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ABSTRACT: Infection dynamics have been studied extensively on complex networks,
yielding insight into the effects of heterogeneity in contact patterns on
disease spread. Somewhat separately, metapopulations have provided a paradigm
for modeling systems with spatially extended and "patchy" organization. In this
paper we expand on the use of multitype networks for combining these paradigms,
such that simple contagion models can include complexity in the agent
interactions and multiscale structure. We first present a generalization of the
Volz-Miller mean-field approximation for Susceptible-Infected-Recovered (SIR)
dynamics on multitype networks. We then use this technique to study the special
case of epidemic fronts propagating on a one-dimensional lattice of
interconnected networks - representing a simple chain of coupled population
centers - as a necessary first step in understanding how macro-scale disease
spread depends on micro-scale topology. Using the formalism of front
propagation into unstable states, we derive the effective transport
coefficients of the linear spreading: asymptotic speed, characteristic
wavelength, and diffusion coefficient for the leading edge of the pulled
fronts, and analyze their dependence on the underlying graph structure. We also
derive the epidemic threshold for the system and study the front profile for
various network configurations. To our knowledge, this is the first such
application of front propagation concepts to random network models.
04/2013;
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ABSTRACT: Small non-coding RNAs (ncRNAs) are important components of many regulatory pathways in bacteria and play key roles in regulating factors important for virulence. Carbon catabolite repression control is modulated by small RNAs (crcZ or crcZ and crcY) in Pseudomonas aeruginosa and Pseudomonas putida. In this study, we demonstrate that expression of crcZ and crcX (formerly designated psr1 and psr2, respectively) is dependent upon RpoN together with the two-component system CbrAB, and is influenced by the carbon source present in the medium in the model plant pathogen Pseudomonas syringae pv tomato DC3000. The distribution of the members of the Crc ncRNA family was also determined by screening available genomic sequences of the Pseudomonads. Interestingly, variable numbers of the Crc family members exist in Pseudomonas genomes. The ncRNAs are comprised of three main subfamilies, named CrcZ, CrcX and CrcY. Most importantly the CrcX subfamily appears to be unique to all P. syringae strains sequenced to date.
RNA biology 01/2013; 10(2). · 5.56 Impact Factor
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ABSTRACT: Pseudomonas syringae pv. tomato DC3000 encodes fifteen sigma factors. The majority are members of the extracytoplasmic function class of sigma factors, including five that belong to iron starvation sub-group. In this study we identified the genes controlled by three iron starvation sigma factors. Their regulons are composed of a small number of genes likely to be involved with iron uptake.
Applied and environmental microbiology 11/2012; · 3.69 Impact Factor
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ABSTRACT: The diversity of regulatory systems encoded by bacteria provides an indication of the variety of stresses and interactions that these organisms encounter in nature. We have been investigating how the plant pathogen Pseudomonas syringae pv. tomato DC3000 responds to iron limitation and have focused on the iron starvation (IS) sigma factors to identify regulon members and to explore the mechanistic details of genetic control for this class of regulators. In the study described in this report, we used chromatin immunoprecipitation paired with high-throughput sequencing (ChIP-Seq) to screen the genome for locations associated with binding of the P. syringae IS sigma factor PSPTO_1203. We used multiple methods to demonstrate differential regulation of two genes identified in the ChIP-Seq screen and characterize the promoter elements that facilitate PSPTO_1203-dependent regulation. The genes regulated by PSPTO_1203 encode a TonB-dependent transducer (PSPTO_1206) and a cytoplasmic membrane protein (PSPTO_2145), which is located in the P. syringae pyoverdine cluster. Additionally, we identified siderophores that induce the activity of PSPTO_1203 and used this information to investigate the functional components of the signal transduction cascade.
