VTechWorks Repository :: Browsing by Author "Laubenbacher, Reinhard C."

Browsing by Author "Laubenbacher, Reinhard C."

Now showing 1 - 20 of 44

ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
Hinkelmann, Franziska; Brandon, Madison; Guang, Bonny; McNeill, Rustin; Blekherman, Grigoriy; Veliz-Cuba, Alan; Laubenbacher, Reinhard C. (2011-07-20)
Background Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. Results We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Conclusions Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics.
The Algebra of Systems Biology
Veliz-Cuba, Alan A. (Virginia Tech, 2010-07-05)
In order to understand biochemical networks we need to know not only how their parts work but also how they interact with each other. The goal of systems biology is to look at biological systems as a whole to understand how interactions of the parts can give rise to complex dynamics. In order to do this efficiently, new techniques have to be developed. This work shows how tools from mathematics are suitable to study problems in systems biology such as modeling, dynamics prediction, reverse engineering and many others. The advantage of using mathematical tools is that there is a large number of theory, algorithms and software available. This work focuses on how algebra can contribute to answer questions arising from systems biology.
An Algebraic Approach to Reverse Engineering with an Application to Biochemical Networks
Stigler, Brandilyn Suzanne (Virginia Tech, 2005-08-04)
One goal of systems biology is to predict and modify the behavior of biological networks by accurately monitoring and modeling their responses to certain types of perturbations. The construction of mathematical models based on observation of these responses, referred to as reverse engineering, is an important step in elucidating the structure and dynamics of such networks. Continuous models, described by systems of differential equations, have been used to reverse engineer biochemical networks. Of increasing interest is the use of discrete models, which may provide a conceptual description of the network. In this dissertation we introduce a discrete modeling approach, rooted in computational algebra, to reverse-engineer networks from experimental time series data. The algebraic method uses algorithmic tools, including Groebner-basis techniques, to build the set of all discrete models that fit time series data and to select minimal models from this set. The models used in this work are discrete-time finite dynamical systems, which, when defined over a finite field, are described by systems of polynomial functions. We present novel reverse-engineering algorithms for discrete models, where each algorithm is suitable for different amounts and types of data. We demonstrate the effectiveness of the algorithms on simulated networks and conclude with a description of an ongoing project to reverse-engineer a real gene regulatory network in yeast.
Algebraic Geometry of Bayesian Networks
Garcia-Puente, Luis David (Virginia Tech, 2004-01-30)
We develop the necessary theory in algebraic geometry to place Bayesian networks into the realm of algebraic statistics. This allows us to create an algebraic geometry--statistics dictionary. In particular, we study the algebraic varieties defined by the conditional independence statements of Bayesian networks. A complete algebraic classification, in terms of primary decomposition of polynomial ideals, is given for Bayesian networks on at most five random variables. Hidden variables are related to the geometry of higher secant varieties. Moreover, a complete algebraic classification, in terms of generating sets of polynomial ideals, is given for Bayesian networks on at most three random variables and one hidden variable. The relevance of these results for model selection is discussed.
Algebraic Methods for Modeling Gene Regulatory Networks
Murrugarra Tomairo, David M. (Virginia Tech, 2012-07-18)
So called discrete models have been successfully used in engineering and computational systems biology. This thesis discusses algebraic methods for modeling and analysis of gene regulatory networks within the discrete modeling context. The first chapter gives a background for discrete models and put in context some of the main research problems that have been pursued in this field for the last fifty years. It also outlines the content of each subsequent chapter. The second chapter focuses on the problem of inferring dynamics from the structure (topology) of the network. It also discusses the characterization of the attractor structure of a network when a particular class of functions control the nodes of the network. Chapters~3 and 4 focus on the study of multi-state nested canalyzing functions as biologically inspired functions and the characterization of their dynamics. Chapter 5 focuses on stochastic methods, specifically on the development of a stochastic modeling framework for discrete models. Stochastic discrete modeling is an alternative approach from the well-known mathematical formalizations such as stochastic differential equations and Gillespie algorithm simulations. Within the discrete setting, a framework that incorporates propensity probabilities for activation and degradation is presented. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations. Finally, Chapter 6 discusses future research directions inspired by the work presented here.
