Microbial systems biology using chemical combinations

Predicting the behavior of microbial organisms is an enormous challenge given their vast complexity. Mathematical models can describe this complexity, allowing researchers to predict new behaviors and invent new hypotheses to explain and treat diseases, but they require huge amounts of data. Among these are perturbation studies, where perturbations have proven important and are greatly facilitated by the advent of mutant libraries and small-molecule chemical perturbations that can easily probe the functions of genes. Single chemical or genetic perturbations can associate individual components with pathways or phenotypes. Functional relationships between pathways and modules of components are most effectively obtained from combined perturbation experiments. Chemicals combinations can provide a detailed look at the type of interaction between pathways.;This thesis undertakes a variety of projects to explore how chemical combinations can be used towards microbial systems biology. Using simulations and experiments of drug combinations we show that classifying drug interactions and using drug interaction profiles from pair wise drug combination screens can provide valuable insights into network connectivity (Nature MSB, 2007). Next, synergistic interactions are shown to be more therapeutically selective compared to the individual components (Nature Biotechnology, 2009). The finding is supported by chemical-genetic screens of human cells, and simulated combinations from a genome-scale model of bacteria in different environments. Next we investigate the relationship between the responses to 3 or more drugs and the functional complexity of a biological system (Nature MSB, 2008). Finally, we confirm the relationship between biological robustness and high order synergy in an Escherichia Coli proliferation experiment and find high order synergies that would not have been expected from lower order pairs (In prep).;All together, these projects show how combinations of chemical perturbations can provide useful information about how complex biological systems really work and how to best control their behavior. Chemical combinations are helpful for gaining insight into biology on many scales, ranging from single pathways up to whole systems and measured phenotypes.