The effect of network topology on the stability of discrete state models of genetic control

Boolean networks have been proposed as potentially useful models for genetic control. An important aspect of these networks is the stability of their dynamics in response to small perturbations. Previous approaches to stability have assumed uncorrelated random network structure. Real gene networks typically have nontrivial topology significantly different from the random network paradigm. In order to address such situations, we present a general method for determining the stability of large Boolean networks of any specified network topology and predicting their steady-state behavior in response to small perturbations. Additionally, we generalize to the case where individual genes have a distribution of `expression biases,' and we consider non-synchronous update, as well as extension of our method to non-Boolean models in which there are more than two possible gene states. We find that stability is governed by the maximum eigenvalue of a modified adjacency matrix, and we test this result by comparison with numerical simulations. We also discuss the possible application of our work to experimentally inferred gene networks.Comment: 25 pages, 4 figures; added supplementary information, fixed typos and figure, reformatte

Emergent complex neural dynamics

A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range of cortical configurations in a flexible manner remains a fundamental problem in neuroscience. One plausible solution is the involvement of universal mechanisms of emergent complex phenomena evident in dynamical systems poised near a critical point of a second-order phase transition. We review recent theoretical and empirical results supporting the notion that the brain is naturally poised near criticality, as well as its implications for better understanding of the brain

Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments

A transcription factor contributes to pathogenesis and virulence in streptococcus pneumoniae

To date, the role of transcription factors (TFs) in the progression of disease for many pathogens is yet to be studied in detail. This is probably due to transient, and generally low expression levels of TFs, which are the central components controlling the expression of many genes during the course of infection. However, a small change in the expression or specificity of a TF can radically alter gene expression. In this study, we combined a number of quality-based selection strategies including structural prediction of modulated genes, gene ontology and network analysis, to predict the regulatory mechanisms underlying pathogenesis of Streptococcus pneumoniae (the pneumococcus). We have identified two TFs (SP_0676 and SP_0927 [SmrC]) that might control tissue-specific gene expression during pneumococcal translocation from the nasopharynx to lungs, to blood and then to brain of mice. Targeted mutagenesis and mouse models of infection confirmed the role of SP_0927 in pathogenesis and virulence, and suggests that SP_0676 might be essential to pneumococcal viability. These findings provide fundamental new insights into virulence gene expression and regulation during pathogenesis.Layla K. Mahdi, Esmaeil Ebrahimie, David L. Adelson, James C. Paton, Abiodun D. Ogunniy

MscS-like mechanosensitive channels in plants and microbes

The challenge of osmotic stress is something all living organisms must face as a result of environmental dynamics. Over the past three decades, innovative research and cooperation across disciplines have irrefutably established that cells utilize mechanically gated ion channels to release osmolytes and prevent cell lysis during hypoosmotic stress. Early electrophysiological analysis of the inner membrane of Escherichia coli identified the presence of three distinct mechanosensitive activities. The subsequent discoveries of the genes responsible for two of these activities, the mechanosensitive channels of large (MscL) and small (MscS) conductance, led to the identification of two diverse families of mechanosensitive channels. The latter of these two families, the MscS family, consists of members from bacteria, archaea, fungi, and plants. Genetic and electrophysiological analysis of these family members has provided insight into how organisms use mechanosensitive channels for osmotic regulation in response to changing environmental and developmental circumstances. Furthermore, determining the crystal structure of E. coli MscS and several homologues in several conformational states has contributed to our understanding of the gating mechanisms of these channels. Here we summarize our current knowledge of MscS homologues from all three domains of life and address their structure, proposed physiological functions, electrophysiological behaviors, and topological diversity

A GFP-lacZ Bicistronic Reporter System for Promoter Analysis in Environmental Gram-Negative Bacteria

Here, we describe a bicistronic reporter system for the analysis of promoter activity in a variety of Gram-negative bacteria at both the population and single-cell levels. This synthetic genetic tool utilizes an artificial operon comprising the gfp and lacZ genes that are assembled in a suicide vector, which is integrated at specific sites within the chromosome of the target bacterium, thereby creating a monocopy reporter system. This tool was instrumental for the complete in vivo characterization of two promoters, Pb and Pc, that drive the expression of the benzoate and catechol degradation pathways, respectively, of the soil bacterium Pseudomonas putida KT2440. The parameterization of these promoters in a population (using β-galactosidase assays) and in single cells (using flow cytometry) was necessary to examine the basic numerical features of these systems, such as the basal and maximal levels and the induction kinetics in response to an inducer (benzoate). Remarkably, GFP afforded a view of the process at a much higher resolution compared with standard lacZ tests; changes in fluorescence faithfully reflected variations in the transcriptional regimes of individual bacteria. The broad host range of the vector/reporter platform is an asset for the characterization of promoters in different bacteria, thereby expanding the diversity of genomic chasses amenable to Synthetic Biology methods

Salivary gland branching morphogenesis: a quantitative systems analysis of the Eda/Edar/NFκB paradigm

<p>Abstract</p> <p>Background</p> <p>Ectodysplasin-A appears to be a critical component of branching morphogenesis. Mutations in mouse <it>Eda </it>or human <it>EDA </it>are associated with absent or hypoplastic sweat glands, sebaceous glands, lacrimal glands, salivary glands (SMGs), mammary glands and/or nipples, and mucous glands of the bronchial, esophageal and colonic mucosa. In this study, we utilized <it>Eda</it>^{<it>Ta </it>}(Tabby) mutant mice to investigate how a marked reduction in functional Eda propagates with time through a defined genetic subcircuit and to test the proposition that canonical NFκB signaling is sufficient to account for the differential expression of developmentally regulated genes in the context of <it>Eda </it>polymorphism.</p> <p>Results</p> <p>The quantitative systems analyses do not support the stated hypothesis. For most NFκB-regulated genes, the observed time course of gene expression is nearly unchanged in Tabby (<it>Eda</it>^<it>Ta</it>) as compared to wildtype mice, as is NFκB itself. Importantly, a subset of genes is dramatically differentially expressed in Tabby (<it>Edar</it>, <it>Fgf8</it>, <it>Shh</it>, <it>Egf</it>, <it>Tgfa</it>, <it>Egfr</it>), strongly suggesting the existence of an alternative Eda-mediated transcriptional pathway pivotal for SMG ontogeny. Experimental and <it>in silico </it>investigations have identified C/EBPα as a promising candidate.</p> <p>Conclusion</p> <p>In Tabby SMGs, upregulation of the Egf/Tgfα/Egfr pathway appears to mitigate the potentially severe abnormal phenotype predicted by the downregulation of Fgf8 and Shh. Others have suggested that the buffering of the phenotypic outcome that is coincident with variant Eda signaling could be a common mechanism that permits viable and diverse phenotypes, normal and abnormal. Our results support this proposition. Further, if branching epithelia use variations of a canonical developmental program, our results are likely applicable to understanding the phenotypes of other branching organs affected by <it>Eda </it>(<it>EDA</it>) mutation.</p

Beyond Networks: Search for Relevant Subsets in Complex Systems

Networks are often used to represent the relations among the variables of a dynamical system. The properties of network topology are usually exploited to understand the organization of the system. Nevertheless, the dynamical organization of a system might considerably differ from its topological one. In this paper, we describe a method to identify \u201crelevant subsets\u201d of variables. The variables belonging to a relevant subset should be strongly integrated and should have a much weaker interaction with the other system variables. Extending previous works on neural networks, an information-theoretic measure is introduced, i.e., the Dynamical Cluster Index, in order to identify candidate relevant subsets. The method solely relies on observations of the variables\u2019 values in tim