Global stability analysis of fractional-order gene regulatory networks with time delay

Fractional-order gene regulatory networks with time delay(DFCIRNs)have proven that they are more suitable to model gene regulation mechanism than integer-order.In this paper,a novel DFGRN is proposed.The existence and uniqueness of the equilibrium point for the DFGRN are proved under certain conditions.On this basis,the conditions on the global asymptotic stability are established by using the Lyapunov method and comparison theorem for the DFGRN,and the stability conditions are dependent on the fractional-order q.Finally,numerical simulations show that the obtained results arc reasonable.

Target Entrapment Based on Adaptive Transformation of Gene Regulatory Networks

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.

Fluctuation Resonance of Feed Forward Loops in Gene Regulatory Networks

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.

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

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.

Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

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.

Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

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.

A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

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.

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

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.

Research of Gene Regulatory Network with Multi-Time Delay Based on Bayesian Network

The gene regulatory network was reconstructed according to time-series microarray data getting from hybridization at different time between gene chips to analyze coordination and restriction between genes. An algorithm for controlling the gene expression regulatory network of the whole cell was designed using Bayesian network which provides an effective aided analysis for gene regulatory network.

The application of hidden markov model in building genetic regulatory network

The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.

H2 and H∞-Feedback Control Design for Nonlinear Gene Networks via Successive Galerkin’s Approximation

This paper presents a design method of H2 and H∞-feedback control loop for nonlinear smooth gene networks that are in control affine form. Formulaic solution methodology for solving the nonlinear partial differential equations, namely the Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Isaacs equations through successive Galerkin’s approximation is implemented and the results are compared. Throughout the implementation, there were several caveats that need to be further resolved for practical applications in general cases. Such issues and the clarification of causes are mathematically established and reviewed.

The Simulation Gene Regulatory Boolean Network based on the Seauential Circuit

Along with the completion of HGP （human genome project）, huge amounts of genetic data constantly emerge. Research suggests that genes are not in independent existence and the expression of a gene will promote or inhibit the expression of another gene; if the expression of a gene makes the biochemical environment of ceils changed, the expression of a series of genes will be affected. In order to get a better understanding of the relationship between genes, all sorts of gene regulatory network models have been established by scientists. In this paper, a variety of gene regulatory networks are first introduced according to the process of this subject research, and then the most basic network （i.e. Boolean network） is emphatically analyzed, and then a new method （i.e. Boolean network based on the theory of circuit） to describe Boolean network is drawn forth. After the shortcomings of the Boolean network proposed in the past are analyzed, a simulation circuit Boolean model is established using EDA technology in order to improve the Boolean network.

Expression and regulatory network of long noncoding RNA in rats after spinal cord hemisection injury

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.

Identification of potential key molecules and signaling pathways for psoriasis based on weighted gene co-expression network analysis

BACKGROUND Psoriasis is a chronic inflammatory skin disease,the pathogenesis of which is more complicated and often requires long-term treatment.In particular,moderate to severe psoriasis usually requires systemic treatment.Psoriasis is also associated with many diseases,such as cardiometabolic diseases,malignant tumors,infections,and mood disorders.Psoriasis can appear at any age,and lead to a substantial burden for individuals and society.At present,psoriasis is still a treatable,but incurable,disease.Previous studies have found that micro RNAs(mi RNAs)play an important regulatory role in the progression of various diseases.Currently,mi RNAs studies in psoriasis and dermatology are relatively new.Therefore,the identification of key mi RNAs in psoriasis is helpful to elucidate the molecular mechanism of psoriasis.AIM To identify key molecular markers and signaling pathways to provide potential basis for the treatment and management of psoriasis.METHODS The mi RNA and m RNA data were obtained from the Gene Expression Omnibus database.Then,differentially expressed m RNAs(DEm RNAs)and differentially expressed mi RNAs(DEmi RNAs)were screened out by limma R package.Subsequently,DEm RNAs were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomics functional enrichment.The“WGCNA”R package was used to analyze the co-expression network of all mi RNAs.In addition,we constructed mi RNA-m RNA regulatory networks based on identified hub mi RNAs.Finally,in vitro validation was performed.All experimental procedures were approved by the ethics committee of Chinese PLA General Hospital(S2021-012-01).RESULTS A total of 639 DEm RNAs and 84 DEmi RNAs were identified.DEm RNAs screening criteria were adjusted P(adj.P)value<0.01 and|log Fold Change|(|log FC|)>1.DEmi RNAs screening criteria were adj.P value<0.01 and|logFC|>1.5.KEGG functional analysis demonstrated that DEm RNAs were significantly enriched in immune-related biological functions,for example,tolllike receptor signaling pathway,cytokine-cytokine receptor interaction,and chemokine signaling pathway.In weighted gene co-expression network analysis,turquoise module was the hub module.Moreover,10 hub mi RNAs were identified.Among these 10 hub mi RNAs,only 8 hub mi RNAs predicted the corresponding target m RNAs.97 negatively regulated mi RNA-m RNA pairs were involved in the mi RNA-m RNA regulatory network,for example,hsa-mi R-21-5 pclaudin 8(CLDN8),hsa-mi R-30 a-3 p-interleukin-1 B(IL-1 B),and hsa-mi R-181 a-5 p/hsa-mi R-30 c-2-3 p-C-X-C motif chemokine ligand 9(CXCL9).Real-time polymerase chain reaction results showed that IL-1 B and CXCL9 were up-regulated and CLDN8 was down-regulated in psoriasis with statistically significant differences.CONCLUSION The identification of potential key molecular markers and signaling pathways provides potential research directions for further understanding the molecular mechanisms of psoriasis.This may also provide new research ideas for the prevention and treatment of psoriasis in the future.

Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat

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.

A Vertex Network Model of Arabidopsis Leaf Growth

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.

基于miRNA-gene调控网络的miRNA重要性评估

MicroRNAs(miRNAs)是一类非编码短序列RNAs分子,它能够与靶基因特异性结合,进而影响靶基因对应的蛋白质产量。单个miRNA可能调节多个基因的表达,同时,单个基因的表达也可能被多个miRNAs所调控。已有研究表明,miRNA可以作为潜在的肿瘤标志物用于癌症的早期诊断和治疗。评价不同miRNAs的重要性对于探究miRNA在各种疾病中的作用具有重要的意义。因此,文章基于miRNA与基因之间的调控网络,提出了一种miRNA重要性评价指标。通过对该指标对miRNAs进行打分排序,排名靠前的miRNAs与疾病相关可能性越大,GO富集分析结果表明,排名靠前的miRNAs具有重要的生物学功能。基于排名靠前的100个miRNAs表达特征,训练SVM分类模型对各种癌症进行分类,实验证明其平均精度达到90%以上。总之,文章提出的miRNA重要性指标对于疾病相关miRNAs的识别以及应用具有重要的指导意义。

骨形态发生蛋白/Wnt信号通路调控成骨:揭示骨骼形成和重塑的分子机制

背景:成骨细胞是负责骨骼形成和重塑的主要细胞类型,其功能的正常发挥受到多种信号通路的精密调控,其中,骨形态发生蛋白和Wnt信号通路在成骨过程中起着关键作用。目的:综述了骨形态发生蛋白/Wnt信号通路在调控成骨细胞功能中的作用,并分析其在不同生理和病理条件下的变化,以期进一步揭示骨骼形成和重塑的分子机制。方法:以中文检索词“BMP信号通路,Wnt信号通路,成骨”及英文检索词“BMP signaling pathway,Wnt signaling pathway,osteogenesis”在中国知网、万方和PubMed数据库中检索研究原著,检索时限为各数据库建库至2023年6月的相关文献,最终筛选61篇文献进行分析总结。采用文献综述的方法,对骨形态发生蛋白/Wnt信号通路调控成骨作用的研究进行梳理和分析。结果与结论:①骨形态发生蛋白和Wnt信号通路在成骨细胞的分化、增殖和成熟过程中发挥重要作用。骨形态发生蛋白信号通路主要通过激活Smad蛋白来调控成骨相关基因的表达,Smad蛋白被激活后进入细胞核,调控与成骨相关的基因表达,与骨形态发生蛋白不同,Wnt信号通路主要依赖于β-catenin的激活来发挥生物学效应。②不同生理和病理状态下,骨形态发生蛋白/Wnt信号通路的调控作用会受到多种因素的影响。生长因子及激素及机械应力等会在一定程度上影响骨形态发生蛋白/Wnt信号通路的活性。③骨形态发生蛋白/Wnt信号通路在调控成骨过程中与其他信号通路相互作用,共同构成复杂的调控网络。④中药和天然化合物可以通过调控信号通路来促进骨骼健康,为中药治疗骨骼疾病提供了新的可能性。⑤未来研究可进一步探讨骨形态发生蛋白/Wnt信号通路与其他信号通路的相互作用以及其在不同生理和病理条件下的变化,解析此复杂网络中的关键节点和调控机制,为骨骼相关疾病的治疗提供更精确的靶点,也为揭示骨骼形成和重塑的分子机制提供新的思路。

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis

The plant genome produces an extremely large collection of long noncoding RNAs(lncRNAs)that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes.Here,we mapped the transcriptional heterogeneity of lncRNAs and their associated gene reg-ulatory networks at single-cell resolution.We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing(scRNA-seq)datasets from juvenile Arabidopsis seedlings.We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers.We further demon-strated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity.In addi-tion,we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs,and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs.The analysis results are available at the single-cell-based plant lncRNA atlas data-base(scPLAD;https://biobigdata.nju.edu.cn/scPLAD/).Overall,this work demonstrates the power of inte-grative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.