurofibrillary tangles and senile plaques are widely recognized as the pathological hallmarks of AD, which are mainly composed of tau and β-amyloid (Aβ) respectively. Recent failures of drugs targeting Aβ have led scientists to scrutinize the crucial impact of tau in neurodegenerative diseases. Mutated or abnormal phosphorylated tau protein loses affinity with microtubules and assembles into pathological accumulations. The aggregation process closely correlates to two amyloidogenic core of PHF6 (306VQIVYK311) and PHF6* (275VQIINK280) fragments. Moreover, tau accumulations display diverse morphological characteristics in different diseases, which increases the difficulty of providing a unifying neuropathological criterion for early diagnosis.

    Results: This review mainly summarizes atomic-resolution structures of tau protein in the monomeric, oligomeric and fibrillar states, as well as the promising inhibitors designed to prevent tau aggregation or disaggregate tau accumulations, recently revealed by experimental and computational studies. We also systematically sort tau functions, their relationship with tau structures and the potential pathological processes of tau protein.

    Conclusion: The current progress on tau structures at atomic level of detail expands our understanding of tau aggregation and related pathology. We discuss the difficulties in determining the source of neurotoxicity and screening effective inhibitors. We hope this review will inspire new clues for designing medicines against tau aggregation and shed light on AD diagnosis and therapies.

  • REVIEW
    Lei Li, Yumei Li, Xudong Zou, Fuduan Peng, Ya Cui, Eric J. Wagner, Wei Li
    Quantitative Biology, 2022, 10(1): 44-54. https://doi.org/10.15302/J-QB-021-0252

    Background: Genome-wide association studies (GWAS) have identified thousands of genomic non-coding variants statistically associated with many human traits and diseases, including cancer. However, the functional interpretation of these non-coding variants remains a significant challenge in the post-GWAS era. Alternative polyadenylation (APA) plays an essential role in post-transcriptional regulation for most human genes. By employing different poly(A) sites, genes can either shorten or extend the 3′-UTRs that contain cis-regulatory elements such as miRNAs or RNA-binding protein binding sites. Therefore, APA can affect the mRNA stability, translation, and cellular localization of proteins. Population-scale studies have revealed many inherited genetic variants that potentially impact APA to further influence disease susceptibility and phenotypic diversity, but systematic computational investigations to delineate the connections are in their earliest states.

    Results: Here, we discuss the evolving definitions of the genetic basis of APA and the modern genomics tools to identify, characterize, and validate the genetic influences of APA events in human populations. We also explore the emerging and surprisingly complex molecular mechanisms that regulate APA and summarize the genetic control of APA that is associated with complex human diseases and traits.

    Conclusion: APA is an intermediate molecular phenotype that can translate human common non-coding variants to individual phenotypic variability and disease susceptibility.

  • REVIEW
    Hua Tang, Zihuai He
    Quantitative Biology, 2021, 9(2): 168-184. https://doi.org/10.15302/J-QB-021-0249

    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.

  • RESEARCH ARTICLE
    Yuwei Huang, Huidan Chang, Xiaoyi Chen, Jiayue Meng, Mengyao Han, Tao Huang, Liyun Yuan, Guoqing Zhang
    Quantitative Biology, 2023, 11(2): 163-174. https://doi.org/10.15302/J-QB-022-0311

    Background: The precise and efficient analysis of single-cell transcriptome data provides powerful support for studying the diversity of cell functions at the single-cell level. The most important and challenging steps are cell clustering and recognition of cell populations. While the precision of clustering and annotation are considered separately in most current studies, it is worth attempting to develop an extensive and flexible strategy to balance clustering accuracy and biological explanation comprehensively.

    Methods: The cell marker-based clustering strategy (cmCluster), which is a modified Louvain clustering method, aims to search the optimal clusters through genetic algorithm (GA) and grid search based on the cell type annotation results.

    Results: By applying cmCluster on a set of single-cell transcriptome data, the results showed that it was beneficial for the recognition of cell populations and explanation of biological function even on the occasion of incomplete cell type information or multiple data resources. In addition, cmCluster also produced clear boundaries and appropriate subtypes with potential marker genes. The relevant code is available in GitHub website (huangyuwei301/cmCluster).

