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Advances in biotechnologies rapidly increase the number of molecules of a cell which can be observed simultaneously. This includes expression levels of thousands or ten-thousands of genes as well as concentration levels of metabolites or proteins. Such Profile data, observed at different times or at different experimental conditions (e.g., heat or dry stress), show how the biological experiment is reflected on the molecular level. This information is helpful to understand the molecular behaviour and to identify molecules or combination of molecules that characterise specific biological condition (e.g., disease). This work shows the potentials of component extraction algorithms to identify the major factors which influenced the observed data. This can be the expected experimental factors such as the time or temperature as well as unexpected factors such as technical artefacts or even unknown biological behaviour. Extracting components means to reduce the very high-dimensional data to a small set of new variables termed components. Each component is a combination of all original variables. The classical approach for that purpose is the principal component analysis (PCA). It is shown that, in contrast to PCA which maximises the variance only, modern approaches such as independent component analysis (ICA) are more suitable for analysing molecular data. The condition of independence between components of ICA fits more naturally our assumption of individual (independent) factors which influence the data. This higher potential of ICA is demonstrated by a crossing experiment of the model plant Arabidopsis thaliana (Thale Cress). The experimental factors could be well identified and, in addition, ICA could even detect a technical artefact. However, in continuously observations such as in time experiments, the data show, in general, a nonlinear distribution. To analyse such nonlinear data, a nonlinear extension of PCA is used. This nonlinear PCA (NLPCA) is based on a neural network algorithm. The algorithm is adapted to be applicable to incomplete molecular data sets. Thus, it provides also the ability to estimate the missing data. The potential of nonlinear PCA to identify nonlinear factors is demonstrated by a cold stress experiment of Arabidopsis thaliana. The results of component analysis can be used to build a molecular network model. Since it includes functional dependencies it is termed functional network. Applied to the cold stress data, it is shown that functional networks are appropriate to visualise biological processes and thereby reveals molecular dynamics.
Sucrose synthase (Susy) is a key enzyme of sucrose metabolism, catalysing the reversible conversion of sucrose and UDP to UDP-glucose and fructose. Therefore, its activity, localization and function have been studied in various plant species. It has been shown that Susy can play a role in supplying energy in companion cells for phloem loading (Fu and Park, 1995), provides substrates for starch synthesis (Zrenner et al., 1995), and supplies UDP-glucose for cell wall synthesis (Haigler et al., 2001). Analysis of the Arabidopsis genome identifies six Susy isoforms. The expression of these isoforms was investigated using promoter-reporter gene constructs (GUS) and real time RT-PCR. Although these isoforms are closely related at the protein level they have radically different spatial and temporal patterns of expression in the plant with no two isoforms showing the same distribution. More than one isoform is expressed in all organs examined. Some of them have high but specific expression in particular organs or developmental stages whilst others are constantly expressed throughout the whole plant and across various stages of development. The in planta function of the six Susy isoforms were explored through analysis of T-DNA insertion mutants and RNAi lines. Plants without the expression of individual isoforms show no differences in growth and development, and are not significantly different from wild type plants in soluble sugars, starch and cellulose contents under all growth conditions investigated. Analysis of T-DNA insertion mutant lacking Sus3 isoform that was exclusively expressed in stomata cells only had a minor influence on guard cell osmoregulation and/or bioenergetics. Although none of the sucrose synthases appear to be essential for normal growth under our standard growth conditions, they may be necessary for growth under stress conditions. Different isoforms of sucrose synthase respond differently to various abiotic stresses. It has been shown that oxygen deprivation up regulates Sus1 and Sus4 and increases total Susy activity. However, the analysis of the plants with reduced expression of both Sus1 and Sus4 revealed no obvious effects on plant performance under oxygen deprivation. Low temperature up regulates Sus1 expression but the loss of this isoform has no effect on the freezing tolerance of non acclimated and cold acclimated plants. These data provide a comprehensive overview of the expression of this gene family which supports some of the previously reported roles for Susy and indicates the involvement of specific isoforms in metabolism and/or signalling.