1
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Chee FT, Harun S, Mohd Daud K, Sulaiman S, Nor Muhammad NA. Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 189:1-12. [PMID: 38604435 DOI: 10.1016/j.pbiomolbio.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/18/2023] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.
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Affiliation(s)
- Fong Ting Chee
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Sarahani Harun
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - Suhaila Sulaiman
- FGV R&D Sdn Bhd, FGV Innovation Center, PT23417 Lengkuk Teknologi, Bandar Baru Enstek, 71760 Nilai, Negeri Sembilan, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
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2
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Xie S, Zeng D, Wang Y. Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components. Biometrics 2024; 80:ujae033. [PMID: 38708763 DOI: 10.1093/biomtc/ujae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/21/2024] [Accepted: 04/09/2024] [Indexed: 05/07/2024]
Abstract
Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.
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Affiliation(s)
- Shanghong Xie
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
| | - Donglin Zeng
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States
| | - Yuanjia Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, United States
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3
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Hao X, Wang S, Fu Y, Liu Y, Shen H, Jiang L, McLamore ES, Shen Y. The WRKY46-MYC2 module plays a critical role in E-2-hexenal-induced anti-herbivore responses by promoting flavonoid accumulation. PLANT COMMUNICATIONS 2024; 5:100734. [PMID: 37859344 PMCID: PMC10873895 DOI: 10.1016/j.xplc.2023.100734] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 10/08/2023] [Accepted: 10/17/2023] [Indexed: 10/21/2023]
Abstract
Volatile organic compounds (VOCs) play key roles in plant-plant communication, especially in response to pest attack. E-2-hexenal is an important component of VOCs, but it is unclear whether it can induce endogenous plant resistance to insects. Here, we show that E-2-hexenal activates early signaling events in Arabidopsis (Arabidopsis thaliana) mesophyll cells, including an H2O2 burst at the plasma membrane, the directed flow of calcium ions, and an increase in cytosolic calcium concentration. Treatment of wild-type Arabidopsis plants with E-2-hexenal increases their resistance when challenged with the diamondback moth Plutella xylostella L., and this phenomenon is largely lost in the wrky46 mutant. Mechanistically, E-2-hexenal induces the expression of WRKY46 and MYC2, and the physical interaction of their encoded proteins was verified by yeast two-hybrid, firefly luciferase complementation imaging, and in vitro pull-down assays. The WRKY46-MYC2 complex directly binds to the promoter of RBOHD to promote its expression, as demonstrated by luciferase reporter, yeast one-hybrid, chromatin immunoprecipitation, and electrophoretic mobility shift assays. This module also positively regulates the expression of E-2-hexenal-induced naringenin biosynthesis genes (TT4 and CHIL) and the accumulation of total flavonoids, thereby modulating plant tolerance to insects. Together, our results highlight an important role for the WRKY46-MYC2 module in the E-2-hexenal-induced defense response of Arabidopsis, providing new insights into the mechanisms by which VOCs trigger plant defense responses.
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Affiliation(s)
- Xin Hao
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shuyao Wang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yu Fu
- State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yahui Liu
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Hongyu Shen
- University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
| | - Libo Jiang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Eric S McLamore
- Department of Agricultural Sciences, Clemson University, Clemson, SC 29634, USA
| | - Yingbai Shen
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
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4
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Wang B, Wang L, Peng J, Hong M, Xu W. The identification of piecewise non-linear dynamical system without understanding the mechanism. CHAOS (WOODBURY, N.Y.) 2023; 33:2894488. [PMID: 37276571 DOI: 10.1063/5.0147847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/16/2023] [Indexed: 06/07/2023]
Abstract
This paper designs an algorithm to distill the piecewise non-linear dynamical system from the data without prior knowledge. The system to be identified does not have to be written as a known model term or be thoroughly understood. We exploit the fact that an unknown piecewise non-linear system can be decomposed into the Fourier series as long as its equations of motion are Riemann integrable. Based on this property, we reduce the challenge of finding the correct model to discovering the Fourier series approximation. However, the Fourier series approximation of the piecewise function is inaccurate. The new method takes advantage of this weakness to determine whether the model has piecewise features and to find a way to discover the discontinuity set. Then, the dynamical system on each segment is identified as a pure Fourier series. Identification of intricate models can be achieved in simple steps. The results show that the method can accurately discover the equation of motion and precisely capture the non-smooth characteristic. Next, the prediction and further detailed analysis can be carried out.
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Affiliation(s)
- Bochen Wang
- Department of Applied Probability and Statistics, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, Peoples Republic of China
| | - Liang Wang
- Department of Applied Probability and Statistics, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, Peoples Republic of China
| | - Jiahui Peng
- Department of Applied Probability and Statistics, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, Peoples Republic of China
| | - Mingyue Hong
- Department of Applied Probability and Statistics, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, Peoples Republic of China
| | - Wei Xu
- Department of Applied Probability and Statistics, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, Peoples Republic of China
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5
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Najnin T, Saimon SH, Sunter G, Ruan J. A Network-Based Approach for Improving Annotation of Transcription Factor Functions and Binding Sites in Arabidopsis thaliana. Genes (Basel) 2023; 14:genes14020282. [PMID: 36833209 PMCID: PMC9957447 DOI: 10.3390/genes14020282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/12/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Transcription factors are an integral component of the cellular machinery responsible for regulating many biological processes, and they recognize distinct DNA sequence patterns as well as internal/external signals to mediate target gene expression. The functional roles of an individual transcription factor can be traced back to the functions of its target genes. While such functional associations can be inferred through the use of binding evidence from high-throughput sequencing technologies available today, including chromatin immunoprecipitation sequencing, such experiments can be resource-consuming. On the other hand, exploratory analysis driven by computational techniques can alleviate this burden by narrowing the search scope, but the results are often deemed low-quality or non-specific by biologists. In this paper, we introduce a data-driven, statistics-based strategy to predict novel functional associations for transcription factors in the model plant Arabidopsis thaliana. To achieve this, we leverage one of the largest available gene expression compendia to build a genome-wide transcriptional regulatory network and infer regulatory relationships among transcription factors and their targets. We then use this network to build a pool of likely downstream targets for each transcription factor and query each target pool for functionally enriched gene ontology terms. The results exhibited sufficient statistical significance to annotate most of the transcription factors in Arabidopsis with highly specific biological processes. We also perform DNA binding motif discovery for transcription factors based on their target pool. We show that the predicted functions and motifs strongly agree with curated databases constructed from experimental evidence. In addition, statistical analysis of the network revealed interesting patterns and connections between network topology and system-level transcriptional regulation properties. We believe that the methods demonstrated in this work can be extended to other species to improve the annotation of transcription factors and understand transcriptional regulation on a system level.