    Conclusions: We speculate that cmCluster provides researchers effective screening strategies to improve the accuracy of subsequent biological analysis, reduce artificial bias, and facilitate the comparison and analysis of multiple studies.

  • REVIEW
    Quan Cheng, Xuan Wang, Xian-En Zhang, Chengchen Xu, Feng Li
    Quantitative Biology, 2023, 11(1): 1-14. https://doi.org/10.15302/J-QB-022-0306

    Background: As one of the representative protein materials, protein nanocages (PNCs) are self-assembled supramolecular structures with multiple advantages, such as good monodispersity, biocompatibility, structural addressability, and facile production. Precise quantitative functionalization is essential to the construction of PNCs with designed purposes.

    Results: With three modifiable interfaces, the interior surface, outer surface, and interfaces between building blocks, PNCs can serve as an ideal platform for precise multi-functionalization studies and applications. This review summarizes the currently available methods for precise quantitative functionalization of PNCs and highlights the significance of precise quantitative control in fabricating PNC-based materials or devices. These methods can be categorized into three groups, genetic, chemical, and combined modification.

    Conclusion: This review would be constructive for those who work with biosynthetic PNCs in diverse fields.

  • REVIEW
    Haiyan Gong, Zhengyuan Chen, Yuxin Tang, Minghong Li, Sichen Zhang, Xiaotong Zhang, Yang Chen
    Quantitative Biology, 2023, 11(2): 122-142. https://doi.org/10.15302/J-QB-022-0322

    Background: As parts of the cis-regulatory mechanism of the human genome, interactions between distal enhancers and proximal promoters play a crucial role. Enhancers, promoters, and enhancer-promoter interactions (EPIs) can be detected using many sequencing technologies and computation models. However, a systematic review that summarizes these EPI identification methods and that can help researchers apply and optimize them is still needed.

    Results: In this review, we first emphasize the role of EPIs in regulating gene expression and describe a generic framework for predicting enhancer-promoter interaction. Next, we review prediction methods for enhancers, promoters, loops, and enhancer-promoter interactions using different data features that have emerged since 2010, and we summarize the websites available for obtaining enhancers, promoters, and enhancer-promoter interaction datasets. Finally, we review the application of the methods for identifying EPIs in diseases such as cancer.

    Conclusions: The advance of computer technology has allowed traditional machine learning, and deep learning methods to be used to predict enhancer, promoter, and EPIs from genetic, genomic, and epigenomic features. In the past decade, models based on deep learning, especially transfer learning, have been proposed for directly predicting enhancer-promoter interactions from DNA sequences, and these models can reduce the parameter training time required of bioinformatics researchers. We believe this review can provide detailed research frameworks for researchers who are beginning to study enhancers, promoters, and their interactions.

  • REVIEW
    Huanhuan Zhu, Xiang Zhou
    Quantitative Biology, 2021, 9(2): 107-121. https://doi.org/10.1007/s40484-020-0207-4

    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.

  • REVIEW
    Junting Wang, Huan Tao, Hao Li, Xiaochen Bo, Hebing Chen
    Quantitative Biology, 2023, 11(2): 109-121. https://doi.org/10.15302/J-QB-022-0317

    Background: The hierarchical three-dimensional (3D) architectures of chromatin play an important role in fundamental biological processes, such as cell differentiation, cellular senescence, and transcriptional regulation. Aberrant chromatin 3D structural alterations often present in human diseases and even cancers, but their underlying mechanisms remain unclear.

    Results: 3D chromatin structures (chromatin compartment A/B, topologically associated domains, and enhancer-promoter interactions) play key roles in cancer development, metastasis, and drug resistance. Bioinformatics techniques based on machine learning and deep learning have shown great potential in the study of 3D cancer genome.

    Conclusion: Current advances in the study of the 3D cancer genome have expanded our understanding of the mechanisms underlying tumorigenesis and development. It will provide new insights into precise diagnosis and personalized treatment for cancers.