Journal of bacteriology 08/2011; 193(20):5775-83. · 3.94 Impact Factor
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Bronwyn G Butcher,
Philip A Bronstein, Christopher R Myers,
Paul V Stodghill,
James J Bolton,
Eric J Markel,
Melanie J Filiatrault,
Bryan Swingle,
Ahmed Gaballa,
John D Helmann,
David J Schneider,
Samuel W Cartinhour
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ABSTRACT: The plant pathogen Pseudomonas syringae pv. tomato DC3000 (DC3000) is found in a wide variety of environments and must monitor and respond to various environmental signals such as the availability of iron, an essential element for bacterial growth. An important regulator of iron homeostasis is Fur (ferric uptake regulator), and here we present the first study of the Fur regulon in DC3000. Using chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq), 312 chromosomal regions were highly enriched by coimmunoprecipitation with a C-terminally tagged Fur protein. Integration of these data with previous microarray and global transcriptome analyses allowed us to expand the putative DC3000 Fur regulon to include genes both repressed and activated in the presence of bioavailable iron. Using nonradioactive DNase I footprinting, we confirmed Fur binding in 41 regions, including upstream of 11 iron-repressed genes and the iron-activated genes encoding two bacterioferritins (PSPTO_0653 and PSPTO_4160), a ParA protein (PSPTO_0855), and a two-component system (TCS) (PSPTO_3382 to PSPTO_3380).
Journal of bacteriology 07/2011; 193(18):4598-611. · 3.94 Impact Factor
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Melanie J Filiatrault,
Paul V Stodghill, Christopher R Myers,
Philip A Bronstein,
Bronwyn G Butcher,
Hanh Lam,
George Grills,
Peter Schweitzer,
Wei Wang,
David J Schneider,
Samuel W Cartinhour
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ABSTRACT: RNA-Seq has provided valuable insights into global gene expression in a wide variety of organisms. Using a modified RNA-Seq approach and Illumina's high-throughput sequencing technology, we globally identified 5'-ends of transcripts for the plant pathogen Pseudomonas syringae pv. tomato str. DC3000. A substantial fraction of 5'-ends obtained by this method were consistent with results obtained using global RNA-Seq and 5'RACE. As expected, many 5'-ends were positioned a short distance upstream of annotated genes. We also captured 5'-ends within intergenic regions, providing evidence for the expression of un-annotated genes and non-coding RNAs, and detected numerous examples of antisense transcription, suggesting additional levels of complexity in gene regulation in DC3000. Importantly, targeted searches for sequence patterns in the vicinity of 5'-ends revealed over 1200 putative promoters and other regulatory motifs, establishing a broad foundation for future investigations of regulation at the genomic and single gene levels.
PLoS ONE 01/2011; 6(12):e29335. · 4.09 Impact Factor
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ABSTRACT: Non-coding RNAs (ncRNAs) are important components of many regulatory pathways and have key roles in regulating diverse functions. In the Pseudomonads, the two-component system, GacA/S, directly regulates at least two well-characterized ncRNAs, RsmZ and RsmY, which act by sequestration of translation repressor proteins to control expression of various exoproducts. Pseudomonas fluorescens CHA0 possesses a third ncRNA, RsmX, which also participates in this regulatory pathway. In this study we confirmed expression of five rsmX ncRNAs in Pseudomonas syringae pv. tomato DC3000, and determined the distribution of the members of the rsmX ncRNA family by screening available genomic sequences of the Pseudomonads. Variable numbers of the rsmX family exist in Pseudomonas genomes, with up to five paralogs in Pseudomonas syringae strains. In Pseudomonas syringae pv. tomato DC3000, the rsmX genes are 112 to 120 nucleotides in size and are predicted by structural analysis to contain multiple exposed GGA motifs, which is consistent with structural features of the Rsm ncRNAs. We also found that these rsmX ncRNA genes share a conserved upstream region suggesting that their expression is dependent upon the global response regulator, GacA.
RNA biology 09/2010; 7(5):508-16. · 5.56 Impact Factor
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ABSTRACT: In this paper, we describe the context sensitivity problem encountered in partitioning a heterogeneous biological sequence into statistically homogeneous segments. After showing signatures of the problem in the bacterial genomes of Escherichia coli K-12 MG1655 and Pseudomonas syringae DC3000, when these are segmented using two entropic segmentation schemes, we clarify the contextual origins of these signatures through mean-field analyses of the segmentation schemes. Finally, we explain why we believe all sequence segmentation schems are plagued by the context sensitivity problem.