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Affiliation(s)
- Tanzira Najnin
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Sakhawat Hossain Saimon
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Garry Sunter
- Department of Biological Sciences, Northern Illinois University, DeKalb, IL 60115, USA
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
- Correspondence:
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6
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Wang Q, Dong A, Zhao J, Wang C, Griffin C, Gragnoli C, Xue F, Wu R. Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis. Front Microbiol 2022; 13:998813. [PMID: 36338093 PMCID: PMC9631484 DOI: 10.3389/fmicb.2022.998813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/09/2022] [Indexed: 09/07/2024] Open
Abstract
Aerobic vaginitis (AV) is a complex vaginal dysbiosis that is thought to be caused by the micro-ecological change of the vaginal microbiota. While most studies have focused on how changes in the abundance of individual microbes are associated with the emergence of AV, we still do not have a complete mechanistic atlas of the microbe-AV link. Network modeling is central to understanding the structure and function of any microbial community assembly. By encapsulating the abundance of microbes as nodes and ecological interactions among microbes as edges, microbial networks can reveal how each microbe functions and how one microbe cooperate or compete with other microbes to mediate the dynamics of microbial communities. However, existing approaches can only estimate either the strength of microbe-microbe link or the direction of this link, failing to capture full topological characteristics of a network, especially from high-dimensional microbial data. We combine allometry scaling law and evolutionary game theory to derive a functional graph theory that can characterize bidirectional, signed, and weighted interaction networks from any data domain. We apply our theory to characterize the causal interdependence between microbial interactions and AV. From functional networks arising from different functional modules, we find that, as the only favorable genus from Firmicutes among all identified genera, the role of Lactobacillus in maintaining vaginal microbial symbiosis is enabled by upregulation from other microbes, rather than through any intrinsic capacity. Among Lactobacillus species, the proportion of L. crispatus to L. iners is positively associated with more healthy acid vaginal ecosystems. In a less healthy alkaline ecosystem, L. crispatus establishes a contradictory relationship with other microbes, leading to population decrease relative to L. iners. We identify topological changes of vaginal microbiota networks when the menstrual cycle of women changes from the follicular to luteal phases. Our network tool provides a mechanistic approach to disentangle the internal workings of the microbiota assembly and predict its causal relationships with human diseases including AV.
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Affiliation(s)
- Qian Wang
- Department of Obstetrics and Gynecology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Female Reproductive Health and Eugenics, Tianjin, China
| | - Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Jinshuai Zhao
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Chen Wang
- Department of Obstetrics and Gynecology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Female Reproductive Health and Eugenics, Tianjin, China
| | - Christipher Griffin
- Applied Research Laboratory, The Pennsylvania State University, State College, PA, United States
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States
- Division of Endocrinology, Department of Medicine, Creighton University School of Medicine, Omaha, NE, United States
- Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome, Italy
| | - Fengxia Xue
- Department of Obstetrics and Gynecology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Female Reproductive Health and Eugenics, Tianjin, China
| | - Rongling Wu
- Center for Statistical Genetics, Department of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA, United States
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7
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Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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Zhang N, Nanshan M, Cao J. A Joint estimation approach to sparse additive ordinary differential equations. STATISTICS AND COMPUTING 2022; 32:69. [PMID: 36033975 PMCID: PMC9395913 DOI: 10.1007/s11222-022-10117-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by considering the likelihood, ODE fidelity and sparse regularization simultaneously. We design a block coordinate descent algorithm for optimizing the non-convex and non-differentiable objective function. The global convergence of the algorithm is established. The simulation study and two applications demonstrate the superior performance of the proposed method in estimation and improved performance of identifying the sparse structure.
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Affiliation(s)
- Nan Zhang
- School of Data Science, Fudan University, Shanghai, China
| | - Muye Nanshan
- School of Data Science, Fudan University, Shanghai, China
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada
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9
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Chen C, Shen B, Ma T, Wang M, Wu R. A statistical framework for recovering pseudo-dynamic networks from static data. Bioinformatics 2022; 38:2481-2487. [PMID: 35218338 PMCID: PMC9991900 DOI: 10.1093/bioinformatics/btac038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases. RESULTS We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constituting these networks had pivotal and biologically relevant roles associated with the vascular system. We also provided the selection consistency of the proposed learning procedure and evaluated its utility through extensive simulations. AVAILABILITY AND IMPLEMENTATION DRDNet is implemented in the R language, and the source codes are available at https://github.com/chencxxy28/DRDnet/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chixiang Chen
- Division of Biostatistics and Bioinformatics, University of Maryland School of Medicine, Baltimore, MD 21201, USA.,Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Biyi Shen
- Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA
| | - Ming Wang
- Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
| | - Rongling Wu
- Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
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10
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Liu Y, Li L, Wang X. A nonlinear sparse neural ordinary differential equation model for multiple functional processes. CAN J STAT 2022; 50:59-85. [PMID: 35530428 PMCID: PMC9075179 DOI: 10.1002/cjs.11666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 07/06/2021] [Indexed: 11/06/2022]
Abstract
In this article, we propose a new sparse neural ordinary differential equation (ODE) model to characterize flexible relations among multiple functional processes. We characterize the latent states of the functions via a set of ordinary differential equations. We then model the dynamic changes of the latent states using a deep neural network (DNN) with a specially designed architecture and a sparsity-inducing regularization. The new model is able to capture both nonlinear and sparse dependent relations among multivariate functions. We develop an efficient optimization algorithm to estimate the unknown weights for the DNN under the sparsity constraint. We establish both the algorithmic convergence and selection consistency, which constitute the theoretical guarantees of the proposed method. We illustrate the efficacy of the method through simulations and a gene regulatory network example.
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Affiliation(s)
- Yijia Liu
- Department of Statistics, Purdue University
| | - Lexin Li
- Department of Biostatistics and Epidemiology, University of California at Berkeley
| | - Xiao Wang
- Department of Statistics, Purdue University
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11
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Shojaie A, Fox EB. Granger Causality: A Review and Recent Advances. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2022; 9:289-319. [PMID: 37840549 PMCID: PMC10571505 DOI: 10.1146/annurev-statistics-040120-010930] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Introduced more than a half-century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience. Despite this popularity, the validity of this framework for inferring causal relationships among time series has remained the topic of continuous debate. Moreover, while the original definition was general, limitations in computational tools have constrained the applications of Granger causality to primarily simple bivariate vector autoregressive processes. Starting with a review of early developments and debates, this article discusses recent advances that address various shortcomings of the earlier approaches, from models for high-dimensional time series to more recent developments that account for nonlinear and non-Gaussian observations and allow for subsampled and mixed-frequency time series.
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Affiliation(s)
- Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, Washington 98195-4322, USA
| | - Emily B Fox
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA
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12
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Abstract
Ordinary differential equation (ODE) is widely used in modeling biological and physical processes in science. In this article, we propose a new reproducing kernel-based approach for estimation and inference of ODE given noisy observations. We do not assume the functional forms in ODE to be known, or restrict them to be linear or additive, and we allow pairwise interactions. We perform sparse estimation to select individual functionals, and construct confidence intervals for the estimated signal trajectories. We establish the estimation optimality and selection consistency of kernel ODE under both the low-dimensional and high-dimensional settings, where the number of unknown functionals can be smaller or larger than the sample size. Our proposal builds upon the smoothing spline analysis of variance (SS-ANOVA) framework, but tackles several important problems that are not yet fully addressed, and thus extends the scope of existing SS-ANOVA as well. We demonstrate the efficacy of our method through numerous ODE examples.
