Quantitative Biology

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Synthetic biology: a new approach to study biological pattern formation
Chenli Liu, Xiongfei Fu, Jian-Dong Huang
Quant Biol    2013, 1 (4): 246-252.   https://doi.org/10.1007/s40484-013-0021-3
Abstract   HTML   PDF (232KB)

The principles and molecular mechanisms underlying biological pattern formation are difficult to elucidate in most cases due to the overwhelming physiologic complexity associated with the natural context. The understanding of a particular mechanism, not to speak of underlying universal principles, is difficult due to the diversity and uncertainty of the biological systems. Although current genetic and biochemical approaches have greatly advanced our understanding of pattern formation, the progress mainly relies on experimental phenotypes obtained from time-consuming studies of gain or loss of function mutants. It is prevailingly considered that synthetic biology will come to the application age, but more importantly synthetic biology can be used to understand the life. Using periodic stripe pattern formation as a paradigm, we discuss how to apply synthetic biology in understanding biological pattern formation and hereafter foster the applications like tissue engineering.

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Computational methodology for ChIP-seq analysis
Hyunjin Shin, Tao Liu, Xikun Duan, Yong Zhang, X. Shirley Liu
Quant Biol    2013, 1 (1): 54-70.   https://doi.org/10.1007/s40484-013-0006-2
Abstract   HTML   PDF (634KB)

Chromatin immunoprecipitation coupled with massive parallel sequencing (ChIP-seq) is a powerful technology to identify the genome-wide locations of DNA binding proteins such as transcription factors or modified histones. As more and more experimental laboratories are adopting ChIP-seq to unravel the transcriptional and epigenetic regulatory mechanisms, computational analyses of ChIP-seq also become increasingly comprehensive and sophisticated. In this article, we review current computational methodology for ChIP-seq analysis, recommend useful algorithms and workflows, and introduce quality control measures at different analytical steps. We also discuss how ChIP-seq could be integrated with other types of genomic assays, such as gene expression profiling and genome-wide association studies, to provide a more comprehensive view of gene regulatory mechanisms in important physiological and pathological processes.

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Personal genomes, quantitative dynamic omics and personalized medicine
George I. Mias, Michael Snyder
Quant. Biol.    2013, 1 (1): 71-90.   https://doi.org/10.1007/s40484-013-0005-3
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The rapid technological developments following the Human Genome Project have made possible the availability of personalized genomes. As the focus now shifts from characterizing genomes to making personalized disease associations, in combination with the availability of other omics technologies, the next big push will be not only to obtain a personalized genome, but to quantitatively follow other omics. This will include transcriptomes, proteomes, metabolomes, antibodyomes, and new emerging technologies, enabling the profiling of thousands of molecular components in individuals. Furthermore, omics profiling performed longitudinally can probe the temporal patterns associated with both molecular changes and associated physiological health and disease states. Such data necessitates the development of computational methodology to not only handle and descriptively assess such data, but also construct quantitative biological models. Here we describe the availability of personal genomes and developing omics technologies that can be brought together for personalized implementations and how these novel integrated approaches may effectively provide a precise personalized medicine that focuses on not only characterization and treatment but ultimately the prevention of disease.

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Developing bioimaging and quantitative methods to study 3D genome
Juntao Gao, Xusan Yang, Mohamed Nadhir Djekidel, Yang Wang, Peng Xi, Michael Q. Zhang
Quant. Biol.    2016, 4 (2): 129-147.   https://doi.org/10.1007/s40484-016-0065-2
Abstract   HTML   PDF (2526KB)

The recent advances in chromosome configuration capture (3C)-based series molecular methods and optical super-resolution (SR) techniques offer powerful tools to investigate three dimensional (3D) genomic structure in prokaryotic and eukaryotic cell nucleus. In this review, we focus on the progress during the last decade in this exciting field. Here we at first introduce briefly genome organization at chromosome, domain and sub-domain level, respectively; then we provide a short introduction to various super-resolution microscopy techniques which can be employed to detect genome 3D structure. We also reviewed the progress of quantitative and visualization tools to evaluate and visualize chromatin interactions in 3D genome derived from Hi-C data. We end up with the discussion that imaging methods and 3C-based molecular methods are not mutually exclusive - - - - actually they are complemental to each other and can be combined together to study 3D genome organization.

