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    Taoyu Chen, Qi Lei, Minglei Shi, Tingting Li
    Quantitative Biology, 2021, 9(3): 255-266.

    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.

    Shilei Zhao, Tong Sha, Chung-I Wu, Yongbiao Xue, Hua Chen
    Quantitative Biology, 2021, 9(3): 304-316.

    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.

    Cecylia S. Lupala, Xuanxuan Li, Jian Lei, Hong Chen, Jianxun Qi, Haiguang Liu, Xiao-Dong Su
    Quantitative Biology, 2021, 9(1): 61-72.

    Background: A novel coronavirus (the SARS-CoV-2) has been identified in January 2020 as the causal pathogen for COVID-19 , a pandemic started near the end of 2019. The Angiotensin converting enzyme 2 protein (ACE2) utilized by the SARS-CoV as a receptor was found to facilitate the infection of SARS-CoV-2, initiated by the binding of the spike protein to human ACE2.

    Methods: Using homology modeling and molecular dynamics (MD) simulation methods, we report here the detailed structure and dynamics of the ACE2 in complex with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein.

    Results: The predicted model is highly consistent with the experimentally determined structures, validating the homology modeling results. Besides the binding interface reported in the crystal structures, novel binding poses are revealed from all-atom MD simulations. The simulation data are used to identify critical residues at the complex interface and provide more details about the interactions between the SARS-CoV-2 RBD and human ACE2.

    Conclusion: Simulations reveal that RBD binds to both open and closed state of ACE2. Two human ACE2 mutants and rat ACE2 are modeled to study the mutation effects on RBD binding to ACE2. The simulations show that the N-terminal helix and the K353 are very important for the tight binding of the complex, the mutants are found to alter the binding modes of the CoV2-RBD to ACE2.

    Guanyu Wang
    Quantitative Biology, 2021, 9(1): 73-83.

    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.

    Olha Kholod, Chi-Ren Shyu, Jonathan Mitchem, Jussuf Kaifi, Dmitriy Shin
    Quantitative Biology, 2020, 8(4): 336-346.

    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.

    Chongzhi Zang, Yiren Wang, Weiqun Peng
    Quantitative Biology, 2020, 8(4): 359-368.

    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.

    Peng Wang, Luonan Chen
    Quantitative Biology, 2020, 8(3): 195-202.

    Background: Phase transition and phase separation as well as their tipping points are penetrating phenomena in biology and are intrinsic properties of biological systems ranging from basic molecule complexes to cells and all way up to entire ecosystems.

    Results: For example, phase separation has been established as a key mechanism for biological molecules such as protein or RNA to form membraneless organelles to perform complex biological functions. Phase transitions are commonly observed during cellular differentiation, and generally, there are the tipping points or critical states just before the phase transitions. And the stability of ecosystem and extinction of species are systematic manifestation of phase transitions. All phase transition and phase separation phenomena display switch-like behavior and critical transitions.

    Conclusion: Here we summarize the concepts regarding the epithelial-to-mesenchymal transition (EMT) as a type of phase changes and the implication of critical transitions in EMT, and discuss open questions and challenges in this fast-moving field.

    Huixia Ren, Mengdi Zhao, Bo Liu, Ruixiao Yao, Qi liu, Zhipeng Ren, Zirui Wu, Zongmao Gao, Xiaojing Yang, Chao Tang
    Quantitative Biology, 2020, 8(3): 245-255.

    Background: Time-lapse live cell imaging of a growing cell population is routine in many biological investigations. A major challenge in imaging analysis is accurate segmentation, a process to define the boundaries of cells based on raw image data. Current segmentation methods relying on single boundary features have problems in robustness when dealing with inhomogeneous foci which invariably happens in cell population imaging.

    Methods: Combined with a multi-layer training set strategy, we developed a neural-network-based algorithm — Cellbow.

    Results: Cellbow can achieve accurate and robust segmentation of cells in broad and general settings. It can also facilitate long-term tracking of cell growth and division. To facilitate the application of Cellbow, we provide a website on which one can online test the software, as well as an ImageJ plugin for the user to visualize the performance before software installation.

    Conclusion: Cellbow is customizable and generalizable. It is broadly applicable to segmenting fluorescent images of diverse cell types with no further training needed. For bright-field images, only a small set of sample images of the specific cell type from the user may be needed for training.

    Xiaotu Ma, Sasi Arunachalam, Yanling Liu
    Quantitative Biology, 2020, 8(2): 95-108.

    Background: The past decade has witnessed a rapid progress in our understanding of the genetics of cancer and its progression. Probabilistic and statistical modeling played a pivotal role in the discovery of general patterns from cancer genomics datasets and continue to be of central importance for personalized medicine.

    Results: In this review we introduce cancer genomics from a probabilistic and statistical perspective. We start from (1) functional classification of genes into oncogenes and tumor suppressor genes, then (2) demonstrate the importance of comprehensive analysis of different mutation types for individual cancer genomes, followed by (3) tumor purity analysis, which in turn leads to (4) the concept of ploidy and clonality, that is next connected to (5) tumor evolution under treatment pressure, which yields insights into cancer drug resistance. We also discuss future challenges including the non-coding genomic regions, integrative analysis of genomics and epigenomics, as well as early cancer detection.

    Conclusion: We believe probabilistic and statistical modeling will continue to play important roles for novel discoveries in the field of cancer genomics and personalized medicine.

    Jianan Lin, Zhengqing Ouyang
    Quantitative Biology, 2020, 8(2): 119-129.

    Background: RNA binding proteins (RBPs) play essential roles in the regulation of RNA metabolism. Recent studies have disclosed that RBPs achieve their functions via binding to their targets in a position-dependent pattern on RNAs. However, few studies have systematically addressed the associations between the RBP’s functions and their positional binding preferences.

    Methods: Here, we present large-scale analyses on the functional targets of human RBPs by integrating the enhanced cross-linking and immunoprecipitation followed by sequencing (eCLIP-seq) datasets and the shRNA knockdown followed by RNA-seq datasets that are deposited in the integrated ENCyclopedia of DNA Elements in the human genome (ENCODE) data portal.

    Results: We found that (1) binding to the translation termination site and the 3′ untranslated region is important to most human RBPs in the RNA decay regulation; (2) RBPs’ binding and regulation follow a cell-type specific pattern.

    Conclusions: These analysis results show the strong relationship between the binding position and the functions of RBPs, which provides novel insights into the RBPs’ regulation mechanisms.