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Affiliation(s)
- Xiaowu Dai
- Department of Economics and Simons Institute for the Theory of Computing, the University of California, Berkeley, Berkeley, CA
| | - Lexin Li
- Department of Economics and Simons Institute for the Theory of Computing, the University of California, Berkeley, Berkeley, CA
- Department of Biostatistics and Epidemiology, the University of California, Berkeley, Berkeley, CA
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13
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Song X, Liu H, Bu D, Xu H, Ma Q, Pei D. Rejuvenation remodels transcriptional network to improve rhizogenesis in mature Juglans tree. TREE PHYSIOLOGY 2021; 41:1938-1952. [PMID: 34014320 DOI: 10.1093/treephys/tpab038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/03/2021] [Indexed: 06/12/2023]
Abstract
Adventitious rooting of walnut species (Juglans L.) is known to be rather difficult, especially for mature trees. The adventitious root formation (ARF) capacities of mature trees can be significantly improved by rejuvenation. However, the underlying gene regulatory networks (GRNs) of rejuvenation remain largely unknown. To characterize such regulatory networks, we carried out the transcriptomic study using RNA samples of the cambia and peripheral tissues on the bottom of rejuvenated and mature walnut (Juglans hindsii × J. regia) cuttings during the ARF. The RNA sequencing data suggested that zeatin biosynthesis, energy metabolism and substance metabolism were activated by rejuvenation, whereas photosynthesis, fatty acid biosynthesis and the synthesis pathways for secondary metabolites were inhibited. The inter- and intra-module GRNs were constructed using differentially expressed genes. We identified 35 hub genes involved in five modules associated with ARF. Among these hub genes, particularly, beta-glucosidase-like (BGLs) family members involved in auxin metabolism were overexpressed at the early stage of the ARF. Furthermore, BGL12 from the cuttings of Juglans was overexpressed in Populus alba × P. glandulosa. Accelerated ARF and increased number of ARs were observed in the transgenic poplars. These results provide a high-resolution atlas of gene activity during ARF and help to uncover the regulatory modules associated with the ARF promoted by rejuvenation.
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Affiliation(s)
- Xiaobo Song
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, the Chinese Academy of Forestry, 1958 Box, Beijing 100091, China
| | - Hao Liu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, the Chinese Academy of Forestry, 1958 Box, Beijing 100091, China
| | - Dechao Bu
- Institute of Computing Technology, Chinese Academy of Sciences, No.6 Kexueyuan South Road Zhongguancun, Haidian District, Beijing 100190, China
| | - Huzhi Xu
- Forestry Bureau of Luoning County, Luoning County, Luoyang City, Henan Province 471700, China
| | - Qingguo Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, the Chinese Academy of Forestry, 1958 Box, Beijing 100091, China
| | - Dong Pei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, the Chinese Academy of Forestry, 1958 Box, Beijing 100091, China
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14
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Yang B, Bao W, Zhang W, Wang H, Song C, Chen Y, Jiang X. Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model. BMC Bioinformatics 2021; 22:448. [PMID: 34544363 PMCID: PMC8451084 DOI: 10.1186/s12859-021-04367-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 09/09/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. RESULTS In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented. CONCLUSIONS When tested on three real gene expression datasets from E. coli and Human Cell, the experiment results suggest that CVODE model could improve 20-50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.
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Affiliation(s)
- Bin Yang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China
| | - Wenzheng Bao
- School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, 221018, China.
| | - Wei Zhang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China
| | - Haifeng Wang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China
| | - Chuandong Song
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Xiuying Jiang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China
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15
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Wang H, Ye M, Fu Y, Dong A, Zhang M, Feng L, Zhu X, Bo W, Jiang L, Griffin CH, Liang D, Wu R. Modeling genome-wide by environment interactions through omnigenic interactome networks. Cell Rep 2021; 35:109114. [PMID: 33979624 DOI: 10.1016/j.celrep.2021.109114] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/11/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022] Open
Abstract
How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.
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Affiliation(s)
- Haojie Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yaru Fu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Miaomiao Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Li Feng
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xuli Zhu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wenhao Bo
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christopher H Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Dan Liang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
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16
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Sun BM, Zeng D, Wang Y. Modeling Temporal Biomarkers With Semiparametric Nonlinear Dynamical Systems. Biometrika 2021; 108:199-214. [PMID: 34326552 PMCID: PMC8315107 DOI: 10.1093/biomet/asaa042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Dynamical systems based on differential equations are useful for modeling the temporal evolution of biomarkers. These systems can characterize the temporal patterns of biomarkers and inform the detection of interactions among biomarkers. Existing statistical methods for dynamical systems mostly target single time-course data based on a linear model or generalized additive model. Hence, they cannot adequately capture the complex interactions among biomarkers; neither can they take into account the heterogeneity between systems or subjects. in this work, we propose a semiparametric dynamical system based on multi-index models for multiple subjects time-course data. Our model accounts for between-subject heterogeneity by introducing system-level or subject-level covariates to dynamic systems, and it allows for nonlinear relationship and interaction between the combined biomarkers and the temporal rate of each biomarker. For estimation and inference, we consider a two-step procedure based on integral equations from the proposed model. We propose an algorithm that iterates between the estimation of the link function through splines and the estimation of index parameters and that allows for regularization to achieve sparsity. We prove model identifiability and derive the asymptotic properties of the estimated model parameters. A benefit of our approach is to pool information from multiple subjects to identify the interaction among biomarkers. We apply the method to analyze electroencephalogram (EEG) data for patients affected by alcohol dependence. The results reveal new insight on patients' brain activities and demonstrate differential interaction patterns in patients compared to health control subjects.
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Affiliation(s)
- By Ming Sun
- Department of Biostatistics, Columbia University, 722 West 168th St. New York, U.S
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, 722 West 168th St. New York, U.S. & Department of Psychiatry, Columbia University Irving Medical Center
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17
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Clairon Q. A regularization method for the parameter estimation problem in ordinary differential equations via discrete optimal control theory. J Stat Plan Inference 2021. [DOI: 10.1016/j.jspi.2020.04.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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18
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Zhang Q, Su Z, Guo Y, Zhang S, Jiang L, Wu R. Genome-wide association studies of callus differentiation for the desert tree, Populus euphratica. TREE PHYSIOLOGY 2020; 40:1762-1777. [PMID: 32761189 DOI: 10.1093/treephys/tpaa098] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 07/31/2020] [Indexed: 05/22/2023]
Abstract
Callus differentiation is a key developmental process in plant regeneration from cells. A better understanding of the genetic architecture of callus differentiation timing can help improve tissue transformation and the efficiency of artificial propagation. In this study, we investigated genotypic variation in callus differentiation capacity among 297 diverse P. euphratica trees sampled from a natural population. We employed a genome-wide association study (GWAS) of binary and growth-based parameters to identify loci and characterize the genetic architecture and genetic network underlying regulation of callus differentiation in P. euphratica. The results of this GWAS experiment suggested potential associations controlling whether the callus could differentiate and the process of callus differentiation. We identified multiple significant quantitative trait loci (QTLs), including the genes LOG1 and LOG7 and a locus containing WOX1. We reconstructed a genetic network that visualizes how each QTL interacts uniquely with other variants, and several core QTLs were detected that are involved in the degree of callus differentiation, providing potential targets for selection. This study represents one of the first to identify genetic variants affecting callus differentiation in a forest tree. Our results suggest that callus differentiation may be a typical qualitative-quantitative trait controlled by a major gene as well as polygenes across the genome of P. euphratica. This GWAS will help to design more complex and specific molecular tools for systematically manipulating organ regeneration.
