Robustness of Transcriptional Regulation in Yeast-like Model Boolean Networks

We investigate the dynamical properties of the transcriptional regulation of gene expression in the yeast Saccharomyces Cerevisiae within the framework of a synchronously and deterministically updated Boolean network model. By means of a dynamically determinant subnetwork, we explore the robustness of transcriptional regulation as a function of the type of Boolean functions used in the model that mimic the influence of regulating agents on the transcription level of a gene. We compare the results obtained for the actual yeast network with those from two different model networks, one with similar in-degree distribution as the yeast and random otherwise, and another due to Balcan et al., where the global topology of the yeast network is reproduced faithfully. We, surprisingly, find that the first set of model networks better reproduce the results found with the actual yeast network, even though the Balcan et al. model networks are structurally more similar to that of yeast.Comment: 7 pages, 4 figures, To appear in Int. J. Bifurcation and Chaos, typos were corrected and 2 references were adde

Critical Dynamics in Genetic Regulatory Networks: Examples from Four Kingdoms

The coordinated expression of the different genes in an organism is essential to sustain functionality under the random external perturbations to which the organism might be subjected. To cope with such external variability, the global dynamics of the genetic network must possess two central properties. (a) It must be robust enough as to guarantee stability under a broad range of external conditions, and (b) it must be flexible enough to recognize and integrate specific external signals that may help the organism to change and adapt to different environments. This compromise between robustness and adaptability has been observed in dynamical systems operating at the brink of a phase transition between order and chaos. Such systems are termed critical. Thus, criticality, a precise, measurable, and well characterized property of dynamical systems, makes it possible for robustness and adaptability to coexist in living organisms. In this work we investigate the dynamical properties of the gene transcription networks reported for S. cerevisiae, E. coli, and B. subtilis, as well as the network of segment polarity genes of D. melanogaster, and the network of flower development of A. thaliana. We use hundreds of microarray experiments to infer the nature of the regulatory interactions among genes, and implement these data into the Boolean models of the genetic networks. Our results show that, to the best of the current experimental data available, the five networks under study indeed operate close to criticality. The generality of this result suggests that criticality at the genetic level might constitute a fundamental evolutionary mechanism that generates the great diversity of dynamically robust living forms that we observe around us

Phylogeny in Aid of the Present and Novel Microbial Lineages: Diversity in Bacillus

Bacillus represents microbes of high economic, medical and biodefense importance. Bacillus strain identification based on 16S rRNA sequence analyses is invariably limited to species level. Secondly, certain discrepancies exist in the segregation of Bacillus subtilis strains. In the RDP/NCBI databases, out of a total of 2611 individual 16S rDNA sequences belonging to the 175 different species of the genus Bacillus, only 1586 have been identified up to species level. 16S rRNA sequences of Bacillus anthracis (153 strains), B. cereus (211 strains), B. thuringiensis (108 strains), B. subtilis (271 strains), B. licheniformis (131 strains), B. pumilus (83 strains), B. megaterium (47 strains), B. sphaericus (42 strains), B. clausii (39 strains) and B. halodurans (36 strains) were considered for generating species-specific framework and probes as tools for their rapid identification. Phylogenetic segregation of 1121, 16S rDNA sequences of 10 different Bacillus species in to 89 clusters enabled us to develop a phylogenetic frame work of 34 representative sequences. Using this phylogenetic framework, 305 out of 1025, 16S rDNA sequences presently classified as Bacillus sp. could be identified up to species level. This identification was supported by 20 to 30 nucleotides long signature sequences and in silico restriction enzyme analysis specific to the 10 Bacillus species. This integrated approach resulted in identifying around 30% of Bacillus sp. up to species level and revealed that B. subtilis strains can be segregated into two phylogenetically distinct groups, such that one of them may be renamed