Quantitative Biology

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Regulation by competition: a hidden layer of gene regulatory network
Lei Wei, Ye Yuan, Tao Hu, Shuailin Li, Tianrun Cheng, Jinzhi Lei, Zhen Xie, Michael Q. Zhang, Xiaowo Wang
Quant. Biol.    2019, 7 (2): 110-121.   https://doi.org/10.1007/s40484-018-0162-5
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Background: Molecular competition brings about trade-offs of shared limited resources among the cellular components, and thus introduces a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. Several molecular competition scenarios have been observed recently, but there is still a lack of systematic quantitative understanding to reveal the essence of molecular competition.

Methods: Here, by abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically integrate experimental evidences in these processes and analyzed general properties shared behind them from steady-state behavior to dynamic responses.

Results: We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets.

Conclusions: This work uncovered the complexity and generality of molecular competition effect as a hidden layer of gene regulatory network, and therefore provided a unified insight and a theoretical framework to understand and employ competition in both natural and synthetic systems.

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Predicting enhancer-promoter interaction from genomic sequence with deep neural networks
Shashank Singh, Yang Yang, Barnabás Póczos, Jian Ma
Quant. Biol.    2019, 7 (2): 122-137.   https://doi.org/10.1007/s40484-019-0154-0
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Background: In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions.

Methods: Here we report a new computational method (named “SPEID”) using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given.

Results: Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes.

Conclusions: This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.

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Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network
Wen Yang, Xia Wang, Kenan Li, Yuanru Liu, Ying Liu, Rui Wang, Honglin Li
Quant. Biol.    2019, 7 (1): 30-41.   https://doi.org/10.1007/s40484-018-0163-4
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Background: Arachidonic acid (AA) metabolic network is activated in the most inflammatory related diseases, and small-molecular drugs targeting AA network are increasingly available. However, side effects of above mentioned drugs have always been the biggest obstacle. (+)-2-(1-hydroxyl-4-oxocyclohexyl) ethyl caffeate (HOEC), a natural product acted as an inhibitor of 5-lipoxygenase (5-LOX) and 15-LOX in vitro, exhibited weaker therapeutic effect in high dose than that in low dose to collagen induced arthritis (CIA) rats. In this study, we tried to elucidate the potential regulatory mechanism by using quantitative pharmacology.

Methods: First, we generated an experimental data set by monitoring the dynamics of AA metabolites’ concentration in A23187 stimulated and different doses of HOEC co-incubated RAW264.7. Then we constructed a dynamic model of A23187-stimulated AA metabolic model to evaluate how a model-based simulation of AA metabolic data assists to find the most suitable treatment dose by predicting the pharmacodynamics of HOEC.

Results: Compared to the experimental data, the model could simulate the inhibitory effect of HOEC on 5-LOX and 15-LOX, and reproduced the increase of the metabolic flux in the cyclooxygenase (COX) pathway. However, a concomitant, early-stage of stimulation-related decrease of prostaglandins (PGs) production in HOEC incubated RAW264.7 cells was not simulated in the model.

Conclusion: Using the model, we predict that higher dose of HOEC disrupts the flux balance in COX and LOX of the AA network, and increased COX flux can interfere the curative effects of LOX inhibitor on resolution of inflammation which is crucial for the efficient and safe drug design.

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Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach
Xinzhe Xiao, Zehui Chen, Zengrui Wu, Tianduanyi Wang, Weihua Li, Guixia Liu, Bo Zhang, Yun Tang
Quant. Biol.    2019, 7 (1): 42-53.   https://doi.org/10.1007/s40484-019-0165-x
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Background: The antineoplastic activity of Chelidonium majus has been reported, but its mechanism of action (MoA) is unsuspected. The emerging theory of systems pharmacology may be a useful approach to analyze the complicated MoA of this multi-ingredient traditional Chinese medicine (TCM).

Methods: We collected the ingredients and related compound-target interactions of C. majus from several databases. The bSDTNBI (balanced substructure-drug-target network-based inference) method was applied to predict each ingredient’s targets. Pathway enrichment analysis was subsequently conducted to illustrate the potential MoA, and prognostic genes were identified to predict the certain types of cancers that C. majus might be beneficial in treatment. Bioassays and literature survey were used to validate the in silico results.

