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Aging of biological systems is controlled by various processes which have a potential impact on gene expression. Here we report a genome-wide transcriptome analysis of the fungal aging model Podospora anserina. Total RNA of three individuals of defined age were pooled and analyzed by SuperSAGE (serial analysis of gene expression). A bioinformatics analysis identified different molecular pathways to be affected during aging. While the abundance of transcripts linked to ribosomes and to the proteasome quality control system were found to decrease during aging, those associated with autophagy increase, suggesting that autophagy may act as a compensatory quality control pathway. Transcript profiles associated with the energy metabolism including mitochondrial functions were identified to fluctuate during aging. Comparison of wild-type transcripts, which are continuously down-regulated during aging, with those down-regulated in the long-lived, copper-uptake mutant grisea, validated the relevance of age-related changes in cellular copper metabolism. Overall, we (i) present a unique age-related data set of a longitudinal study of the experimental aging model P. anserina which represents a reference resource for future investigations in a variety of organisms, (ii) suggest autophagy to be a key quality control pathway that becomes active once other pathways fail, and (iii) present testable predictions for subsequent experimental investigations.
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models.
Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.