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Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing gene regulatory networks

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Journal of Bionic Engineering 2011年第1期8卷 98-106页

作者： Guixia Liu Lei Liu Chunyu Liu Ming Zheng Lanying Su Chunguang Zhou College of Computer Science and Technology Jilin University Changchun 130012 P. R. China Affiliated Hospital of Changchun University of Chinese Medicine Changchun 13002 I P. R. China

Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fu^zy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory nctworks＋ but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without lhctitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast, The results show that this approach can work effectively.

关键词： neuro-fuzzy network biological knowledge regulators gene regulatory networks

A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for gene regulatory networks Modeled with Structural Equation Models

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Computer Modeling in Engineering & Sciences 2020年第7期124卷 303-313页

作者： Yan Li Dayou Liu Yungang Zhu Jie Liu College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China

Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but different.The differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory relationships.In a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between them.However,in this way,the similarities between the pairwise GRNs are not taken into account.Several joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach apparently.In this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure.Then,a Bayesian inference method is used to make joint differential analysis by solving the integrated model.We evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different settings.The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet obviously.In the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.

关键词： gene regulatory networks structural equation models joint

Stability of gene regulatory networks with Lvy noise

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Science China(Information Sciences) 2017年第7期60卷 230-242页

作者： Qi LUO Yangyang GONG Chunxue JIA School of Information and Control Nanjing University of Information Science and TechnologyJiangsu Engineering Research Center on Meteorological Energy Using and ControlCollaborative Innovation Center of Atmospheric Environment and Equipment Technology

The stability of gene regulatory networks has attracted substantial research efforts in the field of systematic biology. Actual gene regulatory networks are always subject to noise interference and disruption to the organism either internally or externally. Specifically, the special case of instantaneous mutation may exist in gene regulatory networks at the m RNA or protein level. Compared with other existing models, a L′evy noisedriven gene regulatory network model has been proved to be more realistic, since it is a powerful tool to describe the above special case. On the basis of previous studies, we developed a theoretical proof of the L′evy noisedriven gene regulatory network, and carried out a large number of numerical simulations for validation. Based on adequate analysis of the simulation examples, the sufficient conditions were investigated and are presented herein to obtain the global asymptotic stability of gene regulatory networks with time-varying delays and L′evy noise.

关键词： L′evy noise gene regulatory networks time-varying delays stability analysis instantaneous mutation

Modeling gene regulatory networks Based on Nonlinear State-space Model

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Modeling Gene Regulatory Networks Based on Nonlinear State-s...

Reverse Engineering gene regulatory networks Based on Dynamic Threshold Condition Mutual Information With Resampling Strategy

第36届中国控制会议

作者： Zuo Zhou Ruirui Ji School of Physics and Electromechanical Engineering Hexi University School of Automation and Information Engineering Xi'an University of Technology

ISBN: (纸本)9781538629185

In the gene regulatory networks（GRNs）,gene expression is usually regulated by some regulatory factors（or transcription factors）,but the regulatory factors’ activity is difficult to measure,and the regulatory effect among genes is typically nonlinear.This paper uses nonlinear Gaussian state-space model to construct gene regulatory networks.In the model,genes are considered as observation variables and regulatory factors are considered as internal state variables,it is more consistent with the actual biological systems.To identify the system,the unscented Kalman filter（UKF） algorithm is applied to estimate the states and parameters,Bayes information criterion（BIC） is applied to determine the dimension of the state variables.We model the regulatory networks of yeast genes’ expression data with the method,the results show that the nonlinear state-space model can improve the accuracy of constructing regulation networks,and UKF algorithm can effectively estimate the parameters and states of nonlinear state-space model.

关键词： gene regulatory networks Unscented Kalman Filter Bayes Information Criterion Nonlinear State-space Model

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Reverse Engineering Gene Regulatory Networks Based on Dynami...