Results: Systems pharmacology analysis demonstrated that C. majus exerted experimental or putative interactions with 18 cancer-associated pathways, and might specifically act on 13 types of cancers. Chelidonine, sanguinarine, chelerythrine, berberine, and coptisine, which are the predominant components of C. majus, may suppress the cancer genes by regulating cell cycle, inducing cell apoptosis and inhibiting proliferation.

Conclusions: The antineoplastic MoA of C. majus was investigated by systems pharmacology approach. C. majus exhibited promising pharmacological effect against cancer, and may consequently be useful material in further drug development. The alkaloids are the key components in C. majus that exhibit anticancer activity.

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Characterizing robustness and sensitivity of convolutional neural networks for quantitative analysis of mitochondrial morphology
Xiaoqi Chai, Qinle Ba, Ge Yang
Quant. Biol.    2018, 6 (4): 344-358.   https://doi.org/10.1007/s40484-018-0156-3
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Background: Quantitative analysis of mitochondrial morphology plays important roles in studies of mitochondrial biology. The analysis depends critically on segmentation of mitochondria, the image analysis process of extracting mitochondrial morphology from images. The main goal of this study is to characterize the performance of convolutional neural networks (CNNs) in segmentation of mitochondria from fluorescence microscopy images. Recently, CNNs have achieved remarkable success in challenging image segmentation tasks in several disciplines. So far, however, our knowledge of their performance in segmenting biological images remains limited. In particular, we know little about their robustness, which defines their capability of segmenting biological images of different conditions, and their sensitivity, which defines their capability of detecting subtle morphological changes of biological objects.

Methods: We have developed a method that uses realistic synthetic images of different conditions to characterize the robustness and sensitivity of CNNs in segmentation of mitochondria. Using this method, we compared performance of two widely adopted CNNs: the fully convolutional network (FCN) and the U-Net. We further compared the two networks against the adaptive active-mask (AAM) algorithm, a representative of high-performance conventional segmentation algorithms.

Results: The FCN and the U-Net consistently outperformed the AAM in accuracy, robustness, and sensitivity, often by a significant margin. The U-Net provided overall the best performance.

Conclusions: Our study demonstrates superior performance of the U-Net and the FCN in segmentation of mitochondria. It also provides quantitative measurements of the robustness and sensitivity of these networks that are essential to their applications in quantitative analysis of mitochondrial morphology.

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WaveNano: a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets
Sheng Wang, Zhen Li, Yizhou Yu, Xin Gao
Quant. Biol.    2018, 6 (4): 359-368.   https://doi.org/10.1007/s40484-018-0155-4
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Background: The Oxford MinION nanopore sequencer is the recently appealing third-generation genome sequencing device that is portable and no larger than a cellphone. Despite the benefits of MinION to sequence ultra-long reads in real-time, the high error rate of the existing base-calling methods, especially indels (insertions and deletions), prevents its use in a variety of applications.

Methods: In this paper, we show that such indel errors are largely due to the segmentation process on the input electrical current signal from MinION. All existing methods conduct segmentation and nucleotide label prediction in a sequential manner, in which the errors accumulated in the first step will irreversibly influence the final base-calling. We further show that the indel issue can be significantly reduced via accurate labeling of nucleotide and move labels directly from the raw signal, which can then be efficiently learned by a bi-directional WaveNet model simultaneously through feature sharing. Our bi-directional WaveNet model with residual blocks and skip connections is able to capture the extremely long dependency in the raw signal. Taking the predicted move as the segmentation guidance, we employ the Viterbi decoding to obtain the final base-calling results from the smoothed nucleotide probability matrix.

Results: Our proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda phage.

Conclusions: The signal-level nanopore base-caller WaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.

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Cited: Crossref(1)
ShapeShifter: a novel approach for identifying and quantifying stable lariat intronic species in RNAseq data
Allison J Taggart, William G Fairbrother
Quant. Biol.    2018, 6 (3): 267-274.   https://doi.org/10.1007/s40484-018-0141-x
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Background: Most intronic lariats are rapidly turned over after splicing. However, new research suggests that some introns may have additional post-splicing functions. Current bioinformatics methods used to identify lariats require a sequencing read that traverses the lariat branchpoint. This method provides precise branchpoint sequence and position information, but is limited in its ability to quantify abundance of stabilized lariat species in a given RNAseq sample. Bioinformatic tools are needed to better address these emerging biological questions.