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Delineating the respective impacts of stochastic curl- and grad-forces in a family of idealized core genetic commitment circuits
Marc Turcotte
Quant. Biol.    2016, 4 (2): 69-83.   https://doi.org/10.1007/s40484-016-0070-5
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Stochastic dynamics pervades gene regulation. Despite being random, the dynamics displays a kind of innate structure. In fact, two stochastic forces combine driving efforts: one force originates from the gradient of the underlying stochastic potential, and the other originates from the mathematical curl of the probability flux. The curl force gives rise to rotation. The gradient force gives rise to drift. Together they give rise to helical behavior. Here, it is shown that around and about the vicinity of attractive fixed points, the gradient force naturally wanes but the curl force is found to remain high. This leads to a locally noticeably different type of stochastic track near and about attractive fixed points, compared to tracks in regions where drift dominates. The consistency of this observation with the experimental fact that, in biology, fate commitment appears to not be a-priory locked-in, but rather necessitating active maintenance, is discussed. Hence attractive fixed-points are not only fuzzy, but may effectively be, locally, “more free”.

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From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis
Gaowei Wang, Xiaomei Zhu, Leroy Hood, Ping Ao
Quant Biol    2013, 1 (1): 32-49.   https://doi.org/10.1007/s40484-013-0007-1
Abstract   HTML   PDF (516KB)

Experimental evidences and theoretical analyses have amply suggested that in cancer genesis and progression genetic information is very important but not the whole. Nevertheless, “cancer as a disease of the genome” is still currently the dominant doctrine. With such a background and based on the fundamental properties of biological systems, a new endogenous molecular-cellular network theory for cancer was recently proposed by us. Similar proposals were also made by others. The new theory attempts to incorporate both genetic and environmental effects into one single framework, with the possibility to give a quantitative and dynamical description. It is asserted that the complex regulatory machinery behind biological processes may be modeled by a nonlinear stochastic dynamical system similar to a noise perturbed Morse-Smale system. Both qualitative and quantitative descriptions may be obtained. The dynamical variables are specified by a set of endogenous molecular-cellular agents and the structure of the dynamical system by the interactions among those biological agents. Here we review this theory from a pedagogical angle which emphasizes the role of modularization, hierarchy and autonomous regulation. We discuss how the core set of assumptions is exemplified in detail in one of the simple, important and well studied model organisms, Phage lambda. With this concrete and quantitative example in hand, we show that the application of the hypothesized theory in human cancer, such as hepatocellular carcinoma (HCC), is plausible, and that it may provide a set of new insights on understanding cancer genesis and progression, and on strategies for cancer prevention, cure, and care.

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Modeling stochastic noise in gene regulatory systems
Arwen Meister, Chao Du, Ye Henry Li, Wing Hung Wong
Quant. Biol.    2014, 2 (1): 1-29.   https://doi.org/10.1007/s40484-014-0025-7
Abstract   HTML   PDF (3945KB)

The Master equation is considered the gold standard for modeling the stochastic mechanisms of gene regulation in molecular detail, but it is too complex to solve exactly in most cases, so approximation and simulation methods are essential. However, there is still a lack of consensus about the best way to carry these out. To help clarify the situation, we review Master equation models of gene regulation, theoretical approximations based on an expansion method due to N.G. van Kampen and R. Kubo, and simulation algorithms due to D.T. Gillespie and P. Langevin. Expansion of the Master equation shows that for systems with a single stable steady-state, the stochastic model reduces to a deterministic model in a first-order approximation. Additional theory, also due to van Kampen, describes the asymptotic behavior of multistable systems. To support and illustrate the theory and provide further insight into the complex behavior of multistable systems, we perform a detailed simulation study comparing the various approximation and simulation methods applied to synthetic gene regulatory systems with various qualitative characteristics. The simulation studies show that for large stochastic systems with a single steady-state, deterministic models are quite accurate, since the probability distribution of the solution has a single peak tracking the deterministic trajectory whose variance is inversely proportional to the system size. In multistable stochastic systems, large fluctuations can cause individual trajectories to escape from the domain of attraction of one steady-state and be attracted to another, so the system eventually reaches a multimodal probability distribution in which all stable steady-states are represented proportional to their relative stability. However, since the escape time scales exponentially with system size, this process can take a very long time in large systems.

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Target specificity of the CRISPR-Cas9 system
Xuebing Wu, Andrea J. Kriz, Phillip A. Sharp
Quant. Biol.    2014, 2 (2): 59-70.   https://doi.org/10.1007/s40484-014-0030-x
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The CRISPR-Cas9 system, naturally a defense mechanism in prokaryotes, has been repurposed as an RNA-guided DNA targeting platform. It has been widely used for genome editing and transcriptome modulation, and has shown great promise in correcting mutations in human genetic diseases. Off-target effects are a critical issue for all of these applications. Here we review the current status on the target specificity of the CRISPR-Cas9 system.

