• Interdisciplinary Bio Central
• /
• 제3권2호
• /
• pp.8.1-8.6
• /
• 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.

• Bioinformatics and Biosystems
• /
• 제2권1호
• /
• pp.12-16
• /
• 2007

DNA repair means a collection of processes that a cell identifies and corrects damage to genome sequence. The DNA repair processes are important because a genome would not be able to maintain its essential cellular functions without the processes. In this research, we make some gene regulatory networks of DNA repair in S. cerevisiae to know how each gene interacts with others. Two approaches are adapted to make the networks; Bayesian Network and ARACNE. After construction of gene regulatory networks based on the two approaches, the two networks are compared to each other to predict which genes have important roles in the DNA repair processes by finding conserved interactions and looking for hubs. In addition, each interaction between genes in the networks is validated with interaction information in S. cerevisiae genome database to support the meaning of predicted interactions in the networks.

• 한국미생물학회:학술대회논문집
• /
• 한국미생물학회 2006년도 International Meeting of the Microbiological Society of Korea
• /
• pp.86-88
• /
• 2006

• 한국통신학회논문지
• /
• 제40권10호
• /
• pp.2054-2061
• /
• 2015

최근 생물학적으로 영감을 받은 모델링 기술은 단순한 현장 상호작용과 제한된 정보와 함께 이들의 강인성과 확장성, 적응성에 대해 상당한 관심을 받고 있다. 이러한 모델링 기술들 중, 유전자 조절 네트워크(Gene Regulatory Networks)(GRNs)은 세포로부터 생물학적 유기체의 발생과 자연 진화에 대한 이해에서 핵심적인 역할을 하고 있다. 본 논문은 GRN 원리를 무선 센서 네트워크 시스템에 적용하고 시간지연 요건을 충족하는 동시에 에너지 균형을 달성할 수 있는 분산화된 노드 스케쥴링 설계 기법을 제안한다. 각 센서 노드는 소비된 에너지 수준과 지연시간에 반응하여 자동으로 자신의 상태를 스케줄링하며, 이는 GRN 모델에서 영감을 받은 유전자 발현과 단백질 농도 조절 모델에 의해 제어된다. 시뮬레이션 결과는 제안된 방법이 에너지 균형뿐만 아니라 원하는 시간 지연에서 성능을 달성하고 있다는 점을 보여준다.

• 정보처리학회논문지D
• /
• 제14D권1호
• /
• pp.9-20
• /
• 2007

유전자들의 그룹은 복잡한 상호작용들을 통해 세포의 기능이 조절되며 이러한 상호작용을 하는 유전자 그룹들을 유전자 조절 네트워크 (GRNs: Gene Regulatory Networks)라고 한다. 이전의 유전자 발현 분석 기법인 군집화와 분류는 단지 상동성에 의한 유전자들 사이의 소속을 결정하는 데에는 유용하나 분자 활동에서의 같은 클래스에서 발견되어지는 유전자들 사이의 조절 관계를 식별할 수 없다. 더욱이 유전자들이 어떻게 연관되는 지와 유전자들이 서로 어떻게 조절하는지에 대한 매커니즘의 이해가 필요하다. 따라서 이 논문에서는 시계열 마이크로어레이 데이터로부터의 유전자들의 조절 관계를 발견하기 위해서 빈발 패턴 마이닝과 연쇄 규칙을 이용한 새로운 접근법을 제안하였다. 이 기법에서는 먼저, 빈발 패턴 마이닝 적용을 위한 적절한 데이터 변환 방법을 제안하였고 FP-growth을 이용하여 유전자 발현 패턴들을 발견한다. 그런 다음, 연쇄 규칙을 이용하여 빈발한 유전자 패턴들로부터 유전자 조절 네트워크를 구축하였다. 마지막으로 제안된 기법의 검증은 공개된 유전자들의 조절 관계와 실험 결과의 일치함을 보임으로써 평가하였다.

• 한국생물정보학회:학술대회논문집
• /
• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
• /
• pp.339-343
• /
• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• Asian Pacific Journal of Cancer Prevention
• /
• 제16권2호
• /
• pp.457-463
• /
• 2015

The pathogenesis of mantle cell lymphoma, a special subtype of lymphoma that is invasive and indolent and has a median survival of 3 to 4 years, is still partially unexplained. Much research about genes and miRNAs has been conducted in recent years, but interactions and regulatory relations of genetic elements which may play a vital role in genesis of MCL have attracted only limited attention. The present study concentrated on regulatory relations about genes and miRNAs contributing to MCL pathogenesis. Numerous experimentally validated raw data were organized into three topology networks, comprising differentially expressed, associated and global examples. Comparison of similarities and dissimilarities of the three regulating networks, paired with the analysis of the interactions between pairs of elements in every network, revealed that the differentially expressed network illuminated the carcinogenicity mechanism of MCL and the related network further described the regulatory relations involved, including prevention, diagnosis, development and therapy. Three kinds of regulatory relations for host genes including miRNAs, miRNAs targeting genes and genes regulating miRNAs were concluded macroscopically. Regulation of the differentially expressed miRNAs was also analyzed, in terms of abnormal gene expression affecting the MCL pathogenesis. Special regulatory relations were uncovered. For example, auto-regulatory loops were found in the three topology networks, key pathways of the nodes being highlighted. The present study focused on a novel point of view revealing important influencing factors for MCL pathogenesis.

• Bioinformatics and Biosystems
• /
• 제2권1호
• /
• pp.24-27
• /
• 2007

Human cell regulatory mechanism is one of suspicious problems among biologists. Here we tried to uncover the human breast cancer cell regulatory mechanism from gene expression data (Marc J. Van de vijver, et. al., 2002) using a module network algorithm which is suggested by Segal, et. al.(2003) Finally, we derived a module network which consists of 50 modules and 10 tree depths. Moreover, to validate this candidate network, we applied a GO enrichment test and known transcription factor-target relationships from Transfac(R) (V. Matys, et. al, 2006) and HPRD database (Peri, S. et al., 2003).

• Bulletin of the Korean Chemical Society
• /
• 제29권1호
• /
• pp.252-254
• /
• 2008

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2014년도 추계학술발표대회
• /
• pp.849-852
• /
• 2014

Reverse engineering of gene regulatory network is a challenging task in computational biology. To detect a regulatory relationship among genes from time series data is called reverse engineering. Reverse engineering helps to discover the architecture of the underlying gene regulatory network. Besides, it insights into the disease process, biological process and drug discovery. There are many statistical approaches available for reverse engineering of gene regulatory network. In our paper, we propose pairwise mutual information for the reverse engineering of a gene regulatory network from time series data. Firstly, we create random boolean networks by the well-known $Erd{\ddot{o}}s-R{\acute{e}}nyi$ model. Secondly, we generate artificial time series data from that network. Then, we calculate pairwise mutual information for predicting the network. We implement of our system on java platform. To visualize the random boolean network graphically we use cytoscape plugins 2.8.0.