Fossil viruses, redox paradigms and predictive metabolism from a systems biology perspective

Abstract

One of the goals of systems biology is to develop a model which encapsulates the molecular, structural and temporal complexity of a living organism. While modern omics experiments can deliver a high resolution view of an organism's molecular complexity, methods for correlating the information from multiple biomolecular systems (i.e. genes, proteins and metabolites) and their changes over time remain greatly underdeveloped. Presented in this research are: (1) methods for understanding the inter-relation of multiple biomolecular systems correlating genomics, proteomics and metabolomics experiments; (2) techniques for machine learning based metabolic biomarker selection; (3) robotics technology for real-time measurement of changes in metabolism. The methods for correlating information from multiple biomolecular systems have provided a new perspective of biomolecular adaptation and evolutionary relationships in the thermophilic archaea. The techniques for biomarker selection have provided a method to assess the reliability of biomarkers in experiments where limited samples are available. The new technology has provided an engineered system for automated analysis of metabolic patterns and how they change over time. Together, these results have created a framework for future improvement of our understanding of biology through the use of molecular biology, machine learning and robotics.