The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic set...The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.展开更多
Objective:Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression;however,the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer...Objective:Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression;however,the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer remain elusive.It is important to decipher the comprehensive epigenetic regulatory network in breast cancer cells to identify master epigenetic regulators and potential therapeutic targets.Methods:We employed high-throughput sequencing-based high-throughput screening(HTS^(2))to effectively detect changes in the expression of 2,986 genes following the knockdown of 400 epigenetic regulators.Then,bioinformatics analysis tools were used for the resulting gene expression signatures to investigate the epigenetic regulations in breast cancer.Results:Utilizing these gene expression signatures,we classified the epigenetic regulators into five distinct clusters,each characterized by specific functions.We discovered functional similarities between BAZ2B and SETMAR,as well as CLOCK and CBX3.Moreover,we observed that CLOCK functions in a manner opposite to that of HDAC8 in downstream gene regulation.Notably,we constructed an epigenetic regulatory network based on the gene expression signatures,which revealed 8 distinct modules and identified 10 master epigenetic regulators in breast cancer.Conclusions:Our work deciphered the extensive regulation among hundreds of epigenetic regulators.The identification of 10 master epigenetic regulators offers promising therapeutic targets for breast cancer treatment.展开更多
Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we...Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings.This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets,addressing batch effects and conserving biological variance.This integration spans a broad spectrum of tissues,including both below-and above-ground parts.Utilizing a rigorous approach for cell type annotation,we identified 47 distinct cell types or states,largely expanding our current view of plant cell compositions.We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression.Taken together,our study not only offers extensive plant cell atlas exploration that serves as a valuable resource,but also provides molecular insights into gene-regulatory programs that varies from different cell types.展开更多
BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new ...BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis(UC).METHODS We obtained gene expression profiles of circRNAs,miRNAs,and mRNAs in UC from the Gene Expression Omnibus dataset.The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions.Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs.We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram,whose efficacy was tested with the C-index,receiver operating characteristic curve(ROC),and decision curve analysis(DCA).RESULTS A circRNA-miRNA-mRNA regulatory network was obtained,containing 12 circRNAs,three miRNAs,and 38 mRNAs.Two optimal prognostic-related differentially expressed circRNAs,hsa_circ_0085323 and hsa_circ_0036906,were included to construct a predictive nomogram.The model showed good discrimination,with a C-index of 1(>0.9,high accuracy).ROC and DCA suggested that the nomogram had a beneficial diagnostic ability.CONCLUSION This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC.The circRNa-miRNA-mRNA network in UC could be more clinically significant.展开更多
The feed forward loop (FFL), wherein a gene X can regulate target gene Z alone or cooperatively with gene Y, is one of the most important motifs in gene regulatory networks. Gene expression often involves a small nu...The feed forward loop (FFL), wherein a gene X can regulate target gene Z alone or cooperatively with gene Y, is one of the most important motifs in gene regulatory networks. Gene expression often involves a small number of reactant molecules and thus internal molecular fluctuation is considerable. Here we studied how an FFL responds to small external signal inputs at gene X, with particular attention paid to the fluctuation resonance (FR) phenomenon of gene Z. We found that for all coherent FFLs, where the sign of the direct regulation path from X to Z is the same as the overall sign of the indirect path via Y, the FR shows a regular single peak, while for incoherent FFLs, the FR exhibits distinct bimodal shapes. The results indicate that one could use small external signals to help identify the regulatory structure of an unknown FFL in complex gene networks.展开更多
In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task i...In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.展开更多
Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level.Many studies have developed sample-specific or cell-type-specific gene regulatory networks fro...Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level.Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples.Here,we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.展开更多
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 actu...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.展开更多