  • RESEARCH ARTICLE
    Lei Zhang, Ji Lv, Ming Xiao, Li Yang, Le Zhang
    Quantitative Biology, 2021, 9(3): 292-303. https://doi.org/10.15302/J-QB-021-0236

    Background: A traditional Chinese medicine formula, Youdujing (YDJ) ointment, is widely used for treatment of human papilloma virus-related diseases, such as cervical cancer. However, the underlying mechanisms by which active compounds of YDJ alleviates cervical cancer are still unclear.

    Methods: We applied a comprehensive network pharmacology approach to explore the key mechanisms of YDJ by integrating potential target identification, network analysis, and enrichment analysis into classical molecular docking procedures. First, we used network and enrichment analyses to identify potential therapeutic targets. Second, we performed molecular docking to investigate the potential active compounds of YDJ. Finally, we carried out a network-based analysis to unravel potentially effective drug combinations.

    Results: Network analysis yielded four potential therapeutic targets: ESR1, NFKB1, TNF, and AKT1. Molecular docking highlighted that these proteins may interact with four potential active compounds of YDJ: E4, Y2, Y20, and Y21. Finally, we found that Y2 or Y21 can act alone or together with E4 to trigger apoptotic cascades via the mitochondrial apoptotic pathway and estrogen receptors.

    Conclusion: Our study not only explained why YDJ is effective for cervical cancer treatment, but also lays a strong foundation for future clinical studies based on this traditional medicine.

  • RESEARCH ARTICLE
    Katherine A. Knutson, Wei Pan
    Quantitative Biology, 2021, 9(2): 185-200. https://doi.org/10.1007/s40484-020-0202-9

    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 https://github.com/kathalexknuts/ADIDP.

  • RESEARCH ARTICLE
    Kang Kang, Xue Sun, Lizhong Wang, Xiaotian Yao, Senwei Tang, Junjie Deng, Xiaoli Wu, WeGene Research Team, Can Yang, Gang Chen
    Quantitative Biology, 2021, 9(2): 201-215. https://doi.org/10.1007/s40484-020-0209-2

    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.

  • RESEARCH ARTICLE
    Darren Wethington, Sayak Mukherjee, Jayajit Das
    Quantitative Biology, 2023, 11(1): 59-71. https://doi.org/10.15302/J-QB-022-0308

    Background: Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model.

    Methods: We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.

    Results: We carry out a series of in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.

    Conclusions: These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

  • RESEARCH ARTICLE
    Xue-Ying Li, Xiao Chen, Chao-Gan Yan
    Quantitative Biology, 2022, 10(4): 366-380. https://doi.org/10.15302/J-QB-021-0270

    Background: As one of the leading causes of global disability, major depressive disorder (MDD) places a noticeable burden on individuals and society. Despite the great expectation on finding accurate biomarkers and effective treatment targets of MDD, studies in applying functional magnetic resonance imaging (fMRI) are still faced with challenges, including the representational ambiguity, small sample size, low statistical power, relatively high false positive rates, etc. Thus, reviewing studies with solid methodology may help achieve a consensus on the pathology of MDD.

    Methods: In this systematic review, we screened fMRI studies on MDD through strict criteria to focus on reliable studies with sufficient sample size, adequate control of head motion, and a proper multiple comparison control strategy.

    Results: We found consistent evidence regarding the dysfunction within and among the default mode network (DMN), the frontoparietal network (FPN), and other brain regions. However, controversy remains, probably due to the heterogeneity of participants and data processing strategies.

    Conclusion: Future studies are recommended to apply a comprehensive set of neuro-behavioral measurements, consider the heterogeneity of MDD patients and other potentially confounding factors, apply surface-based neuroscientific network fMRI approaches, and advance research transparency and open science by applying state-of-the-art pipelines along with open data sharing.

  • PROFILE
    Lirong Zhang, Junjie Liu
    Quantitative Biology, 2022, 10(3): 221-223. https://doi.org/10.15302/J-QB-021-0242
  • REVIEW
    Yueshan Zhao, Min Zhang, Da Yang
    Quantitative Biology, 2022, 10(4): 307-320. https://doi.org/10.15302/J-QB-022-0299

    Background: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects.