05/2009;
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ABSTRACT: In this paper, we extend a previously developed recursive entropic segmentation scheme for applications to biological sequences. Instead of Bernoulli chains, we model the statistically stationary segments in a biological sequence as Markov chains, and define a generalized Jensen-Shannon divergence for distinguishing between two Markov chains. We then undertake a mean-field analysis, based on which we identify pitfalls associated with the recursive Jensen-Shannon segmentation scheme. Following this, we explain the need for segmentation optimization, and describe two local optimization schemes for improving the positions of domain walls discovered at each recursion stage. We also develop a new termination criterion for recursive Jensen-Shannon segmentation based on the strength of statistical fluctuations up to a minimum statistically reliable segment length, avoiding the need for unrealistic null and alternative segment models of the target sequence. Finally, we compare the extended scheme against the original scheme by recursively segmenting the Escherichia coli K-12 MG1655 genome.
05/2009;
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ABSTRACT: In this paper, the multiclass supervised training problem is considered when a discrete set of classes is assumed. Upon generating affine models for finite data sets, we have observed the invariance of certain measures of performance after a trained classifier has been presented with test data of unknown classification. Specifically, after constructing mappings between training vectors and their desired targets, the class membership and ranking of test data has been found to remain either invariant or close to invariant under a transformation of the set of target vectors. Therefore, we derive conditions explaining how this type of invariance can arise when the multiclass problem is phrased in the context of linear networks. A bioinformatics example is then presented in order to demonstrate various principles outlined in this work.
IEEE Transactions on Neural Networks 04/2009; 20(5):745-57. · 2.95 Impact Factor
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ABSTRACT: We demonstrate the use of a variational method to determine a quantitative lower bound on the rate of convergence of Markov chain Monte Carlo (MCMC) algorithms as a function of the target density and proposal density. The bound relies on approximating the second largest eigenvalue in the spectrum of the MCMC operator using a variational principle and the approach is applicable to problems with continuous state spaces. We apply the method to one dimensional examples with Gaussian and quartic target densities, and we contrast the performance of the random walk Metropolis-Hastings algorithm with a "smart" variant that incorporates gradient information into the trial moves, a generalization of the Metropolis adjusted Langevin algorithm. We find that the variational method agrees quite closely with numerical simulations. We also see that the smart MCMC algorithm often fails to converge geometrically in the tails of the target density except in the simplest case we examine, and even then care must be taken to choose the appropriate scaling of the deterministic and random parts of the proposed moves. Again, this calls into question the utility of smart MCMC in more complex problems. Finally, we apply the same method to approximate the rate of convergence in multidimensional Gaussian problems with and without importance sampling. There we demonstrate the necessity of importance sampling for target densities which depend on variables with a wide range of scales.
Physical Review E 11/2008; 78(4 Pt 2):046704. · 2.26 Impact Factor
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ABSTRACT: The functioning of many biochemical networks is often robust-remarkably stable under changes in external conditions and internal reaction parameters. Much recent work on robustness and evolvability has focused on the structure of neutral spaces, in which system behavior remains invariant to mutations. Recently we have shown that the collective behavior of multiparameter models is most often sloppy: insensitive to changes except along a few 'stiff' combinations of parameters, with an enormous sloppy neutral subspace. Robustness is often assumed to be an emergent evolved property, but the sloppiness natural to biochemical networks offers an alternative nonadaptive explanation. Conversely, ideas developed to study evolvability in robust systems can be usefully extended to characterize sloppy systems.
