Marc Strickert | PubMed Health

PubMed2006

Gene expression patterns reveal tissue-specific signaling networks controlling programmed cell death and ABA- regulated maturation in developing barley seeds.

Gene expression patterns covering over 10,000 seed-expressed sequences were analyzed by macroarray technology in maternal tissue (mainly pericarp) and filial endosperm and embryo during barley seed development from anthesis until late maturation. Defined sets of genes showing distinct expression patterns characterized both tissue type and major developmental phases. The analysis focused on regulatory networks involved in programmed cell death (PCD) and abscisic acid (ABA)-mediated maturation. These processes were similar in the different tissues, but typically involved the expression of alternative members of a common gene family. The analysis of co-expressed gene sets and the identification of cis regulatory elements in orthologous rice gene 'promoter' regions suggest that PCD in the pericarp is mediated by distinct classes of proteases and is under the hormonal control of both jasmonic acid (JA) and ethylene via ethylene-responsive element binding protein (EREBP) transcription factors (TFs). On the other hand, PCD in endosperm apparently involves only the ethylene pathway, but employs distinct gene family members from those active in the pericarp, and a different set of proteases and TFs. JA biosynthetic genes are hardly activated. Accordingly, JA levels are high in the pericarp but low in the endosperm during middle and late developmental stages. Similarly, genes acting in the deduced ABA biosynthetic pathway and signaling network differ between endosperm and embryo. ABA in the endosperm appears to exert an influence over storage product synthesis via SNF1 kinase. In the embryo, ABA seems to influence the acquisition of desiccation tolerance via ABA response element binding factors, but the data also suggest the existence of an ABA-independent but interactive pathway acting via the dehydration-responsive element binding (DREB) 2A TF.

Abscisic Acid↗

PubMedDate not supplied

Recursive self-organizing network models.

Self-organizing models constitute valuable tools for data visualization, clustering, and data mining. Here, we focus on extensions of basic vector-based models by recursive computation in such a way that sequential and tree-structured data can be processed directly. The aim of this article is to give a unified review of important models recently proposed in literature, to investigate fundamental mathematical properties of these models, and to compare the approaches by experiments. We first review several models proposed in literature from a unifying perspective, thereby making use of an underlying general framework which also includes supervised recurrent and recursive models as special cases. We shortly discuss how the models can be related to different neuron lattices. Then, we investigate theoretical properties of the models in detail: we explicitly formalize how structures are internally stored in different context models and which similarity measures are induced by the recursive mapping onto the structures. We assess the representational capabilities of the models, and we shortly discuss the issues of topology preservation and noise tolerance. The models are compared in an experiment with time series data. Finally, we add an experiment for one context model for tree-structured data to demonstrate the capability to process complex structures.

Artifacts↗