Cell atlas of CCl_(4)-induced progressive liver fibrosis reveals stage-specific responses

第40届中国控制会议

作者： Jie Xu Guanxue Yang Guohai Liu Guanxiao Yang School of Electrical and Information Engineering Jiangsu University

Recently, constructing relatively accurate gene regulatory networks has become a hot research direction in the field of bioinformatics. Path consistency algorithm based on conditional mutual information（PCACMI） is a practical algorithm to reconstruct gene regulation networks, but the threshold is fixed, which will affect the accuracy of the reconstructed networks. So we improve PCACMI and design a new algorithm termed dynamic threshold condition mutual information（DTCMI）. In the new algorithm, the threshold is related to the maximal element of the weight matrix of different orders, and the value of threshold will change with maximal weights. In addition, in order to improve the accuracy of the reconstructed network, we firstly employ resampling strategy by utilizing the jackknife to deal with the gene expression data. Finally, we reconstruct the networks by using gene knock-out expression data from the stochastic differential equation and DREAM4 challenges. The results show that the performance of DTCMI is more effective.

关键词： gene regulatory networks Mutual information Jackknife Network inference

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Zoological Research 2023年第3期44卷 451-466页

作者： Peng-Cheng Guo Jing Zuo Ke-Ke Huang Guang-Yao Lai Xiao Zhang Juan An Jin-Xiu Li Li Li Liang Wu Yi-Ting Lin Dong-Ye Wang Jiang-Shan Xu Shi-Jie Hao Yang Wang Rong-Hai Li Wen Ma Yu-Mo Song Chang Liu Chuan-Yu Liu Zhen Dai Yan Xu Amar Deep Sharma Michael Ott Qing Ou-Yang Feng Huo Rong Fan Yong-Yin Li Jin-Lin Hou Giacomo Volpe Long-Qi Liu Miguel A.Esteban Yi-Wei Lai State Key Laboratory for Zoonotic Diseases Key Laboratory for Zoonosis Research of Ministry of EducationInstitute of ZoonosisCollege of Veterinary MedicineJilin UniversityChangchunJilin 130062China BGI-Hangzhou HangzhouZhejiang 310012China BGI-Shenzhen ShenzhenGuangdong 518083China Key Laboratory of Biological Targeting Diagnosis Therapy and Rehabilitation of Guangdong Higher Education InstitutesFifth Affiliated Hospital of Guangzhou Medical UniversityGuangzhouGuangdong 510799China Laboratory of Integrative Biology Guangzhou Institutes of Biomedicine and HealthChinese Academy of SciencesGuangzhouGuangdong 510530China Joint School of Life Sciences Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical UniversityGuangzhouGuangdong 510530China School of Life Sciences Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui 230026China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhouGuangdong 510530China Biotherapy Centre Third Affiliated Hospital of Sun Yat-sen UniversityGuangzhouGuangdong 510630China Department of Gastroenterology Hepatology and EndocrinologyHannover Medical SchoolHannover 30625Germany Department of Hepatobiliary Surgery and Liver Transplant Center General Hospital of Southern Theater CommandGuangzhouGuangdong 510010China Department of Infectious Diseases Nanfang HospitalSouthern Medical UniversityGuangdong Provincial Key Laboratory of Viral Hepatitis ResearchGuangzhouGuangdong 510515China Hematology and Cell Therapy Unit IRCCS-Istituto Tumori‘Giovanni Paolo II’Bari 70124Italy Institute of Experimental Hematology Hannover Medical SchoolHannover 30625Germany

Chronic liver injury leads to progressive liver fibrosis and ultimately cirrhosis,a major cause of morbidity and mortality worldwide.However,there are currently no effective anti-fibrotic therapies available,especially for latestage patients,which is partly attributed to the major knowledge gap regarding liver cell heterogeneity and cellspecific responses in different fibrosis stages.To reveal the multicellular networks regulating mammalian liver fibrosis from mild to severe phenotypes,we generated a single-nucleus transcriptomic atlas encompassing 49919nuclei corresponding to all main liver cell types at different stages of murine carbon tetrachloride(CCl_(4))-induced progressive liver fibrosis.Integrative analysis distinguished the sequential responses to injury of hepatocytes,hepatic stellate cells and endothelial cells.Moreover,we reconstructed the cell-cell interactions and gene regulatory networks implicated in these processes.These integrative analyses uncovered previously overlooked aspects of hepatocyte proliferation exhaustion and disrupted pericentral metabolic functions,dysfunction for clearance by apoptosis of activated hepatic stellate cells,accumulation of pro-fibrotic signals,and the switch from an anti-angiogenic to a pro-angiogenic program during CCl_(4)-induced progressive liver fibrosis.Our dataset thus constitutes a useful resource for understanding the molecular basis of progressive liver fibrosis using a relevant animal model.