    Results: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens.

    Conclusion: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.

  • REVIEW
    Huawei Zhu, Yin Li
    Quantitative Biology, 2023, 11(2): 143-154. https://doi.org/10.15302/J-QB-022-0314

    Background: Light-driven synthetic microbial consortia are composed of photoautotrophs and heterotrophs. They exhibited better performance in stability, robustness and capacity for handling complex tasks when comparing with axenic cultures. Different from general microbial consortia, the intrinsic property of photosynthetic oxygen evolution in light-driven synthetic microbial consortia is an important factor affecting the functions of the consortia.

    Results: In light-driven microbial consortia, the oxygen liberated by photoautotrophs will result in an aerobic environment, which exerts dual effects on different species and processes. On one hand, oxygen is favorable to the synthetic microbial consortia when they are used for wastewater treatment and aerobic chemical production, in which biomass accumulation and oxidized product formation will benefit from the high energy yield of aerobic respiration. On the other hand, the oxygen is harmful to the synthetic microbial consortia when they were used for anaerobic processes including biohydrogen production and bioelectricity generation, in which the presence of oxygen will deactivate some biological components and compete for electrons.

    Conclusions: Developing anaerobic processes in using light-driven synthetic microbial consortia represents a cost-effective alternative for production of chemicals from carbon dioxide and light. Thus, exploring a versatile approach addressing the oxygen dilemma is essential to enable light-driven synthetic microbial consortia to get closer to practical applications.

  • RESEARCH ARTICLE
    Mateusz Chiliński, Anup Kumar Halder, Dariusz Plewczynski
    Quantitative Biology, 2023, 11(2): 155-162. https://doi.org/10.15302/J-QB-022-0315

    Background: With the development of rapid and cheap sequencing techniques, the cost of whole-genome sequencing (WGS) has dropped significantly. However, the complexity of the human genome is not limited to the pure sequence—and additional experiments are required to learn the human genome’s influence on complex traits. One of the most exciting aspects for scientists nowadays is the spatial organisation of the genome, which can be discovered using spatial experiments (e.g., Hi-C, ChIA-PET). The information about the spatial contacts helps in the analysis and brings new insights into our understanding of the disease developments.

    Methods: We have used an ensemble of deep learning with classical machine learning algorithms. The deep learning network we used was DNABERT, which utilises the BERT language model (based on transformers) for the genomic function. The classical machine learning models included support vector machines (SVMs), random forests (RFs), and K-nearest neighbor (KNN). The whole approach was wrapped together as deep hybrid learning (DHL).

    Results: We found that the DNABERT can be used to predict the ChIA-PET experiments with high precision. Additionally, the DHL approach has increased the metrics on CTCF and RNAPII sets.

    Conclusions: DHL approach should be taken into consideration for the models utilising the power of deep learning. While straightforward in the concept, it can improve the results significantly.

  • RESEARCH ARTICLE
    Shilei Zhao, Tong Sha, Chung-I Wu, Yongbiao Xue, Hua Chen
    Quantitative Biology, 2021, 9(3): 304-316. https://doi.org/10.15302/J-QB-021-0256

    Background: The availability of vaccines provides a promising solution to contain the COVID-19 pandemic. However, it remains unclear whether the large-scale vaccination can succeed in containing the COVID-19 pandemic and how soon. We developed an epidemiological model named SUVQC (Suceptible-Unquarantined-Vaccined-Quarantined-Confirmed) to quantitatively analyze and predict the epidemic dynamics of COVID-19 under vaccination.

    Methods: In addition to the impact of non-pharmaceutical interventions (NPIs), our model explicitly parameterizes key factors related to vaccination, including the duration of immunity, vaccine efficacy, and daily vaccination rate etc. The model was applied to the daily reported numbers of confirmed cases of Israel and the USA to explore and predict trends under vaccination based on their current epidemic statuses and intervention measures. We further provided a formula for designing a practical vaccination strategy, which simultaneously considers the effects of the basic reproductive number of COVID-19, intensity of NPIs, duration of immunological memory after vaccination, vaccine efficacy and daily vaccination rate.