WRKY transcription factors(TFs)have been identified as important core regulators in the responses of plants to biotic and abiotic stresses.Cultivated peanut(Arachis hypogaea)is an important oil and protein crop.Previo...WRKY transcription factors(TFs)have been identified as important core regulators in the responses of plants to biotic and abiotic stresses.Cultivated peanut(Arachis hypogaea)is an important oil and protein crop.Previous studies have identified hundreds of WRKY TFs in peanut.However,their functions and regulatory networks remain unclear.Simultaneously,the AdWRKY40 TF is involved in drought tolerance in Arachis duranensis and has an orthologous relationship with the AhTWRKY24 TF,which has a homoeologous relationship with AhTWRKY106 TF in A.hypogaea cv.Tifrunner.To reveal how the homoeologous AhTWRKY24 and AhTWRKY106 TFs regulate the downstream genes,DNA affinity purification sequencing(DAP-seq)was performed to detect the binding sites of TFs at the genome-wide level.A total of 3486 downstream genes were identified that were collectively regulated by the AhTWRKY24 and AhTWRKY106 TFs.The results revealed that W-box elements were the binding sites for regulation of the downstream genes by AhTWRKY24 and AhTWRKY106 TFs.A gene ontology enrichment analysis indicated that these downstream genes were enriched in protein modification and reproduction in the biological process.In addition,RNA-seq data showed that the AhTWRKY24 and AhTWRKY106 TFs regulate differentially expressed genes involved in the response to drought stress.The AhTWRKY24 and AhTWRKY106 TFs can specifically regulate downstream genes,and they nearly equal the numbers of downstream genes from the two A.hypogaea cv.Tifrunner subgenomes.These results provide a theoretical basis to study the functions and regulatory networks of AhTWRKY24 and AhTWRKY106 TFs.展开更多
Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks ...Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks share structural properties with natural transcriptional regulatory networks. Specifically, these networks can display scale-free and small-world structures. We also find that these networks have a higher probability to operate in the ordered regimen, and a lower probability to operate in the chaotic regimen. That is, the dynamics of these networks is similar to that of natural networks. The results show that the structure and dynamics inherent in natural networks may be in part due to their method of generation rather than being exclusively shaped by subsequent evolution under natural selection.展开更多
Recently, self-sustained oscillatory genetic regulatory networks (GRNs) have attracted significant attention in the biological field. Given a GRN, it is important to anticipate whether the network could generate osc...Recently, self-sustained oscillatory genetic regulatory networks (GRNs) have attracted significant attention in the biological field. Given a GRN, it is important to anticipate whether the network could generate oscillation with proper parameters, and what the key ingredients for the oscillation are. In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered. In particular, some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed. Moreover, there are some anti-oscillatory motifs which have a strong oscillation repressing effect. Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.展开更多
Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thali...Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thaliana.This mechanistically challenging process results from multiple inputs including gene interactions,cellular geometry,growth rates,and coordinated cell divisions.To better understand how this complex genetic and cellular information controls leaf growth,we developed a mathematical model of flat leaf production.This two-dimensional model describes the gene interactions in a vertex network of cells which grow and divide according to physical forces and genetic information.Interestingly,the model predicts the presence of an unknown additional factor required for the formation of biologically realistic gene expression domains and iterative cell division.This two-dimensional model will form the basis for future studies into robustness of adaxial-abaxial patterning.展开更多
Network motifs hold a very important status in genetic regulatory networks. This paper aims to analyse the dynamical property of the network motifs in genetic regulatory networks. The main result we obtained is that t...Network motifs hold a very important status in genetic regulatory networks. This paper aims to analyse the dynamical property of the network motifs in genetic regulatory networks. The main result we obtained is that the dynamical property of a single motif is very simple with only an asymptotically stable equilibrium point, but the combination of several motifs can make more complicated dynamical properties emerge such as limit cycles. The above-mentioned result shows that network motif is a stable substructure in genetic regulatory networks while their combinations make the genetic regulatory network more complicated.展开更多
This paper investigates the stability of the equilibria of the piecewise-linear models of genetic regulatory networks on the intersection of the thresholds of all variables. It first studies circling trajectories and ...This paper investigates the stability of the equilibria of the piecewise-linear models of genetic regulatory networks on the intersection of the thresholds of all variables. It first studies circling trajectories and derives some stability conditions by quantitative analysis in the state transition graph. Then it proposes a common Lyapunov function for convergence analysis of the piecewise-linear models and gives a simple sign condition. All the obtained conditions are only related to the constant terms on the right-hand side of the differential equation after bringing the equilibrium to zero.展开更多
In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecis...In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.展开更多
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-specif...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.展开更多