关键词： Liver fibrosis Toxicity Single-cell and single-nucleus RNA-sequencing Hepatocytes Hepatic stellate cells Angiogenesis Cell-cell interactions gene regulatory networks

gene regulatory network inference based on causal discovery integrating with graph neural network

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Quantitative Biology 2023年第4期11卷 434-450页

作者： Ke Feng Hongyang Jiang Chaoyi Yin Huiyan Sun School of Artificial Intelligence Jilin UniversityChangchunChina international Center of Future Science Jilin UniversityChangchunChina Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of EducationChangchunChina

gene regulatory network (GRN) inference from gene expression data is asignificant approach to understanding aspects of the biological system.Compared with generalized correlation-based methods, causality-inspiredones seem more rational to infer regulatory relationships. We proposeGRINCD, a novel GRN inference framework empowered by graph representationlearning and causal asymmetric learning, considering both linearand non-linear regulatory relationships. First, high-quality representation ofeach gene is generated using graph neural network. Then, we apply theadditive noise model to predict the causal regulation of each regulator-targetpair. Additionally, we design two channels and finally assemble them forrobust prediction. Through comprehensive comparisons of our frameworkwith state-of-the-art methods based on different principles on numerousdatasets of diverse types and scales, the experimental results show that ourframework achieves superior or comparable performance under variousevaluation metrics. Our work provides a new clue for constructing GRNs,and our proposed framework GRINCD also shows potential in identifyingkey factors affecting cancerdevelopment.

关键词： causal discovery ensemble learning gene regulatory network inference gene regulatory networks graph neural network key regulators of disease development

Integrated miRNA and mRNA Analysis Identified Potential Mechanisms and Targets of Qianggan Extracts in Preventing Nonalcoholic Steatohepatitis

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World Journal of Traditional Chinese Medicine 2022年第1期8卷 77-86页

作者： Jie Huang Meng Li Wen-Jun Zhou Ze-Min Yao Guang Ji Li Zhang Ming-Zhe Zhu China-Canada Center of Research for Digestive Diseases Institute of Digestive DiseasesLonghua HospitalShanghai University of Traditional Chinese MedicineShanghaiChina Department of Biochemistry Microbiology and ImmunologyOttawa Institute of Systems BiologyUniversity of OttawaOttawaCanada School of Public Health Shanghai University of Traditional Chinese MedicineShanghaiChina

Objective: Qianggan(QG) extract is a patented traditional Chinese medicine that has been widely used for the clinical treatment of nonalcoholic steatohepatitis(NASH). However, its mechanism remains unclear. Methods: The efficacy of QG was evaluated in mice with methionine-and-choline-deficient diet-induced NASH by measuring serum alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase levels and by H and E staining of liver sections. Microarray and bioinformatics analyses were performed to obtain hepatic micro RNA(mi RNA) and m RNA expression profiles and to mine potential mechanisms and therapeutic targets. Furthermore, representative mi RNA and m RNA expression levels were validated by quantitative real-time polymerase chain reaction(q RT-PCR). Results: QG extract significantly improved NASH. Twelve differentially expressed mi RNAs and 1124 differentially changed m RNAs were identified as potential targets of QG extract. Integrated analysis detected 976 mi RNA–m RNA regulatory pairs, and networks including 11 mi RNAs and 427 m RNAs were constructed by Cytoscape. Hub nodes including mi R-7050-5p, mi R-212-3p, Bcl2l11, and Kras were filtered out. gene Ontology and Kyoto Encyclopedia of genes and Genomes pathway analyses revealed that 427 m RNAs were enriched in pathways including apoptotic process, immune response, Fox O signaling pathway, and natural killer cell-mediated cytotoxicity. We also constructed a protein–protein interaction network with 254 nodes, and identified hub genes including Kras, Fasl, and Ncam1. Finally, the results of q RT-PCR were in good accordance with microarray data. Conclusion: This study identified important hub mi RNAs and m RNAs involved in the mechanism of QG extract and which might provide potential therapeutic targets for patients with NASH.