Current Opinion in Biotechnology 08/2008; 19(4):389-95. · 7.71 Impact Factor
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ABSTRACT: Systematic comparison of the current repertoire of virulence-associated genes for three Pseudomonas syringae strains with complete genome sequences, P. syringae pv. tomato DC3,000, P. syringae pv. phaseolicola 1448A, and P. syringae pv. syringae B728a, is prompted by recent advances in virulence factor identification in P. syringae and other bacteria. Among these are genes linked to epiphytic fitness, plant- and insect-active toxins, secretion pathways, and virulence regulators, all reflected in the recently updated DC3,000 genome annotation. Distribution of virulence genes in relation to P. syringae genome organization was analyzed to distinguish patterns of conservation among genomes and association between genes and mobile genetic elements. Variable regions were identified on the basis of deviation in sequence composition and gaps in syntenic alignment among the three genomes. Mapping gene location relative to the genome structure revealed strong segregation of the HrpL regulon with variable genome regions (VR), divergent distribution patterns for toxin genes depending on association with plant or insect pathogenesis, and patterns of distribution for other virulence genes that highlight potential sources of strain-to-strain differences in host interaction. Distribution of VR among other sequenced bacterial genomes was analyzed and future plans for characterization of this potential reservoir of virulence genes are discussed.
Molecular Plant-Microbe Interactions 07/2008; 21(6):685-700. · 4.43 Impact Factor
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ABSTRACT: Bacteria that survive under variable conditions possess an assortment of genetic regulators to meet these challenges. The group IV or extracytoplasmic function (ECF) sigma factors regulate gene expression in response to specific environmental signals by altering the promoter specificity of RNA polymerase. We have undertaken a study of PvdS, a group IV sigma factor encoded by Pseudomonas syringae pv. tomato DC3000 (DC3000), a plant pathogen that is likely to encounter variations in nutrient availability as well as plant host defences. The gene encoding PvdS was previously identified by sequence similarity to the Pseudomonas aeruginosa orthologue, which directs transcription of genes encoding the biosynthesis of pyoverdine, a siderophore involved in iron acquisition, and is responsible for the characteristic fluorescence of the pseudomonads. We identified 15 promoters regulated by PvdS in DC3000 and characterized the promoter motif using computational analysis. Mutagenesis of conserved nucleotides within the motif interfered with promoter function and the degree of the effect was different depending on which region of the motif was mutated. Hidden Markov models constructed from alignments of sequence motifs extracted from DC3000 and PAO1 were used to query genomes of DC3000 and other fluorescent pseudomonads for similar motifs. We conclude that the role of PvdS as a regulator of pyoverdine synthesis is conserved among the fluorescent pseudomonads, but the promoters recognized by PvdS orthologues may differ subtly from species to species.
Molecular Microbiology 06/2008; 68(4):871-89. · 5.01 Impact Factor
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ABSTRACT: Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.
Annals of the New York Academy of Sciences 01/2008; 1115:203-11. · 3.15 Impact Factor
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ABSTRACT: Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
PLoS Computational Biology 11/2007; 3(10):1871-78. · 5.22 Impact Factor
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ABSTRACT: We have built an open-source software system for the modeling of biomolecular reaction networks, SloppyCell, which is written in Python and makes substantial use of third-party libraries for numerics, visualization, and parallel programming. We highlight here some of the powerful features that Python provides that enable SloppyCell to do dynamic code synthesis, symbolic manipulation, and parallel exploration of complex parameter spaces.
05/2007;
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ABSTRACT: Quantitative computational models play an increasingly important role in
modern biology. Such models typically involve many free parameters, and
assigning their values is often a substantial obstacle to model development.
Directly measuring \emph{in vivo} biochemical parameters is difficult, and
collectively fitting them to other data often yields large parameter
uncertainties. Nevertheless, in earlier work we showed in a
growth-factor-signaling model that collective fitting could yield
well-constrained predictions, even when it left individual parameters very
poorly constrained. We also showed that the model had a `sloppy' spectrum of
parameter sensitivities, with eigenvalues roughly evenly distributed over many
decades. Here we use a collection of models from the literature to test whether
such sloppy spectra are common in systems biology. Strikingly, we find that
every model we examine has a sloppy spectrum of sensitivities. We also test
several consequences of this sloppiness for building predictive models. In
particular, sloppiness suggests that collective fits to even large amounts of
ideal time-series data will often leave many parameters poorly constrained.