Algebraic theory for discrete models in systems biology
Hinkelmann, Franziska (Virginia Tech, 2011-08-01)
This dissertation develops algebraic theory for discrete models in systems biology. Many discrete model types can be translated into the framework of polynomial dynamical systems (PDS), that is, time- and state-discrete dynamical systems over a finite field where the transition function for each variable is given as a polynomial. This allows for using a range of theoretical and computational tools from computer algebra, which results in a powerful computational engine for model construction, parameter estimation, and analysis methods. Formal definitions and theorems for PDS and the concept of PDS as models of biological systems are introduced in section 1.3. Constructing a model for given time-course data is a challenging problem. Several methods for reverse-engineering, the process of inferring a model solely based on experimental data, are described briefly in section 1.3. If the underlying dependencies of the model components are known in addition to experimental data, inferring a "good" model amounts to parameter estimation. Chapter 2 describes a parameter estimation algorithm that infers a special class of polynomials, so called nested canalyzing functions. Models consisting of nested canalyzing functions have been shown to exhibit desirable biological properties, namely robustness and stability. The algorithm is based on the parametrization of nested canalyzing functions. To demonstrate the feasibility of the method, it is applied to the cell-cycle network of budding yeast. Several discrete model types, such as Boolean networks, logical models, and bounded Petri nets, can be translated into the framework of PDS. Section 3 describes how to translate agent-based models into polynomial dynamical systems. Chapter 4, 5, and 6 are concerned with analysis of complex models. Section 4 proposes a new method to identify steady states and limit cycles. The method relies on the fact that attractors correspond to the solutions of a system of polynomials over a finite field, a long-studied problem in algebraic geometry which can be efficiently solved by computing Gröbner bases. Section 5 introduces a bit-wise implementation of a Gröbner basis algorithm for Boolean polynomials. This implementation has been incorporated into the core engine of Macaulay 2. Chapter 6 discusses bistability for Boolean models formulated as polynomial dynamical systems.
Algorithms for modeling and simulation of biological systems; applications to gene regulatory networks
Vera-Licona, Martha Paola (Virginia Tech, 2007-06-06)
Systems biology is an emergent field focused on developing a system-level understanding of biological systems. In the last decade advances in genomics, transcriptomics and proteomics have gathered a remarkable amount data enabling the possibility of a system-level analysis to be grounded at a molecular level. The reverse-engineering of biochemical networks from experimental data has become a central focus in systems biology. A variety of methods have been proposed for the study and identification of the system's structure and/or dynamics. The objective of this dissertation is to introduce and propose solutions to some of the challenges inherent in reverse-engineering of biological systems. First, previously developed reverse engineering algorithms are studied and compared using data from a simulated network. This study draws attention to the necessity for a uniform benchmark that enables an ob jective comparison and performance evaluation of reverse engineering methods. Since several reverse-engineering algorithms require discrete data as input (e.g. dynamic Bayesian network methods, Boolean networks), discretization methods are being used for this purpose. Through a comparison of the performance of two network inference algorithms that use discrete data (from several different discretization methods) in this work, it has been shown that data discretization is an important step in applying network inference methods to experimental data. Next, a reverse-engineering algorithm is proposed within the framework of polynomial dynamical systems over finite fields. This algorithm is built for the identification of the underlying network structure and dynamics; it uses as input gene expression data and, when available, a priori knowledge of the system. An evolutionary algorithm is used as the heuristic search method for an exploration of the solution space. Computational algebra tools delimit the search space, enabling also a description of model complexity. The performance and robustness of the algorithm are explored via an artificial network of the segment polarity genes in the D. melanogaster. Once a mathematical model has been built, it can be used to run simulations of the biological system under study. Comparison of simulated dynamics with experimental measurements can help refine the model or provide insight into qualitative properties of the systems dynamical behavior. Within this work, we propose an efficient algorithm to describe the phase space, in particular to compute the number and length of all limit cycles of linear systems over a general finite field. This research has been partially supported by NIH Grant Nr. RO1GM068947-01.
