Construction Of Genetic Networks Research Articles

ДИНАМИКА АЛЛЕЛОФОНДА ЛИВЕНСКОЙ ПОРОДЫ СВИНЕЙ НА ОСНОВЕ МИКРОСАТЕЛЛИТНОГО АНАЛИЗА

Using a multilocus panel of microsatellites, the allele pool of Livni breed populations was assessed in five-year dynamics (from 2018 to 2022) and in comparison with commercial breeds (Large White and Landrace). We identified alleles, which were found in Livni breed and absent in commercial breeds. However, there was a tendency towards the loss of certain alleles in the dynamics of the analyzed period. It was shown that observed heterozygosity in the population of Livni breed in 2022 was significantly higher (P<0.05) compared to those estimated in commercial breeds. Based on principal component analysis, genetic network construction, and cluster analysis, the preservation of authentic genomic components, which were predominant in Livni breed, in the modern population is shown.

Association analysis of agronomic traits and construction of genetic networks by resequencing of 306 sugar beet (Beta vulgaris L.) lines

Due to the relatively brief domestication history of sugar beet (Beta vulgaris ssp. vulgaris), our understanding of the genomic diversity and functional genes in its cultivars is limited, resulting in slow breeding progress. To address this issue, a total of 306 germplasm materials of major cultivars and breeding lines from China, the USA, and Europe were selected for genome resequencing. We investigated population structure and genetic diversity and performed selective scanning of genomic regions, identifying six novel genes associated with important agronomic traits: the candidate genes DFAX2 and P5CS for skin roughness; the candidate genes FRO5, GL24, and PPR91 for root yield and sugar yield, and the pleiotropic candidate gene POLX for flourishing growth vigour, plant height, crown size, flesh coarseness, and sugar yield. In addition, we constructed a protein–protein interaction network map and a phenotype-gene network map, which provide valuable information for identifying and characterizing functional genes affecting agronomic traits in sugar beet. Overall, our study sheds light on the future improvement of sugar beet agronomic traits at the molecular level.

Achieving large dynamic range control of gene expression with a compact RNA transcription-translation regulator.

RNA transcriptional regulators are emerging as versatile components for genetic network construction. However, these regulators suffer from incomplete repression in their OFF state, making their dynamic range less than that of their protein counterparts. This incomplete repression causes expression leak, which impedes the construction of larger synthetic regulatory networks as leak propagation can interfere with desired network function. To address this, we demonstrate how naturally derived antisense RNA-mediated transcriptional regulators can be configured to regulate both transcription and translation in a single compact RNA mechanism that functions in Escherichia coli. Using in vivo gene expression assays, we show that a combination of transcriptional termination and ribosome binding site sequestration increases repression from 85% to 98%, or activation from 10-fold to over 900-fold, in response to cognate antisense RNAs. We also show that orthogonal repressive versions of this mechanism can be created through engineering minimal antisense RNAs. Finally, to demonstrate the utility of this mechanism, we use it to reduce network leak in an RNA-only cascade. We anticipate these regulators will find broad use as synthetic biology moves beyond parts engineering to the design and construction of more sophisticated regulatory networks.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Extracting Various Classes of Data From Biological Text Using the Concept of Existence Dependency.

One of the key goals of biological natural language processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependences. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called EDCC. EDCC aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined earlier, as follows: 1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules; and 2) it applies a semantic relationship extraction model that extracts information from different structural forms of constituents in sentences. EDCC can be used to extract different types of data from biological texts for purposes such as protein function prediction, genetic network construction, and protein-protein interaction detection. We evaluated the quality of EDCC by comparing it experimentally with six systems. Results showed marked improvement.

Analysis and Construction of Genetic Network for Mice Brain Microarray Datasets

This paper intends to find out target genes about memory and learning via microarray analysis. Microarrays are often used to store and manage large amounts of data; however, there is no consensus as to how to best analyze microarray data. This paper uses computational algorithms to analyze gene samples from mice with various calcium channel phenotypes. Min-max normalization was used first to normalize the data. Then, analysis of variance was applied to detect genetic differences among the genes. Finally, Pearson correlation coefficients were calculated to identify the regulatory network of the genes. This analysis model can be applied to efficiently analyze complicated gene expression data. It can also be used to examine the biological functions and regulations of target genes.

