[논문]SOMPS 알고리즘을 이용한 세포주기 조절 유전자 검출

강용석; 배철수

doi:10.5762/kais.2012.13.9.3952

[국내논문] SOMPS 알고리즘을 이용한 세포주기 조절 유전자 검출
Detecting cell cycle-regulated genes using Self-Organizing Maps with statistical Phase Synchronization (SOMPS) algorithm 원문보기

한국산학기술학회논문지 = Journal of the Korea Academia-Industrial cooperation Society, v.13 no.9, 2012년, pp.3952 - 3961

초록
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세포주기조절유전자를 식별하는 계산방법을 개발하는 것은 시스템 생물학의 중요한 주제중 하나이다. 이전 방법의 대부분은 세포주기 조절 유전자를 식별하는 표현신호의 주기적인 특성으로 간주한다. 그러나, 세포주기 조절유전자는 상대적으로 세포 네트워크를 기반으로 서로 활성화된 상대적으로 많은 상호 작용을 일으킨다고 가정한다. 본 연구에서는 세포주기 분석에 변수 위상동기화 이론을 적용하여, "통계적상 동기화를 이용한 자가조직지도 (SOMPS)", 즉, 자가조직지도와 다변수 통계 동기화 방법으로 이루어진 방법을 사용하여 여러 개의 하위집합과의 상호작용을 발생시키고자 한다. 평가방법으로 SOMPS방법 알고리즘이 세포주기조절 유전자를 방법으로 기존에 사용되는 방법들과 같이 유용할 것으로 보인다.

Abstract ▼ AI-Helper

Developing computational methods for identifying cell cycle-regulated genes has been one of important topics in systems biology. Most of previous methods consider the periodic characteristics of expression signals to identify the cell cycle-regulated genes. However, we assume that cell cycle-regulated genes are relatively active having relatively many interactions with each other based on the underlying cellular network. Thus, we are motivated to apply the theory of multivariate phase synchronization to the cell cycle expression analysis. In this study, we apply the method known as "Self-Organizing Maps with statistical Phase Synchronization (SOMPS)", which is the combination of self-organizing map and multivariate phase synchronization, producing several subsets of genes that are expected to have interactions with each other in their subset (Kim, 2008). Our evaluation experiments show that the SOMPS algorithm is able to detect cell cycle-regulated genes as much as one of recently reported method that performs better than most existing methods.

Keyword

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문제 정의

This study focuses on the oscillating systems containing internal source of energy, which are transformed into oscillatory movements. The SOMPS algorithm is basically based on the theory of phase synchronization, which is generally considered as the complete coincidence of the states of individual systems (Rosenblum et al.
The purpose of this experiment is to show the effectiveness of SOMPS algorithm for identifying the signals from a certain specific process. In this study, it is assumed that a certain group of gene expression levels during cell cycle can be explained as the synchronization of a large ensemble of oscillators.
This study shows that it is feasible to identify cell cycle-regulated genes using the theory of phase synchronization. With the theory of multivariate phase synchronization, SOMPS is able to detect groups of genes, and genes from each group are considered as being "active"having relatively many interactions with others during cell cycle.

제안 방법

With the theory of multivariate phase synchronization, SOMPS is able to detect groups of genes, and genes from each group are considered as being "active"having relatively many interactions with others during cell cycle. Therefore, this study provides a novel insight to the identification of cell cycle-regulated genes, compared to other previous methods that mainly consider the periodic characteristic of expression signals during cell cycle.

후속연구

It means that these mean fields could be considered as the estimated activity of cell cycle related transcription factors. Therefore, SOMPS has a potential application to the study of transcriptional regulatory networks, and the further study of SOMPS for transcriptional regulatory networks is left as future study.

참고문헌 (27)

Albert,B., Bray,D., Lewis,J., Raff,M., Roberts,K. and Watson,J.D. Molecular Biology of the Cell. Garland Publishing, New York & London.
Allefeld,C. and Kurths,J. An approach to multivariate phase synchronization analysis and its application to event-related potentials., Int. J. Bifurcation and Chaos, 14, pp.417-426. 2004.

상세보기
Bhattacharya,J."Reduced degree of long-range phase synchrony in pathological human brain",, Acta Neurobiol. Exp., 61, 309-318.2001.

상세보기
Cho,R.J., Campbell,M.Jm, Winzeler,E.A., Steinmetz,L., Conway,A., Wodicka,L., Wolfsberg,T.G., Gabrielian,A.E., Landsman,D., Lockhart,D.J., et al."A genome-wide transcriptional analysis of the mitotic cell cycle"., Mol. Cell, 2, 65-73.1998.

상세보기
Cho,R.J., Huang,M., Campbell,M.J., Dong,H., Steinmetz,L., Sapinoso,L., Hampton,G., Elledge,S.J., Davis,R.W. and Lockhart,D.J. "Transcriptional regulation and function during the human cell cycle"., Nat. Genet., 27, 48-54. 2001.