A Biclustering Approach to Combinatorial Transcription Control
Srinivasan, Venkataraghavan (Virginia Tech, 2005-07-06)
Combinatorial control of transcription is a well established phenomenon in the cell. Multiple transcription factors often bind to the same transcriptional control region of a gene and interact with each other to control the expression of the gene. It is thus necessary to consider the joint conservation of sequence pairs in order to identify combinations of binding sites to which the transcription factors bind. Conventional motif finding algorithms fail to address this issue. We propose a novel biclustering algorithm based on random sampling to identify candidate binding site combinations. We establish bounds on the various parameters to the algorithm and study the conditions under which the algorithm is guaranteed to identify candidate binding sites. We analyzed a yeast cell cycle gene expression data set using our algorithm and recovered certain novel combinations of binding sites, besides those already reported in the literature.
Bifurcation Analysis and Qualitative Optimization of Models in Molecular Cell Biology with Applications to the Circadian Clock
Conrad, Emery David (Virginia Tech, 2006-04-14)
Circadian rhythms are the endogenous, roughly 24-hour rhythms that coordinate an organism's interaction with its cycling environment. The molecular mechanism underlying this physiological process is a cell-autonomous oscillator comprised of a complex regulatory network of interacting DNA, RNA and proteins that is surprisingly conserved across many different species. It is not a trivial task to understand how the positive and negative feedback loops interact to generate an oscillator capable of a) maintaining a 24-hour rhythm in constant conditions; b) entraining to external light and temperature signals; c) responding to pulses of light in a rather particular, predictable manner; and d) compensating itself so that the period is relatively constant over a large range of temperatures, even for mutations that affect the basal period of oscillation. Mathematical modeling is a useful tool for dealing with such complexity, because it gives us an object that can be quickly probed and tested in lieu of the experiment or actual biological system. If we do a good job designing the model, it will help us to understand the biology better by predicting the outcome of future experiments. The difficulty lies in properly designing a model, a task that is made even more difficult by an acute lack of quantitative data. Thankfully, our qualitative understanding of a particular phenomenon, i.e. the observed physiology of the cell, can often be directly related to certain mathematical structures. Bifurcation analysis gives us a glimpse of these structures, and we can use these glimpses to build our models with greater confidence. In this dissertation, I will discuss the particular problem of the circadian clock and describe a number of new methods and tools related to bifurcation analysis. These tools can effectively be applied during the modeling process to build detailed models of biological regulatory with greater ease.
A combinatorial approach to scientific exploration of gene expression data: An integrative method using Formal Concept Analysis for the comparative analysis of microarray data
Potter, Dustin Paul (Virginia Tech, 2005-08-03)
Functional genetics is the study of the genes present in a genome of an organism, the complex interplay of all genes and their environment being the primary focus of study. The motivation for such studies is the premise that gene expression patterns in a cell are characteristic of its current state. The availability of the entire genome for many organisms now allows scientists unparalleled opportunities to characterize, classify, and manipulate genes or gene networks involved in metabolism, cellular differentiation, development, and disease. System-wide studies of biological systems have been made possible by the advent of high-throughput and large-scale tools such as microarrays which are capable of measuring the mRNA levels of all genes in a genome. Tools and methods for the integration, visualization, and modeling of the large-scale data obtained in typical systems biology experiments are indispensable. Our work focuses on a method that integrates gene expression values obtained from microarray experiments with biological functional information related to the genes measured in order to make global comparisons of multiple experiments. In our method, the integrated data is represented as a lattice and, using appropriate measures, a reference experiment can be compared to samples from a database of similar experiments, and a ranking of similarity is returned. In this work, support for the validity of our method is demonstrated both theoretically and empirically: a mathematical description of the lattice structure with respect to the integrated information is developed and the method is applied to data sets of both simulated and reported microarray experiments. A fast algorithm for constructing the lattice representation is also developed.
