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Quantitative Biology

  • Cover Illustration

    2017, Vol.5  No.2

    Monkey King with Golden Hoop: in Journey to the West, a classic Chinese mythological novel, a monkey called Sun Wukong helped his master Monk Tang Sanzang overcome various trials and tribulations during the pilgrimage for Buddhist scriptures. Although Sun Wukong possessed great power and talent, he cannot reach the final destination and get the real Buddhist scripture without Tang Sanzang’s control via the incantation of the golden hoop. The golde [Detail] ...

    Table of Contents

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, Volume 5 Issue 2 Previous Issue   
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REVIEW
Engineering synthetic optogenetic networks for biomedical applications
Meiyan Wang, Yuanhuan Yu, Jiawei Shao, Boon Chin Heng, Haifeng Ye
Quant. Biol.. 2017, 5 (2): 111-123.   DOI: 10.1007/s40484-017-0105-6
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Background: Recently, optogenetics based on genetically encoded photosensitive proteins has emerged as an innovative technology platform to revolutionize manipulation of cellular behavior through light stimulation. It has enabled user defined control of various cellular behaviors with spatiotemporal precision and minimal invasiveness, creating unprecedented opportunities for biomedical applications.

Results: This article reviews current advances in optogenetic networks designed for the treatment of human diseases. We highlight the advantages of these optogenetic networks, as well as emerging questions and future perspectives.

Conclusions: Various optogenetic systems have been engineered to control biological processes at all levels using light and applied for numerous diseases, such as metabolic disorders, cancer, and immune diseases. Continued development of optogenetic modules will be necessary to precisely control of gene expression magnitude towards clinical medical practice in the context of real-world problems.

Synthetic biology provides the platform and tools to design artificial regulators to rationally and precisely control the biological activities of cells. Most of the devices were responsive to chemical inducer systems and had inherent limitations in regard to precise spatiotemporal control of gene expression. Optogenetics has emerged and begun to revolutionize manipulation of cellular behavior by harnessing light to easily spatiotemporally control transgene expression. This article reviews the most recent advances in optogenetic networks which were generated and applied to biomedical applications, various challenges and future perspectives in clinical therapies.
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Control of synthetic gene networks and its applications
David J Menn, Ri-Qi Su, Xiao Wang
Quant. Biol.. 2017, 5 (2): 124-135.   DOI: 10.1007/s40484-017-0106-5
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Background: One of the underlying assumptions of synthetic biology is that biological processes can be engineered in a controllable way.

Results: Here we discuss this assumption as it relates to synthetic gene regulatory networks (GRNs). We first cover the theoretical basis of GRN control, then address three major areas in which control has been leveraged: engineering and analysis of network stability, temporal dynamics, and spatial aspects.

Conclusion: These areas lay a strong foundation for further expansion of control in synthetic GRNs and pave the way for future work synthesizing these disparate concepts.

Controlling the behavior of gene networks is the basis of much of synthetic biology. Here we review major theoretical concepts underpinning gene regulatory network (GRN) control and how these concepts are implemented to organize biological parts into functional and predictable synthetic GRNs. We present several contexts in which theory and practice have been synthesized in constructed GRNs to generate biologically relevant behaviors: multistability, designed temporal dynamics, and spatial patterning. These proof-of-concept works set researchers up to engineer more complex and controllable circuits in the future.
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Current progresses of 3D bioprinting based tissue engineering
Zeyu Zhang, Xiu-Jie Wang
Quant. Biol.. 2017, 5 (2): 136-142.   DOI: 10.1007/s40484-017-0103-8
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Background: The shortage of available organs for transplantation is the major obstacle hindering the application of regenerative medicine, and has also become the desperate problem faced by more and more patients nowadays. The recent development and application of 3D printing technique in biological research (bioprinting) has revolutionized the tissue engineering methods, and become a promising solution for tissue regeneration.

Results: In this review, we summarize the current application of bioprinting in producing tissues and organoids, and discuss the future directions and challenges of 3D bioprinting.

Conclusions: Currently, 3D bioprinting is capable to generate patient-specialized bone, cartilage, blood vascular network, hepatic unit and other simple components/tissues, yet pure cell-based functional organs are still desired.

Recent advances in 3D printing has prompted the development of tissue engineering methods, enabling researchers to design and fabricate native-like simple organs to an unprecedented level of resolution and resemblance. Here, we review the commonly used 3D printing strategy in tissue engineering and the current progresses of 3D printed organs, we also discuss the future perspectives regarding the improvement of tissue engineering.
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Quantum conformational transition in biological macromolecule
Liaofu Luo, Jun Lv
Quant. Biol.. 2017, 5 (2): 143-158.   DOI: 10.1007/s40484-016-0087-9
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Background: Recently we proposed a quantum theory on the conformational change of biomolecule, deduced several equations on protein folding rate from the first principles and discussed the experimental tests of the theory. The article is a review of these works.