Versatile RNA-sensing transcriptional regulators for engineering genetic networks

The widespread natural ability of RNA to sense small molecules and regulate genes has become an important tool for synthetic biology in applications as diverse as environmental sensing and metabolic engineering. Previous work in RNA synthetic biology has engineered RNA mechanisms that independently regulate multiple targets and integrate regulatory signals. However, intracellular regulatory networks built with these systems have required proteins to propagate regulatory signals. In this work, we remove this requirement and expand the RNA synthetic biology toolkit by engineering three unique features of the plasmid pT181 antisense-RNA-mediated transcription attenuation mechanism. First, because the antisense RNA mechanism relies on RNA-RNA interactions, we show how the specificity of the natural system can be engineered to create variants that independently regulate multiple targets in the same cell. Second, because the pT181 mechanism controls transcription, we show how independently acting variants can be configured in tandem to integrate regulatory signals and perform genetic logic. Finally, because both the input and output of the attenuator is RNA, we show how these variants can be configured to directly propagate RNA regulatory signals by constructing an RNA-meditated transcriptional cascade. The combination of these three features within a single RNA-based regulatory mechanism has the potential to simplify the design and construction of genetic networks by directly propagating signals as RNA molecules.

3J1522 遺伝子ネットワークの合理的な構築を容易とする人工遺伝子の発現の動的制御(バイオエンジニアリング2,第49回年会講演予稿集)

3J1522 遺伝子ネットワークの合理的な構築を容易とする人工遺伝子の発現の動的制御(バイオエンジニアリング2,第49回年会講演予稿集)

Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values. The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes. dargenio@bmsr.usc.edu Supplementary material is available at Bioinformatics online.

Computational identification of the normal and perturbed genetic networks involved in myeloid differentiation and acute promyelocytic leukemia

A dissection of the genetic networks and circuitries is described for two form of leukaemia. Integrating transcription factor binding and gene expression profiling, networks are revealed that underly this important human disease.

High-Throughput Discovery of Germ-Cell-Specific Genes

As a specialized cell population that plays the unique role of transmitting genetic information to subsequent generations, germ cells have been intensively studied to unravel their unique physiology from the specification of primordial germ cells to the fateful reunion of gametes during fertilization. For their differential expression, germ-cell-specific genes are the keys to understanding these unique features. In the last decade, the emerging methodologies designed for large-scale and high-throughput analysis have created an ever-increasing amount of data. Among these methodologies, expressed sequence tag libraries, serial analysis of gene expression, and microarrays provide valuable expression data that can be further analyzed. Using the mouse as a model system, we describe a strategy starting from the quick identification of germ-cell-specific genes using public domain expression data to the functional characterization of the identified genes using targeted gene disruption. This strategy should accelerate the process to fill in the missing pieces of the germ cell physiology puzzle and the construction of genetic networks to help us to understand the etiology of infertility. Furthermore, these identified germ-cell-specific genes may lead to the development of new contraceptives targeted specifically to germ cells.

Comparative Analysis Workbench for Genetic Networks

To understand the nature of cellular function, it is necessary to study the behavior of genes in a holistic rather than in an individual manner because the expression and activities of genes are not isolated or independent of each other. There is lot of genetic network analysis method such as Boolean-based model, Bayesian model and Differential equation based model. However there is no agreement that which one is best, since the reliability of genetic analysis result is highly depends on the quality and the characteristics of input data. Since the experimental biologist is finally responsible for verifying the meaningfulness of the genetic network, we need a system to help biologist to compare genetic networks. Recently there is an interesting work on comparative study of the genetic networks[4]. They gave a few quality and quantitative measures to help to estimate the significance and reliability of the gene networks which one is more reliable and acceptable. In order to help these kinds of comparative study, we develop a gene regulatory network analysis and visualization tool, GENAW(GEnetic Network Analysis Workbench for microarray). GENAW accepts the expression profile of microarrays and finally produces a set of genetic network model. This system supports three well-known methods for genetic network construction (Bayesian, Boolean, Differential equation). So GENAW user enables to investigate the common or relatively different features of 3 different genetic models which were generated by a given data. Moreover GENAW provides a simple network simulation tools to efficiently investigate the regulatory relationships among genes by adjusting interaction structure and the pathway structure.