A computational model of invasive aspergillosis in the lung and the role of iron
Oremland, Matthew; Michels, Kathryn R.; Bettina, Alexandra M.; Lawrence, Chris; Mehrad, Borna; Laubenbacher, Reinhard C. (BMC, 2016)
Background: Invasive aspergillosis is a severe infection of immunocompromised hosts, caused by the inhalation of the spores of the ubiquitous environmental molds of the Aspergillus genus. The innate immune response in this infection entails a series of complex and inter-related interactions between multiple recruited and resident cell populations with each other and with the fungal cell; in particular, iron is critical for fungal growth. Results: A computational model of invasive aspergillosis is presented here; the model can be used as a rational hypothesis-generating tool to investigate host responses to this infection. Using a combination of laboratory data and published literature, an in silico model of a section of lung tissue was generated that includes an alveolar duct, adjacent capillaries, and surrounding lung parenchyma. The three-dimensional agent-based model integrates temporal events in fungal cells, epithelial cells, monocytes, and neutrophils after inhalation of spores with cellular dynamics at the tissue level, comprising part of the innate immune response. Iron levels in the blood and tissue play a key role in the fungus’ ability to grow, and the model includes iron recruitment and consumption by the different types of cells included. Parameter sensitivity analysis suggests the model is robust with respect to unvalidated parameters, and thus is a viable tool for an in silico investigation of invasive aspergillosis. Conclusions: Using laboratory data from a mouse model of invasive aspergillosis in the context of transient neutropenia as validation, the model predicted qualitatively similar time course changes in fungal burden, monocyte and neutrophil populations, and tissue iron levels. This model lays the groundwork for a multi-scale dynamic mathematical model of the immune response to Aspergillus species.
Data integration and visualization for systems biology data
Cheng, Hui (Virginia Tech, 2010-10-27)
Systems biology aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins and metabolites. Comprehensive visualization tools for exploring multivariate data are needed to gain insight into the physiological processes reflected in these molecular profiles. Data fusion methods are required to integratively study high-throughput transcriptomics, metabolomics and proteomics data combined before systems biology can live up to its potential. In this work I explored mathematical and statistical methods and visualization tools to resolve the prominent issues in the nature of systems biology data fusion and to gain insight into these comprehensive data. In order to choose and apply multivariate methods, it is important to know the distribution of the experimental data. Chi square Q-Q plot and violin plot were applied to all M. truncatula data and V. vinifera data, and found most distributions are right-skewed (Chapter 2). The biplot display provides an effective tool for reducing the dimensionality of the systems biological data and displaying the molecules and time points jointly on the same plot. Biplot of M. truncatula data revealed the overall system behavior, including unidentified compounds of interest and the dynamics of the highly responsive molecules (Chapter 3). The phase spectrum computed from the Fast Fourier transform of the time course data has been found to play more important roles than amplitude in the signal reconstruction. Phase spectrum analyses on in silico data created with two artificial biochemical networks, the Claytor model and the AB2 model proved that phase spectrum is indeed an effective tool in system biological data fusion despite the data heterogeneity (Chapter 4). The difference between data integration and data fusion are further discussed. Biplot analysis of scaled data were applied to integrate transcriptome, metabolome and proteome data from the V. vinifera project. Phase spectrum combined with k-means clustering was used in integrative analyses of transcriptome and metabolome of the M. truncatula yeast elicitation data and of transcriptome, metabolome and proteome of V. vinifera salinity stress data. The phase spectrum analysis was compared with the biplot display as effective tools in data fusion (Chapter 5). The results suggest that phase spectrum may perform better than the biplot. This work was funded by the National Science Foundation Plant Genome Program, grant DBI-0109732, and by the Virginia Bioinformatics Institute.
