Endometrium Cell Surface Abnormalities in the Syrian Hamster as a Result of In Utero Exposure to Diethylstilbestrol

Scanning electron microscopy (SEM) was used to observe changes in the hamster endometrium cell surface following in utero pre- and/or postnatal exposure to diethylstilbestrol (DES). Some of the changes in cell surfaces are associated with alterations in cell sizes and shapes (from columnar to cuboidal and/or squamous) and in microvilli and mucous secretion. In all cases, DES treated uteri show mucosal cell surface pleomorphism, apocrine secretion and cystic accumulation of secretory material. Microvillous pleomorphism and peculiar linkages attaching one microvillus to others were investigated. Although the function and nature of such linkages is unclear, their presence seems to be more prominent in the in utero DES treated hamster endometrium. These infrastructures may provide a support for the microvilli distributed on the mucosal cell surfaces, i.e., a morphological compromise between the single microvillous surface and the microridged structures. These interconnections may represent glycocalyx material or remodeling of cell surfaces toward squamous epithelium

A critical review on modelling formalisms and simulation tools in computational biosystems

Integration of different kinds of biological processes is an ultimate goal for whole-cell modelling. We briefly review modelling formalisms that have been used in Systems Biology and identify the criteria that must be addressed by an integrating framework capable of modelling, analysing and simulating different biological networks. Aware that no formalism can fit all purposes we realize Petri nets as a suitable model for Metabolic Engineering and take a deeper perspective on the role of this formalism as an integrating framework for regulatory and metabolic networks.Research supported by PhD grant SFRH/BD/35215/2007 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal program

An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing

The authors would like to thank the support on this research by the CRISP Project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD

Whole-genome phylogenies of the family Bacillaceae and expansion of the sigma factor gene family in the Bacillus cereus species-group

<p>Abstract</p> <p>Background</p> <p>The <it>Bacillus cereus </it><it>sensu lato </it>group consists of six species (<it>B. anthracis</it>, <it>B. cereus</it>, <it>B. mycoides</it>, <it>B. pseudomycoides</it>, <it>B. thuringiensis</it>, and <it>B. weihenstephanensis</it>). While classical microbial taxonomy proposed these organisms as distinct species, newer molecular phylogenies and comparative genome sequencing suggests that these organisms should be classified as a single species (thus, we will refer to these organisms collectively as the <it>Bc </it>species-group). How do we account for the underlying similarity of these phenotypically diverse microbes? It has been established for some time that the most rapidly evolving and evolutionarily flexible portions of the bacterial genome are regulatory sequences and transcriptional networks. Other studies have suggested that the sigma factor gene family of these organisms has diverged and expanded significantly relative to their ancestors; sigma factors are those portions of the bacterial transcriptional apparatus that control RNA polymerase recognition for promoter selection. Thus, examining sigma factor divergence in these organisms would concurrently examine both regulatory sequences and transcriptional networks important for divergence. We began this examination by comparison to the sigma factor gene set of <it>B. subtilis</it>.</p> <p>Results</p> <p>Phylogenetic analysis of the <it>Bc </it>species-group utilizing 157 single-copy genes of the family <it>Bacillaceae </it>suggests that several taxonomic revisions of the genus <it>Bacillus </it>should be considered. Within the <it>Bc </it>species-group there is little indication that the currently recognized species form related sub-groupings, suggesting that they are members of the same species. The sigma factor gene family encoded by the <it>Bc </it>species-group appears to be the result of a dynamic gene-duplication and gene-loss process that in previous analyses underestimated the true heterogeneity of the sigma factor content in the <it>Bc </it>species-group.</p> <p>Conclusions</p> <p>Expansion of the sigma factor gene family appears to have preferentially occurred within the extracytoplasmic function (ECF) sigma factor genes, while the primary alternative (PA) sigma factor genes are, in general, highly conserved with those found in <it>B. subtilis</it>. Divergence of the sigma-controlled transcriptional regulons among various members of the <it>Bc </it>species-group likely has a major role in explaining the diversity of phenotypic characteristics seen in members of the <it>Bc </it>species-group.</p

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p