Pseudomonas aeruginosa AMPR transcriptional regulatory network

In Enterobacteriaceae, the transcriptional regulator AmpR, a member of the LysR family, regulates the expression of a chromosomal β-lactamase AmpC. The regulatory repertoire of AmpR is broader in Pseudomonas aeruginosa, an opportunistic pathogen responsible for numerous acute and chronic infections including cystic fibrosis. Previous studies showed that in addition to regulating ampC, P. aeruginosa AmpR regulates the sigma factor AlgT/U and production of some quorum sensing (QS)-regulated virulence factors. In order to better understand the ampR regulon, the transcriptional profiles generated using DNA microarrays and RNA-Seq of the prototypic P. aeruginosa PAO1 strain with its isogenic ampR deletion mutant, PAOΔampR were analyzed. Transcriptome analysis demonstrates that the AmpR regulon is much more extensive than previously thought influencing the differential expression of over 500 genes. In addition to regulating resistance to β-lactam antibiotics via AmpC, AmpR also regulates non-β-lactam antibiotic resistance by modulating the MexEF-OprN efflux pump. Virulence mechanisms including biofilm formation, QS-regulated acute virulence, and diverse physiological processes such as oxidative stress response, heat-shock response and iron uptake are AmpR-regulated. Real-time PCR and phenotypic assays confirmed the transcriptome data. Further, Caenorhabditis elegans model demonstrates that a functional AmpR is required for full pathogenicity of P. aeruginosa. AmpR, a member of the core genome, also regulates genes in the regions of genome plasticity that are acquired by horizontal gene transfer. The extensive AmpR regulon included other transcriptional regulators and sigma factors, accounting for the extensive AmpR regulon. Gene expression studies demonstrate AmpR-dependent expression of the QS master regulator LasR that controls expression of many virulence factors. Using a chromosomally tagged AmpR, ChIP-Seq studies show direct AmpR binding to the lasR promoter. The data demonstrates that AmpR functions as a global regulator in P. aeruginosa and is a positive regulator of acute virulence while negatively regulating chronic infection phenotypes. In summary, my dissertation sheds light on the complex regulatory circuit in P. aeruginosa to provide a better understanding of the bacterial response to antibiotics and how the organism coordinately regulates a myriad of virulence factors.

Pseudomonas Aeruginosa AmpR Transcriptional Regulatory Network

In Enterobacteriaceae, the transcriptional regulator AmpR, a member of the LysR family, regulates the expression of a chromosomal β-lactamase AmpC. The regulatory repertoire of AmpR is broader in Pseudomonas aeruginosa, an opportunistic pathogen responsible for numerous acute and chronic infections including cystic fibrosis. Previous studies showed that in addition to regulating ampC, P. aeruginosa AmpR regulates the sigma factor AlgT/U and production of some quorum sensing (QS)-regulated virulence factors. In order to better understand the ampR regulon, the transcriptional profiles generated using DNA microarrays and RNA-Seq of the prototypic P. aeruginosa PAO1 strain with its isogenic ampR deletion mutant, PAO∆ampR were analyzed. Transcriptome analysis demonstrates that the AmpR regulon is much more extensive than previously thought influencing the differential expression of over 500 genes. In addition to regulating resistance to β-lactam antibiotics via AmpC, AmpR also regulates non-β-lactam antibiotic resistance by modulating the MexEF-OprN efflux pump. Virulence mechanisms including biofilm formation, QS-regulated acute virulence, and diverse physiological processes such as oxidative stress response, heat-shock response and iron uptake are AmpR-regulated. Real-time PCR and phenotypic assays confirmed the transcriptome data. Further, Caenorhabditis elegans model demonstrates that a functional AmpR is required for full pathogenicity of P. aeruginosa. AmpR, a member of the core genome, also regulates genes in the regions of genome plasticity that are acquired by horizontal gene transfer. The extensive AmpR regulon included other transcriptional regulators and sigma factors, accounting for the extensive AmpR regulon. Gene expression studies demonstrate AmpR-dependent expression of the QS master regulator LasR that controls expression of many virulence factors. Using a chromosomally tagged AmpR, ChIP-Seq studies show direct AmpR binding to the lasR promoter. The data demonstrates that AmpR functions as a global regulator in P. aeruginosa and is a positive regulator of acute virulence while negatively regulating chronic infection phenotypes. In summary, my dissertation sheds light on the complex regulatory circuit in P. aeruginosa to provide a better understanding of the bacterial response to antibiotics and how the organism coordinately regulates a myriad of virulence factors.

Reflected Backward Stochastic Differential Equation with Jumps and RCLL Obstacle

In this paper we study one-dimensional reflected backward stochastic differential equation when the noise is driven by a Brownian motion and an independent Poisson point process when the solution is forced to stay above a right continuous left-hand limited obstacle. We prove existence and uniqueness of the solution by using a penalization method combined with a monotonic limit theorem.

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering

On a Stochastic Partial Differential Equation with a Noisy Term

2000 Mathematics Subject Classification: 60H15, 60H40

Stochastic differential equations with random coefficients

In this paper we establish the existence and uniqueness of a solution for different types of stochastic differential equation with random initial conditions and random coefficients. The stochastic integral is interpreted as a generalized Stratonovich integral, and the techniques used to derive these results are mainly based on the path properties of the Brownian motion, and the definition of the Stratonovich integral.

Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations

This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.

Fractional Fokker-Planck-Kolmogorov type Equations and their Associated Stochastic Differential Equations

MSC 2010: 26A33, 35R11, 35R60, 35Q84, 60H10 Dedicated to 80-th anniversary of Professor Rudolf Gorenflo

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Evolution of the gene regulatory network controlling flower dorsoventral asymmetry

Tese de Doutoramento em Biologia de Plantas.

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model.

MOTIVATION: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes. RESULTS: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

A regulatory network for the efficient control of transgene expression.

BACKGROUND: Expression of heterologous genes in mammalian cells or organisms for therapeutic or experimental purposes often requires tight control of transgene expression. Specifically, the following criteria should be met: no background gene activity in the off-state, high gene expression in the on-state, regulated expression over an extended period, and multiple switching between on- and off-states. METHODS: Here, we describe a genetic switch system for controlled transgene transcription using chimeric repressor and activator proteins functioning in a novel regulatory network. In the off-state, the target transgene is actively silenced by a chimeric protein consisting of multimerized eukaryotic transcriptional repression domains fused to the DNA-binding tetracycline repressor. In the on-state, the inducer drug doxycycline affects both the derepression of the target gene promoter and activation by the GAL4-VP16 transactivator, which in turn is under the control of an autoregulatory feedback loop. RESULTS: The hallmark of this new system is the efficient transgene silencing in the off-state, as demonstrated by the tightly controlled expression of the highly cytotoxic diphtheria toxin A gene. Addition of the inducer drug allows robust activation of transgene expression. In stably transfected cells, this control is still observed after months of repeated cycling between the repressed and activated states of the target genes. CONCLUSIONS: This system permits tight long-term regulation when stably introduced into cell lines. The underlying principles of this network system should have general applications in biotechnology and gene therapy.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.