Differential gene expression and immune regulatory mechanisms in parasite-resistant hair and susceptible wool sheep infected with the parasitic nematode, Haemonchus contortus
MacKinnon, Kathryn Michelle (Virginia Tech, 2007-07-23)
Among sheep producers, the parasitic nematode Haemonchus contortus is a major animal health concern. Caribbean hair sheep are more resistant than conventional wool breeds to this blood-feeding, abomasal parasite. Our objective was to determine differences in the immune response associated with parasite-resistant hair and susceptible wool lambs infected with 10,000 H. contortus and in uninfected controls. Animals were sacrificed and abomasum and lymph node tissues were collected at 3 or 27 days post-infection (PI), and for controls on day 17, 27, or 38 relative to d 0 of infected animals. Blood and fecal samples were collected throughout the study. Lower fecal egg counts, higher packed cell volumes, and heavier lymph nodes of infected hair compared to wool lambs, suggests hair lambs have increased parasite resistance. Greater tissue infiltration of eosinophils (P < 0.05) was observed in hair compared to wool sheep by 3 days PI, with no breed differences in globule leukocytes. Total serum IgA and IgE were greater in control hair versus wool sheep (P < 0.05). After 3, 5, and 21 of infection, total serum IgA (P< 0.05), total lymph node IgE (P < 0.01), but not total serum IgE were greater in hair sheep compared to wool sheep. Gene expression was measured between hair and wool lambs for abomasal and lymph node tissues using bovine cDNA microarrays and real-time RT-PCR. Microarray analysis revealed cell survival, endosome function, gut motility, and anti-coagulation pathways are important in abomasal and lymph node tissues during H. contortus infection. Immune genes, including IL-4, IL-4 Ra, IL-12 Rb1, and IL-12 Rb2, are also highly represented in abomasal or lymph node tissue of infected animals. Eleven genes were evaluated using real-time RT-PCR and included TH1 and TH2 cytokines, cytokine receptors, and IgE. Parasite infection leads to increased expression of IL-13 and IgE in both tissues and breeds when compared to control animals. Breed comparison of gene expression shows resistant hair sheep produce a stronger modified TH2-type immune response during infection. Differential cell infiltration, antibody production, and regulation of TH2 cytokines between breeds may be partially responsible for differences in parasite resistance.
The effect of negative feedback loops on the dynamics of Boolean networks
Sontag, Eduardo; Veliz-Cuba, Alan; Laubenbacher, Reinhard C.; Jarrah, Abdul Salam (CELL PRESS, 2008-07)
Feedback loops play an important role in determining the dynamics of biological networks. To study the role of negative feedback loops, this article introduces the notion of distance-to-positive-feedback which, in essence, captures the number of independent negative feedback loops in the network, a property inherent in the network topology. Through a computational study using Boolean networks, it is shown that distance-to-positive-feedback has a strong influence on network dynamics and correlates very well with the number and length of limit cycles in the phase space of the network. To be precise, it is shown that, as the number of independent negative feedback loops increases, the number (length) of limit cycles tends to decrease (increase). These conclusions are consistent with the fact that certain natural biological networks exhibit generally regular behavior and have fewer negative feedback loops than randomized networks with the same number of nodes and same connectivity.
Functional genomics through metabolite profiling and gene expression analysis in Arabidopsis thaliana
Cortes Bermudez, Diego Fernando (Virginia Tech, 2008-07-25)
In the post-genomic era, one of the most important goals for the community of plant biologists is to take full advantage of the knowledge generated by the Arabidopsis thaliana genome project, and to employ state-of-the-art functional genomics techniques to assign function to each gene. This will be achieved through a complete understanding of what all cellular components do, and how they interact with one another to produce a phenotype. Among the proteins encoded by the Arabidopsis genome are 24 related carboxyl methyltransferases that belong to the SABATH family. Several of the SABATH methyltransferases convert plant hormones, like jasmonic acid, indole-3-acetic acid, salicylic acid, gibberellins, and other plant constituents into methyl esters, thereby regulating the biological activity of these molecules and, consequently, myriad important physiological processes. Our research aims to decipher the function of proteins belonging to the SABATH family by applying a combination of genomics tools, including genome-wide expression analysis and gas-chromatography coupled with mass spectrometry-based metabolite profiling. Our results, combined with available biochemical information, provide a better understanding of the physiological role of SABATH methyltransferases, further insights into secondary plant metabolism and deeper knowledge of the consequences of modulating the expression of SABATH methyltransferases, both at the genome-wide expression and metabolite levels.
