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.

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.

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.

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.

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.

Comparative transcriptome analysis between abundant and deficient spore strains provides novel insight into gene regulatory networks and mechanisms of monospore production in bladed Bangiales

As an important seedling source,monospores closely associate with yields in nori farming.However,the molecular mechanism underlying differences in monospore production for different strains remains unknown.Comparative transcriptome analysis was performed to examine gene expression differences between the spore abundant wild-type strain(WT)and spore deficient mutant(Y1)of Pyropia chauhanii.The WT strain that produces monospores in abundance exhibited more differentially expressed genes(DEGs)in both number and higher fold-changes than the Y1 strain incapable of producing monospores,indicating that the specific regulation of genes is involved in monospore production.Three lists of DEGs were obtained between the two strains using intersection and displayed in Venn diagram:one expressed only in WT strain,another expressed only in Y1 strain,and the third shared in both strains.DEGs annotated as homologous genes of Arabidopsis thaliana in these 3 lists were curated for online functional enrichment analysis on Metascape website.Gene regulatory networks of WT were functionally enriched in the processing,proteolysis,and transport of proteins,especially within the small GTPase protein family,which might be account for the monospore production ability,whereas Y1 were functionally enriched in the metabolism of essential substance and utilization of indispensable energy,which might be account for the rapid growth of blades.We found the differentially enriched gene regulatory networks between strains might be the intrinsic mechanisms of the different monospore production traits.These findings provide novel insights into the genes and regulatory networks associated with monospore production abilities,which are essential for developing accurate breeding technologies for optimal release of monospores and increase of total nori production.

Gene Regulatory Networks in the Genomics Era

The precise regulation of gene expression is critical to the nor- mal development and biological function of all organisms. Dysregulation of gene expression during early development can result in a spectrum of failures ranging from minor defects to the termination of development. In adult life, dysregulation can lead to the uncontrolled cell proliferation of cancer or pro- grammed cell death leading to neurodegenerative diseases. The regulation of gene expression is controlled by multiple systems with more being discovered. The immediate regulators are transcription factors which bind to specific sequences in the promoter or enhancer regions of individual genes. The activity of transcription factors can be regulated by the presence of other transcription factors and cofactors, methylation status of the promoter or enhancer region,

Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method

Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on conditional mutual information test （PCA-CMI）. In the PC-based algorithms the separator set is determined to detect the dependency between variables. The PCHMS algorithm attempts to select the set in the smart way. For this purpose, the edges of resulted skeleton are directed based on PC algorithm direction rule and mutual information test （MIT） score. Then the separator set is selected according to the directed network by considering a suitable sequential order of genes. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network of Escherichia coll. Results show that applying the PCHMS algorithm improves the precision of learning the structure of the GRNs in comparison with current popular approaches.

Towards precise reconstruction of gene regulatory networks by data integration

Background： More and more high-throughput datasets are available from multiple levels of measuring gene regulations. The reverse engineering of gene regulatory networks from these data offers a valuable research paradigm to decipher regulatory mechanisms. So far, numerous methods have been developed for reconstructing gene regulatory networks. Results： In this paper, we provide a review of bioinformatics methods for inferring gene regulatory network from omics data. To achieve the precision reconstruction of gene regulatory networks, an intuitive alternative is to integrate these available resources in a rational framework. We also provide computational perspectives in the endeavors of inferring gene regulatory networks from heterogeneous data. We highlight the importance of multi-omics data integration with prior knowledge in gene regulatory network inferences. Conclusions： We provide computational perspectives of inferring gene regulatory networks from multiple omics data and present theoretical analyses of existing challenges and possible solutions. We emphasize on prior knowledge and data integration in network inferences owing to their abilities of identifying regulatory causality.

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.

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.

MINI-EX:Integrative inference of single-cell gene regulatory networks in plants

Multicellular organisms,such as plants,are characterized by highly specialized and tightly regulated cell populations,establishing specific morphological structures and executing distinct functions.Gene regulatory networks(GRNs)describe condition-specific interactions of transcription factors(TFs)regulating the expression of target genes,underpinning these specific functions.As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking,limiting our understanding of the organization of specific cell types in both model species and crops,we developed MINI-EX(Motif-Informed Network Inference based on single-cell EXpression data),an integrative approach to infer cell-type-specific networks in plants.MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons,resulting in networks with increased accuracy.Next,regulons are assigned to different cell types,leveraging cell-specific expression,and candidate regulators are prioritized using network centrality measures,functional annotations,and expression specificity.This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest.We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data,and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools.MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice,leaf development in Arabidopsis,and ear development in maize,enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.

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.

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.

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.

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.

Oscillatory Gene Expression by the microRAN Mediating Delayed Negative Feedback Loop

More and more experiments show that microRNAs can regulate gene expression by stimulating degradation of mRNA or repression of translation of mRNA. In this paper, we incorporate the microRNA into a previous mathematical model of gene expression through forming a microRNA-induced silencing complex(RISC). Our findings demonstrate the dynamical behavior of the constructed system. By Hopf theories, we derive the theoretical results of globally asymptotical stability and provide the sufficient conditions for the oscillation of the simple gene regulatory system, and by numerical simulation further demonstrate how the amplitudes against the change of delay in the gene regulatory network.

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

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.