Spike architecture influences both grain weight and grain number per spike,which are the two major components of grain yield in bread wheat(Triticum aestivum L.).However,the complex wheat genome and the influence of var...Spike architecture influences both grain weight and grain number per spike,which are the two major components of grain yield in bread wheat(Triticum aestivum L.).However,the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits.Here,we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat.We identified 170 loci that are responsible for variations in spike length,spikelet number per spike,and grain number per spike through genome-wide association study and meta-QTL analyses.We constructed gene regulatory networks for young inflorescences at the double ridge stage and thefloret primordium stage,in which the spikelet meristem and thefloret meristem are predominant,respec-tively,by integrating transcriptome,histone modification,chromatin accessibility,eQTL,and protein–pro-tein interactome data.From these networks,we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits.The functions of TaZF-B1,VRT-B2,and TaSPL15-A/D in establishment of wheat spike architecture were verified.This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.展开更多
Plants have an ability to flower under optimal seasonal conditions to ensure reproductive success.Photoperiod and temperature are two important season-dependent factors of plant flowering.The floral transition of plan...Plants have an ability to flower under optimal seasonal conditions to ensure reproductive success.Photoperiod and temperature are two important season-dependent factors of plant flowering.The floral transition of plants depends on accurate measurement of changes in photoperiod and temperature.Recent advances in molecular biology and genetics on Arabidopsis and rice reveals that the regulation of plant flowering by photoperiod and temperature are involved in a complicated gene network with different regulatory pathways,and new evidence and understanding were provided in the regulation of rice flowering.Here,we summarize and analyze different flowering regulatory pathways in detail in rice based on previous studies and our results,including short-day promotion,long-day suppression,long-day induction of flowering,night break,different light-quality and temperature regulation pathways.展开更多
Long noncoding RNAs(lncRNAs)participate in a variety of biological processes and diseases.However,the expression and function of lncRNAs after spinal cord injury has not been extensively analyzed.In this study of righ...Long noncoding RNAs(lncRNAs)participate in a variety of biological processes and diseases.However,the expression and function of lncRNAs after spinal cord injury has not been extensively analyzed.In this study of right side hemisection of the spinal cord at T10,we detected the expression of lncRNAs in the proximal tissue of T10 lamina at different time points and found 445 lncRNAs and 6522 mRNA were differentially expressed.We divided the differentially expressed lncRNAs into 26 expression trends and analyzed Profile 25 and Profile 2,the two expression trends with the most significant difference.Our results showed that the expression of 68 lncRNAs in Profile 25 rose first and remained high 3 days post-injury.There were 387 mRNAs co-expressed with the 68 lncRNAs in Profile 25.The co-expression network showed that the co-expressed genes were mainly enriched in cell division,inflammatory response,FcγR-mediated cell phagocytosis signaling pathway,cell cycle and apoptosis.The expression of 56 lncRNAs in Profile2 first declined and remained low after 3 days post-injury.There were 387 mRNAs co-expressed with the 56 lncRNAs in Profile 2.The co-expression network showed that the co-expressed genes were mainly enriched in the chemical synaptic transmission process and in the signaling pathway of neuroactive ligand-receptor interaction.The results provided the expression and regulatory network of the main lncRNAs after spinal cord injury and clarified their co-expressed gene enriched biological processes and signaling pathways.These findings provide a new direction for the clinical treatment of spinal cord injury.展开更多
基金supported in part by the National Science and Technol-ogy Major Project(No.2021ZD0111502)the National Nat-ural Science Foundation of China(Nos.62176147,62476163)+2 种基金the Science and Technology Planning Project of Guangdong Province of China(Nos.2022A1515110660,2021JC06X549)the STU Scientific Research Foundation for Talents(No.NTF21001)Guangdong Basic and Applied Basic Research Foundation(No.2023B1515120020)。
文摘The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.
基金supported by grants from the National Natural Science Foundation of China(Grant No.82172723)the Natural Science Foundation of Sichuan(Grant Nos.2023NSFSC1828 and 2022NSFSC1289)+2 种基金the“Xinglin Scholar”Scientific Research Promotion Plan of Chengdu University of Transitional Chinese Medicine(Grant No.BSH2021003)the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(Grant No.ZYYCXTD-D-202209)the Research Funding of Department of Science and Technology of Qinghai Province(Grant No.2023-ZJ-729)。
文摘Objective:Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression;however,the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer remain elusive.It is important to decipher the comprehensive epigenetic regulatory network in breast cancer cells to identify master epigenetic regulators and potential therapeutic targets.Methods:We employed high-throughput sequencing-based high-throughput screening(HTS^(2))to effectively detect changes in the expression of 2,986 genes following the knockdown of 400 epigenetic regulators.Then,bioinformatics analysis tools were used for the resulting gene expression signatures to investigate the epigenetic regulations in breast cancer.Results:Utilizing these gene expression signatures,we classified the epigenetic regulators into five distinct clusters,each characterized by specific functions.We discovered functional similarities between BAZ2B and SETMAR,as well as CLOCK and CBX3.Moreover,we observed that CLOCK functions in a manner opposite to that of HDAC8 in downstream gene regulation.Notably,we constructed an epigenetic regulatory network based on the gene expression signatures,which revealed 8 distinct modules and identified 10 master epigenetic regulators in breast cancer.Conclusions:Our work deciphered the extensive regulation among hundreds of epigenetic regulators.The identification of 10 master epigenetic regulators offers promising therapeutic targets for breast cancer treatment.