The Genome-Wide Early Temporal Response of Saccharomyces cerevisiae to Oxidative Stress Induced by Cumene Hydroperoxide
Sha, Wei; Martins, Ana M.; Laubenbacher, Reinhard C.; Mendes, Pedro; Shulaev, Vladimir (PLOS, 2013-09-20)
Oxidative stress is a well-known biological process that occurs in all respiring cells and is involved in pathophysiological processes such as aging and apoptosis. Oxidative stress agents include peroxides such as hydrogen peroxide, cumene hydroperoxide, and linoleic acid hydroperoxide, the thiol oxidant diamide, and menadione, a generator of superoxide, amongst others. The present study analyzed the early temporal genome-wide transcriptional response of Saccharomyces cerevisiae to oxidative stress induced by the aromatic peroxide cumene hydroperoxide. The accurate dataset obtained, supported by the use of temporal controls, biological replicates and well controlled growth conditions, provided a detailed picture of the early dynamics of the process. We identified a set of genes previously not implicated in the oxidative stress response, including several transcriptional regulators showing a fast transient response, suggesting a coordinated process in the transcriptional reprogramming. We discuss the role of the glutathione, thioredoxin and reactive oxygen species-removing systems, the proteasome and the pentose phosphate pathway. A data-driven clustering of the expression patterns identified one specific cluster that mostly consisted of genes known to be regulated by the Yap1p and Skn7p transcription factors, emphasizing their mediator role in the transcriptional response to oxidants. Comparison of our results with data reported for hydrogen peroxide identified 664 genes that specifically respond to cumene hydroperoxide, suggesting distinct transcriptional responses to these two peroxides. Genes up-regulated only by cumene hydroperoxide are mainly related to the cell membrane and cell wall, and proteolysis process, while those down-regulated only by this aromatic peroxide are involved in mitochondrial function.
In silico cell biology and biochemistry: a systems biology approach
Camacho, Diogo Mayo (Virginia Tech, 2007-06-01)
In the post-"omic" era the analysis of high-throughput data is regarded as one of the major challenges faced by researchers. One focus of this data analysis is uncovering biological network topologies and dynamics. It is believed that this kind of research will allow the development of new mathematical models of biological systems as well as aid in the improvement of already existing ones. The work that is presented in this dissertation addresses the problem of the analysis of highly complex data sets with the aim of developing a methodology that will enable the reconstruction of a biological network from time series data through an iterative process. The first part of this dissertation relates to the analysis of existing methodologies that aim at inferring network structures from experimental data. This spans the use of statistical tools such as correlations analysis (presented in Chapter 2) to more complex mathematical frameworks (presented in Chapter 3). A novel methodology that focuses on the inference of biological networks from time series data by least squares fitting will then be introduced. Using a set of carefully designed inference rules one can gain important information about the system which can aid in the inference process. The application of the method to a data set from the response of the yeast Saccharomyces cerevisiae to cumene hydroperoxide is explored in Chapter 5. The results show that this method can be used to generate a coarse-level mathematical model of the biological system at hand. Possible developments of this method are discussed in Chapter 6.