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Affiliation(s)
- Qianru Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Zhifang Su
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yunqian Guo
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shilong Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA
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19
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Jiang L, Griffin CH, Wu R. SEGN: Inferring real-time gene networks mediating phenotypic plasticity. Comput Struct Biotechnol J 2020; 18:2510-2521. [PMID: 33005313 PMCID: PMC7516210 DOI: 10.1016/j.csbj.2020.08.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 08/27/2020] [Accepted: 08/29/2020] [Indexed: 12/13/2022] Open
Abstract
The capacity of an organism to alter its phenotype in response to environmental perturbations changes over developmental time and is a process determined by multiple genes that are co-expressed in intricate but organized networks. Characterizing the spatiotemporal change of such gene networks can offer insight into the genomic signatures underlying organismic adaptation, but it represents a major methodological challenge. Here, we integrate the holistic view of systems biology and the interactive notion of evolutionary game theory to reconstruct so-called systems evolutionary game networks (SEGN) that can autonomously detect, track, and visualize environment-induced gene networks along the time axis. The SEGN overcomes the limitations of traditional approaches by inferring context-specific networks, encapsulating bidirectional, signed, and weighted gene-gene interactions into fully informative networks, and monitoring the process of how networks topologically alter across environmental and developmental cues. Based on the design principle of SEGN, we perform a transcriptional plasticity study by culturing Euphrates poplar, a tree that can grow in the saline desert, in saline-free and saline-stress conditions. SEGN characterize previously unknown gene co-regulation that modulates the time trajectories of the trees' response to salt stress. As a marriage of multiple disciplines, SEGN shows its potential to interpret gene interdependence, predict how transcriptional co-regulation responds to various regimes, and provides a hint for exploring the mass, energetic, or signal basis that drives various types of gene interactions.
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Affiliation(s)
- Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christopher H. Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA
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20
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Van den Broeck L, Gordon M, Inzé D, Williams C, Sozzani R. Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling. Front Genet 2020; 11:457. [PMID: 32547596 PMCID: PMC7270862 DOI: 10.3389/fgene.2020.00457] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 04/14/2020] [Indexed: 12/26/2022] Open
Abstract
Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.
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Affiliation(s)
- Lisa Van den Broeck
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
| | - Max Gordon
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, United States
| | - Dirk Inzé
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.,VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Cranos Williams
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, United States
| | - Rosangela Sozzani
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
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21
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Zhang T, Sun Y, Li H, Yan G, Tanabe S, Miao R, Wang Y, Caffo BS, Quigg MS. Bayesian inference of a directional brain network model for intracranial EEG data. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Li H, Wang Y, Tanabe S, Sun Y, Yan G, Quigg MS, Zhang T. Mapping epileptic directional brain networks using intracranial EEG data. Biostatistics 2019; 22:613-628. [PMID: 31879751 DOI: 10.1093/biostatistics/kxz056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 11/26/2019] [Accepted: 11/29/2019] [Indexed: 11/13/2022] Open
Abstract
The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another as directional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. As such, effective epilepsy diagnosis and treatment require accurate identification of directional connections among regions, i.e., mapping of epileptic patients' brain networks. This article aims to understand the epileptic brain network using intracranial electroencephalographic data-recordings of epileptic patients' brain activities in many regions. The most popular models for directional connectivity use ordinary differential equations (ODE). However, ODE models are sensitive to data noise and computationally costly. To address these issues, we propose a high-dimensional state-space multivariate autoregression (SSMAR) model for the brain's directional connectivity. Different from standard multivariate autoregression and SSMAR models, the proposed SSMAR features a cluster structure, where the brain network consists of several clusters of densely connected brain regions. We develop an expectation-maximization algorithm to estimate the proposed model and use it to map the interregional networks of epileptic patients in different seizure stages. Our method reveals the evolution of brain networks during seizure development.
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Affiliation(s)
- Huazhang Li
- Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA
| | - Yaotian Wang
- Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA
| | - Seiji Tanabe
- Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA
| | - Yinge Sun
- Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA
| | - Guofen Yan
- Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA
| | - Mark S Quigg
- Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA
| | - Tingting Zhang
- Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA
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23
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Abstract
The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex. Understanding the implications of normalization at the population level is hindered by the heterogeneity of cortical neurons, which differ in the composition of their normalization pools and semi-saturation constants. Here we introduce a geometric approach to investigate contextual modulation in neural populations and study how the representation of stimulus orientation is transformed by the presence of a mask. We find that population responses can be embedded in a low-dimensional space and that an affine transform can account for the effects of masking. The geometric analysis further reveals a link between changes in discriminability and bias induced by the mask. We propose the geometric approach can yield new insights into the image processing computations taking place in early visual cortex at the population level while coping with the heterogeneity of single cell behavior.
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Affiliation(s)
- Dario L Ringach
- Departments of Neurobiology and Psychology, UCLA, Los Angeles, CA, 90095, USA.
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24
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Chen C, Jiang L, Fu G, Wang M, Wang Y, Shen B, Liu Z, Wang Z, Hou W, Berceli SA, Wu R. An omnidirectional visualization model of personalized gene regulatory networks. NPJ Syst Biol Appl 2019; 5:38. [PMID: 31632690 PMCID: PMC6789114 DOI: 10.1038/s41540-019-0116-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 09/18/2019] [Indexed: 01/09/2023] Open
Abstract
Gene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the individual's response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene co-expression, but such graphs cannot quantify how gene-gene interactions vary among individuals and how they alter structurally across spatiotemporal gradients. Here, we develop a general framework for inferring informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from routine transcriptional experiments. This framework is constructed by a system of quasi-dynamic ordinary differential equations (qdODEs) derived from the combination of ecological and evolutionary theories. We reconstruct idopNetworks using genomic data from a surgical experiment and illustrate how network structure is associated with surgical response to infrainguinal vein bypass grafting and the outcome of grafting. idopNetworks may shed light on genotype-phenotype relationships and provide valuable information for personalized medicine.