    Results: In Israel, 53.83% of the population is fully vaccinated, and under the current NPI intensity and vaccination scheme, the pandemic is predicted to end between May 14, 2021, and May 16, 2021, assuming immunity persists for 180 days to 365 days. If NPIs are not implemented after March 24, 2021, the pandemic will end later, between July 4, 2021, and August 26, 2021. For the USA, if we assume the current vaccination rate (0.268% per day) and intensity of NPIs, the pandemic will end between January 20, 2022, and October 19, 2024, assuming immunity persists for 180 days to 365 days. However, assuming immunity persists for 180 days and no NPIs are implemented, the pandemic will not end and instead reach an equilibrium state, with a proportion of the population remaining actively infected.

    Conclusions: Overall, the daily vaccination rate should be decided according to vaccine efficacy and immunity duration to achieve herd immunity. In some situations, vaccination alone cannot stop the pandemic, and NPIs are necessary to supplement vaccination and accelerate the end of the pandemic. Considering that vaccine efficacy and duration of immunity may be reduced for new mutant strains, it is necessary to remain cautiously optimistic about the prospect of ending the pandemic under vaccination.

  • RESEARCH ARTICLE
    Weiran Chen, Md Wahiduzzaman, Quan Li, Yixue Li, Guangyong Zheng, Tao Huang
    Quantitative Biology, 2022, 10(4): 333-340. https://doi.org/10.15302/J-QB-022-0295

    Background: Single-cell RNA sequencing (scRNA-seq) technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specific biological processes. Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples. Therefore, benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA-seq data. However, recent comparing efforts were constrained in sequencing platforms or analyzing pipelines. There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity, precision, and so on.

    Methods: We downloaded public scRNA-seq data that was generated by two distinct sequencers, NovaSeq 6000 and MGISEQ 2000. Then data was processed through the Drop-seq-tools, UMI-tools and Cell Ranger pipeline respectively. We calculated multiple measurements based on the expression profiles of the six platform-pipeline combinations.

    Results: We found that all three pipelines had comparable performance, the Cell Ranger pipeline achieved the best performance in precision while UMI-tools prevailed in terms of sensitivity and marker calling.

    Conclusions: Our work provided an insight into the selection of scRNA-seq data processing tools for two sequencing platforms as well as a framework to evaluate platform-pipeline combinations.

  • RESEARCH ARTICLE
    Xian-Fang Wang, Zhi-Yong Du, Qi-Meng Li, Yi-Feng Liu, Shao-Hui Ma, Jui-Wei Cui
    Quantitative Biology, 2023, 11(1): 94-103. https://doi.org/10.15302/J-QB-022-0307

    Background: The COVID-19 has a huge negative impact on people’s health. Traditional Chinese Medicine (TCM) has a good effect on viral pneumonia. It is of great practical significance to study its pharmacology.

    Methods: The ingredients and targets of each herb in Maxing Shigan Decoction which obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and the related targets of COVID-19 were screened by GeneCards database based on the network pharmacology. Venn was used to analyze the intersection target between active ingredients and diseases. Cytoscape software was used to construct an active ingredient-disease target network. The Protein-Protein Interaction network was constructed by STRING database and Cytohubba was used to screen out the key targets. Gene Ontology (GO) functional enrichment analysis and KEGG pathway analysis were performed by David database.

    Results: In this study, a total of 134 active ingredients and 229 related targets, 198 targets of COVID-19 and 48 common targets of drug-disease were chosen. Enrichment items and pathways were obtained through GO and KEGG pathway analysis. The predicted active ingredients were quercetin, kaempferol, luteolin, naringenin, glycyrol, and the key targets involved IL6, MAPK3, MAPK8, CASP3, IL10, etc. The results showed that the active ingredients of Maxing Shigan Decoction acted on multiple targets which played roles in the treatment of COVID-19 by regulating inflammation, immune system and other pathways.