상세보기
de Lichtenberg,U., Jensen,L.J., Fausboll,A., Jensen,T.S., Bork,P. and Brunak,S. "Comparison of computational methods for the identification of cell cycle-regulated genes"., Bioinformatics, 21, 1164-1171.2005.

상세보기
Gabor,D. Theory of communication., JIEE (London), 93, 429-457.1946.
Huygens,Ch.Horologium Oscillatorium. Apud F. Muguet, Parisiis, France.
Johannson,D., Lindgren,P., and Berglund,A. "A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcriptio"n., Bioinformatics, 19, 467-473. 2003.

상세보기
Kim, C.S. "Self-Organizing Maps with Statistical Phase Synchronization (SOMPS) for analyzing cell cycle-specific gene expression data., Statistical Applications in Genetics and Molecular Biology", 7:Iss. 1, Article 1.2008.

상세보기
Kohonen,T. Self-Organizing Maps. Springer-Verlag, Berlin. 1995.
Lee,T.I., Rinaldi,N.J., Robert,F., Odom,D.T., Bar-Joseph,Z., Gerber,G.K., Hannett,N., Harbison,C.T., Thompson,C.M., Simon,I., et al. "Transcriptional regulatory networks in Saccharomyces cerevisiae"., Science, 298, 799-804.2002.

상세보기
Lu,X., Zhang,W., Qin,Z.S., Kwasi,K.E. and Liu,J.S.Statistical resynchronization and Bayesian detection of peridically expressed genes., Nucleic Acids Res., 32, 447-455. 2004.

상세보기
Luan,Y. and Li,H. "Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data"., Bioinformatics, 20, 332-339.2004.

상세보기
Mewes,H.W., Frishman,D., Guldner,U., Mannhaupt,G., Mayer,K., Mokrejs,M., Morgenstern,B., Munsterkoetter,M., Rudd,S. and Weil,B. MIPS: a database for genomes and protein sequences., Nucleic Acids Res., 30, 31-34.

상세보기
Pikovsky,A.S. "On the interaction of strange attractors"., Phys. B, 55, 149-154. 1984a.
Pikovsky,A.S. "Synchronization and stochastization of the ensemble of autogenerators by external noise"., Radiophys. Quant. Electron, 27, 576-581. 1984b.
Pikovsky,A., Rosenblum,M. and Kurths,J. Synchronization: a universal concept in nonlinear science. . Cambridge University Press. 2001.
Qian,J., Dolled-Filhart,M., Lin,J., Yu,H. and Gerstein,M. Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions., J. Mol. Biol., 314, 1053-1066. 2001.

상세보기
Rosenblum,M.G., Pikovsky,A.S. and Kurths,J. "Phase synchronization of chaotic oscillators"., Phys. Rev. Lett., 76, 1804-1807. 1996.

상세보기
Rustici,G., Mata,J., Kivinen,K., Lio,P., Penkett,C.J., Burns,G., Hayles,J., Brazma,A., Nurse,P. and Bahler,J. Periodic gene expression program of the fission yeast cell cycle., Nat. Genet., 36, 809-817. 2004.

상세보기
Simon,I., Barnett,J., Hannett,N., Harbison,C.T., Ranaldi,N.J., Volkert,T.L., Wyrick,J.J., Zeitlinger,J., Gifford,D.K., Jaakola,T.S., et al."Serial regulation of transcriptional regulators in the yeast cell cycle"., Cell, 106, 697-708. 2001.

상세보기
Spellman,P.T., Sherlock,G., Zhang,M.Q., Lyer,V.R., Anders,K., Eisen,M.B., Brown,P.Q., Botstein,D. and Futcher,B. "Comprehensive identification of cell cycle-regulated genes of the yeast S. cerevisiae by microarray hybridization"., Mol. Biol. Cell, 9, 3273-3297. 1998.

상세보기
Strogatz,S.H. "From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators"., Physica D, 143, 1-20. 2000.

상세보기
Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., Matese, J. C., Perou, C. M., Hurt, M. M., Brown, P. O., et al. "Identification of gene periodically expressed in the human cell cycle and their expression in tumors"., Mol. Biol. Cell, 13, 1977-2000, 2002.

상세보기
Zhao,L.P., Prentice,R. and Breeden,L. "Statistical modeling of large microarray data sets to identifying stimulus-response profiles"., Proc. Natl. Acad. Sci. USA, 98, 5631-5636, 2001.

상세보기
Ik-Hyun Lee, Tae-Sun Choi, "Shape From Focus Algorithm with Optimization of Focus Measure for Cell Image" Journal of Korea Institute of Information, Electronics, and Communication Technology, Vol.3, No.3, pp.8-13, 2010.

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