Bibliography of: Gene Expression Regulation

  1. Collado-Vides, J., Hofestadt, R., Mavrovouniotis, M.L., and Michal, G.. "Modeling and simulation of gene regulation and metabolic pathways." Biosystems. 49 (1). 1999. pp. 79-82.
    [ .pdf ] [ PubMed ]

    Keywords: *Gene Expression Regulation ; *Metabolism ; *Models Genetic


  2. Hofestadt, R., Mavrovouniotis, M.L., Collado-Vides, J., and Loffler, M.. "Modeling and simulation of metabolic pathways, gene regulation and cell differentiation. October 22-27, 1995. International Conference and Research Center for Computer Science, Schloss Dagstuhl, Saarland, Germany." Bioessays. 18 (4). 1996. pp. 333-5.
    [ PubMed ]

    Keywords: Cell Differentiation ; *Computer Simulation ; Gene Expression Regulation ; Information Systems ; Metabolism ; *Models Biological


  3. Kawashima, T., Kawashima, S., Kohara, Y., Kanehisa, M., and Makabe, K.W.. "Update of MAGEST: Maboya Gene Expression patterns and Sequence Tags." Nucleic Acids Res. 30 (1). 2002. pp. 119-20.
    [ .pdf ] [ PubMed ]

    MAGEST is a database for maternal gene expression information for an ascidian, Halocynthia roretzi. The ascidian has become an animal model in developmental biological research because it shows a simple developmental process, and belongs to one of the chordate groups. Various data are deposited into the MAGEST database, e.g. the 3'- and 5'-tag sequences from the fertilized egg cDNA library, the results of similarity searches against GenBank and the expression data from whole mount in situ hybridization. Over the last 2 years, the data retrieval systems have been improved in several aspects, and the tag sequence entries have increased to over 20 000 clones. Additionally, we constructed a database, translated MAGEST, for the amino acid fragment sequences predicted from the EST data sets. Using this information comprehensively, we should obtain new information on gene functions. The MAGEST database is accessible via the Internet at http://www.genome.ad.jp/magest/.

    Keywords: Amino Acid Sequence ; DNA Complementary_genetics ; *Databases Genetic ; *Expressed Sequence Tags ; Forecasting ; *Gene Expression Regulation ; Developmental ; Gene Library ; In Situ Hybridization ; Information Storage and Retrieval ; Internet ; RNA Messenger Stored_biosynthesis ; Urochordata_*embryology ; Urochordata_*genetics ; Urochordata_metabolism ; Zygote_metabolism


  4. Matsuno, H., Doi, A., Nagasaki, M., and Miyano, S.. "Hybrid Petri net representation of gene regulatory network." Pac Symp Biocomput. 2000. pp. 341-52.
    [ .pdf ] [ PubMed ]

    It is important to provide a representation method of gene regulatory networks which realizes the intuitions of biologists while keeping the universality in its computational ability. In this paper, we propose a method to exploit hybrid Petri net (HPN) for representing gene regulatory networks. The HPN is an extension of Petri nets which have been used to represent many kinds of systems including stochastic ones in the field of computer sciences and engineerings. Since the HPN has continuous and discrete elements, it can easily handle biological factors such as protein and mRNA concentrations. We demonstrate that, by using HPNs, it is possible to translate biological facts into HPNs in a natural manner. It should be also emphasized that a hierarchical approach is taken for our construction of the genetic switch mechanism of lambda phage which is realized by using HPNs. This hierarchical approach with HPNs makes easier the arrangement of the components in the gene regulatory network based on the biological facts and provides us a prospective view of the network. We also show some computational results of the protein dynamics of the lambda phage mechanism that is simulated and observed by implementing the HPN on a currently available tool.

    Keywords: Bacteriophage lambda_genetics ; Bacteriophage lambda_growth and development ; Computer Simulation ; Gene Expression Regulation ; Gene Expression Regulation Viral ; Genes Viral ; *Models Genetic ; Operon ; Repressor Proteins_genetics ; Stochastic Processes ; Viral Proteins_genetics