Tests over our model collection are consistent with this suggestion. This
difficulty with collective fits may seem to argue for direct parameter
measurements, but sloppiness also implies that such measurements must be
formidably precise and complete to usefully constrain many model predictions.
We confirm this implication in our signaling model. Our results suggest that
sloppy sensitivity spectra are universal in systems biology models. The
prevalence of sloppiness highlights the power of collective fits and suggests
that modelers should focus on predictions rather than on parameters.
01/2007;
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ABSTRACT: Pseudomonas syringae strains translocate large and distinct collections of effector proteins into plant cells via the type III secretion system (T3SS). Mutations in T3SS-encoding hrp genes are unable to elicit the hypersensitive response or pathogenesis in nonhost and host plants, respectively. Mutations in individual effectors lack strong phenotypes, which has impeded their discovery. P. syringae effectors are designated Hop (Hrp outer protein) or Avr (avirulence) proteins. Some Hop proteins are considered to be extracellular T3SS helpers acting at the plant-bacterium interface. Identification of complete sets of effectors and related proteins has been enabled by the application of bioinformatic and high-throughput experimental techniques to the complete genome sequences of three model strains: P. syringae pv. tomato DC3000, P. syringae pv. phaseolicola 1448A, and P. syringae pv. syringae B728a. Several recent papers, including three in this issue of Molecular Plant-Microbe Interactions, address the effector inventories of these strains. These studies establish that active effector genes in P. syringae are expressed by the HrpL alternative sigma factor and can be predicted on the basis of cis Hrp promoter sequences and N-terminal amino-acid patterns. Among the three strains analyzed, P. syringae pv. tomato DC3000 has the largest effector inventory and P. syringae pv. syringae B728a has the smallest. Each strain has several effector genes that appear inactive. Only five of the 46 effector families that are represented in these three strains have an active member in all of the strains. Web-based community resources for managing and sharing growing information on these complex effector arsenals should help future efforts to understand how effectors promote P. syringae virulence.
Molecular Plant-Microbe Interactions 12/2006; 19(11):1151-8. · 4.43 Impact Factor
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Adriana O Ferreira, Christopher R Myers,
Jeffrey S Gordon,
Gregory B Martin,
Monica Vencato,
Alan Collmer,
Misty D Wehling,
James R Alfano,
Gabriel Moreno-Hagelsieb,
Warren F Lamboy,
Genevieve DeClerck,
David J Schneider,
Samuel W Cartinhour
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ABSTRACT: Pseudomonas syringae pv. tomato DC3000 is a model pathogen of tomato and Arabidopsis that uses a hypersensitive response and pathogenicity (Hrp) type III secretion system (T3SS) to deliver virulence effector proteins into host cells. Expression of the Hrp system and many effector genes is activated by the HrpL alternative sigma factor. Here, an open reading frame-specific whole-genome microarray was constructed for DC3000 and used to comprehensively identify genes that are differentially expressed in wild-type and deltahrpL strains. Among the genes whose differential regulation was statistically significant, 119 were upregulated and 76 were downregulated in the wild-type compared with the deltahrpL strain. Hierarchical clustering revealed a subset of eight genes that were upregulated particularly rapidly. Gibbs sampling of regions upstream of HrpL-activated operons revealed the Hrp promoter as the only identifiable regulatory motif and supported an iterative refinement involving real-time polymerase chain reaction testing of additional HrpL-activated genes and refinements in a hidden Markov model that can be used to predict Hrp promoters in P. syringae strains. This iterative bioinformatic-experimental approach to a comprehensive analysis of the HrpL regulon revealed a mix of genes controlled by HrpL, including those encoding most type III effectors, twin-arginine transport (TAT) substrates, other regulatory proteins, and proteins involved in the synthesis or metabolism of phytohormones, phytotoxins, and myo-inositol. This analysis provides an extensively verified, robust method for predicting Hrp promoters in P. syringae genomes, and it supports subsequent identification of effectors and other factors that likely are important to the host-specific virulence of P. syringae.
Molecular Plant-Microbe Interactions 12/2006; 19(11):1167-79. · 4.43 Impact Factor
