Thanks to spectacular advances in the techniques for identifying proteins separated by two-dimensional electrophoresis and in methods for large-scale analysis of proteome variations, proteomics is becoming an essential methodology in various fields of plant sciences. Plant proteomics would be most useful when combined with other functional genomics tools and approaches. A combination of microarray and proteomics analysis will indicate whether gene regulation is controlled at the level of transcription or translation and protein accumulation. In this review, we described the catalogues of the rice proteome which were constructed in our program, and functional characterization of some of these proteins was discussed. Mass-spectrometry is a most prevalent technique to identify rapidly a large of proteins in proteome analysis. However, the conventional Western blotting/sequencing technique us still used in many laboratories. As a first step to efficiently construct protein data-file in proteome analysis of major cereals, we have analyzed the N-terminal sequences of 100 rice embryo proteins and 70 wheat spike proteins separated by two-dimensional electrophoresis. Edman degradation revealed the N-terminal peptide sequences of only 31 rice proteins and 47 wheat proteins, suggesting that the rest of separated protein spots are N-terminally blocked. To efficiently determine the internal sequence of blocked proteins, we have developed a modified Cleveland peptide mapping method. Using this above method, the internal sequences of all blocked rice proteins (i. e., 69 proteins) were determined. Among these 100 rice proteins, thirty were proteins for which homologous sequence in the rice genome database could be identified. However, the rest of the proteins lacked homologous proteins. This appears to be consistent with the fact that about 30% of total rice cDNA have been deposited in the database. Also, the major proteins involved in the growth and development of rice can be identified using the proteome approach. Some of these proteins, including a calcium-binding protein that fumed out to be calreticulin, gibberellin-binding protein, which is ribulose-1,5-bisphosphate carboxylase/oxygenase activate in rice, and leginsulin-binding protein in soybean have functions in the signal transduction pathway. Proteomics is well suited not only to determine interaction between pairs of proteins, but also to identify multisubunit complexes. Currently, a protein-protein interaction database for plant proteins (http://genome .c .kanazawa-u.ac.jp/Y2H)could be a very useful tool for the plant research community. Recently, we are separated proteins from grain filling and seed maturation in rice to perform ESI-Q-TOF/MS and MALDI-TOF/MS. This experiment shows a possibility to easily and rapidly identify a number of 2-DE separated proteins of rice by ESI-Q-TOF/MS and MALDI-TOF/MS. Therefore, the Information thus obtained from the plant proteome would be helpful in predicting the function of the unknown proteins and would be useful in the plant molecular breeding. Also, information from our study could provide a venue to plant breeder and molecular biologist to design their research strategies precisely.

Societies are important sources of new information for users. However, most of these societies still rely on traditional, or rather ancient methods for gathering and servicing the information. Furthermore, most of the societies are trying to electrify processes such as managing members and paper submission as well as the process managing the information for service but are limited due to financial and technical reasons. Therefore, KISTI(Korea Institute of Science and Technology Information) has developed the ${\ulcorner}$KISTl-ACOMS (KISTI-Article Contribution Management System)${\lrcorner}$ as part of the national project for automating the process of processing academic information by societies, in order to convert journals published by academic societies in Korea into an electronic form and make them accessible on the Internet. This system has been developed in the year 2001 and has since been distributed to societies free of charge. The number of societies requesting the service has risen recently, which prompted us to take more recommendations of the societies that adopt this system into account in expanding and standardizing the area of service being provided by the system.

[ $\bigcirc$ ] In the growth simulation using genetic coefficients calculated with fooled data under various condition, WAGT was not higher and LAI, WLVG, WSO were higher, but WST was similar before grain-filling stage after the became lower because of higher translocation of carbohydrates than in the growth simulation using genetic coefficients calculated with data under high nitrogen applicated condition. $\bigcirc$ Genetic coefficients should be calculated with data showing potential in ORYZA2000, but under 180 kg and 240 kg N condition in 2003, plants were infected by panicle blast and also yield was not higher than under 120 kg N condition showing not potential condition and therefore not appropriate for genetic coefficients estimation compared with pooled data from various condition.

[ $\bigcirc$ ] In the growth simulation without changing of module with ORYZA2000, dry matter, LAI and leaf nitrogen content(FNLV) were estimated well under high nitrogen applicated condition, but overestimated under low nitrogen applicated condition. $\bigcirc$ Nitrogen stress factor on the SLA was introduced into ORYZA2000 because especially overestimated LAI under low nitrogen applicated condition was originated from SLA decrease with leaf nitrogen(FNLV) decrease. $\bigcirc$ In the growth simulation with modified SLA modified module, LAI was estimated well under even low nitrogen applicated condition, but dry matter was hardly changed compared with default. $\bigcirc$ Simulated plant nitrogen content and dry matter have no clear difference between modules, but compared with observed values, panicle weight(WSO) and rough rice yield(WRR14) were overestimated under high nitrogen applicated because of lodging, pest, disease and low nitrogen use efficiency.

[ $\bigcirc$ ] In the phenology model of ORYZA2000, the effect of photoperiod on the developmental rate was a little ignored because most crop parameters were measured with IRRI varieties which are insensitive to photoperiod, therefore it is very difficult to apply this phenology model directly to Korean varieties which are usually sensitive to photoperiod. $\bigcirc$ After introducing PPFAC and PPSE to improve the phenology model, the precision of heading date prediction was improved but not satisfied. $\bigcirc$ In the growth simulation using data from several regions, yield tended to be overestimated under high nitrogen applicated condition. $\bigcirc$ The precision of yield was much improved by introducing nitrogen use efficiency, but still different between regions because of different soil fertility or property of irrigation water between regions