基金supported by the National Natural Science Foundation of China (No.32070656)the Nanjing University Deng Feng Scholars Program+1 种基金the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions,China Postdoctoral Science Foundation funded project (No.2022M711563)Jiangsu Funding Program for Excellent Postdoctoral Talent (No.2022ZB50)
文摘Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings.This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets,addressing batch effects and conserving biological variance.This integration spans a broad spectrum of tissues,including both below-and above-ground parts.Utilizing a rigorous approach for cell type annotation,we identified 47 distinct cell types or states,largely expanding our current view of plant cell compositions.We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression.Taken together,our study not only offers extensive plant cell atlas exploration that serves as a valuable resource,but also provides molecular insights into gene-regulatory programs that varies from different cell types.
基金Supported by the National Natural Science Foundation of China,No.81774093,No.81904009,No.81974546 and No.82174182Key R&D Project of Hubei Province,No.2020BCB001.
文摘BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis(UC).METHODS We obtained gene expression profiles of circRNAs,miRNAs,and mRNAs in UC from the Gene Expression Omnibus dataset.The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions.Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs.We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram,whose efficacy was tested with the C-index,receiver operating characteristic curve(ROC),and decision curve analysis(DCA).RESULTS A circRNA-miRNA-mRNA regulatory network was obtained,containing 12 circRNAs,three miRNAs,and 38 mRNAs.Two optimal prognostic-related differentially expressed circRNAs,hsa_circ_0085323 and hsa_circ_0036906,were included to construct a predictive nomogram.The model showed good discrimination,with a C-index of 1(>0.9,high accuracy).ROC and DCA suggested that the nomogram had a beneficial diagnostic ability.CONCLUSION This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC.The circRNa-miRNA-mRNA network in UC could be more clinically significant.
基金ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (No.20673106).
文摘The feed forward loop (FFL), wherein a gene X can regulate target gene Z alone or cooperatively with gene Y, is one of the most important motifs in gene regulatory networks. Gene expression often involves a small number of reactant molecules and thus internal molecular fluctuation is considerable. Here we studied how an FFL responds to small external signal inputs at gene X, with particular attention paid to the fluctuation resonance (FR) phenomenon of gene Z. We found that for all coherent FFLs, where the sign of the direct regulation path from X to Z is the same as the overall sign of the indirect path via Y, the FR shows a regular single peak, while for incoherent FFLs, the FR exhibits distinct bimodal shapes. The results indicate that one could use small external signals to help identify the regulatory structure of an unknown FFL in complex gene networks.
基金supported by National Natural Science Foundation of China (Grant Nos. 60433020, 60175024 and 60773095)European Commission under grant No. TH/Asia Link/010 (111084)the Key Science-Technology Project of the National Education Ministry of China (Grant No. 02090),and the Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, P. R. China
文摘In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.
基金supported by the National Key Research and Development Program of China(2017YFA0505500)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38040400)+1 种基金National Science Foundation of China(31771476 and 31930022)Shanghai Municipal Science and Technology Major Project(2017SHZDZX01)。
文摘Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level.Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples.Here,we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.
基金Acknowledgement This paper is supported by National Natural Science Foundation of China (Grant No. 60973092 and No. 60873146), the National High Technology Research and Development Program of China (Grant No.2009 AA02Z307), the "211 Project" of Jilin University, the Key Laboratory for Symbol Computation and Knowledge Engineering (Ministry of Education, China), and the Key Laboratory for New Technology of Biological Recognition of Jilin Province (No. 20082209).