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Affiliation(s)
- Chixiang Chen
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA 17033 USA
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033 USA
| | - Libo Jiang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083 China
| | - Guifang Fu
- Department of Mathematical Sciences, SUNY Binghamton University, Binghamton, NY 13902 USA
| | - Ming Wang
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA 17033 USA
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033 USA
| | - Yaqun Wang
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ 08854 USA
| | - Biyi Shen
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033 USA
| | - Zhenqiu Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033 USA
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Heaven, CT 06520 USA
| | - Wei Hou
- Department of Family, Population & Preventive Medicine, Stony Brook School of Medicine, Stony Brook, NY 11794 USA
| | - Scott A. Berceli
- Malcom Randall VA Medical Center, Gainesville, FL 32610 USA
- Department of Surgery, University of Florida, Box 100128, Gainesville, FL 32610 USA
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32610 USA
| | - Rongling Wu
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA 17033 USA
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033 USA
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25
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Bizzarri M, Giuliani A, Pensotti A, Ratti E, Bertolaso M. Co-emergence and Collapse: The Mesoscopic Approach for Conceptualizing and Investigating the Functional Integration of Organisms. Front Physiol 2019; 10:924. [PMID: 31427981 PMCID: PMC6690009 DOI: 10.3389/fphys.2019.00924] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 07/09/2019] [Indexed: 11/13/2022] Open
Abstract
The fall of reductionist approaches to explanation leaves biology with an unescapable challenge: how to decipher complex systems. This entails a number of very critical questions, the most basic ones being: "What do we mean by 'complex'?" and "What is the system we should look for?" In complex systems, constraints belong to a higher level that the molecular one and their effect reduces and constrains the manifold of the accessible internal states of the system itself. Function is related but not deterministically imposed by the underlying structure. It is quite unlikely that such kind of complexity could be grasped by current approaches focusing on a single organization scale. The natural co-emergence of systems, parts and properties can be adopted as a hypothesis-free conceptual framework to understand functional integration of organisms, including their hierarchical or multilevel patterns, and including the way scientific practice proceeds in approaching such complexity. External, "driving" factors - order parameters and control parameters provided by the surrounding microenvironment - are always required to "push" the components' fate into well-defined developmental directions. In the negative, we see that in pathological processes such as cancer, organizational fluidity, collapse of levels and dynamic heterogeneity make it hard to even find a level of observation for a stable explanandum to persist in scientific practice. Parts and the system both lose their properties once the system is destabilized. The mesoscopic approach is our proposal to conceptualizing, investigating and explaining in biology. "Mesoscopic way of thinking" is increasingly popular in the epistemology of biology and corresponds to looking for an explanation (and possibly a prediction) where "non-trivial determinism is maximal": the "most microscopic" level of organization is not necessarily the place where "the most relevant facts do happen." A fundamental re-thinking of the concept of causality is also due for order parameters to be carefully and correctly identified. In the biological realm, entities have relational properties only, as they depend ontologically on the context they happen to be in. The basic idea of a relational ontology is that, in our inventory of the world, relations are somehow prior to the relata (i.e., entities).
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Affiliation(s)
- Mariano Bizzarri
- Systems Biology Group Lab, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Alessandro Giuliani
- Department of Environment and Health, Istituto Superiore di Sanità, Rome, Italy
| | | | - Emanuele Ratti
- Reilly Center for Science, Technology, and Values, University of Notre Dame, Notre Dame, IN, United States
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26
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Zhao Q, Zhang Y. Ensemble Method of Feature Selection and Reverse Construction of Gene Logical Network Based on Information Entropy. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001420590041] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we propose a novel ensemble gene selection method to obtain a gene subset. Then we provide a reverse construction method of gene network derived from expression profile data of the gene subset. The uncertainty coefficient based on information entropy are used to define the existence of logical relations among these genes. If the uncertainty coefficient between some genes exceeds predefined thresholds, the gene nodes will be connected by directed edges. Thus, a gene network is generated, which we define as gene logical network. This method is applied to the breast cancer data including control group and experimental group, with comparisons of the 2nd-order logic type distribution, average degree as well as average path length of the networks. It is found that these structures with different networks are quite distinct. By the comparison of the degree difference between control group and experimental group, the key genes are picked up. By defining the dynamics evolution rules of state transition based on the logical regulation among the key genes in the network, the dynamic behaviors for normal breast cells and cells with cancer of different stages are simulated numerically. Some of them are highly related to the development of breast cancer through literature inquiry. The study may provide a useful revelation to the biological mechanism in the formation and development of cancer.
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Affiliation(s)
- Qingfeng Zhao
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China
- Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, P. R. China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China
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27
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Haliki E, Alpagut Keskin N, Masalci O. Boolean gene regulatory network model of centromere function in Saccharomyces cerevisiae. J Biol Phys 2019; 45:235-251. [PMID: 31175490 DOI: 10.1007/s10867-019-09526-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 05/09/2019] [Indexed: 10/26/2022] Open
Abstract
Centromeres, a highly conserved locus of eukaryotic chromosomes, have critical function for genome stability and integrity. Because their centromeric DNA sequences are necessary and sufficient for kinetochore recruitment and DNA segregation, point centromeres of Saccharomyces cerevisiae chromosomes provide an attractive system for the study of the regulation of centromere function. Using the mathematical model of Boolean gene regulatory networks, the gene regulatory dynamics of centromere region of S. cerevisiae (budding yeast), which is actively involved in the cell-cycle, has been examined. A gene regulatory network containing the relevant centromere genes of the model organism from biological databases was established and all possible cellular phenotypes subjected to a synchronous gene regulation and attracted to several basins. Gene expression in the largest attractor was compared with the biological data by obtaining changes in the cell-cycle. We show that the model for centromere function recovers a single cyclic attractor. The trajectory flow diagram plotted over all initial conditions of the system also shows good correspondence with the cell-cycle phases. Although other upstream signals are possibly involved in the regulation of centromere genes, proposed interactions with selected cell-cycle genes were sufficient to recover whole cell-cycle process. To truly clarify these proposed regulatory interactions of candidate genes for centromere function, profiling and analyzing their expression levels over time with expanded nodes/edges are required. Moreover, a previously modeled gene knock-down mechanism applied to the network and robustness versus knock-down was interpreted based on the obtained consequences.
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28
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Jiang L, Sun L, Ye M, Wang J, Wang Y, Bogard M, Lacaze X, Fournier A, Beauchêne K, Gouache D, Wu R. Functional mapping of N deficiency‐induced response in wheat yield‐component traits by implementing high‐throughput phenotyping. THE PLANT JOURNAL 2019; 97:1105-1119. [PMID: 30536457 DOI: 10.1111/tpj.14186] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 11/09/2018] [Accepted: 11/23/2018] [Indexed: 05/25/2023]
Affiliation(s)
- Libo Jiang
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
| | - Lidan Sun
- Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding National Engineering Research Center for Floriculture College of Landscape Architecture Beijing Forestry University Beijing 100083 China
| | - Meixia Ye
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
| | - Jing Wang
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
| | - Yaqun Wang
- Department of Biostatistics Rutgers University New Brunswick NJ 08901 USA
| | - Matthieu Bogard
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Xavier Lacaze
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Antoine Fournier
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Katia Beauchêne
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - David Gouache
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Rongling Wu
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
- Center for Statistical Genetics Departments of Public Health Sciences and Statistics Pennsylvania State University Hershey PA 17033 USA
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29
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30
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Clairon Q, Brunel NJB. Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations. J Stat Plan Inference 2019. [DOI: 10.1016/j.jspi.2018.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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31
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Wu L, Qiu X, Yuan YX, Wu H. Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach. J Am Stat Assoc 2019; 114:657-667. [PMID: 34385718 DOI: 10.1080/01621459.2017.1423074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Ordinary differential equations (ODEs) are widely used to model the dynamic behavior of a complex system. Parameter estimation and variable selection for a "Big System" with linear ODEs are very challenging due to the need of nonlinear optimization in an ultra-high dimensional parameter space. In this article, we develop a parameter estimation and variable selection method based on the ideas of similarity transformation and separable least squares (SLS). Simulation studies demonstrate that the proposed matrix-based SLS method could be used to estimate the coefficient matrix more accurately and perform variable selection for a linear ODE system with thousands of dimensions and millions of parameters much better than the direct least squares (LS) method and the vector-based two-stage method that are currently available. We applied this new method to two real data sets: a yeast cell cycle gene expression data set with 30 dimensions and 930 unknown parameters and the Standard & Poor 1500 index stock price data with 1250 dimensions and 1,563,750 unknown parameters, to illustrate the utility and numerical performance of the proposed parameter estimation and variable selection method for big systems in practice.