Methods: Based on the general equation of the conformation-transitional rate several theoretical results are deduced and compared with experimental data through bioinformatics methods.

Results: The temperature dependence and the denaturant concentration dependence of the protein folding rate are deduced and compared with experimental data. The quantitative relation between protein folding rate and torsional mode number (or chain length) is deduced and the obtained formula can be applied to RNA folding as well. The quantum transition theory of two-state protein is successfully generalized to multi-state protein folding. Then, how to make direct experimental tests on the quantum property of the conformational transition of biomolecule is discussed, which includes the study of protein photo-folding and the observation of the fluctuation of the fluorescence intensity emitted from the protein folding/unfolding event. Finally, the potential applications of the present quantum folding theory to molecular biological problems are sketched in two examples: the glucose transport across membrane and the induced pluripotency in stem cell.

Conclusions: The above results show that the quantum mechanics provides a unifying and logically simple theoretical starting point in studying the conformational change of biological macromolecules. The far-reaching results in practical application of the theory are expected.

Quantum theory on the conformational change of biomolecule is reviewed. The protein folding is looked as a quantum transition between torsion states of the chain of amino acids. It means that the protein could “jump” from one shape to another without necessarily forming the shapes in between. The review emphasizes the checking of the new theory against experimental data. All comparisons (including on the non-Arrhenius temperature dependence of the folding rate) show that the quantum mechanism does exist in the conformational transition of biomolecules and the quantum mechanics provides a unifying and logically simple starting point for studying these problems.
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RESEARCH ARTICLE
Elastic restricted Boltzmann machines for cancer data analysis
Sai Zhang, Muxuan Liang, Zhongjun Zhou, Chen Zhang, Ning Chen, Ting Chen, Jianyang Zeng
Quant. Biol.. 2017, 5 (2): 159-172.   DOI: 10.1007/s40484-017-0092-7
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Background: Restricted Boltzmann machines (RBMs) are endowed with the universal power of modeling (binary) joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the pN problem and is also ubiquitous in other bioinformatics and computational biology fields. The pN problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue.

Methods: We propose a novel RBMs model, called elastic restricted Boltzmann machines (eRBMs), which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence (CD) algorithm, we develop the elastic contrastive divergence (eCD) algorithm which can train eRBMs efficiently.

Results: We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task — predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods.

Conclusions: The proposed eRBMs are capable of dealing with the p N problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.

Analysis of biological data, especially the cancer data, often suffers from the “pN” problem, in which the feature dimension greatly outnumbers the sample size. In this study, we proposed a novel model, called elastic restricted Boltzmann machines (eRBMs), that incorporates the elastic regularization term into traditional restricted Boltzmann machines (RBMs) to tackle this problem and balance the model complexity and sensitivity. Both theoretical analysis and tests on predicting dichotomized survival time using real cancer data demonstrated the superiority of eRBMs over other traditional methods in modeling the statistical characteristics of input features with very limited samples.
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A systematic analysis of intrinsic regulators for HIV-1 R5 to X4 phenotypic switch
Wei Yu, Yu Wu
Quant. Biol.. 2017, 5 (2): 173-182.   DOI: 10.1007/s40484-017-0107-4
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Background: Human immunodeficiency virus isolates most often use chemokine receptor CCR5 or CXCR4 as a co-receptor to enter target cells. During early stages of HIV-1 infection, CCR5-tropic viruses are the predominant species. The CXCR4-tropic viruses may emerge late in infection. Recognition of factors influencing this phenotypic switch may give some hints on the antiviral strategies like anti-HIV/AIDS drugs, gene therapy and vaccines.

Methods: To investigate the mechanism that triggers R5 to X4 phenotypic switch, we performed a systematic sensitivity analysis based on a five-dimensional model with time-varying parameters. We studied the sensitivity of each factor to the CCR5-to-CXCR4 tropism switch and acquired some interesting outcomes beyond expectation.

Results: The death rate of free virus (dV), rate that uninfected CD4+ T cells arise from precursors (s) and proliferate as stimulated by antigens (r), and in vivo viral burst size (N) are four robust factors which are constantly observed to have a strong correlation with the evolution of viral phenotype for most patients longitudinally.