文摘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.
基金funded by the Start-up Foundation for High Talents of Qingdao Agricultural University(No.665/1120012)the Natural Science Foundation of Shandong Province,China(ZR2019QC017)+4 种基金the National Key Research and Development Program,China(2022YFD2300101-1)the Key Research and Development Program of Shandong Province,China(2021LZGC003 and 2021LZGC026-03)Peanut Seed Industry Project in Shandong Province,China(2022LZGC007)the Science&Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta,China(2022SZX18)the Graduate Student Innovation Program of Qingdao Agricultural University(QNYCX23001).
文摘WRKY transcription factors(TFs)have been identified as important core regulators in the responses of plants to biotic and abiotic stresses.Cultivated peanut(Arachis hypogaea)is an important oil and protein crop.Previous studies have identified hundreds of WRKY TFs in peanut.However,their functions and regulatory networks remain unclear.Simultaneously,the AdWRKY40 TF is involved in drought tolerance in Arachis duranensis and has an orthologous relationship with the AhTWRKY24 TF,which has a homoeologous relationship with AhTWRKY106 TF in A.hypogaea cv.Tifrunner.To reveal how the homoeologous AhTWRKY24 and AhTWRKY106 TFs regulate the downstream genes,DNA affinity purification sequencing(DAP-seq)was performed to detect the binding sites of TFs at the genome-wide level.A total of 3486 downstream genes were identified that were collectively regulated by the AhTWRKY24 and AhTWRKY106 TFs.The results revealed that W-box elements were the binding sites for regulation of the downstream genes by AhTWRKY24 and AhTWRKY106 TFs.A gene ontology enrichment analysis indicated that these downstream genes were enriched in protein modification and reproduction in the biological process.In addition,RNA-seq data showed that the AhTWRKY24 and AhTWRKY106 TFs regulate differentially expressed genes involved in the response to drought stress.The AhTWRKY24 and AhTWRKY106 TFs can specifically regulate downstream genes,and they nearly equal the numbers of downstream genes from the two A.hypogaea cv.Tifrunner subgenomes.These results provide a theoretical basis to study the functions and regulatory networks of AhTWRKY24 and AhTWRKY106 TFs.
文摘Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks share structural properties with natural transcriptional regulatory networks. Specifically, these networks can display scale-free and small-world structures. We also find that these networks have a higher probability to operate in the ordered regimen, and a lower probability to operate in the chaotic regimen. That is, the dynamics of these networks is similar to that of natural networks. The results show that the structure and dynamics inherent in natural networks may be in part due to their method of generation rather than being exclusively shaped by subsequent evolution under natural selection.
基金Project supported by the National Natural Science Foundation of China (Grant No. 10975015)the National Basic Research Program of China (Grant No. 2007CB814800)
文摘Recently, self-sustained oscillatory genetic regulatory networks (GRNs) have attracted significant attention in the biological field. Given a GRN, it is important to anticipate whether the network could generate oscillation with proper parameters, and what the key ingredients for the oscillation are. In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered. In particular, some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed. Moreover, there are some anti-oscillatory motifs which have a strong oscillation repressing effect. Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.
基金supported by the NSF#2039489 to A.Y.H and the NSF#1813071 to C.-S.C.
文摘Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thaliana.This mechanistically challenging process results from multiple inputs including gene interactions,cellular geometry,growth rates,and coordinated cell divisions.To better understand how this complex genetic and cellular information controls leaf growth,we developed a mathematical model of flat leaf production.This two-dimensional model describes the gene interactions in a vertex network of cells which grow and divide according to physical forces and genetic information.Interestingly,the model predicts the presence of an unknown additional factor required for the formation of biologically realistic gene expression domains and iterative cell division.This two-dimensional model will form the basis for future studies into robustness of adaxial-abaxial patterning.
基金Project supported by the National Natural Science Foundation of China (Grant No 10672093) and Innovation Foundation of , Shanghai University for Postgraduates, China.
文摘Network motifs hold a very important status in genetic regulatory networks. This paper aims to analyse the dynamical property of the network motifs in genetic regulatory networks. The main result we obtained is that the dynamical property of a single motif is very simple with only an asymptotically stable equilibrium point, but the combination of several motifs can make more complicated dynamical properties emerge such as limit cycles. The above-mentioned result shows that network motif is a stable substructure in genetic regulatory networks while their combinations make the genetic regulatory network more complicated.