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Affiliation(s)
- Leqin Wu
- Department of Mathematics, Jinan University, Guangzhou, China
| | - Xing Qiu
- Department of Biostatistics and Computational Biology University of Rochester, Rochester, New York, U.S.A
| | - Ya-Xiang Yuan
- Academy of Mathematics and System Sciences Chinese Academy of Sciences, Beijing, China
| | - Hulin Wu
- Department of Biostatistics, University of Texas Health Science Center at Houston, Houston, TX, U.S.A
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32
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Chen G, Ramírez JC, Deng N, Qiu X, Wu C, Zheng WJ, Wu H. Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5289627. [PMID: 30649296 PMCID: PMC6333964 DOI: 10.1093/database/bay145] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 12/11/2018] [Indexed: 11/14/2022]
Abstract
Motivation Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse. Results We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series' metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study's description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.
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Affiliation(s)
- Guocai Chen
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | | | - Nan Deng
- Department of Biostatistics & Data Science, School of Public Health, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, New York, USA
| | - Canglin Wu
- TechWave International. Inc., Houston, Texas, USA
| | - W Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Hulin Wu
- Department of Biostatistics & Data Science, School of Public Health, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
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33
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Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform. Sci Rep 2018; 8:17787. [PMID: 30542062 PMCID: PMC6290780 DOI: 10.1038/s41598-018-36180-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 11/16/2018] [Indexed: 02/06/2023] Open
Abstract
Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot topic of system biology in recent years. In order to reduce the computation load for large-scale GRN identification, a parallel algorithm based on restricted gene expression programming (RGEP), namely MPRGEP, is proposed to infer instantaneous and time-delayed regulatory relationships between transcription factors and target genes. In MPRGEP, the structure and parameters of time-delayed S-system (TDSS) model are encoded into one chromosome. An original hybrid optimization approach based on genetic algorithm (GA) and gene expression programming (GEP) is proposed to optimize TDSS model with MapReduce framework. Time-delayed GRNs (TDGRN) with hundreds of genes are utilized to test the performance of MPRGEP. The experiment results reveal that MPRGEP could infer more accurately gene regulatory network than other state-of-art methods, and obtain the convincing speedup.
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34
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Xue H, Wu S, Wu Y, Idarraga JCR, Wu H. Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks. Stat Med 2018; 37:2630-2644. [PMID: 29722041 PMCID: PMC6940146 DOI: 10.1002/sim.7669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 01/18/2018] [Accepted: 03/08/2018] [Indexed: 11/12/2022]
Abstract
Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae.
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Affiliation(s)
- Hongqi Xue
- iCardiac Technologies, 150 Allens Creek Road, Rochester, NY 14618, USA
| | - Shuang Wu
- Biogen, 300 Binney Street, Cambridge, MA 02142, USA
| | - Yichao Wu
- Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL 60607-7045, USA
| | | | - Hulin Wu
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, 1200 Pressler Street, RAS E833, Houston, TX 77030, USA
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35
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Clairon Q, Brunel NJB. Optimal Control and Additive Perturbations Help in Estimating Ill-Posed and Uncertain Dynamical Systems. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2017.1319841] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Quentin Clairon
- School of Mathematics and Statistics, University of Newcastle, UK
| | - Nicolas J.-B. Brunel
- ENSIIE and Laboratoire de Mathématiques et Modélisation d’Evry, Université d’Evry Val d’Essonne, UMR CNRS, France
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36
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Hung LH, Shi K, Wu M, Young WC, Raftery AE, Yeung KY. fastBMA: scalable network inference and transitive reduction. Gigascience 2018; 6:1-10. [PMID: 29020744 PMCID: PMC5632288 DOI: 10.1093/gigascience/gix078] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 08/10/2017] [Indexed: 11/15/2022] Open
Abstract
Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/).
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Affiliation(s)
- Ling-Hong Hung
- Institute of Technology, University of Washington, Tacoma Campus, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, U.S.A
| | - Kaiyuan Shi
- Institute of Technology, University of Washington, Tacoma Campus, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, U.S.A
| | - Migao Wu
- Institute of Technology, University of Washington, Tacoma Campus, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, U.S.A
| | - William Chad Young
- Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, U.S.A
| | - Adrian E. Raftery
- Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, U.S.A
| | - Ka Yee Yeung
- Institute of Technology, University of Washington, Tacoma Campus, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, U.S.A
- Correspondence address. Ka Yee Yeung, Institute of Technology, University of Washington, Tacoma Campus, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, U.S.A.; Tel: 253-692-4924; Fax: 253-692-5862; E-mail:
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37
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Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data. Sci Rep 2018; 8:6790. [PMID: 29717206 PMCID: PMC5931614 DOI: 10.1038/s41598-018-25064-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 04/09/2018] [Indexed: 12/30/2022] Open
Abstract
Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlinear biochemical network model. The framework is based on the expectation-maximization algorithm combined with particle smoother and sparse regularization techniques. In this method, a “redundant” model consisting of an excessive number of nodes and regulatory paths is iteratively updated by eliminating unnecessary paths, resulting in an inference of the most likely model. Using artificial single-cell time-course data showing heterogeneous oscillatory behaviors, we demonstrated that this algorithm successfully inferred the true network without any prior knowledge of network topology or parameter values. Furthermore, we showed that both the regulatory paths among nodes and the optimal number of nodes in the network could be systematically determined. The method presented in this study provides a general framework for inferring a nonlinear biochemical network model from heterogeneous single-cell time-course data.
