Stochastic Simulations of Cellular Processes in Bacteria

Primer pla d'un edifici entre mitgeres. Consta de planta baixa, planta entresòl, tres plantes pis, planta terrat i amb un volum remuntat, reculat del pla de façana. La planta baixa i l'entresòl formen una gran peça

Extrinsic noise driven phenotype switching in a self-regulating gene

Due to inherent noise in intracellular networks cellular decisions can be random, so genetically identical cells can display different phenotypic behavior even in identical environments. Most previous work in understanding the decision-making process has focused on the role of intrinsic noise in these systems. Yet, especially in the high copy-number regime, extrinsic noise has been shown to be much more significant. Here, using a prototypical example of a bistable self-regulating gene model, we develop a theoretical framework describing the combined effect of intrinsic and extrinsic noise on the dynamics of stochastic genetic switches. Employing our theory and Monte Carlo simulations, we show that extrinsic noise not only significantly alters the lifetimes of the phenotypic states, but can induce bistability in unexpected regions of parameter space, and may fundamentally change the escape mechanism. These results have implications for interpreting experimentally observed heterogeneity in cellular populations and for stochastic modeling of cellular decision processes.Comment: 5 pages, 4 figure

Horizontal gene transfer of zinc and non-zinc forms of bacterial ribosomal protein S4

<p>Abstract</p> <p>Background</p> <p>The universal ribosomal protein S4 is essential for the initiation of small subunit ribosomal assembly and translational accuracy. Being part of the information processing machinery of the cell, the gene for S4 is generally thought of as being inherited vertically and has been used in concatenated gene phylogenies. Here we report the evolution of ribosomal protein S4 in relation to a broad sharing of zinc/non-zinc forms of the gene and study the scope of horizontal gene transfer (HGT) of S4 during bacterial evolution.</p> <p>Results</p> <p>In this study we present the complex evolutionary history of ribosomal protein S4 using 660 bacterial genomes from 16 major bacterial phyla. According to conserved characteristics in the sequences, S4 can be classified into C+ (zinc-binding) and C- (zinc-free) variants, with 26 genomes (mainly from the class <it>Clostridia</it>) containing genes for both. A maximum likelihood phylogenetic tree of the S4 sequences was incongruent with the standard bacterial phylogeny, indicating a departure from strict vertical inheritance. Further analysis using the genome content near the S4 genes, which are usually located in a conserved gene cluster, showed not only that HGT of the C- gene had occurred at various stages of bacterial evolution, but also that both the C- and C+ genes were present before the individual phyla diverged. To explain the latter, we theorize that a gene pool existed early in bacterial evolution from which bacteria could sample S4 gene variants, according to environmental conditions. The distribution of the C+/- variants for seven other zinc-binding ribosomal proteins in these 660 bacterial genomes is consistent with that seen for S4 and may shed light on the evolutionary pressures involved.</p> <p>Conclusion</p> <p>The complex history presented for "core" protein S4 suggests the existence of a gene pool before the emergence of bacterial lineages and reflects the pervasive nature of HGT in subsequent bacterial evolution. This has implications for both theoretical models of evolution and practical applications of phylogenetic reconstruction as well as the control of zinc economy in bacterial cells.</p

MultiSeq: unifying sequence and structure data for evolutionary analysis

BACKGROUND: Since the publication of the first draft of the human genome in 2000, bioinformatic data have been accumulating at an overwhelming pace. Currently, more than 3 million sequences and 35 thousand structures of proteins and nucleic acids are available in public databases. Finding correlations in and between these data to answer critical research questions is extremely challenging. This problem needs to be approached from several directions: information science to organize and search the data; information visualization to assist in recognizing correlations; mathematics to formulate statistical inferences; and biology to analyze chemical and physical properties in terms of sequence and structure changes. RESULTS: Here we present MultiSeq, a unified bioinformatics analysis environment that allows one to organize, display, align and analyze both sequence and structure data for proteins and nucleic acids. While special emphasis is placed on analyzing the data within the framework of evolutionary biology, the environment is also flexible enough to accommodate other usage patterns. The evolutionary approach is supported by the use of predefined metadata, adherence to standard ontological mappings, and the ability for the user to adjust these classifications using an electronic notebook. MultiSeq contains a new algorithm to generate complete evolutionary profiles that represent the topology of the molecular phylogenetic tree of a homologous group of distantly related proteins. The method, based on the multidimensional QR factorization of multiple sequence and structure alignments, removes redundancy from the alignments and orders the protein sequences by increasing linear dependence, resulting in the identification of a minimal basis set of sequences that spans the evolutionary space of the homologous group of proteins. CONCLUSION: MultiSeq is a major extension of the Multiple Alignment tool that is provided as part of VMD, a structural visualization program for analyzing molecular dynamics simulations. Both are freely distributed by the NIH Resource for Macromolecular Modeling and Bioinformatics and MultiSeq is included with VMD starting with version 1.8.5. The MultiSeq website has details on how to download and use the software

Experimental and computational determination of tRNA dynamics

AbstractAs the molecular representation of the genetic code, tRNA plays a central role in the translational machinery where it interacts with several proteins and other RNAs during the course of protein synthesis. These interactions exploit the dynamic flexibility of tRNA. In this minireview, we discuss the effects of modified bases, ions, and proteins on tRNA structure and dynamics and the challenges of observing its motions over the cycle of translation

Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism’s biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria

Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations

AbstractSimulation of in vivo cellular processes with the reaction–diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel efficiency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

Metabolic Model and Maps. A zip file archive containing the iST807 metabolic model in the SBML format, an Excel spreadsheet including descriptions of the metabolic model and the metabolic map in formats compatible with Cytoscape and Escher. (ZIP 1392 KB