    Conclusions: The main contribution of this paper is to use data to mine the principles of the treatment of COVID-19 from the pharmacology of these prescriptions, and the results can be provided theoretical reference for medical workers.

  • RESEARCH ARTICLE
    Zongwei Guo, Fangkui Yin, Peiran Wang, Ting Song, Zhichao Zhang
    Quantitative Biology, 2021, 9(3): 329-340. https://doi.org/10.15302/J-QB-021-0237

    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.

  • RESEARCH ARTICLE
    Eric Durandau, Serge Pelet
    Quantitative Biology, 2021, 9(3): 341-358. https://doi.org/10.15302/J-QB-021-0240

    Background: Commitment to a new cell cycle is controlled by a number of cellular signals. Mitogen-activated protein kinase (MAPK) pathways, which transduce multiple extracellular cues, have been shown to be interconnected with the cell cycle and can modulate its progression.

    Methods: In budding yeast, we have introduced fluorescent biosensors that monitor in real time the signaling activity of the MAPKs Fus3 and Kss1 and the cyclin-dependent kinase (CDK) in individual cells. We have quantified in hundreds of live single cells the interplay between the MAPKs regulating the mating response and the CDK controlling cell cycle progression.

    Results: Different patterns of MAPK activity dynamics could be identified by clustering cells based on their CDK activity, denoting the tight relationship between these two cellular signals. Our data suggest that beyond the already well-established mechanisms of regulation between the MAPK and the CDK, additional mechanisms remain to be identified.

    Conclusion: A tight interplay between MAPK pathways and the cell cycle is essential to control cellular proliferation and cell fate decisions.

  • REVIEW
    Huan Du, Meng Li, Yang Liu
    Quantitative Biology, 2023, 11(1): 15-30. https://doi.org/10.15302/J-QB-022-0313

    Background: Synthetic microbial communities, with different strains brought together by balancing their nutrition and promoting their interactions, demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications. The potential of such microbial communities has not been explored, due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.

    Results: Genome-scale metabolic models (GEM) have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities, since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbe-habitats and microbe-microbe interactions. In this work, we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities: predicting multi-species interactions, exploring environmental impacts on microbial phenotypes, and optimizing community-level performance.

    Conclusions: Although at the infancy stage, GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities. Compared to other methods, especially the use of laboratory cultures, GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality, such as identifying community members, determining media composition, evaluating microbial interaction potential or selecting the best community configuration. Future efforts should be made to overcome the limitations of the approaches, ranging from quality control of GEM reconstructions to community-level modeling algorithms, so that more applications of GEMs in studying phenotypes of microbial communities can be expected.

  • REVIEW
    Shi Shuyu, Si Wen, Ouyang Xiaoyi, Wei Ping
    Quantitative Biology, 2021, 9(4): 378-399. https://doi.org/10.15302/J-QB-021-0262

    Background: The concept of phase separation has been used to describe and interpret physicochemical phenomena in biological systems for decades. Many intracellular macromolecules undergo phase separation, where it plays important roles in gene regulation, cellular signaling, metabolic reactions and so on, due to its unique dynamic properties and biological effects. As the noticeable importance of phase separation, pioneer researchers have explored the possibility to introduce the synthetically engineered phase separation for applicable cell function.

    Results: In this article, we illustrated the application value of phase separation in synthetic biology. We described main states of phase separation in detail, summarized some ways to implement synthetic condensates and several methods to regulate phase separation, and provided a substantial amount of identical examples to illuminate the applications and perspectives of phase separation in synthetic biology.

    Conclusions: Multivalent interactions implement phase separation in synthetic biology. Small molecules, light control and spontaneous interactions induce and regulate phase separation. The synthetic condensates are widely used in signal amplifications, designer orthogonally non-membrane-bound organelles, metabolic pathways, gene regulations, signaling transductions and controllable platforms. Studies on quantitative analysis, more standardized modules and precise spatiotemporal control of synthetic phase separation may promote the further development of this field.