Iron acquisition and oxidative stress response in aspergillus fumigatus
Brandon, Madison; Howard, Brad; Lawrence, Christopher B.; Laubenbacher, Reinhard C. (BMC, 2015)
Background: Aspergillus fumigatus is a ubiquitous airborne fungal pathogen that presents a life-threatening health risk to individuals with weakened immune systems. A. fumigatus pathogenicity depends on its ability to acquire iron from the host and to resist host-generated oxidative stress. Gaining a deeper understanding of the molecular mechanisms governing A. fumigatus iron acquisition and oxidative stress response may ultimately help to improve the diagnosis and treatment of invasive aspergillus infections. Results: This study follows a systems biology approach to investigate how adaptive behaviors emerge from molecular interactions underlying A. fumigatus iron regulation and oxidative stress response. We construct a Boolean network model from known interactions and simulate how changes in environmental iron and superoxide levels affect network dynamics. We propose rules for linking long term model behavior to qualitative estimates of cell growth and cell death. These rules are used to predict phenotypes of gene deletion strains. The model is validated on the basis of its ability to reproduce literature data not used in model generation. Conclusions: The model reproduces gene expression patterns in experimental time course data when A. fumigatus is switched from a low iron to a high iron environment. In addition, the model is able to accurately represent the phenotypes of many knockout strains under varying iron and superoxide conditions. Model simulations support the hypothesis that intracellular iron regulates A. fumigatus transcription factors, SreA and HapX, by a post-translational, rather than transcriptional, mechanism. Finally, the model predicts that blocking siderophore-mediated iron uptake reduces resistance to oxidative stress. This indicates that combined targeting of siderophore-mediated iron uptake and the oxidative stress response network may act synergistically to increase fungal cell killing.
Ironing Out the Host-fungal Interaction in Airway Epithelial Cells
Lee, Shernita (Virginia Tech, 2014-04-10)
Aspergillus fumigatus is a ubiquitous fungus associated with several airway complications and diseases including asthma, allergies, cystic fibrosis, and most commonly invasive aspergillosis. The airway epithelium, a protective barrier, is the first anatomical site to interact with A. fumigatus. Although this host-fungal interaction is often asymptomatic for immunocompetent individuals, for immunocompromised persons, due to a weakened competence of the immune system, they have an increased likelihood of fungal infection. This dissertation aims to investigate the effect of A. fumigatus on the transcriptional response of human airway epithelial cells, focusing on the relationship between innate immunity and iron regulation from the host perspective. The trace element iron is needed by both the fungus and the host for cellular maintenance and survival, but tightly controlled iron regulation in the host is required to prevent oxidative stress and cell death. The research methods in this dissertation employ a systems biology approach, by incorporating mathematical modeling, RNA-seq analysis, and experimental biology techniques to assess the role of airway epithelial cells in the host-fungal interaction. Both the quantitative and qualitative research design allows for characterization of airway epithelial cells and the downstream changes in iron importer genes. This study addresses literature gaps through analysis of the host transcriptome using multiple time points, by performing an extensive evaluation of the effect of cytokines on iron importer genes, and conceptualization of a comprehensive mathematical model of the airway epithelial cell. The major findings suggest the following: 1) airway epithelial cells avidly respond to A. fumigatus through modification of the expression of immune response related genes at different infection stages, 2) during A. fumigatus co-incubation with airway epithelial cells, the iron importers genes respond in strikingly different ways, and 3) cytokines have a significant effect on the increase in expression of an iron importer gene. We illuminated the role of airway epithelial cells in fungal recognition and activation of the immune response in signaling cascades that consequently modify iron importer genes and hope to use this information as a platform to discover potential therapeutic targets.
A Mathematical Model of a Denitrification Metabolic Network in Pseudomonas aeruginosa
Arat, Seda (Virginia Tech, 2012-11-30)
Lake Erie, one of the Great Lakes in North America, has witnessed recurrent summertime low oxygen dead zones for decades. This is a yearly phenomenon that causes microbial production of the greenhouse gas nitrous oxide from denitrification. Complete denitrification is a microbial process of reduction of nitrate to nitrogen gas via nitrite, nitric oxide, and greenhouse gas nitrous oxide. After scanning the microbial community in Lake Erie, Pseudomonas aeruginosa is decided to be examined, not because it is abundant in Lake Erie, but because it can perform denitrification under anaerobic conditions. This study focuses on a mathematical model of the metabolic network in Pseudomonas aeruginosa under denitrification and testable hypotheses generation using polynomial dynamical systems and stochastic discrete dynamical systems. Analysis of the long-term behavior of the system changing the concentration level of oxygen, nitrate, and phosphate suggests that phosphate highly affects the denitrification performance of the network.

Browsing by Author "Laubenbacher, Reinhard C."

Results Per Page

Sort Options