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Stochastic physics, complex systems and biology The 1st Gordon Research Conference on “Stochastic Physics in Biology”, chaired by K. A. Dill, was held on January 23–28, 2011, in Ventura, CA.
Hong Qian
Quant. Biol.    2013, 1 (1): 50-53.   https://doi.org/10.1007/s40484-013-0002-6
Abstract   HTML   PDF (103KB)

In complex systems, the interplay between nonlinear and stochastic dynamics, e.g., J. Monod’s necessity and chance, gives rise to an evolutionary process in Darwinian sense, in terms of discrete jumps among attractors, with punctuated equilibria, spontaneous random “mutations” and “adaptations”. On an evolutionary time scale it produces sustainable diversity among individuals in a homogeneous population rather than convergence as usually predicted by a deterministic dynamics. The emergent discrete states in such a system, i.e., attractors, have natural robustness against both internal and external perturbations. Phenotypic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a perspective.

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Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data
Ming Hu, Ke Deng, Zhaohui Qin, Jun S. Liu
Quant. Biol.    2013, 1 (2): 156-174.   https://doi.org/10.1007/s40484-013-0016-0
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Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research.

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XFEL data analysis for structural biology
Haiguang Liu, John C. H. Spence
Quant. Biol.    2016, 4 (3): 159-176.   https://doi.org/10.1007/s40484-016-0076-z
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X-ray Free Electron Lasers (XFELs) have advanced research in structure biology, by exploiting their ultra-short and bright X-ray pulses. The resulting “diffraction before destruction” experimental approach allows data collection to outrun radiation damage, a crucial factor that has often limited resolution in the structure determination of biological molecules. Since the first hard X-ray laser (the Linac Coherent Light Source (LCLS) at SLAC) commenced operation in 2009, serial femtosecond crystallography (SFX) has rapidly matured into a method for the structural analysis of nano- and micro-crystals. At the same time, single particle structure determination by coherent diffractive imaging, with one particle (such as a virus) per shot, has been under intense development. In this review we describe these applications of X-ray lasers in structural biology, with a focus particularly on aspects of data analysis for the computational research community. We summarize the key problems in data analysis and model reconstruction, and provide perspectives on future research using computational methods.

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Computational neuroanatomy and co-expression of genes in the adult mouse brain, analysis tools for the Allen Brain Atlas
Pascal Grange, Michael Hawrylycz, and Partha P. Mitra
Quant Biol    2013, 1 (1): 91-100.   https://doi.org/10.1007/s40484-013-0011-5
Abstract   HTML   PDF (563KB)

We review quantitative methods and software developed to analyze genome-scale, brain-wide spatially-mapped gene-expression data. We expose new methods based on the underlying high-dimensional geometry of voxel space and gene space, and on simulations of the distribution of co-expression networks of a given size. We apply them to the Allen Atlas of the adult mouse brain, and to the co-expression network of a set of genes related to nicotine addiction retrieved from the NicSNP database. The computational methods are implemented in BrainGeneExpressionAnalysis (BGEA), a Matlab toolbox available for download.

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SupraBiology 2014: Promoting UK-China collaboration on Systems Biology and High Performance Computing
Ettore Murabito,Riccardo Colombo,Chengkun Wu,Malkhey Verma,Samrina Rehman,Jacky Snoep,Shao-Liang Peng,Naiyang Guan,Xiangke Liao,Hans V. Westerhoff
Quant. Biol.    2015, 3 (1): 46-53.   https://doi.org/10.1007/s40484-015-0039-9
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Mathematics, genetics and evolution
Warren J. Ewens
Quant. Biol.    2013, 1 (1): 9-31.   https://doi.org/10.1007/s40484-013-0003-5
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The importance of mathematics and statistics in genetics is well known. Perhaps less well known is the importance of these subjects in evolution. The main problem that Darwin saw in his theory of evolution by natural selection was solved by some simple mathematics. It is also not a coincidence that the re-writing of the Darwinian theory in Mendelian terms was carried largely by mathematical methods. In this article I discuss these historical matters and then consider more recent work showing how mathematical and statistical methods have been central to current genetical and evolutionary research.

