deciphering the ecology of cystic fibrosis bacterial communities towards systems level integration

Despite over a decade of cystic fibrosis (CF) microbiome research, much remains to be learned about the overall composition, metabolic activities, and pathogenicity of the microbes in CF airways, limiting our understanding of the respiratory microbiome's relation to disease. Systems-level integration and modeling of host–microbiome interactions may allow us to better define the relationships between microbiological characteristics, disease status, and treatment response. In this way, modeling could pave the way for microbiome-based development of predictive models, individualized treatment plans, and novel therapeutic approaches, potentially serving as a paradigm for approaching other chronic infections. In this review, we describe the challenges facing this effort and propose research priorities for a systems biology approach to CF lung disease

Mathematical Modeling of Biofilm Structures Using COMSTAT Data

Mathematical modeling holds great potential for quantitatively describing biofilm growth in presence or absence of chemical agents used to limit or promote biofilm growth. In this paper, we describe a general mathematical/statistical framework that allows for the characterization of complex data in terms of few parameters and the capability to (i) compare different experiments and exposures to different agents, (ii) test different hypotheses regarding biofilm growth and interaction with different agents, and (iii) simulate arbitrary administrations of agents. The mathematical framework is divided to submodels characterizing biofilm, including new models characterizing live biofilm growth and dead cell accumulation; the interaction with agents inhibiting or stimulating growth; the kinetics of the agents. The statistical framework can take into account measurement and interexperiment variation. We demonstrate the application of (some of) the models using confocal microscopy data obtained using the computer program COMSTAT

SPATIOTEMPORAL IMPACT OF PHAGE EXPOSURE ON BIOFILM SYSTEMS

When single-celled prokaryotic organisms, one of the simplest forms of life, develop the ability to exhibit complex emergent properties such as social cooperation, resource capture, and enhanced survivability, the individual limitations of existence can be overcome which would otherwise be unlikely. Emergent properties of biofilms such as matrix production, quorum sensing, and coordinated lifecycle offers structural and functional advantages which makes them highly successful at evading destruction by antimicrobials and immune defenses. With few, if any, novel antibiotics in the clinical pipeline, there is a resurgence of interest in alternatives such as phage therapy, the practice of bacterial viruses known as bacteriophages that infect and lyse bacteria to treat infections. In this thesis, we explore the understudied impact of phage titer on biofilm dynamics and outcomes. We determined that the biofilm developmental stage at the time of phage addition modulates its response. These responses vary as a function of the phage dose and can be broadly organized into four distinct classes. In each of these classes, we observe that high phage doses restrain the biofilm from transitioning into the next stage of their developmental cycle. A paradoxical aspect of this result is that mature biofilms exposed to high phage titers are enhanced by phage treatment. Despite this apparently unwanted outcome, the inhibition of biofilm dispersion in phage-treated samples could potentially minimize the further spread of infections to other locations. These results comprehensively demonstrate predictable biofilm outcomes versus phage dosage and biofilm age, and will provide guidance in advancing phage-based personalized medicine when generalized treatments fail. Collectively, this dissertation derives insights on the advantages and limitations of phages to inhibit, control, and eliminate biofilms.Ph.D

Exploring New Horizons in Microbiome Research

Leading scientists in microbiome research met at Lake Titisee, Germany, in April 2014 to discuss the current state of the field, the most urgent and unresolved questions, state-of-the-art technological advances, and new avenues of future research. We summarize some of the concepts and themes discussed at this meeting

Use of wavelet-packet transforms to develop an engineering model for multifractal characterization of mutation dynamics in pathological and nonpathological gene sequences

This study uses dynamical analysis to examine in a quantitative fashion the information coding mechanism in DNA sequences. This exceeds the simple dichotomy of either modeling the mechanism by comparing DNA sequence walks as Fractal Brownian Motion (fbm) processes. The 2-D mappings of the DNA sequences for this research are from Iterated Function System (IFS) (Also known as the Chaos Game Representation (CGR)) mappings of the DNA sequences. This technique converts a 1-D sequence into a 2-D representation that preserves subsequence structure and provides a visual representation. The second step of this analysis involves the application of Wavelet Packet Transforms, a recently developed technique from the field of signal processing. A multi-fractal model is built by using wavelet transforms to estimate the Hurst exponent, H. The Hurst exponent is a non-parametric measurement of the dynamism of a system. This procedure is used to evaluate gene-coding events in the DNA sequence of cystic fibrosis mutations. The H exponent is calculated for various mutation sites in this gene. The results of this study indicate the presence of anti-persistent, random walks and persistent sub-periods in the sequence. This indicates the hypothesis of a multi-fractal model of DNA information encoding warrants further consideration.;This work examines the model\u27s behavior in both pathological (mutations) and non-pathological (healthy) base pair sequences of the cystic fibrosis gene. These mutations both natural and synthetic were introduced by computer manipulation of the original base pair text files. The results show that disease severity and system information dynamics correlate. These results have implications for genetic engineering as well as in mathematical biology. They suggest that there is scope for more multi-fractal models to be developed