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38
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Jiang L, Shi C, Ye M, Xi F, Cao Y, Wang L, Zhang M, Sang M, Wu R. A computational‐experimental framework for mapping plant coexistence. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.12981] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Libo Jiang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Chaozhong Shi
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Meixia Ye
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Feifei Xi
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Yige Cao
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Lina Wang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Miaomiao Zhang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Mengmeng Sang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Rongling Wu
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
- Center for Statistical GeneticsPennsylvania State University Hershey PA USA
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39
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Masiello MG, Verna R, Cucina A, Bizzarri M. Physical constraints in cell fate specification. A case in point: Microgravity and phenotypes differentiation. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 134:55-67. [PMID: 29307754 DOI: 10.1016/j.pbiomolbio.2018.01.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 12/30/2017] [Accepted: 01/02/2018] [Indexed: 12/12/2022]
Abstract
Data obtained by studying mammalian cells in absence of gravity strongly support the notion that cell fate specification cannot be understood according to the current molecular model. A paradigmatic case in point is provided by studying cell populations growing in absence of gravity. When the physical constraint (gravity) is 'experimentally removed', cells spontaneously allocate into two morphologically different phenotypes. Such phenomenon is likely enacted by the intrinsic stochasticity, which, in turn, is successively 'canalized' by a specific gene regulatory network. Both phenotypes are thermodynamically and functionally 'compatibles' with the new, modified environment. However, when the two cell subsets are reseeded into the 1g gravity field the two phenotypes collapse into one. Gravity constraints the system in adopting only one phenotype, not by selecting a pre-existing configuration, but more precisely shaping it de-novo through the modification of the cytoskeleton three-dimensional structure. Overall, those findings highlight how macro-scale features are irreducible to lower-scale explanations. The identification of macroscale control parameters - as those depending on the field (gravity, electromagnetic fields) or emerging from the cooperativity among the field's components (tissue stiffness, cell-to-cell connectivity) - are mandatory for assessing boundary conditions for models at lower scales, thus providing a concrete instantiation of top-down effects.
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Affiliation(s)
- Maria Grazia Masiello
- Department of Experimental Medicine, Sapienza University of Rome, viale Regina Elena 324, 00161 Rome, Italy; Department of Surgery "PietroValdoni", Sapienza University of Rome, via A. Scarpa 14, 00161 Rome, Italy.
| | - Roberto Verna
- Department of Experimental Medicine, Sapienza University of Rome, viale Regina Elena 324, 00161 Rome, Italy.
| | - Alessandra Cucina
- Department of Surgery "PietroValdoni", Sapienza University of Rome, via A. Scarpa 14, 00161 Rome, Italy; Azienda Policlinico Umberto I, viale del Policlinico 155, 00161 Rome, Italy.
| | - Mariano Bizzarri
- Department of Experimental Medicine, Sapienza University of Rome, viale Regina Elena 324, 00161 Rome, Italy.
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40
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Niu M, Macdonald B, Rogers S, Filippone M, Husmeier D. Statistical inference in mechanistic models: time warping for improved gradient matching. Comput Stat 2018; 33:1091-1123. [PMID: 31258254 PMCID: PMC6560940 DOI: 10.1007/s00180-017-0753-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 07/19/2017] [Indexed: 10/31/2022]
Abstract
Inference in mechanistic models of non-linear differential equations is a challenging problem in current computational statistics. Due to the high computational costs of numerically solving the differential equations in every step of an iterative parameter adaptation scheme, approximate methods based on gradient matching have become popular. However, these methods critically depend on the smoothing scheme for function interpolation. The present article adapts an idea from manifold learning and demonstrates that a time warping approach aiming to homogenize intrinsic length scales can lead to a significant improvement in parameter estimation accuracy. We demonstrate the effectiveness of this scheme on noisy data from two dynamical systems with periodic limit cycle, a biopathway, and an application from soft-tissue mechanics. Our study also provides a comparative evaluation on a wide range of signal-to-noise ratios.
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Affiliation(s)
- Mu Niu
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Benn Macdonald
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Simon Rogers
- Department of Computer Science, University of Glasgow, Glasgow, UK
| | | | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
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41
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Song J, Carey M, Zhu H, Miao H, Ramírez JC, Wu H. Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations. ACTA ACUST UNITED AC 2018; 11:135-153. [PMID: 34531927 DOI: 10.1504/ijcbdd.2018.10011910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Reactivation of latently infected cells has emerged as an important strategy for eradication of HIV. However, genetic mechanisms of regulation after reactivation remain unclear. We describe a five-step pipeline to study the dynamics of the gene regulatory network following a viral reactivation using high-dimensional ordinary differential equations. Our pipeline implements a combination of five different methods, by detecting temporally differentially expressed genes (step 1), clustering genes with similar temporal expression patterns into a small number of response modules (step2), performing a functional enrichment analysis within each gene response module (step 3), identifying a network structure based on the gene response modules using ordinary differential equations (ODE) and a high-dimensional variable selection technique (step 4), and obtaining a gene regulatory model based on refined parameter estimates using nonlinear least squares (step 5). We applied our pipeline to a time course gene expression data of latently infected T-cells following a latency-reversion.
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Affiliation(s)
- Jaejoon Song
- Department of Biostatistics, The University of Texas MD, Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030, USA
| | - Michelle Carey
- Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, Canada, H3A 0B9
| | - Hongjian Zhu
- Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, 77030, USA
| | - Hongyu Miao
- Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, 77030, USA
| | - Juan Camilo Ramírez
- Faculty of Computer Engineering, Universidad Antonio Nariño, Cl. 58a Bis 3794, Bogotá, Cundinamarca, Colombia
| | - Hulin Wu
- Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, USA, 77030, USA
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42
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Zhang T, Yin Q, Caffo B, Sun Y, Boatman-Reich D. Bayesian inference of high-dimensional, cluster-structured ordinary differential equation models with applications to brain connectivity studies. Ann Appl Stat 2017. [DOI: 10.1214/17-aoas1021] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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43
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Nie Y, Wang L, Cao J. Estimating time-varying directed gene regulation networks. Biometrics 2017; 73:1231-1242. [PMID: 28369708 DOI: 10.1111/biom.12685] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 02/01/2017] [Accepted: 02/01/2017] [Indexed: 11/29/2022]
Abstract
The problem of modeling the dynamical regulation process within a gene network has been of great interest for a long time. We propose to model this dynamical system with a large number of nonlinear ordinary differential equations (ODEs), in which the regulation function is estimated directly from data without any parametric assumption. Most current research assumes the gene regulation network is static, but in reality, the connection and regulation function of the network may change with time or environment. This change is reflected in our dynamical model by allowing the regulation function varying with the gene expression and forcing this regulation function to be zero if no regulation happens. We introduce a statistical method called functional SCAD to estimate a time-varying sparse and directed gene regulation network, and simultaneously, to provide a smooth estimation of the regulation function and identify the interval in which no regulation effect exists. The finite sample performance of the proposed method is investigated in a Monte Carlo simulation study. Our method is demonstrated by estimating a time-varying directed gene regulation network of 20 genes involved in muscle development during the embryonic stage of Drosophila melanogaster.