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Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes
Rui Liu, Kazuyuki Aihara, Luonan Chen
Quant Biol    2013, 1 (2): 105-114.   https://doi.org/10.1007/s40484-013-0008-0
Abstract   HTML   PDF (772KB)

Non-smooth or even abrupt state changes exist during many biological processes, e.g., cell differentiation processes, proliferation processes, or even disease deterioration processes. Such dynamics generally signals the emergence of critical transition phenomena, which result in drastic changes of system states or eventually qualitative changes of phenotypes. Hence, it is of great importance to detect such transitions and further reveal their molecular mechanisms at network level. Here, we review the recent advances on dynamical network biomarkers (DNBs) as well as the related theoretical foundation, which can identify not only early signals of the critical transitions but also their leading networks, which drive the whole system to initiate such transitions. In order to demonstrate the effectiveness of this novel approach, examples of complex diseases are also provided to detect pre-disease stage, for which traditional methods or biomarkers failed.

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The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network
Guodong Liu, Antonio Marras, Jens Nielsen
Quant. Biol.    2014, 2 (1): 30-46.   https://doi.org/10.1007/s40484-014-0027-5
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Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model. While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate transcriptional regulatory interactions to genome-scale metabolic models in a quantitative manner.

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Mathematical modeling reveals the mechanisms of feedforward regulation in cell fate decisions in budding yeast
Wenlong Li,Ming Yi,Xiufen Zou
Quant. Biol.    2015, 3 (2): 55-68.   https://doi.org/10.1007/s40484-015-0043-0
Abstract   HTML   PDF (1870KB)

The determination of cell fate is one of the key questions of developmental biology. Recent experiments showed that feedforward regulation is a novel feature of regulatory networks that controls reversible cellular transitions. However, the underlying mechanism of feedforward regulation-mediated cell fate decision is still unclear. Therefore, using experimental data, we develop a full mathematical model of the molecular network responsible for cell fate selection in budding yeast. To validate our theoretical model, we first investigate the dynamical behaviors of key proteins at the Start transition point and the G1/S transition point; a crucial three-node motif consisting of cyclin (Cln1/2), Substrate/Subunit Inhibitor of cyclin-dependent protein kinase (Sic1) and cyclin B (Clb5/6) is considered at these points. The rapid switches of these important components between high and low levels at two transition check points are demonstrated reasonably by our model. Many experimental observations about cell fate decision and cell size control are also theoretically reproduced. Interestingly, the feedforward regulation provides a reliable separation between different cell fates. Next, our model reveals that the threshold for the amount of WHIskey (Whi5) removed from the nucleus is higher at the Reentry point in pheromone-arrested cells compared with that at the Start point in cycling cells. Furthermore, we analyze the hysteresis in the cell cycle kinetics in response to changes in pheromone concentration, showing that Cln3 is the primary driver of reentry and Cln1/2 is the secondary driver of reentry. In particular, we demonstrate that the inhibition of Cln1/2 due to the accumulation of Factor ARrest (Far1) directly reinforces arrest. Finally, theoretical work verifies that the three-node coherent feedforward motif created by cell FUSion (Fus3), Far1 and STErile (Ste12) ensures the rapid arrest and reversibility of a cellular state. The combination of our theoretical model and the previous experimental data contributes to the understanding of the molecular mechanisms of the cell fate decision at the G1 phase in budding yeast and will stimulate further biological experiments in future.

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Experimental design and model reduction in systems biology
Jenny E. Jeong, Qinwei Zhuang, Mark K. Transtrum, Enlu Zhou, Peng Qiu
Quant. Biol.    2018, 6 (4): 287-306.   https://doi.org/10.1007/s40484-018-0150-9
Abstract   HTML   PDF (1923KB)

Background: In systems biology, the dynamics of biological networks are often modeled with ordinary differential equations (ODEs) that encode interacting components in the systems, resulting in highly complex models. In contrast, the amount of experimentally available data is almost always limited, and insufficient to constrain the parameters. In this situation, parameter estimation is a very challenging problem. To address this challenge, two intuitive approaches are to perform experimental design to generate more data, and to perform model reduction to simplify the model. Experimental design and model reduction have been traditionally viewed as two distinct areas, and an extensive literature and excellent reviews exist on each of the two areas. Intriguingly, however, the intrinsic connections between the two areas have not been recognized.

Results: Experimental design and model reduction are deeply related, and can be considered as one unified framework. There are two recent methods that can tackle both areas, one based on model manifold and the other based on profile likelihood. We use a simple sum-of-two-exponentials example to discuss the concepts and algorithmic details of both methods, and provide Matlab-based code and implementation which are useful resources for the dissemination and adoption of experimental design and model reduction in the biology community.

