Computational approaches to deciphering regulatory circuits in mycobacterium tuberculosis from chip-seq data, and developing theoretical strategies to combat drug-resistant infections
MetadataShow full item record
This thesis consists of two related studies directed at aspects of M. tuberculosis biology. The first focuses on deciphering gene-regulatory circuits from ChIP-seq data, and the second focuses on alternative strategies for combatting drug-resistant infections. The first study describes Binding Resolution Amplifier and Cooperative Interaction Locator (BRACIL), a post-peak-caller computational method that predicts transcription-factor (TF) binding sites with high-resolution as well as cooperative TF interactions derived from ChIP-seq data. BRACIL integrates ChIP-seq coverage with motif discovery from a signal-processing perspective and uses a blind-deconvolution algorithm that predicts binding-site locations and magnitudes. BRACIL also explicitly considers a second-order signal, represented by DNA fragments with two sites bound simultaneously, and uses it to predict cooperative interaction. Cooperative interaction indicates that the binding to a first site influences the probability of binding to a second site. This method estimates the probability of a binding configuration from the ChIP-seq coverage and performs a likelihood ratio test to predict cooperative interaction. As a proof of principle, I validated this method using M. tuberculosis transcription factor DosR. The second study focuses on strategies to fight antibiotic resistance. In particular, recent reports have shown the existence of treatment conditions (called "antiR") that select against drug-resistant strains. I used a mathematical model of infection dynamics and immunity to simulate the growth of resistant and sensitive pathogens under different treatment conditions (no drugs, antibiotic present, and antiR), and could show how a precisely timed combination of treatments can defeat resistant strains. This analysis suggested that a time- scheduled, multi-treatment therapy could lead to complete elimination of both sensitive and resistant strains. Also, my results indicated that the time necessary to turn a resistant infection into a sensitive one ("tclear") depends on the experimentally measurable rates of pathogen division, growth and plasmid loss. Additionally, I estimated tclear for a specific case, using available empirical data, and found that resistance may be lost up to 15 times faster under antiR treatment as compared to a no-treatment regime. Finally, an extension of these findings to population models provides quantitative support for therapeutic plans to clear antibiotic-resistant infections, including novel drug-cycling strategies.