关键词： Fatty liver gene regulatory networks microarray analysis Qianggan extract Traditional Chinese Medicine

Neuroprotectants attenuate hypobaric hypoxia-induced brain injuries in cynomolgus monkeys

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Zoological Research 2020年第1期41卷 3-19页

作者： Pei Zhang Jie-Si Chen Qi-Ye Li Long-Xiang Sheng Yi-Xing Gao Bing-Zheng Lu Wen-Bo Zhu Xiao-Yu Zhan Yuan Li Zhi-Bing Yuan Gang Xu Bi-Tao Qiu Min Yan Chun-Xue Guo You-Qiong Wang Yi-Jun Huang Jing-Xia Zhang Fu-Yu Liu Zhong-Wei Tang Sui-Zhen Lin David NCooper Huan-Ming Yang Jian Wang Yu-Qi Gao Wei Yin Guo-Jie Zhang Guang-Mei Yan State Key Laboratory of genetic Resources and Evolution Kunming Institute of ZoologyChinese Academy of SciencesKunmingYunnan 650223China BGI-Shenzhen ShenzhenGuangdong 518083China Section for Ecology and Evolution Department of BiologyUniversity of CopenhagenCopenhagen DK-2100Denmark Department of Pharmacology Zhongshan School of MedicineSun Yat-Sen UniversityGuangzhouGuangdong 510080China Institute of Medicine and Hygienic Equipment for High Altitude Region College of High Altitude Military MedicineThird Military Medical UniversityChongqing 400038China Key Laboratory of High Altitude Medicine of People's Liberation Army Chongqing 400038China Key Laboratory of High Altitude Environmental Medicine Third Military Medical UniversityMinistry of EducationChongqing 400038China School of Pharmaceutical Sciences Sun Yat-Sen UniversityGuangzhouGuangdong 510006China Guangzhou Cellprotek Pharmaceutical Co.Ltd. GuangzhouGuangdong 510663China Institute of Medical genetics School of MedicineCardiff UniversityCardiff CF144XNUK James D.Watson Institute of Genome Sciences HangzhouZhejiang 310058China Department of Biochemistry Zhongshan School of MedicineSun Yat-Sen UniversityGuangzhouGuangdong 510080China Center for Excellence in Animal Evolution and genetics Chinese Academy of SciencesKunmingYunnan 650223China China National genebank BGI-ShenzhenShenzhenGuangdong 518120China

Hypobaric hypoxia (HH) exposure can cause serious brain injury as well as life-threatening cerebral edema in severe cases. Previous studies on the mechanisms of HH-induced brain injury have been conducted primarily using non-primate animal models that are genetically distant to humans, thus hindering the development of disease treatment. Here, we report that cynomolgus monkeys (Macaca fascicularis) exposed to acute HH developed human-like HH syndrome involving severe brain injury and abnormal behavior. Transcriptome profiling of white blood cells and brain tissue from monkeys exposed to increasing altitude revealed the central role of the HIF-1 and other novel signaling pathways, such as the vitamin D receptor (VDR) signaling pathway, in co-regulating HH-induced inflammation processes. We also observed profound transcriptomic alterations in brains after exposure to acute HH, including the activation of angiogenesis and impairment of aerobic respiration and protein folding processes, which likely underlie the pathological effects of HH-induced brain injury. Administration of progesterone (PROG) and steroid neuroprotectant 5α-androst-3β,5,6β-triol (TRIOL) significantly attenuated brain injuries and rescued the transcriptomic changes induced by acute HH. Functional investigation of the affected genes suggested that these two neuroprotectants protect the brain by targeting different pathways, with PROG enhancing erythropoiesis and TRIOL suppressing glutamate-induced excitotoxicity. Thus, this study advances our understanding of the pathology induced by acute HH and provides potential compounds for the development of neuroprotectant drugs for therapeutic treatment.

关键词： Acute hypobaric hypoxia Cynomolgus monkeys Brain injury Neuroprotectant gene regulatory networks