Conclusions: From a geometric perspective, we consider the experimental data as a point in a high-dimensional data space and the mathematical model as a manifold living in this space. Parameter estimation can be viewed as a projection of the data point onto the manifold. By examining the singularity around the projected point on the manifold, we can perform both experimental design and model reduction. Experimental design identifies new experiments that expand the manifold and remove the singularity, whereas model reduction identifies the nearest boundary, which is the nearest singularity that suggests an appropriate form of a reduced model. This geometric interpretation represents one step toward the convergence of experimental design and model reduction as a unified framework.

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A novel method to identify topological domains using Hi-C data
Yang Wang, Yanjian Li, Juntao Gao, Michael Q. Zhang
Quant. Biol.    2015, 3 (2): 81-89.   https://doi.org/10.1007/s40484-015-0047-9
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Over the last decade the 3C-based (Chromosome Conformation Capture, 3C) approaches have been developed to describe the frequency of chromatin interaction. The invention of Hi-C allows us to obtain genome-wide chromatin interaction map. However, it is challenging to develop efficient and robust analytical tools to interpret the Hi-C data. Here we present a new method called Clustering based Hi-C Domain Finder (CHDF), which is based on the difference of interaction intensity inside/outside domains, to identify Hi-C domains. We also compared CHDF with existing methods including Direction Index (DI) and HiCseg. CHDF can define more chromatin domains validated by higher resolution local chromatin structure data (Chromosome Conformation Capture Carbon Copy (5C) data). Using Hi-C data of lower sequencing depth, chromatin structure identified by CHDF is closer to that discovered by data of higher sequencing depth. Furthermore, the implement of CHDF is faster than the other two. Using CHDF, we are potentially able to discover more hints and clues about chromatin structural elements at domain level.

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De novo assembly of transcriptome from next-generation sequencing data
Xuan Li, Yimeng Kong, Qiong-Yi Zhao, Yuan-Yuan Li, Pei Hao
Quant. Biol.    2016, 4 (2): 94-105.   https://doi.org/10.1007/s40484-016-0069-y
Abstract   HTML   PDF (212KB)

Reconstruction of transcriptome by de novo assembly from next generation sequencing (NGS) short-sequence reads provides an essential mean to catalog expressed genes, identify splicing isoforms, and capture the expression detail of transcripts for organisms with no reference genome available. De novo transcriptome assembly faces many unique challenges, including alternative splicing, variable expression level covering a dynamic range of several orders of magnitude, artifacts introduced by reverse transcription, etc. In the current review, we illustrate the grand strategy in applying De Bruijn Graph (DBG) approach in de novo transcriptome assembly. We further analyze many parameters proven critical in transcriptome assembly using DBG. Among them, k-mer length, coverage depth of reads, genome complexity, performance of different programs are addressed in greater details. A multi-k-mer strategy balancing efficiency and sensitivity is discussed and highly recommended for de novo transcriptome assembly. Future direction points to the combination of NGS and third generation sequencing technology that would greatly enhance the power of de novo transcriptomics study.

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Cis-acting regulatory elements: from random screening to quantitative design
Hailin Meng, Yong Wang
Quant. Biol.    2015, 3 (3): 107-114.   https://doi.org/10.1007/s40484-015-0050-1
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The cis-acting regulatory elements, e.g., promoters and ribosome binding sites (RBSs) with various desired properties, are building blocks widely used in synthetic biology for fine tuning gene expression. In the last decade, acquisition of a controllable regulatory element from a random library has been established and applied to control the protein expression and metabolic flux in different chassis cells. However, more rational strategies are still urgently needed to improve the efficiency and reduce the laborious screening and multifaceted characterizations. Building precise computational models that can predict the activity of regulatory elements and quantitatively design elements with desired strength have been demonstrated tremendous potentiality. Here, recent progress on construction of cis-acting regulatory element library and the quantitative predicting models for design of such elements are reviewed and discussed in detail.

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Recent advances in molecular machines based on toehold-mediated strand displacement reaction
Yijun Guo, Bing Wei, Shiyan Xiao, Dongbao Yao, Hui Li, Huaguo Xu, Tingjie Song, Xiang Li, Haojun Liang
Quant. Biol.    2017, 5 (1): 25-41.   https://doi.org/10.1007/s40484-017-0097-2
Abstract   HTML   PDF (4277KB)

Background: The DNA strand displacement reaction, which uses flexible and programmable DNA molecules as reaction components, is the basis of dynamic DNA nanotechnology, and has been widely used in the design of complex autonomous behaviors.