Conclusions: Crucial factors, which are essential to phenotypic switch and disease progression, are almost the same for different patients at different time points, including the production of both virus and CD4+ T cells and the decay of virion. It is also worth mentioning that although the sequence of factors sorted by the influence varies between patients, the trends of influences engendered by most factors as disease progresses are similar inter-patients.

Human immunodeficiency virus isolates most often use chemokine receptor CCR5 or CXCR4 as a co-receptor to enter target cells. During early stages of HIV-1 infection, CCR5-tropic viruses are the predominant species. The CXCR4-tropic viruses may emerge late in infection. To investigate the mechanism that triggers R5 to X4 phenotypic switch, we performed a systematic sensitivity analysis based on a five-dimensional model with time-varying parameters. The results are beyond expectation and recognition of these may give some hints on the antiviral strategies.
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HiC-3DViewer: a new tool to visualize Hi-C data in 3D space
Mohamed Nadhir Djekidel, Mengjie Wang, Michael Q. Zhang, Juntao Gao
Quant. Biol.. 2017, 5 (2): 183-190.   DOI: 10.1007/s40484-017-0091-8
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Background: Although significant progress has been made to map chromatin structure at unprecedented resolution and scales, we are short of tools that enable the intuitive visualization and navigation along the three-dimensional (3D) structure of chromatins. The available tools people have so far are generally script-based or present basic features that do not easily enable the integration of genomic data along with 3D chromatin structure, hence, many scientists find themselves in the obligation to hack tools designed for other purposes such as tools for protein structure study.

Methods: We present HiC-3DViewer, a new browser-based interactive tool designed to provide an intuitive environment for investigators to facilitate the 3D exploratory analysis of Hi-C data along with many useful annotation functionalities. Among the key features of HiC-3DViewer relevant to chromatin conformation studies, the most important one is the 1D-to-2D-to-3D mapping, to highlight genomic regions of interest interactively. This feature enables investigators to explore their data at different levels/angels. Additionally, investigators can superpose different genomic signals (such as ChIP-Seq, SNP) on the top of the 3D structure.

Results: As a proof of principle we applied HiC-3DViewer to investigate the quality of Hi-C data and to show the spatial binding of GATA1 and GATA2 along the genome.

Conclusions: As a user-friendly tool, HiC-3DViewer enables the visualization of inter/intra-chromatin interactions and gives users the flexibility to customize the look-and-feel of the 3D structure with a simple click. HiC-3DViewer is implemented in Javascript and Python, and is freely available at: http://bioinfo.au.tsinghua.edu.cn/member/nadhir/HiC3DViewer/. Supplementary information (User Manual, demo data) is also available at this website.

Recently, many tools have developed to analyze and visualize chromatin conformation data. However, we are short of tools that enable the interactive visualization of the 3D chromatin structure. Here, we introduce HiC3D-Viewer, a new browser-based interactive visualization tool designed to provide an intuitive environment that facilitates the 3D exploratory analysis of Hi-C data. Among the key features of HiC-3DViewer is the 1D-to-2D-to-3D interactive highlight of genomic regions, display of inter- and intra-chromatin interactions and 3D model predictions, in addition to the flexibility to customize the displayed models, which make very valuable for the chromatin structure community.
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PERSPECTIVE
Global quantitative biology can illuminate ontological connections between diseases
Guanyu Wang
Quant. Biol.. 2017, 5 (2): 191-198.   DOI: 10.1007/s40484-017-0104-7
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Owing to its interdisciplinary nature, quantitative biology is playing ever-increasing roles in biological researches. To make quantitative biology even more powerful, it is important to develop a holistic perspective by integrating information from multiple biological levels and by considering related biocomplexity simultaneously. Using complex diseases as an example, I show in this paper how their ontological connections can be revealed by considering the diseases on a common ground. The obtained insights may be useful to the prediction and treatment of the diseases. Although the example involves only with cancer and diabetes, the approaches are applicable to the study of other diseases, or even to other biological problems.

Deep connections may exist between seemingly disparate things. Using complex diseases as an example, the author shows that the connections between diseases can be revealed by using powerful approaches of mathematics and quantitative biology. The obtained insights may be useful to the prediction and treatment of the diseases. It is promising to apply the approaches to study other biological problems.
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MEETING REPORT
The 7th National Conference on Bioinformatics and Systems Biology of China
Zhirong Sun, Kui Hua, Xuegong Zhang, Feng-Biao Guo, Jian Huang
Quant. Biol.. 2017, 5 (2): 199-201.   DOI: 10.1007/s40484-017-0090-9
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