• Title/Summary/Keyword: Gene Modeling

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Biomedical Application of Gene Editing (유전자 교정 기술의 생의학적 응용)

  • Ju-Chan, Park;Hyeon-Ki, Jang
    • Journal of Industrial Technology
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    • v.42 no.1
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    • pp.29-36
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    • 2022
  • The CRISPR system has revolutionized gene editing field. Cas9-mediated gene editing such as Indel induction or HDR enable targeted gene disruption or precise correction of mutation. Moreover, CRISPR-based new editing tools have been developed such as base editors. In this review, we focus on gene editing in human pluripotent stem cells, which is principal technique for gene correction therapy and disease modeling. Pluripotent stem cell-specific drug YM155 enabled selection of target gene-edited pluripotent stem cells. Also, we discussed base editing for treatment of congenital retina disease. Adenine base editor delivery as RNP form provide an approach for genetic disease treatment with safe and precise in vivo gene correction.

G-Networks Based Two Layer Stochastic Modeling of Gene Regulatory Networks with Post-Translational Processes

  • Kim, Ha-Seong;Gelenbe, Erol
    • Interdisciplinary Bio Central
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    • v.3 no.2
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    • pp.8.1-8.6
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    • 2011
  • Background: Thanks to the development of the mathematical/statistical reverse engineering and the high-throughput measuring biotechnology, lots of biologically meaningful genegene interaction networks have been revealed. Steady-state analysis of these systems provides an important clue to understand and to predict the systematic behaviours of the biological system. However, modeling such a complex and large-scale system is one of the challenging difficulties in systems biology. Results: We introduce a new stochastic modeling approach that can describe gene regulatory mechanisms by dividing two (DNA and protein) layers. Simple queuing system is employed to explain the DNA layer and the protein layer is modeled using G-networks which enable us to account for the post-translational protein interactions. Our method is applied to a transcription repression system and an active protein degradation system. The steady-state results suggest that the active protein degradation system is more sensitive but the transcription repression system might be more reliable than the transcription repression system. Conclusions: Our two layer stochastic model successfully describes the long-run behaviour of gene regulatory networks which consist of various mRNA/protein processes. The analytic solution of the G-networks enables us to extend our model to a large-scale system. A more reliable modeling approach could be achieved by cooperating with a real experimental study in synthetic biology.

State-Space Approach to Modeling Dynamics of Gene Regulation in Networks

  • Xiong, Momiao;Jin, Li
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2005.09a
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    • pp.191-196
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    • 2005
  • Genetic networks are a key to unraveling dynamic properties of biological processes and regulation of genes plays an essential role in dynamic behavior of the genetic networks. A popular characterization of regulation of the gene is a kinetic model. However, many kinetic parameters in the genetic regulation have not been available. To overcome this difficulty, in this report, state-space approach to modeling gene regulation is presented. Second-order systems are used to characterize gene regulation. Interpretation of coefficients in the second order systems as resistance, capacitance and inductance is studied. The mathematical methods for transient response analysis of gene regulation to external perturbation are investigated. Criterion for classifying gene into three categories: underdamped, overdamped and critical damped is discussed. The proposed models are applied to yeast cell cycle gene expression data.

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Gray Image Generation Methods Using Genetic Algorithm (유전자 알고리즘을 이용한 흑백 이미지 생성 기법)

  • Cha, Joo Hyoung;Kang, Dong Sung;Song, Moo Sang;Kweon, Tae Hyeon;Woo, Young Woon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.265-267
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    • 2019
  • In this paper, we propose a method to automatically generate gray images similar to existing images using genetic algorithms. We have proposed two techniques for gene modeling, which is the most important design element to apply genetic algorithm to real field problems. Experiments were performed on two different sizes of gray images using each of the proposed techniques. Experimental results show that there is a large difference in the evolutionary performance of each technique in gene modeling for image generation. Therefore, it can be understood that gene modeling should be carefully decided in order to generate an image similar to the existing image in the future, or to learn quickly and naturally to generate an image synthesized from different images.

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Application of UML (Unified Modeling Language) in Object-oriented Analysis of Microarray Information System (UML을 활용한 마이크로어레이 정보시스템의 객체지향분석)

  • Park, Ji-Yeon;Chung, Hee-Joon;Kim, Ju-Han
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2003.10a
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    • pp.147-154
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    • 2003
  • Microarray information system is a complex system to manage, analyze and interpretate microarray gene expression data. Establishment of well-defined development process is very essential for understanding the complexity and organization of the system. We performed object-oriented analysis using Unified Modeling Language (UML) in specifying, visualizing and documenting microarray information system. The object-oriented analysis consists of three major steps: (i) use case modeling to describe various functionalities from the user's perspective (ii) dynamic modeling to illustrate behavioral aspects of the system (iii) object modeling to represent structural aspects of the system. As a result of our modeling activities we provide the UML diagrams showing various views of the microarray information system. We believe that the object-oriented analysis ensures effective documentations and communication of information system requirements. Another useful feature of object-oriented technique is structural continuity to standard microarray data model MAGE-OM (Microarray Gene Expression Object Model). The proposed modeling e(forts can be applicable for integration of biomedical information system.