基金supported by the National Natural Science Foundation of China (Grant No. 60672029)
文摘This paper investigates the stability of the equilibria of the piecewise-linear models of genetic regulatory networks on the intersection of the thresholds of all variables. It first studies circling trajectories and derives some stability conditions by quantitative analysis in the state transition graph. Then it proposes a common Lyapunov function for convergence analysis of the piecewise-linear models and gives a simple sign condition. All the obtained conditions are only related to the constant terms on the right-hand side of the differential equation after bringing the equilibrium to zero.
基金supported by Department of Computer Science Project of University of Jamia Millia Islamia, India (No. 39151-A)
文摘In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.
基金supported by grants from National Natural Science Foundation of China(Nos.61502198,61572226,61472161,61876069)。
文摘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.
基金supported by STI2030-Major Projects (2023ZD0406802)the Fundamental Research Funds for the Central Universities (2662020ZKPY002)+1 种基金the National Key Laboratory of Crop Genetic Improvement Self-Research Program (ZW19A0201)the HZAUAGIS Cooperation Fund 869 (SZYJY2021006).
文摘Spike architecture influences both grain weight and grain number per spike,which are the two major components of grain yield in bread wheat(Triticum aestivum L.).However,the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits.Here,we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat.We identified 170 loci that are responsible for variations in spike length,spikelet number per spike,and grain number per spike through genome-wide association study and meta-QTL analyses.We constructed gene regulatory networks for young inflorescences at the double ridge stage and thefloret primordium stage,in which the spikelet meristem and thefloret meristem are predominant,respec-tively,by integrating transcriptome,histone modification,chromatin accessibility,eQTL,and protein–pro-tein interactome data.From these networks,we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits.The functions of TaZF-B1,VRT-B2,and TaSPL15-A/D in establishment of wheat spike architecture were verified.This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.
基金supported by the National Natural Science Foundation of China(Grant Nos.31171515 and 30871328)the Tianjin Natural Science Foundation of China(Grant No.11JCZDJC17900)+1 种基金the Program of Tianjin Municipal Education Commission,China(Grant No.20090609)the Knowledge Innovation Program of Tianjin Normal University,China(Grant No.52X09039)
文摘Plants have an ability to flower under optimal seasonal conditions to ensure reproductive success.Photoperiod and temperature are two important season-dependent factors of plant flowering.The floral transition of plants depends on accurate measurement of changes in photoperiod and temperature.Recent advances in molecular biology and genetics on Arabidopsis and rice reveals that the regulation of plant flowering by photoperiod and temperature are involved in a complicated gene network with different regulatory pathways,and new evidence and understanding were provided in the regulation of rice flowering.Here,we summarize and analyze different flowering regulatory pathways in detail in rice based on previous studies and our results,including short-day promotion,long-day suppression,long-day induction of flowering,night break,different light-quality and temperature regulation pathways.
文摘Long noncoding RNAs(lncRNAs)participate in a variety of biological processes and diseases.However,the expression and function of lncRNAs after spinal cord injury has not been extensively analyzed.In this study of right side hemisection of the spinal cord at T10,we detected the expression of lncRNAs in the proximal tissue of T10 lamina at different time points and found 445 lncRNAs and 6522 mRNA were differentially expressed.We divided the differentially expressed lncRNAs into 26 expression trends and analyzed Profile 25 and Profile 2,the two expression trends with the most significant difference.Our results showed that the expression of 68 lncRNAs in Profile 25 rose first and remained high 3 days post-injury.There were 387 mRNAs co-expressed with the 68 lncRNAs in Profile 25.The co-expression network showed that the co-expressed genes were mainly enriched in cell division,inflammatory response,FcγR-mediated cell phagocytosis signaling pathway,cell cycle and apoptosis.The expression of 56 lncRNAs in Profile2 first declined and remained low after 3 days post-injury.There were 387 mRNAs co-expressed with the 56 lncRNAs in Profile 2.The co-expression network showed that the co-expressed genes were mainly enriched in the chemical synaptic transmission process and in the signaling pathway of neuroactive ligand-receptor interaction.The results provided the expression and regulatory network of the main lncRNAs after spinal cord injury and clarified their co-expressed gene enriched biological processes and signaling pathways.These findings provide a new direction for the clinical treatment of spinal cord injury.