Methods: We used an unsupervised machine learning approach on sequencing reads from publicly available ENCODE data to learn to identify and quantify lariats based on RNAseq read coverage shape.

Results: We developed ShapeShifter, a novel approach for identifying and quantifying stable lariat species in RNAseq datasets. We learned a characteristic “lariat” curve from ENCODE RNAseq data and were able to estimate abundances for introns based on read coverage. Using this method we discovered new stable introns in these samples that were not represented using the older, branchpoint-traversing read method.

Conclusions: ShapeShifter provides a robust approach towards detecting and quantifying stable lariat species.

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Developing a low-cost milliliter-scale chemostat array for precise control of cellular growth
David Skelding, Samuel F M Hart, Thejas Vidyasagar, Alexander E Pozhitkov, Wenying Shou
Quant. Biol.    2018, 6 (2): 129-141.   https://doi.org/10.1007/s40484-018-0143-8
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Background: Multiplexed milliliter-scale chemostats are useful for measuring cell physiology under various degrees of nutrient limitation and for carrying out evolution experiments. In each chemostat, fresh medium containing a growth rate-limiting metabolite is pumped into the culturing chamber at a constant rate, while culture effluent exits at an equal rate. Although such devices have been developed by various labs, key parameters — the accuracy, precision, and operational range of flow rate — are not explicitly characterized.

Methods: Here we re-purpose a published multiplexed culturing device to develop a multiplexed milliliter-scale chemostat. Flow rates for eight chambers can be independently controlled to a wide range, corresponding to population doubling times of 3~13 h, without the use of expensive feedback systems.

Results: Flow rates are precise, with the maximal coefficient of variation among eight chambers being less than 3%. Flow rates are accurate, with average flow rates being only slightly below targets, i.e., 3%–6% for 13-h and 0.6%–1.0% for 3-h doubling times. This deficit is largely due to evaporation and should be correctable. We experimentally demonstrate that our device allows accurate and precise quantification of population phenotypes.

Conclusions: We achieve precise control of cellular growth in a low-cost milliliter-scale chemostat array, and show that the achieved precision reduces the error when measuring biological processes.

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Cited: Crossref(3)
BMTK: a toolkit for determining modules in biological bipartite networks
Bei Wang, Jinyu Chen, Shihua Zhang
Quant. Biol.    2018, 6 (2): 186-192.   https://doi.org/10.1007/s40484-018-0132-y
Abstract   HTML   PDF (2319KB)

Background: Module detection is widely used to analyze and visualize biological networks. A number of methods and tools have been developed to achieve it. Meanwhile, bipartite module detection is also very useful for mining and analyzing bipartite biological networks and a few methods have been developed for it. However, there is few user-friendly toolkit for this task.

Methods: To this end, we develop an online web toolkit BMTK, which implements seven existing methods.

Results: BMTK provides a uniform operation platform and visualization function, standardizes input and output format, and improves algorithmic structure to enhance computing speed. We also apply this toolkit onto a drug-target bipartite network to demonstrate its effectiveness.

Conclusions: BMTK will be a powerful tool for detecting bipartite modules in diverse bipartite biological networks.

Availability: The web application is freely accessible at http://www.zhanglabtools.net/BMTK.

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MRHCA: a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network
Yu Zhang, Sha Cao, Jing Zhao, Burair Alsaihati, Qin Ma, Chi Zhang
Quant. Biol.    2018, 6 (1): 40-55.   https://doi.org/10.1007/s40484-018-0131-z
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Background: Gene co-expression and differential co-expression analysis has been increasingly used to study co-functional and co-regulatory biological mechanisms from large scale transcriptomics data sets.

Methods: In this study, we develop a nonparametric approach to identify hub genes and modules in a large co-expression network with low computational and memory cost, namely MRHCA.

Results: We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co-expression analysis to E. coli and TCGA cancer data, we have identified significant condition specific activated genes in E. coli and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations.

Conclusion: Our analysis has demonstrated MRHCA can (i) deal with large association networks, (ii) rigorously assess statistical significance for hubs and module sizes, (iii) identify co-expression modules with low associations, (iv) detect small and significant modules, and (v) allow genes to be present in more than one modules, compared with existing methods.

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