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In-silico inferences for expression data using IGAM: Applied to Fuzzy-Clustering & Regulatory Network Modeling (연판 지식을 이용한 유전자 발현 데이터 분석: 퍼지 플러스링과 조절 네트웍 모델링에의 응용)

  • Lee, Philhyone;Hojeong Nam;Lee, Doheon;Lee, Kwang H.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.273-276
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    • 2004
  • Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

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BINGO: Biological Interpretation Through Statistically and Graph-theoretically Navigating Gene $Ontology^{TM}$

  • Lee, Sung-Geun;Yang, Jae-Seong;Chung, Il-Kyung;Kim, Yang-Seok
    • Molecular & Cellular Toxicology
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    • v.1 no.4
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    • pp.281-283
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    • 2005
  • Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.

Semantic Modeling for SNPs Associated with Ethnic Disparities in HapMap Samples

  • Kim, HyoYoung;Yoo, Won Gi;Park, Junhyung;Kim, Heebal;Kang, Byeong-Chul
    • Genomics & Informatics
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    • v.12 no.1
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    • pp.35-41
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    • 2014
  • Single-nucleotide polymorphisms (SNPs) have been emerging out of the efforts to research human diseases and ethnic disparities. A semantic network is needed for in-depth understanding of the impacts of SNPs, because phenotypes are modulated by complex networks, including biochemical and physiological pathways. We identified ethnicity-specific SNPs by eliminating overlapped SNPs from HapMap samples, and the ethnicity-specific SNPs were mapped to the UCSC RefGene lists. Ethnicity-specific genes were identified as follows: 22 genes in the USA (CEU) individuals, 25 genes in the Japanese (JPT) individuals, and 332 genes in the African (YRI) individuals. To analyze the biologically functional implications for ethnicity-specific SNPs, we focused on constructing a semantic network model. Entities for the network represented by "Gene," "Pathway," "Disease," "Chemical," "Drug," "ClinicalTrials," "SNP," and relationships between entity-entity were obtained through curation. Our semantic modeling for ethnicity-specific SNPs showed interesting results in the three categories, including three diseases ("AIDS-associated nephropathy," "Hypertension," and "Pelvic infection"), one drug ("Methylphenidate"), and five pathways ("Hemostasis," "Systemic lupus erythematosus," "Prostate cancer," "Hepatitis C virus," and "Rheumatoid arthritis"). We found ethnicity-specific genes using the semantic modeling, and the majority of our findings was consistent with the previous studies - that an understanding of genetic variability explained ethnicity-specific disparities.

An Effective Method for Generating Images Using Genetic Algorithm (유전자 알고리즘을 이용한 효과적인 영상 생성 기법)

  • Cha, Joo Hyoung;Woo, Young Woon;Lee, Imgeun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.8
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    • pp.896-902
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    • 2019
  • In this paper, we proposed two methods to automatically generate color images similar to existing images using genetic algorithms. Experiments were performed on two different sizes($256{\times}256$, $512{\times}512$) of gray and color images using each of the proposed methods. Experimental results show that there are significant differences in the evolutionary performance of each technique in genetic modeling for image generation. In the results, evolving the whole image into sub-images evolves much more effective than modeling and evolving it into a single gene, and the generated images are much more sophisticated. Therefore, we could find that gene modeling, selection method, crossover method and mutation rate, should be carefully decided in order to generate an image similar to the existing image in the future, or to learn quickly and naturally to generate an image synthesized from different images.

A System for Describing Cis-Regulatory Machinery Unit

  • Kaminuma, Tsuguchika;Takai-Igarashi, Takako;Yukawa, Masumi;Tanaka, Yoshitomo;Tanaka, Hiroshi
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2005.09a
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    • pp.427-430
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    • 2005
  • Studies on cellular pathways and networks are now one of the most actively researched topics in all fields of biomedicine ranging from developmental biology to etiology. Many databases have been developed and quantitative simulation models have been proposed. One of the eventual goals of pathway/network studies is to integrate different types of pathway/network models and databases to simulate overall cellular responses. A bottleneck to this goal is modeling gene expression since the mechanism of this process is not yet fully unveiled. We are developing a small scale computer program called CiRMU (Cis-Regulatory Machinery Unit model) for describing, viewing, analyzing, and modeling the process of gene expression. A prototype system is being designed and implemented for analyzing functions of nuclear receptors.

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