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Affiliation(s)
- Yunlong Nie
- Department of Statistics and Actuarial Science, Simon Fraser University, British Columbia, Canada
| | - LiangLiang Wang
- Department of Statistics and Actuarial Science, Simon Fraser University, British Columbia, Canada
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, British Columbia, Canada
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44
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Ding YP, Ladeiro Y, Morilla I, Bouhnik Y, Marah A, Zaag H, Cazals-Hatem D, Seksik P, Daniel F, Hugot JP, Wainrib G, Tréton X, Ogier-Denis E. Integrative Network-based Analysis of Colonic Detoxification Gene Expression in Ulcerative Colitis According to Smoking Status. J Crohns Colitis 2017; 11:474-484. [PMID: 27702825 DOI: 10.1093/ecco-jcc/jjw179] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 10/03/2016] [Indexed: 02/08/2023]
Abstract
BACKGROUNDS AND AIMS The effect of cigarette smoking [CS] is ambivalent since smoking improves ulcerative colitis [UC] while it worsens Crohn's disease [CD]. Although this clinical relationship between inflammatory bowel disease [IBD] and tobacco is well established, only a few experimental works have investigated the effect of smoking on the colonic barrier homeostasis focusing on xenobiotic detoxification genes. METHODS A comprehensive and integrated comparative analysis of the global xenobiotic detoxification capacity of the normal colonic mucosa of healthy smokers [n = 8] and non-smokers [n = 9] versus the non-affected colonic mucosa of UC patients [n = 19] was performed by quantitative real-time polymerase chain reaction [qRT PCR]. The detoxification gene expression profile was analysed in CD patients [n = 18], in smoking UC patients [n = 5], and in biopsies from non-smoking UC patients cultured or not with cigarette smoke extract [n = 8]. RESULTS Of the 244 detoxification genes investigated, 65 were dysregulated in UC patients in comparison with healthy controls or CD patients. The expression of ≥ 45/65 genes was inversed by CS in biopsies of smoking UC patients in remission and in colonic explants of UC patients exposed to cigarette smoke extract. We devised a network-based data analysis approach for differentially assessing changes in genetic interactions, allowing identification of unexpected regulatory detoxification genes that may play a major role in the beneficial effect of smoking on UC. CONCLUSIONS Non-inflamed colonic mucosa in UC is characterised by a specifically altered detoxification gene network, which is partially restored by tobacco. These mucosal signatures could be useful for developing new therapeutic strategies and biomarkers of drug response in UC.
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Affiliation(s)
- Yong-Ping Ding
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France
| | - Yannick Ladeiro
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France
| | - Ian Morilla
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France.,Université Paris 13, Sorbonne Paris Cité, Villetaneuse, France
| | - Yoram Bouhnik
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France.,Assistance Publique Hôpitaux de Paris, Service de gastroentérologie, MICI et assistance nutritive, Hôpital Beaujon, Clichy la Garenne, France
| | - Assiya Marah
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France
| | - Hatem Zaag
- Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France.,Université Paris 13, Sorbonne Paris Cité, Villetaneuse, France
| | - Dominique Cazals-Hatem
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France.,Assistance Publique Hôpitaux de Paris, Service d'anatomopathologie, Hôpital Beaujon, Clichy la Garenne, France
| | - Philippe Seksik
- INSERM U1157, UMR 7203, F-7502, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Saint-Antoine, Paris, France
| | - Fanny Daniel
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France
| | - Jean-Pierre Hugot
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Robert Debré, Paris, France
| | - Gilles Wainrib
- Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France.,Département d'Informatique, Equipe DATA, Ecole Normale Supérieure, Paris, France
| | - Xavier Tréton
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France.,Assistance Publique Hôpitaux de Paris, Service de gastroentérologie, MICI et assistance nutritive, Hôpital Beaujon, Clichy la Garenne, France
| | - Eric Ogier-Denis
- INSERM, Research Centre of Inflammation BP 416, Paris, France.,Université Paris-Diderot Sorbonne Paris-Cité, Paris, France.,Laboratory of Excellence Labex INFLAMEX, Sorbonne-Paris- Cité, Paris, France
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Hartmann AK, Nuel G. Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data. PLoS One 2017; 12:e0170514. [PMID: 28141825 PMCID: PMC5283676 DOI: 10.1371/journal.pone.0170514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 01/05/2017] [Indexed: 12/04/2022] Open
Abstract
Triplet ordering preferences are used to perform Monte Carlo sampling of the posterior causal orderings originating from the analysis of gene-expression experiments involving observation as well as, usually few, interventions, like knock-outs. The performance of this sampling approach is compared to a previously used sampling via pairwise ordering preference as well as to the sampling of the full posterior distribution. For a fair comparison, the latter approach is restricted to twice the numerical effort of the triplet-based approach. This is done for artificially generated causal, i.e., directed acyclic graphs (DAGs) and for actual experimental data taken from the ROSETTA challenge. The sampling using the triplets ordering turns out to be superior to both other approaches.
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Affiliation(s)
| | - Grégory Nuel
- LPMA, CNRS 7599, Université Pierre et Marie Curie, Paris, France
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Zhang X, Cao J, Carroll RJ. Estimating varying coefficients for partial differential equation models. Biometrics 2017; 73:949-959. [PMID: 28076654 DOI: 10.1111/biom.12646] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 11/01/2016] [Accepted: 12/01/2016] [Indexed: 11/29/2022]
Abstract
Partial differential equations (PDEs) are used to model complex dynamical systems in multiple dimensions, and their parameters often have important scientific interpretations. In some applications, PDE parameters are not constant but can change depending on the values of covariates, a feature that we call varying coefficients. We propose a parameter cascading method to estimate varying coefficients in PDE models from noisy data. Our estimates of the varying coefficients are shown to be consistent and asymptotically normally distributed. The performance of our method is evaluated by a simulation study and by an empirical study estimating three varying coefficients in a PDE model arising from LIDAR data.
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Affiliation(s)
- Xinyu Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China and Statistics and Mathematics College, Yunnan University of Finance and Economics, Kunming 650221, China
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, V5A1S6, Canada
| | - Raymond J Carroll
- Department of Statistics, Texas A&M University, 3143 TAMU, College Station, Texas 77843-3143, U.S.A.,School of Mathematical and Physical Sciences, University of Technology, Sydney, PO Box 123, Broadway, New South Wales 2007, Australia
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Chen S, Shojaie A, Witten DM. Network Reconstruction From High-Dimensional Ordinary Differential Equations. J Am Stat Assoc 2017; 112:1697-1707. [PMID: 29618851 DOI: 10.1080/01621459.2016.1229197] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a system of additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.
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Affiliation(s)
- Shizhe Chen
- Department of Biostatistics, University of Washington, WA
| | - Ali Shojaie
- Departments of Biostatistics and Statistics, University of Washington, WA
| | - Daniela M Witten
- Departments of Biostatistics and Statistics, University of Washington, WA
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Paul D, Peng J, Burman P. Nonparametric estimation of dynamics of monotone trajectories. Ann Stat 2016. [DOI: 10.1214/15-aos1409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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49
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Zhou J, Feng HL. Consistent estimation of ordinary differential equation when the transformation parameter is unknown. Stat Probab Lett 2016. [DOI: 10.1016/j.spl.2016.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Yang B, Zhang W, Wang H, Song C, Chen Y. TDSDMI: Inference of time-delayed gene regulatory network using S-system model with delayed mutual information. Comput Biol Med 2016; 72:218-25. [DOI: 10.1016/j.compbiomed.2016.03.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2015] [Revised: 03/04/2016] [Accepted: 03/29/2016] [Indexed: 01/06/2023]
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