Results: In this review, we first briefly introduce the concept of toehold-mediated strand displacement reaction and its kinetics regulation in pure solution. Thereafter, we review the recent progresses in DNA complex circuit, the assembly of AuNPs driven by DNA molecular machines, and the detection of single nucleotide polymorphism (SNP) using DNA toehold exchange probes in pure solution and in interface state. Lastly, the applications of toehold-mediated strand displacement in the genetic regulation and silencing through combining gene circuit with RNA interference systems are reviewed.

Conclusions: The toehold-mediated strand displacement reaction makes DNA an excellent material for the fabrication of molecular machines and complex circuit, and may potentially be used in the disease diagnosis and the regulation of gene silencing in the near future.

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Perspective on the q-bio Summer School and Conference: 2007 – 2014 and beyond
Orna Resnekov,Brian Munsky,William S. Hlavacek
Quant. Biol.    2014, 2 (1): 54-58.   https://doi.org/10.1007/s40484-014-0029-3
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TACO: Taxonomic prediction of unknown OTUs through OTU co-abundance networks
Zohreh Baharvand Irannia, Ting Chen
Quant. Biol.    2016, 4 (3): 149-158.   https://doi.org/10.1007/s40484-016-0073-2
Abstract   HTML   PDF (437KB)

Background: A main goal of metagenomics is taxonomic characterization of microbial communities. Although sequence comparison has been the main method for the taxonomic classification, there is not a clear agreement on similarity calculation and similarity thresholds, especially at higher taxonomic levels such as phylum and class. Thus taxonomic classification of novel metagenomic sequences without close homologs in the biological databases poses a challenge.

Methods: In this study, we propose to use the co-abundant associations between taxa/operational taxonomic units (OTU) across complex and diverse communities to assist taxonomic classification. We developed a Markov Random Field model to predict taxa of unknown microorganisms using co-abundant associations.

Results: Although such associations are intrinsically functional associations, we demonstrate that they are strongly correlated with taxonomic associations and can be combined with sequence comparison methods to predict taxonomic origins of unknown microorganisms at phylum and class levels.

Conclusions: With the ever-increasing accumulation of sequence data from microbial communities, we now take the first step to explore these associations for taxonomic identification beyond sequence similarity.

Availability and Implementation: Source codes of TACO are freely available at the following URL: https://github.com/baharvand/OTU-Taxonomy-Identification implemented in C++, supported on Linux and MS Windows.

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Applications of species accumulation curves in large-scale biological data analysis
Chao Deng, Timothy Daley, Andrew Smith
Quant. Biol.    2015, 3 (3): 135-144.   https://doi.org/10.1007/s40484-015-0049-7
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The species accumulation curve, or collector’s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional sampling. Traditional applications have focused on ecological populations but emerging large-scale applications, for example in DNA sequencing, are orders of magnitude larger and present new challenges. We developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing libraries. This method uses rational function approximations to a classical non-parametric empirical Bayes estimator due to Good and Toulmin [Biometrika, 1956, 43, 45–63]. Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data sets. These include estimating microbial species richness, immune repertoire size, and k-mer diversity for genome assembly applications. We show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be attained. We also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.

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Constructing a Boolean implication network to study the interactions between environmental factors and OTUs
Congmin Zhu, Rui Jiang, Ting Chen
Quant. Biol.    2014, 2 (4): 127-141.   https://doi.org/10.1007/s40484-014-0037-3
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Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next-generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies (BIMS) for extracting Boolean implications (IF-THEN relationships) to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate (FDR) of 0.01, we construct a Boolean implication network between operational taxonomic units (OTUs) and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.

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Structure-based protein-protein interaction networks and drug design
Hammad Naveed, Jingdong J. Han
Quant. Biol.    2013, 1 (3): 183-191.   https://doi.org/10.1007/s40484-013-0018-y
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Proteins carry out their functions by interacting with other proteins and small molecules, forming a complex interaction network. In this review, we briefly introduce classical graph theory based protein-protein interaction networks. We also describe the commonly used experimental methods to construct these networks, and the insights that can be gained from these networks. We then discuss the recent transition from graph theory based networks to structure based protein-protein interaction networks and the advantages of the latter over the former, using two networks as examples. We further discuss the usefulness of structure based protein-protein interaction networks for drug discovery, with a special emphasis on drug repositioning.

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