(613g) Metabolomics-Driven Modeling and Characterization of Metabolism (INVITED SPEAKER)

While rapidly advancing technology is making it increasingly easier, cheaper, and more accessible to perform system-scale measurement of metabolic intermediates in biological systems (known as “metabolomics”), our efforts to exploit the resulting data for modeling of metabolic systems have surprisingly lagged behind. One reason for this is that many of the techniques that are viable for systems-scale metabolic modeling assume a steady state in the system, which limits the utility of measurements of metabolite levels. However, we know from time course metabolomics profiles that biological systems are often not at a steady state, and that these metabolic dynamics play a major role in the regulation of metabolism. This regulation is ultimately what defines the metabolic state or output of a system. As a result, it is critical to try to model metabolic dynamics and regulation at the systems scale.

We have recently developed a computational framework, based on the widely-used Flux Balance Analysis framework, that allows for the exploitation of dynamic metabolomics profiles and ultimately the computationally tractable modeling of metabolic dynamics at the genome scale. We will explain our approach and present our most recent developments to enable scale-up and predictive accuracy of metabolic modeling. We will also describe a machine learning-based approach to identification of unknown regulatory interactions in these metabolic models using metabolomics data. This latter approach can be used not only for the implementation of models in our new computational framework, but for the discovery of regulatory interactions for implementation in arbitrary mathematical frameworks for metabolic modeling. It will provide a valuable tool for the development of metabolic models for applications in metabolic engineering, especially with the rising use of non-conventional chassis organisms for industrial applications where regulation is poorly understood (if at all).